PROCEEDINGS
ICAER - 2014 INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN ENGINEERING RESEARCH
Sponsored By INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ((Registered Under Indian Trust Act, 1882)
Technical Program 6 October, 2014 BPS Sports Club, Goa
Organized By INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT
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About IAETSD: The International Association of Engineering and Technology for Skill Development (IAETSD) is a Professional and non-profit conference organizing company devoted to promoting social, economic, and technical advancements around the world by conducting international academic conferences in various Engineering fields around the world. IAETSD organizes multidisciplinary conferences for academics and professionals in the fields of Engineering. In order to strengthen the skill development of the students IAETSD has established. IAETSD is a meeting place where Engineering students can share their views, ideas, can improve their technical knowledge, can develop their skills and for presenting and discussing recent trends in advanced technologies, new educational environments and innovative technology learning ideas. The intention of IAETSD is to expand the knowledge beyond the boundaries by joining the hands with students, researchers, academics and industrialists etc, to explore the technical knowledge all over the world, to publish proceedings. IAETSD offers opportunities to learning professionals for the exploration of problems from many disciplines of various Engineering fields to discover innovative solutions to implement innovative ideas. IAETSD aimed to promote upcoming trends in Engineering.
About ICAER: The aim objective of ICCTER is to present the latest research and results of scientists related to all engineering departments’ topics. This conference provides opportunities for the different areas delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration. We hope that the conference results constituted significant contribution to the knowledge in these up to date scientific field. The organizing committee of conference is pleased to invite prospective authors to submit their original manuscripts to ICAER 2014. All full paper submissions will be peer reviewed and evaluated based on originality, technical and/or research content/depth, correctness, relevance to conference, contributions, and readability. The conference will be held every year to make it an ideal platform for people to share views and experiences in current trending technologies in the related areas.
Conference Advisory Committee:
Dr. P Paramasivam, NUS, Singapore Dr. Ganapathy Kumar, Nanometrics, USA Mr. Vikram Subramanian, Oracle Public cloud Dr. Michal Wozniak, Wroclaw University of Technology, Dr. Saqib Saeed, Bahria University, Mr. Elamurugan Vaiyapuri, tarkaSys, California Mr. N M Bhaskar, Micron Asia, Singapore Dr. Mohammed Yeasin, University of Memphis Dr. Ahmed Zohaa, Brunel university Kenneth Sundarraj, University of Malaysia Dr. Heba Ahmed Hassan, Dhofar University, Dr. Mohammed Atiquzzaman, University of Oklahoma, Dr. Sattar Aboud, Middle East University, Dr. S Lakshmi, Oman University
Conference Chairs and Review committee:
Dr. Shanti Swaroop, Professor IIT Madras Dr. G Bhuvaneshwari, Professor, IIT, Delhi Dr. Krishna Vasudevan, Professor, IIT Madras Dr.G.V.Uma, Professor, Anna University Dr. S Muttan, Professor, Anna University Dr. R P Kumudini Devi, Professor, Anna University Dr. M Ramalingam, Director (IRS) Dr. N K Ambujam, Director (CWR), Anna University Dr. Bhaskaran, Professor, NIT, Trichy Dr. Pabitra Mohan Khilar, Associate Prof, NIT, Rourkela Dr. V Ramalingam, Professor, Dr.P.Mallikka, Professor, NITTTR, Taramani Dr. E S M Suresh, Professor, NITTTR, Chennai Dr. Gomathi Nayagam, Director CWET, Chennai Prof. S Karthikeyan, VIT, Vellore Dr. H C Nagaraj, Principal, NIMET, Bengaluru Dr. K Sivakumar, Associate Director, CTS. Dr. Tarun Chandroyadulu, Research Associate, NAS
ICAER - 2014 CONTENTS 1
AUTOMATED TOLL COLLECTION SYSTEMUSING RFID
1
2
COMPUTER SIMULATION OF COMPRESSION IGNITION ENGINE THROUGH MATLAB
5
3
AUTOMATED DAM MONITERING SYSTEM
18
4
DESIGN AND ANALYSIS OF A NOVEL LOW ACTUATED VOLTAGE RF MEMS SHUNT CAPACITIVE SWITCH
22
5
DESIGN AND ANALYSIS OF LOW-LEAKAGE HIGH-SPEED DOMINO CIRCUIT FOR WIDE FAN-IN OR GATES
26
6
DEVELOPMENT OF SENSING DEVICE TO DETECT PERSONS HIDING IN A CAR
34
7
EXPLODING SPACE TRAVEL
41
8
GPS TRACKING SYSTEM WITH LICENSE DETECTOR AND SPEED CONTROL
45
9
GREEN BUILDING
51
10
EARLY DETECTION OF BREAST CANCER USING CAD SYSTEM EMPLOYING SVM CLASSIFIER
58
11
MODELING & SIMULATION OF SOLAR PV ARRAY FIELD INCORPORATED WITH SOLAR IRRADIANCE AND TEMPERATURE VARIATION TO ESTIMATE OUTPUT POWER OF SOLAR PV FIELD
62
12
RTOS BASED ELECTRONICS INDUSTRIAL TEMPERATURE AND HUMIDITY MONITORING USING ARM PROCESSOR
69
13
SMART VEHICLE TRACKING SYSTEM USING GSM, GPS AND RC5
73
14
EXPERIMENTAL INVESTIGATION AND PREDICTIVE MODELING FOR SURFACE ROUGHNESS OF DRILLING ON GFRP COMPOSITES
77
15
WIRELESS AD HOC NETWORKS
85
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AUTOMATED TOLL COLLECTION SYSTEM USING RFID 1
Kamichetty Pramodh Kumar, 2Kasturi Sai Ratna Gayatri, 3Reddy Akshay,4Vaduru Tayjo Padmini, 5
Kondaka Jyothirmayi
Department of Information Technology Gitam University Vishakapatnam, India 1
pramodhkumar.12@gmail.com, 2gayatrikasturi94@gmail.com, 3reddyakshay.225@gmail.com, 4 tayjovaduru@gmail.com, 5jyothirmayi94@gmail.com
Abstract- ATCSR (Automated Toll Collection System using RFID) is used for collecting tax automatically. In this we do the identification with the help of radio frequency. A vehicle will hold an RFID (Radio Frequency Identification Device) tag. This tag is nothing but unique identification number assigned to it. The reader will be strategically placed at toll collection centers. Whenever the vehicle passes the toll booth, the tax amount will be deducted from his prepaid balance. New balance will be updated. As vehicles don’t have to stop in a queue, this translates to reduced Traffic congestion at toll plazas and helps in lower fuel consumption. This is a very important advantage of this system.
acceleration. Meanwhile, for the toll authorities also get the benefits mentioned below[4]:
An RFID tag is installed on each vehicle with read/write memory. A reader device reads this data when the vehicle is near the toll system, and compares it with the data in the computer database. This helps us to provide security since vehicles with RFID can be tracked easily. Apart from that we can conserve fuel and also reduce the amount of pollution.
5. Lowered toll collection costs
Keywords: ATCSR, RFID Reader, RFID Tag, Toll Collection.
The existing method for collecting toll tax is a time consuming method. There are more chances of escaping toll payment. It leads to queuing up of following vehicles. Suppose the manual toll collection system is very efficient then for one vehicle to stop and pay taxes total time taken is 50 seconds. And suppose 200 vehicles cross the toll plaza. Then, time taken by 1 vehicle with 60 second average stop in a month is: 50x30= 1500 seconds[8].
1. Fewer or shorter queues at toll plazas 2. Faster and more efficient service (no exchanging toll fees by hand) 3. The ability to make payments by keeping a balance on the card itself 4. The use of postpaid toll statements (no need to request for receipts)
6. Better audit control by centralized user account 7. Expanded capacity without building more infrastructures. II.DRAWBACKS OF EXISTING SYSTEM
I.INTRODUCTION The main idea behind implementing ATCSR is to automate the toll collection process there by reducing the long queues at toll booths using the RFID tags installed on the vehicle. In addition to this, it can not only help in vehicle theft detection but also can track vehicles crossing the signal and over speeding vehicles. This system is used by vehicle owners and the system administrator. Other general advantages for the motorists include fuel savings and reduced mobile emissions by reducing or eliminating deceleration, waiting time and
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Yearly total time taken = 1500x12 = 18000seconds = 5.0 hours On average each vehicle that passes through the toll plaza has to wait 5.0 hours in engine start condition yearly. The
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figure is staggering. If on an average we take 200 vehicles to pass through the toll plaza each day, then yearly 72000 vehicles pass through the toll plaza. And each year 72000 vehicles just stand still for 5.0 hours in engine start condition thereby aiding pollution and wasting fuel and money. This study is if the system is very efficient but what if the vehicle has to wait for 5 minutes? This is a figure considering one toll plaza[8]. If considering 50 toll systems the above figure will drastically increase and the wastage of fuel and money will increase and pollution will also increase.
Assuming cost of 1 liter fuel = Rs.75 Total cost of fuel consumed by 36, 00000 vehicles = 75 x 36, 00,000 = Rs. 270,000,000/- The above is the money wastage under the consideration that the vehicle stops for 60 second at the toll system, and 100 vehicles pass through the toll plaza each day and there are 100 toll plazas. These figures are all in minimum. III.AUTOMATIC TOLL COLLECTION SYSTEM To avoid the above drawbacks we can automate the toll collection system using RFID through his we can overcome the drawbacks. A. Overview: Whenever any person buys a vehicle, one first needs to get his or her vehicle registered at the RTO office. RTO officials will not only assign a number plate to it but also will give a RFID enabled smart card or a tag. This card will have a unique ID feasible to use with that vehicle only. They will also create an account for the use of that particular smart card and maintain transaction history in database [4]. User needs to deposit some minimum amount to this account. Every time a registered vehicle approaches the toll booth, first the Infrared sensors will detect the presence of the vehicle.
Fig 1:Traffic Jam at Tolls Suppose, If there are 100 manual toll-taxes system and everyday 100 vehicles cross through each system, then No of vehicle that pass through one system yearly= 100 x 30 x 12 = 36,000. No of vehicle that pass through 100 system yearly= 100 x 36,000 = 36, 00,000[7]. Vehicle
Days
Tool booth 1 1 100
100 1 36000 30*12 3600000 30*12 TABLE 1: Vehicles Passed through Toll Booth in 1 year This figure indicates that in one year each of the 36, 00,000 vehicles just stand still for about 6.0 hours in engine start condition which causes pollution and increases fuel consumption. Suppose that in 6.0 hours a vehicle uses 1 liter fuel, Total fuel used by all the vehicles: 36, 00,000 x 1 = 36, 00,000 liters[7].
Vehicle 1 3600000
Fuel consumption 1 lit 3600000 lit.
Fig.2:Elements of RFID System
It will in turn activate the RFID circuit to read the RFID enable smart card fixed on the windscreen of the vehicle. Transaction will begin, depending upon the balance available toll will be deducted directly or the vehicle will be directed towards another lane to pay tax manually. The software further updates the details in the Centralized database server. It also
Amount 75/270,000,000/-
TABLE 2: Fuel Consumption and Amount Vehicle Fuel Consumed Amount
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triggers mechanism to generate the bill and will be sent to user as a text message.
When the vehicle crosses the sensor processing unit, the tag will be read by the RFID reader. The tag contains the unique identification number. Then data read by the RFID reader will be taken by Microcontroller which will process the data for authentication of authorized user[6].
Fig.3:SMS Gateway Fig 5:Practical Working
B.Operation:
If the user is a valid user then data will be sent to the processing unit. Then it checks the respective account of that user from the database to ensure weather the user has sufficient amount for toll payment or not. If amount is not sufficient to pay toll then user must have to recharge its account by paying manually. If sufficient amount then user is allow to pass by iron bar which will rise up with the help mechanical assembly after receiving the permission signal from processing unit. If user is not valid then iron bar will remain down and appropriate action will be taken against invalid user.
Start Detect the RFID tag
A
NO
Tag??
YES
A
Stolen Vehicle?
Access the user account
YES
NO
Deduct the toll from account
Read the RFID
Report tono owner Update the Database and account balance
A
NO
Had enough balance ?
YES
Toll amount deduction
END
END
Fig6:Toll amount deduction
Fig 4:Working Procedure
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passing of any defaulter vehicle is implemented, thus assuring security on the roadways. This also reduce the fuel consumption which are now under the threat of exhausting.
Deduct amount Balance
References:
Vehicles Information
[1] The Time’s of India paper April 20, 2012 ”Now Road toll can be paid without stopping at Toll Plazas”.
Details maintained
ID from RFID tags
[2] The Time’s of India paper May 28, 2012 “High-Tech number plates for 20 lakh vehicles soon”.
Process Taking place
[3] ActiveWaveInc. http://www.activewaveinc.com. [4] D. M. Grimes and T. O. Jones, ―Automotive radar: A brief review,ǁ Proc. IEEE, vol. 62, no. 6, pp. 804–822, Jun. 1974.
Details retrieved from Database
Details displayed to user
[5] Smart key Access Control System http://www.smartkey-rfid.com. [6] Wikipedia http://en.wikipedia.org/wiki/electronic_toll_collection. [7] Automated Toll Collection System Using RFID Pranoti Salunke1, Poonam Malle2, Kirti Datir3, Jayshree Dukale4. [8] International Journal of Electrical and Electronics Research ISSN 23486988 (online) Vol. 2, Issue 2, pp: (67-72), Month: April - June 2014.
Fig7:Overall Process
When the user installs an RFID device on his vehicle, he will be provided with a card. This card will be used to debit the amount of money required to pay the toll fee when the vehicle passes through the toll gate. The user needs to keep topping up the amount on his card every time it reaches a certain minimum amount. Whenever the vehicle crosses an automated toll gate, the required fee is automatically debited from the card. An automatically generated message will be sent instead of a receipt to the user’s mobile number. IV.CONCLUSION The electronic toll collection system in expressway based on RFID, a design scheme was put forward. It has characteristics of low cost, high security, far communication distance and high efficiency, etc. It not only can improve technology level of charge, but also improve passage ability of expressway. Electronic toll collection system is an effective measure to reduce management costs and fees, at the same time, greatly reduce noise and pollutant emission of toll station. We can also save the time. In the design of the proposed Electronic toll collection (ETC) system, real time toll collection and anti-theft solution system have been designed. This reduces the manual labour and delays that often occur on roads. This system of collecting tolls is eco-friendly and also results in increased toll lane capacity. Also an anti-theft solution system module which prevents
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Computer Simulation of Compression Ignition Engine through MATLAB K. Hareesh1*, Rohith Teja. N2, B. Konda Reddy3 1,
rgu.konankihareesh.1820@gmail.com, 2, rohithteja.ps91221@gmail.com, 3, bkondareddy.rkv@rgukt.in 1,2
Students in Bachelor of Technology, 3Asst. Professor in Department of Mechanical Engineering. 1,2,3
Rajiv Gandhi University of Knowledge Technology, RK Valley, Andhra Pradesh – 516329.
Abstract: In the present work, a computer simulation has been developed using MATLAB to determine the performance of a four stroke Compression Ignition internal combustion (IC) engine. The modeling of this process begins with the simulation of one cylinder of the four stroke IC engine which is assumed to have an ideal pressure-volume (p-V) relationship allowing for computation of peak performance. The computer simulation is modeled for Ideal Cycle System with encryption of thermodynamic laws of heat transfer and then it is also modeled for the prediction of emissions. The second phase of the model focuses on fuel cycle system where all the real factors are to be considered for the prediction of performance parameters and emissions. Along with the thermodynamic model to compute heat release some standard models like Woschni and Annand models are also used to predict the heat release. Performance parameters computed include brake power and brake specific fuel consumption for an engine's entire operating range. Keywords: Computer simulation, IC engines, Ideal cycle system, Heat release models, Performance parameters, Specific fuel consumption, MATLAB simulation.
1. Introduction One of the major polluting contributors to our environment today is the internal combustion engine, either in the form of spark ignition (Otto) or Diesel versions. In parallel to this serious environmental threat, the main source of fuel for these engines, crude oil, is being depleted at high rates, so that the development of less polluting and more efficient engines is today of extreme importance for engine manufacturers. Also, to this end, the fact of the increasing threat posed by the rivals of the internal combustion engine, for smaller size engines, such as the electric motors, the hybrid engines, the fuel cells and the like corroborates the importance [1]. Experimental work aimed at fuel economy and low pollutants emissions from Diesel and Otto engines International Conference on Advancements in Engineering Research 5
includes successive changes of each of the many parameters involved, which is very demanding in terms of money and time. Today, the development of powerful digital computers leads to the obvious alternative of simulation of the engine performance by a mathematical model. In these models, the effects of various design and operation changes can be estimated in a fast and non-expensive way, provided that the main mechanisms are recognized and correctly modelled [2, 3]. The process of combustion in a Diesel engine is inherently very complex due to its transient and heterogeneous character, controlled mainly by turbulent mixing of fuel and air in the fuel jets issuing from the nozzle holes. High speed photography studies and inwww.iaetsd.in
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cylinder sampling techniques have revealed some interesting features of combustion [4]. The first attempts to simulate the Diesel engine cycle substituted the ‘‘internal combustion’’ by ‘‘external heat addition’’. Apparent heat release rates were empirically correlated to fuel injection rates and eventually used in a thermodynamic cycle calculation to obtain the cylinder pressure in a uniform mixture [5]. Models based on droplet evaporation and combustion, while still in a mono-zone mixture, can only partially take into account the heterogeneous character of Diesel combustion [4]. The need for accurate predictions of exhaust emissions pollutants forced the researchers to attempt developing two zone combustion models [1–4]. Eventually, some multi-zone combustion models have appeared, carrying the expected drawbacks of the first attempts, where the detailed analysis of fuel-air distribution permits calculation of the exhaust gas composition with reasonable accuracy [3-6]. However, this happens under the rising computing time cost when compared to lower zones Diesel combustion models. At this point, it is mentioned that multi-dimensional models have proved useful in examining problems characterized by the need for detailed spatial information and complex interactions of many phenomena simultaneously [6, 7]. However, these are limited by the relative inadequacy of sub-models for turbulence, combustion chemistry and by computer size and cost of operation to crude approximations to the real flow and combustion problems. Therefore, it is felt that a reasonable choice seems to be a two zone model, which includes the effects of changes in engine design and operation on the details of the combustion process through a phenomenological model where the geometric details are fairly well approximated by detailed modelling of the various mechanisms involved [1, 8]. This is going to have the advantage of relative simplicity and very reasonable computer time cost. Thus, the object of the present work concerns a comprehensive two zone model, applied to a direct injection (DI) Diesel engine, similar in broad outline to others, but with several differences that one must International Conference on Advancements in Engineering Research 6
expect from an independent research source. The model contains upgraded jet mixing, heat transfer and chemistry sub-models, using as simply as possible the numerical analysis treatment of the governing differential and algebraic equations, thus leading to good solution convergence with reduced computer time cost [9, 10]. Extending that work further, the present paper, after exposing a rather short description of the model, as a first step, verifies its validity by using data from a vast experimental investigation. This data is taken at the authors laboratory on a fully automated test bed, four stroke, water cooled, standard ‘‘Hydra’’, direct injection, high speed, Diesel engine. Plots of pressure, temperatures in the two zones, nitric oxide (NO) concentration, soot density, efficiency and other interesting quantities are presented as a function of crank angle, for various loads and injection timings, providing insight into the physical mechanisms governing the combustion and pollutants formation. After gaining confidence in the predictive capabilities of the model, the second step follows with an extensive investigation of the sensitivity of the model to variation of the constants used in the fuel preparation and reaction sub-models, which are proved critical to the model predictions. For this purpose, the coincident experimental and predicted points are used as baseline values around which changes to these constants are effected. As a feedback, this leads to a better understanding of the physical mechanisms governed by these constants, explaining the behavior of the combustion and pollutants formation for various fuels and conditions used, as reported in the literature. At the same time, this analysis paves the way for the construction of a reliable and relatively simple multi-zone model, which incorporates in each zone (packet) the philosophy of the present two zone model, while it may also be useful for the construction of a combustion model during transient engine operation [8,11,12].
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mt ma m f
2. Thermodynamic Analysis Two-zone model views the entire combustion in to burned zone and unburned zone as shown in the Figure 1.
mb mi mt i
After amount of mass fraction burned inside the combustion chamber was found out, the next important parameter to find out is instantaneous Volume of the cylinder. This volume can be obtained by the equation in relation with the crank angle as:
D 2 r 1 1 cos 2 sin 2 V Vcl 4
1/ 2
The peculiarity of this process among all other processes of combustion analysis is, here the total combustion range is divided into six regions such as suction, Compression, Combustion, expansion, constant volume heat rejection and exhaust. All the processes in the above listed will follow the ideal relations show in the below Figure 2.
Burned zone
Unburned zone
Figure 1: Two Zone Combustion Model However, the two-zone model recognizes a burned and unburned zone, thus predicting heat transfer and emissions more accurately. The construction of two zone model begins with weibe function to identify burned and unburned regions.
i 1 exp(6.908 d
b1
)
The mass fraction profile follows the ‘S’. So, that’s why it is called as S-function. The burn profile is engine-specific. So, from the mass fraction profile the total amount of mass burned inside the chamber can be find out as:
ma
Figure 2: Diesel Cycle
Pint Vint Rg Tint
But, the prediction of performance parameters in combustion zone is bit complicated and doesn’t follow the ideal relations. Generally in the combustion region, Volume and Temperature does not remains constant which leads to the increasing of
m f mass of fuel
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pressure as the combustion proceeds. Inorder to find the pressure the temperature has to be obtained by using heat equations shown below:
combustion. The following Figure 4a, 4b justify the above information and also gives an idea of PVDiagram:
Qin mb LHV mu C p (Tnew T ) From the above relation new temperature can be obtained as
Tnew T
mb LHV mu C p
Temperature increases rapidly during the combustion and then decreases due to heat convection by cooling water. The temperature in the total cycle changes as show in below Figure 3.
Figure 4(a): Pressure Vs Theta
Figure 3: Variation of Temperature in combustion zone Now the new pressure can be obtained as
Pnew
mt Rg Tnew V
In actual combustion process unlike the idea case the pressure doesn’t remain constant at heat addition process, rather it reaches a maximum value at maximum heat addition point and then decreases. This is because that there was no assumption that the pressure remains constant in the region of International Conference on Advancements in Engineering Research
Figure 4(b): PV Diagram
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BTH
Rated Powers and Efficiencies This section starts with the calculation of total work done in the cycle from where mean effective pressure can be calculated which is equal to the Indicated mean effective pressure when the friction is neglected [5]. The work output can be obtained from
Then the brake specific fuel consumption is determined as
BSFC
P3V3 P4V4 PmaxV2 P1V1 1 1 Then the Indicated power is n I p W (KW), where n = N/2 for four stroke 60 engine From the I p mean effective pressure was obtained
LAnK
Heat Release Models This section starts with the first law of thermodynamics for the four stroke engine energy balance [3] is
(Pa)
U Q W
Friction mean effective pressure for direct injection diesel engine is given [6] as
Where Q the total energy is transferred into the system, W is the work transferred out of the system, and U is the change in internal energy within the system. In differentiating equation the above equation with respect to d dU dQ dW dT mCv d d d d
RPM 2 fmep C 48 0.4 U p 1000 Where C is constant which is equal to 75 Kpa and Up is the mean piston speed. Then friction power is,
Fp
Where Cv
Now, the brake Power and brake mean effective pressure are obtained from the following relations
Where is the specific heat ratio. The above equations can be used to describe the formation of work and the net heat input: dW dV P d d
B p I p Fp
60000 B p LAnK
dQn dQin dQloss d d d Which can be expanded as, dQn d dQ m f LHV loss d d d
Once the all rates powers were obtained calculation of their efficiencies becomes easy. The brake thermal efficiency is obtained [4] as
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is the specific heat of the combustion
chamber gas. Upon dividing the specific heat by the universal gas constant [9], Cv Cv 1 R C p Cv 1
fmep LAnK 60000
bmep
MFVL 3600 Bp
The brake specific fuel consumption varies with the load, fuel and engine model. It is high at full load condition where maximum power output is generated in the form of brake power [4].
from,
60000 I p
MFVL CV
Where MFVL is the fuel in terms of Kg/s
W Pmax V3 V2
imep
Bp
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If we further implements the heat model to get net heat with respect to crank angle [3], the equation becomes: dQn dV 1 dP P V d 1 d 1 d However, it is also necessary to continue discussion to get more accuracy for more complex heat release models. Lastly the change in pressure [6] is defined by: dP 1 dQin dQloss P dV d V d d V d
w 2.28U p C1
V Tr P Pm PrVr
Where C1 the constant is varies with the process. For combustion process C1 =0.00324, U p is mean piston speed, V is the instantaneous volume, Tr is the reference temperature, Pr is the reference pressure, Vr
is the reference volume, P instantaneous pressure at the combustion period and Pm is the motored cylinder pressure.
The rate of heat loss can be expressed as dQloss 1 h A T Tw d This can be modelled by two models namely woschni’s heat transfer model and Annand’s heat transfer model.
Woschni’s Heat Release Model Woschni’s method is a set of empirical equations that predicts the heat transfer coefficient between incylinder gasses and walls [5]. The convective losses between in-cylinder gasses and walls can be predicted using Newton’s law of cooling:
T 4 Tw 4 dQht 1 h A T Tw 5.76 d 100 100
h is the instantaneous heat transfer coefficient,
Figure 5: Rate of change of convective heat transfer coefficient with crank angle
A is the instantaneous heat transfer area, T is the instantaneous bulk gas temperature, and
Tw is the
cylinder wall temperature. With the heat loss modelled angularly, the convective heat transfer coefficient is now defined as:
In compression and expansion processes, Watson and Janota [6] suggested modelling the motored cylinder pressure as a polytrophic process:
h 3.26D 0.2 P 0.8T 0.55 w0.8
V Pm Pr r V
Where that w denotes the woschni’s factor which also changes with the crank angle and it is expressed [11] [12] [4] as,
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With all of these variables previously expressed, the convective heat transfer coefficient and corresponding heat loss could then be calculated.
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Where a is a constant having a value of 0.26 for a two-stroke engine and 0.49 for a four stroke engine [2], and is the instantaneous Reynolds number. The Reynolds number is expressed as:
The change in convective heat transfer coefficient with the crank angle during combustion according woschni’s has represented in the Figure 5.
Annand’s Heat Release Model
Re
Annand’s and Woschni’s heat transfer models differed in the fact that Annand’s approach separated the convective and radiation terms. Annand’s method solved for the heat transfer coefficient by assuming pipe-like fluid dynamics, and using the in-cylinder density, and Reynolds and Nusselt numbers as functions of time.
Where gas is the instantaneous cylinder gas density,
U p is the mean piston velocity, and
gas
dQ 1 hc hr A T Tw d
hc
P RgasT
Where Rgas is the fluid-specific gas constant, and an assumed value of 287[
is the convective heat transfer
J ] was used for this KgK
variable.
coefficient and hr is the radiation heat transfer coefficient. The convective heat transfer coefficient can be extracted from the relationship between the Nusselt number and fluid properties [7] as
hc
gas is the
instantaneous gas viscosity. Since the model assumes ideal gas behavior, the cylinder gas density can be found by rearranging the ideal gas law:
Using Annand’s method, Newton’s law of cooling can be broken into convective and radiation terms [3] as follows:
Where
gasU p D gas
As with the thermal conductivity, the cylinder gas viscosity was modelled using empirical equations. According to Heywood [2], the cylinder gas viscosity can be expressed as:
k gas Nu
D
kg 6 7.457 10. m*s
gas
4.1547 10
8
T 7.4793 10
12
T
Where k gas is the gas thermal conductivity, Nu is the Nusselt number, and D is the cylinder bore. With an iterative solver, the thermal conductivity of the cylinder gas can be modelled using a polynomial curve-fitting of experimental data. Heywood [2] suggests using the curve fitted equation:
k gas 6.1944 10 3 7.3814 10 5 T 1.2491 10 8 T
Although the radiative heat transfer coefficient is small [2], it was decided that radiation should be included in considering overall heat losses in the model. The radiative heat transfer coefficient is defined as [5]: 2
W , units are m* K
Where the instantaneous cylinder temperature and wall temperature must be provided in units of [K]. With known pressure and temperature traces from the calculations, Annand’s method could then be used to calculate heat losses. A comparison of predicted heat
The Nusselt number can be described relative to the Reynolds number and the type of engine: Nu a Re 0.7
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4 w 9 T Tw hr 2 4.25 10 m *K T Tw
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loss rates of both Woishni and Annand model would be shown in the Figure 6.
NOx Model While nitric oxide (NO) and nitrogen dioxide (NO2) are usually grouped together as NOx emissions, nitric oxide is the predominant oxide of nitrogen produced inside the engine cylinder. The principle source of NO is the oxidation of atmospheric nitrogen. However, if the fuel contains significant nitrogen, the oxidation of the fuel nitrogen-containing compounds is an additional source of NO. The mechanism of the formation of NO has been revised and the principle equations governing the formation of NO has formulated as O + N2 = NO + N
(3.1.1)
N + O2 = NO + O
(3.1.2)
N + OH = NO + H
(3.1.3)
The forward rate constant for reaction (3.1.1) and the reverse rate constants for reactions (3.1.2) and (3.1.3) have large activation energies which results in a strong temperature dependence of NO formation. And the NO formation rate is given as
Figure 6: ROHL with respect to crank angle
Total heat losses other than convective, radiative heat transfer are accounted in the below Figure 7.
1 NO / K O2 N 2 d NO 2k1 ON 2 dt 1 k1 NO/ k 2 O2 k3 OH 2
Where K = k1 k1 k 2 k 2 . To introduce the equilibrium assumption it is convenient to use the notations
R1 k1 Oe N 2 e k1 NOe N e , for (3.1.1) R2 k 2 N e O2 e k 2 NOe Oe , for (3.1.2) R3 k 3 N e OH e k 3 NOe H e ,for (3.1.3) Then the NO formation rate equation becomes
2R1 1 NO/NOe d NO dt 1 NO/NOe R1 / R2 R3 The strong temperature dependence of the NO formation rate makes the formation rate very simple
Figure 7: Energy losses International Conference on Advancements in Engineering Research
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and depends on the equilibrium concentration of Oxygen and Nitrogen and it is given as
HC Model Hydrocarbons, or more appropriately organic emissions, are the consequence of incomplete combustion of the hydrocarbon fuel. The level of unburned hydrocarbons (HC) in the exhaust gases is generally specified in terms of the total hydrocarbons concentration expressed in parts per million carbon atoms.
d NO 6 10 69090 1/ 2 exp O2 e N 2 e 1 dt T T2 16
d NO on temperature in dt the exponential term is evident. High temperatures and high oxygen concentrations result in high NO formation rates. The characteristic time for the NO formation process is The strong dependence of
NO
A reasonable fit to the experimental data on unburned HC burn up is the rate expression [9] as
d HC 18735 P 6.7 1015 exp x HC xO2 dt T RT
8 10 T exp58300 / T P1 / 2 16
2
Where x HC , xO2 are the mole fraction of HC and mole
By multiplying this time with NOx formation rate we can get the NOx in to PPM as
fraction of Oxygen respectively.
d NO 1000 6 NOx (PPM) = NO 10 dt 3600
The main contribution of unburned Hydro Carbons are depicted in the Figure 9.
The change in NOx emission in PPM with respect to temperature is shown in the Figure 8.
HC EMISSION CONTRIBUTIONS others 25% Crevices 48%
valves 5%
oil layers 22%
Figure 9: % of factors contributing to HC emissions
CO Emissions Figure 8: NOx emissions with respect to Temperature.
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Carbon monoxide is an intermediate species in the oxidation of hydrocarbon fuels to CO2 and H2O. In fuel-rich regions of a flame, the CO levels are 13
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necessarily high since there is insufficient oxygen for complete combustion. Only if sufficient air is mixed with such gases at sufficiently high temperature can the CO be oxidized. Thus, imperfect mixing can allow carbon monoxide to escape from combustors that are operated fuel-lean overall. Even in premixed combustion systems, carbon monoxide levels can be relatively high due to the high equilibrium concentrations at the flame temperature, particularly in internal combustion engines where the gases are hot prior to ignition due to compression. As the combustion products are cooled by heat or work transfer, the equilibrium CO level decreases. If equilibrium were maintained as the temperature decreased, carbon monoxide emissions from automobiles and other well-mixed Combustors would be very low in fuel-lean operation. The extent to which CO is actually oxidized, however, depends on the kinetics of the oxidation reactions and the manner of cooling. In this section we explore the kinetics of CO oxidation and the mechanisms that allow CO to escape oxidation in locally fuel-lean combustion.
Figure 10: % of emissions with respect to Temperature
3. Simulation Model The thermodynamic simulation model through MATLAB is developed to validate the experimental results for a compression ignition engine to reduce the time consumption in manually computing for comparison of actual data with experimental data. The validation of a mathematical model of a structural dynamic system entails the comparison of predictions from the model to measured results from experiments. There are some more numerous, related reasons for performing validations of mathematical models. For example:
The predominant reaction leading to carbon monoxide oxidation in hydrocarbon combustion is
CO OH CO2 H Where,
k 1 4.4T 1.5 exp372 / T m 3 mol 1 S 1
1. There may be a need or desire to replace experimentation with model predictions, and, of course, a corresponding requirement that the model predictions have some degree of accuracy. The need for a model may arise from the fact that it is impossible to test system behavior or survivability in some regimes of operation.
The rate of carbon monoxide production for the reaction (4.9.2.1) is given by,
R1 k 1 COe OH e The speed of the reaction is expressed in terms of the characteristic reaction time
CO
CO
2. Alternately, the need for validation may arise from a necessity to prove the reliability of a structure under a broad range of operating conditions or environments. Because it may be expensive to simulate the conditions in the laboratory or realize them in the field, an accurate model is required.
R1
The proportions of the emissions of NOx, HC and CO emissions are shown in the Figure 10.
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3. Another reason for model validation is that a system may be undergoing changes in design that require analyses to assure that the design modifications yield acceptable system behaviour. A validated model can be used to assess the system behaviour. In all these situations it is useful to confirm analysts’ abilities to produce accurate models.
between iterations, thus decreasing the overall computation time. Pre-allocated arrays and matrices were also used as a means of setting appropriate properties for the recursion of the program to compare between the graphs of multiple fuel tests.
In this work, the task of developing the MATLAB simulation model for compression ignition engine is for testing different fuels at different conditions to select the best to have high efficiency and less emissions.
Fuel inputs such as mass of the fuel, Calorific value, Lower Heat Values and air-fuel ratio were taken as per the experiment model of the engine.
4. MATLAB Script Procedure
In order to that the main program has divided in to two sub loops where in the first loop with a specified index (i=0:720) calculated instantaneous engine properties discussed in the above section. In addition to the properties of instantaneous properties like volume, pressure and temperature it has also scripted for to calculate work done during the total range of the cycle, Indicated power, friction power, brake power, correction factor for the calculation of brake power and brake specific fuel function.
In selecting a computer program to execute the demands of a two-zone model, Matrix Laboratory (MATLAB) was considered. It was considered as because of its ease of simulation and speed of validation. With keeping all constraints in mind total script was developed in MATLAB for the future use. The bulk of MATLAB code was set up through the use of script and the total script is divided in to some sub sections.the purpose of these sub-sections and the organization of the MATLAB model will be elucidated in subsequent sections. 4.1 Engine Geometry and Atmospheric inputs The MATLAB script began with known engine inputs. The bore, stroke, connecting rod length, number of cylinders, compression ratio, and operating characteristics has to mentioned to the program as inputs for the validation of an experimental details. Based on the inputs script would calculate the area of the cylinder, clearance volume of the cylinder and surface area of the piston head. And the atmospheric conditions were chosen like, the initial inlet Temperature 300K (room temperature) and pressure as 1 atm. 4.2 Pre-allocation of Arrays
4.4 Instantaneous Engine and Fluid Properties
The second loop with specified index in the range of combustion, MATLAB script has statements to cope up with heat release and heat loss model in Woschni and Annand model. 4.5 Plot statements Each plot was sized based on the minimum and maximum variable values, and each plot was given a title appropriate to the variable being plotted. MATLAB script was so developed to have a plots between all the performance parameters as a function of crank angle, PV diagrams and for the relation between pressure and temperature. Plots were modelled for mass-fraction, heat release and heat loss as a function of crank angle. 4.6 Emission Predictions
Through experimentation, it was found that preallocating arrays and matrices drastically improved the efficiency of the program. This prevented MATLAB from having to re-size arrays or matrices International Conference on Advancements in Engineering Research
4.3 Fuel inputs
The NO prediction model was included in the MATLAB script, predicted the quantitative fraction of NO particles. The residence time for NO formation 15
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was calculated and the integrated amount of NO was calculated in PPM.
5. Results and Discussion As the ideal cycle system for four stroke Direct Injection Diesel Engine starts with the calculation of mass fraction profile, it was calibrated and a graph drawn for mass fraction verses crank angle during combustion zone and it is shown in figure 11.
Figure 12: Volume Vs Crank angle
Figure 11: Weib's Mass Fraction profile
Figure 12 shows the Volume profile for the total range of four stroke engine as a function of crank angle and found that it never changes with fuel and it’s properties. It is only depends upon the specifications of the engine. Figure 13 shows the rate of heat transfer during the combustion period according to Thermodynamic analysis. Figure 13: ROHT Vs Crank angle during Combustion
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Figure 14 show the difference between the heat transfer coefficient for both Woschni’s and Annand heat release models and found they follow same trend as they follow for standard cycles systems.
References 1.
2.
3. 4. 5. 6.
7.
8.
Figure 14: Comparison of Heat transfer Coefficients of both Woschni's and Annand Heat Release Models 9.
Using the burned-zone temperature, sub-functions were created to calculate NOx and HC emissions and graphs were generated as shown in Figure 10.
Conclusion 10.
It was found that the model could be used in simulating any diesel engine. This could save an enormous amount of time in tuning an engine, especially when little is known about the engine. With an air-fuel ratio and volumetric efficiency map, Injection-timing could be optimized, thus minimizing wear-and-tear on the engine and dynamometer equipment. Much research could be directed towards refining the model and using it for the improvement of engine performance and reducing the NOx emissions by testing different fuels.
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11.
12.
17
C.D. Rakopoulos, E.G. Giakoumis and D.C. Kyritsis - “Validation and sensitivity analysis of a two zone Diesel engine model for combustion and emissions prediction”, Energy Conversion and Management 45 (2004). Jeremy L. Cuddihy - University of Idaho, “A User-Friendly, Two-Zone Heat Release Model for Predicting Spark-Ignition Engine Performance and Emissions”, May 2014. “Computer Simulation of Compression Engine” by V. Ganesan –1st edition, 2000. J. Heywood, Internal Combustion Engine Fundamentals. Tata Mcgraw Hill Education, 2011. V. Ganesan, Internal Combustion Engines, 6th edition, Tata Mcgraw Hill Education, 2002. Zehra Sahin and Orhan Durgun - “Multi-zone combustion modeling for the prediction of diesel engine cycles and engine performance parameters”, Applied Thermal Engineering 28 (2008). G. P. Blair, Design and Simulation of Four Stroke Engines [R-186]. Society of Automotive Engineers Inc, 1999. C.D. Rakopoulos, K.A. Antonopoulos and D.T. Hountalas -“Multi-zone modeling of combustion and emissions formation in DI diesel engine operating on ethanol–diesel fuel blends”, Energy Conversion and Management 49 (2008) 625–643. Hsing-Pang Liu, Shannon Strank, MikeWerst, Robert Hebner and Jude Osara - “COMBUSTION EMISSIONSMODELING AND TESTING OF CONVENTIONAL DIESEL FUEL”, Proceedings of the ASME 2010, 4th International Conference on Energy Sustainability (May 17-22, 2010). A. Sakhrieh, E. Abu-Nada, I. Al-Hinti, A. AlGhandoor and B. Akash - “Computational thermodynamic analysis of compression ignition engine”, International Communications in Heat and Mass Transfer 37 (2010) 299–303 . D. Descieux, M. Feidt - “One zone thermodynamic model simulation of an ignition compression engine”, Applied Thermal Engineering 27 (2007) 1457–1466. Mike Saris, Nicholas Phillips - Computer Simulated Engine Performance, 2003.
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AUTOMATED DAM MONITERING SYSTEM 1
Kamichetty Pramodh Kumar, 2Kasturi Sai Ratna Gayatri, 3Reddy Akshay,4Vaduru Tayjo Padmini, 5
Kondaka Jyothirmayi
Department of Information Technology Gitam Universitys Vishakapatnam, India 1
pramodhkumar.12@gmail.com, 2gayatrikasturi94@gmail.com, 3reddyakshay.225@gmail.com, 4 tayjovaduru@gmail.com, 5jyothirmayi94@gmail.com
Abstract:
generation using the signal generated from NI 9263 analog output module.
To develop an automated dam controlling system in order to
II.THE SYSTEM SETUP
1. Measure velocity of the water flowing into the dam. 2. Measure pressure at dam walls.
The entire dam controlling system is divided into separate dedicated sub-systems which are explained below.
3. Indicate current water level.
A. Velocity Measuring System:
4. Properly channelize the water for irrigation and power generation.
Pivot tubes can be used to indicate fluid flow velocity by measuring the difference between the static and total pressure in fluids.
5. Monitor vibrations of the dam structure to ensure safety. Our solution helps in improving the ease of monitoring a dam system, thereby enabling measurement of water inflow velocity to dam and pressure measurements in dam walls. During floods the dam is subjected to a heavy volume of water. In orderto ensure the safety of dam from large volume of water in the upstream and to prevent flooding in the downstream, proper channelizing is essential. Our solution provides a scheme in order to channelize the water in dams effectively.
I.INTRODUCTION We introduce an automatic monitoring and control system of dams. This scheme enables easy controlling through its smart sensing and data acquisition technique using NI Compact RIO, LABVIEW8.2.1 .
Fig1: Velocity measurment using pivot tube V = sqrt [2 * {pt -ps} / R]
Firstly data is acquired from various sensors in order to measure water height in dams, velocity of water , pressure in dam walls and vibrations. The output from various sensors is obtained as voltage which is fed into another channel of NI 9201 I/P module. LABVIEW monitors and analyses the data from the sensors and controls the sluice gates for sustaining the required level of water, channelizing the water for irrigation and power
Where, V=velocity of water Pt=total pressure Ps=static pressure R=specific density of water
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Fig2: Wheatstone bridge circuit to find velocity of water Fig3:Working of turbines
The strain gauges form the four arms of the Wheatstone bridge in a full bridge pattern. When resistance of all the four arms of the bridge is same, the bridge is said to be balanced and the output of it will be zero. When the fluid flows along the pitot tube, there comes to picture the static pressure and the total pressure which exert a force on the diaphragms.
WATER POWER=(W*H)/1000KW Where ,W=r*g*v r=water density g=acceleration due to the gravity
This will lead to change in resistance in the four arms of the Wheatstone bridge, which make the bridge unbalanced and produces a corresponding voltage that is proportional to the pressure exerted as its output. So the bridge converts the force change information into milli volt output. This milli volt output voltage is amplified using a voltage amplifier to 0-10 v which is fed to the DAQ .
v=volume per second H= net water head Now, depending upon the volume of water accumulated inside dam ,the channels of the dams can be controlled which is simulated in lab view using LED’S.By allocating 2 different channels we can judiciously make use of water obtained in flood in generating power ,thereby conserving power. Mechanically, it can be achieved using motors of 2 different ratings depending upon the water level.
B. Channelizing System For Irrigation And Power Generation: Now, as our project is concerned about multipurpose dams, 2 channels are devoted for generating power from hydro electric plant. We are more concerned about the input power that is given to the turbine so called water power.
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C. Vibration Measuring System:
Fig5: Mode:2;Frequency:2.61 cycles/sec
Fig4:Basic sketch of an earthen dam Normally structures of dams should be concrete enough to withstand the high velocity of water expected during flood. Vibrations pose a major threat to the dam structure. The hatched portion shown in the diagrams below represent the deformation in the embankment at various frequencies of vibration. Now in our project we can analyze the vibration spectrum with an aid of vibration sensor for which we use LIVM(low impedance voltage mode) accelerometer ,model 3035B1 and a high rate data acquisition card connected to the output display. The power spectrum comprising the frequency and the amplitude of the vibration is obtained using which the alarm signalis issued.
Fig6: Mode:3;Frequency:2.93 cycles/sec
Fig4:Mode:1;Frequency:1.75 cycles/sec
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Fig7: Mode:4;Frequency:3.06 cycles/sec
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IV.BENEFITS This project will very much prove to be economical. Though the initial cost of implementing the system might appear to be a bit high, in the long run, the running cost will be low. The economic benefits that arise out of this scheme will be an eye-catcher. Safety is the main aspect of our design. This project will reduce flooding drastically in the downstream side thus preventing crop damage and civilian losses and will be a boon to the agronomy. This system replaces manual control and as a result human errors are totally eliminated. Moreover our project aims at producing two different powers generated at “hydroelectric power stations” by regulating the water power to the turbine suitably depending upon the volume of the water available. Thus, it helps us optimize the use of water according to the availability of the water.
Fig8: Mode:5;Frequency:3.64 cycles/sec D. Water Level Indicating System: A low AC voltage is applied between the probe electrode and the reference electrode. When the water comes in contact with the electrode tip, a conductive path is established between the sense electrode and the reference electrode. This current is sensed, amplified and made to operate a relay whose contacts in turn can be used for controlling the sluice gates.
Thus the automated dam controlling system measuring velocity, pressure, indicating water level, vibration and channelizing water for irrigation and power generation has been modeled using lab view .
E. Pressure Measuring System:
References:
To measure the pressure we use LL-V pressure sensor from Honeywell. The sensor has been designed for complete submersion in water. The output of the pressure sensor is compatible with the data acquisition card we use. The data from the sensor is acquired by the DAQ and when the pressure increases to dangerous levels, the sluice gates are opened.
1. S K Garg. ‘Irrigation Engineering and Hydraulic Structure’. Khanna publishers,Delhi,India,1996.
V.CONCLUSION
2. A Dhariwal and D G Purohit. ‘Failure of Embankment Dams’. IGC 2000 the Millennium Conference Proceedings 2000, Mumbai, India, p.371-374. 3. S S Bhattacharjee. and P Leger. ‘Application of NLFM Models to Predict Cracking in Concrete Gravity Dam’. J Struct Eng ASCE. 120(4):1255-1271, 1994.
III.THE FRONT PANEL
4. www.ni.com 5. www.honeywell.com 6. Rick Bitter, Taqi Mohiuddin and Matt Nawrocki. ‘LabVIEW: Advanced Programming Techniques’.second edition, CRC publications, 2006. 7. Ian Sinclair. ‘Sensors snd Transducers’, third edition, newness publications, 2001.
Fig9:Front panel of dam monitoring system
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Design and Analysis of a Novel Low Actuated Voltage RF MEMS Shunt Capacitive Switch Prashant Kumar Gupta 1, Tejbir Singh2,Garima Jain3 and DV Avasthi4 1
Meerut Institute of Technology, Meerut,250001,India 3DAV PG College Muzaffarnagar 2,4 Subharti Institute of Technology and Engineering, SVSU, Meerut, 250001, India 1 2 er.prashantkumargupta03@gmail.com, tvsp1282@gmail.com Hence from equation (1) it clears that the pull in voltage VPI can be reduced by three ways1. By reducing go which results in lower actuation voltage. But this way can be used at low frequency applications. Since it will unfavorably affect the high frequency OFF- State RF MEMS Switch performance by compromising the switch isolation (for series switch) or insertion loss (for shunt switch). 2. By reducing the actuation area. But this will not be useful solution since the compactness is prime issue and adaption of MEMS technology is to achieve miniaturization. 3. By reducing the value of spring constant KZ. So fixed-fixed flexures beam structure is used which offers lower spring constant without increasing the size and weight of deceive. Thus fixed-fixed flexures beam structure is important for low actuation voltage. The configuration of fixed-fixed flexures beam structure is shown in “Fig 1”. The spring constant kZ for fixed-fixed flexures beam structure is given by [6],
Abstract— This Paper presents design, analysis, proposed fabrication process and simulation of a novel low actuated voltage shunt capacitive RF MEMS Switch. The Air gap in between the membrane and CPW signal line is 1.5 µm. The lowest actuation voltage of switch is 3 Volts. The proposed fixedfixed flexures beam structure provides excellent RF Characteristics (Isolation -43 dB at 28 GHZ and insertion loss 0.12 dB at 28 GHZ). Index Terms— Actuation voltage, Coplanar wave guide, Spring constant, scattering Parameters.
I. INTRODUCTION In the last decade huge amount of work have done on RF MEMS switches. Which are used as switching devices, working at radio frequencies. Since they exhibits number of advantages over PIN diodes or field effect transistor (FET) switches. For example- Low or near about zero power consumption, very high isolation, very low insertion loss and very good linearity [1,2 and 3]. This leads to it an ideal for wireless equipments working in ground and space for example- mobile cell phones, Base station and satellites. However RF MEMS Switches have some downsides for examples- relatively low switching speed, low power handling, High-voltage, low long term life, and packaging problems. Mainly a large electrostatic force produced at very high voltage 15-60 volts is required for RF MEMS Switches to have satisfactory operation. Since the actuation voltage or the pull in voltage (VPI) of RF MEMS Switch is higher than the standard voltages of CMOS, Which is usually 5 Volts or less. It results in that the RF MEMS switches are not compatible with the control circuits and others. Therefore it is impossible to integrate them in a single chip [4]. Therefore one most important job of RF MEMS Switch is to decrease actuation voltage VPI. The propose of this paper is to design and analyze RF MEMS switch to attain actuation voltage below 4 Volts. This is achieved by using fixed-fixed flexures beam structure. I. DESIGN OF SWITCH The pull in voltage (VPI) is given by [5];
VPI
t kZ 4 Ew l
(2) Where, E is Young’s Modulus of membrane and t is thickness of membrane.
w
l Fig. 1. Fixed-fixed flexures Beam Structure
II.
FBRICATION PROCESS
The coplanar wave guide (CPW) structure preferred for RF MEMS Switch is S/W/S = 84/120/84 µm, H = 600 µm (50 ohm) above silicon substrate as shown in “Fig. 2”. W S
8kZ g o 3 27 A o
(1) Where, kZ is spring constant of membrane, go is gap between the movable membrane and CPW signal line and A is the actuation area.
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H Fig. 2. CPW Structure for switch
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The switch is fabricated by using five masks fabrication Process. 1. A layer of 1 µm thick oxide is deposited on the silicon (Si) substrate as buffer layer as Shown in “Fig. 2(a)”. 2. A layer of 4 µm of gold (Au) is sputtered and patterned for CPW transmission line as Shown in “Fig. 2(b)”. 3. A 0.15µm thick plasma enhanced chemical vapor deposition (PECVD) Si3N4 is deposited and patterned as dielectric layer in between membrane and CPW signal line as Shown in “Fig. 2(c)”. 4. A layer of photo resist with 4 µm thickness is spun coated and patterned to fill in the CPW slots (for obtaining flat membrane) as Shown in “Fig. 2(d)”. 5. A 1.5 µm thick photo resist sacrificial layer is spun coated and patterned as Shown in “Fig. 2(e)”. 6. A 0.6 µm thick layer of gold (Au) thin film is evaporated and wet etched to form bridge as Shown in “Fig. 2(f)”. 7. All the sacrificial photo resist is etched by oxygen plasma etching to free the metal bridge as Shown in “Fig. 2(g)”.
Si
(g) Fig. 3. Fabrication process flow.
The Detailed dimensions of switch are in Table I. TABLE I Detailed dimensions
go (µm)
A (µm)
w
l
(µm)
(µm)
t (µm)
L (µm)
1.5
120×40
10
184
0.6
508
III.
SIMULATION RESULTS A. Electrostatic Performance Simulations are done by using Coventor Ware software and the 3D structure of RF MEMS Switch is shown in “Fig. 4” Displacement Vs. voltage plot shown in “Fig. 5”. This shows that the Pull-in voltage of proposed fixed-fixed flexures beam structure is 3 volts. Displacement of the membrane at the desired pull in voltage is shown in “Fig. 6”. “Fig. 6” shows that at pull in voltage, the membrane and the pull in CPW signal line create a parallel plate capacitor with silicon nitride (Si3N4) as dielectric layer in between membrane and CPW signal line. This referred as down state capacitance (Cd). Since, in upstate of RF MEMS Switch the thickness of dielectric is insignificant as compare to air gap, so the up state capacitance is created by pull in membrane and CPW signal line with air as a dielectric layer. This capacitance is referred as up state capacitance (Cu).
SiO2
(a)
Au CPW (b)
Si3N4 (Dielectric) (c)
Fill-in layer (d)
Sacrificial layer (e)
Au (membrane) (f)
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Fig. 4. Three dimensional model of RF MEMS Switch
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Insertion loss
HFSSDesign1
ANSOFT
0.00 -0.10 I n s e r ti o n L o s s ( d B )
-0.20 -0.30 -0.40
Curve Info
-0.50
dB(S(PORT_1,PORT_2)) Setup1 : Sweep
-0.60 -0.70 -0.80 -0.90 -1.00
0.00
5.00
10.00
15.00
20.00 Freq [GHz]
25.00
30.00
35.00
40.00
Fig. 7. Insertion Loss (UP State)
Fig. 5. Displacement Vs Voltage plot
Isolation
HFSSDesign1
0.00
ANSOFT
Curve Info dB(S(PORT_2,PORT_1)) Setup1 : Sweep
Is o l a ti o n ( d B )
-10.00
-20.00 -30.00
-40.00
-50.00
0.00
5.00
10.00
15.00
20.00 25.00 Freq [GHz]
30.00
35.00
40.00
Fig. 6. Displacement Vs Voltage plot
Fig. 8. Isolation Loss (Down State)
B. RF Performance As we know that S11 and S21 parameters of RF MEMS switch are only calculated by Cu and Cd respectively. The relationships in between S11 and Cu and in between S21 and Cd are expressed as follows [6]-
IV. CONCLUSION Simulated results shows that very low actuation voltage of RF MEMS switch by using fixed-fixed flexure beam structure is achieved i.e. 3 volts, shown in “Fig. 5”. Simulated results also shows excellent RF Characteristics i.e. very low insertion loss -0.12dB at 28 GHZ shown in Fig. 7 and very high Isolation 43dB at 28 GHZ shown in “Fig. 8”. .
2 Cu2 Z o2 S11 4 4 2 S 21 2 2 2 Cd Z o 2
(3)
REFERENCES [1]
(4) Simulation of insertion loss S12 and isolation S21 are done by HFSS Software and the plots for S12 and S21 are shown in “Fig. 7” and “Fig. 8” respectively.
[2]
[3]
[4]
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Reines I. C., Goldsmith C. L., Nordquist C. D., Dyck C. W., Kraus G.M., Plut T. A., Finnegan P. S., Austin F. and Sullivan C. T. A low loss RF MEMS Ku-band integrated switched filter bank [J]. IEEE Microwave & Wireless Components Letters, vol.15, No.2 (2005), pp.74-76. Goldsmith C, Lin T H, Powers B. Micromechanical Membrane Switches for Microwave Applications [C]. In: IEEE MTT-S Int. Microwave Symp. Dig, 1995, pp. 91-96. Anil Kumar Sahu and B K Sarkar ‘A Novel Low Actuation Voltage RF MEMS Shunt Capacitive Switch’ 978-1-4244-48197/091 ©2009 IEEE C. L. Dai, H. J. Peng, M. C. Liu, C. C. Wu and L.J. Yang. Design and Fabrication of RF MEMS Switch by the CMOS Process [J].
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[5]
[6]
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Tamkang Journal of Science and Engineering, Vol. 8, No 3 (2005), pp. 197- 202 D. Balaraman, S. K. Bhattacharya, F Ayazi, and J. Papapolymerou, "Low Cost Low actuation Voltage Copper RF MEMS Switches", lEEE MTT-s Digest 2002, IF-WE-20. Mar. 2006. G. M. Rebeiz, RF MEMS Theory, Design and Technology, John Wiley & Sons Inc., New Jersey, 2003.
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Design and Analysis of Low-Leakage High-Speed Domino Circuit for Wide Fan-In OR Gates. M.Chennakeshavulu1, K.Subramanyam2 Associate Professor, ECE, RGMCET, JNTUA, Nandyal, A.P, India
1
1Onlinechenna@yahoo.com
M.Tech Student, ECE, RGMCET, JNTUA, Nandyal, A.P, India
2
2Subbuking987@gmail.com
Abstract--Domino CMOS logic circuit relations finds a broad variety of applications in microprocessors, digital signal processors, and dynamic memory owing to their high speed and low device count. In this paper a new domino circuit is studied, which has a lower leakage and higher noise immunity, lacking dramatic speed degradation for wide fan-in gates. The system which is utilized in this paper is based on comparison of Power, Propagation Delay, Energy, and Energy Delay Propagation. The studied circuit technique decreases the parasitic capacitance on the dynamic node, yielding a smaller keeper for wide fan-in gates for the fast and robust circuits. Thus, the disputation current and consequently power consumption and delay are reduced. The leakage current is also decreased by exploiting the footer transistor in diode configuration, which results in increased noise immunity. This the studied technique is applying in 90nm, 130nm, and 180nm technology using TANNER tools. Index Terms: Domino logic,leakage-tolerant, noise immunity and wide fan-in. Fig. 1. Standard Footless Domino circuit.
I.INTRODUCTION CMOS gates are mostly designed using static logic and dynamic logic. DYNAMIC logic such as domino
As a result, it is complex to get satisfactory robustness– performance tradeoffs. In this paper comparison-based domino (CCD) circuits for wide fan-in applications in ultra deep sub micrometer technologies are studied. The originality of the studied circuits is concurrently increases performance and decreases leakage power consumption. In this paper we are studied High speed Domino circuits for wide(4,8,16) FAN –IN applications. The most well-liked dynamic logic is the conventional standard domino circuit as shown in Fig. 1. In this design, a pMOS keeper transistor is working to stop any undesired discharging at the dynamic node due to the leakage currents and charge distribution of the pull-down network in the evaluation phase .Hence
logic is widely used in lots of applications to get high performance, which cannot be get with static logic styles [1].But, the major drawback of dynamic logic families is that they are more sensitive to noise than static logic families. When the technology scales down, the supply voltage is reduced for low power, and the threshold voltage (Vth) is also scaled down to reach high performance. while reducing the threshold voltage exponentially increases the subthreshold leakage current, drop of leakage current and improving noise immunity are of main concern in robust and highperformance designs in recent technology generations, especially for wide fan-in dynamic gates [2], robustness and performance significantly degrade with increasing leakage current.
improving the robustness by using keeper transistor. The keeper ratio K is defined as K =
(1)
Where W and L denote the transistor size, and μn and μp are the electron and hole mobilities.Even if the
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keeper upsizing improves noise immunity it increases current contention between the keeper transistor and the evaluation network. Thus, it increases power consumption and evaluation delay of standard domino circuits. These problems are graver in wide fan-in dynamic gates due to the huge number of leaky nMOS transistors connected to the dynamic node. Hence, there is a tradeoff between robustness and performance, and the number of pull-down legs is limited. The existing techniques try to compromise one feature to gain at the expense of the other. Several circuit techniques are studied. These circuit techniques can be divided into 2 categories. In the 1st category, circuit techniques to modify the controlling circuit of the gate voltage of the keeper such as 1.Standrad Footless Domino(SFLD)[1], 2. Conditional-Keeper Domino (CKD)[3] 3. High-Speed Domino (HSD) [4],, 4. Leakage Current Replica (LCR) Keeper Domino [5], & 5. Controlled Keeper by Current-Comparison Domino (CKCCD) [6]. And the 2nd category, designs including the designs to vary the circuit topology of the footer transistor in the evaluation network such as 6. Diode Footed Domino (DFD) [2] and 7. Diode-Partitioned Domino (DPD)[7] These 2 category techniques are explained in given bellow.
larger keeper. The increase in size of delay element improves noise immunity but power dissipation and delay increases. 3. High-Speed Domino (HSD) : Here delay is introduced in clock by using two inverters as shown in Fig.3, in arrange to reduce the current during the PMOS keeper at the initial of evaluation phase which leads to reduce the power dissipation up to some extent. This makes it probable to use physically powerful keeper without performance degradation and improve the noise margin. Other than the power and area overhead of clock delay circuit remains. In precharge phase, when clock becomes LOW, transistor Mp1 turns ON and dynamic node is charged to logic HIGH and in the start of evaluation phase, Mp2 still turns ON which keeps the keeper transistor Mk to OFF condition. After delay completion Mp2 turns OFF.
2. Conditional-Keeper Domino (CKD): It is one of the standard versions of domino logic as shown in Fig.2. Here 2 keeper transistors are used. At the commencement of evaluation phase, the minor keeper K2 charges the dynamic node in case if all the inputs are LOW. Then after delay end, if dynamic node still remains charged, the NAND gate turns the better keeper transistor ON to stay the dynamic node HIGH for the rest of evaluation phase. If the dynamic node has discharged, the keeper transistors remain OFF.
Fig.3 High speed Domino (HSD)[3]
In connecting this, the inputs make the logic function as well as in case if input(s) are logic HIGH then provides discharge path for dynamic node and output changes to logic HIGH and Mk is in OFF condition as a logic high as (VDD-Vtn) voltage, where Vtn is NMOS threshold voltage, is passed to Mk which does not turn it into ON state. And if no input is HIGH then dynamic node is not discharged and output is still LOW. So Mn1 turns ON and pass logic LOW to Mk which turns it ON and dynamic node is kept at logic HIGH. In this way, keeper transistor prevents charge leakage in High speed domino logic. The difficulty with high speed domino logic (HSD) is that a voltage of VDD - Vtn is accepted through the NMOS transistor Mn1 which leads to a small dc current through the keeper transistor and pulldown network and also large noise at input terminals of PDN causes the dynamic node discharge because of no footer transistor. 4. Leakage Current Replica (LCR) Keeper Dom In LCR Keeper domino circuit the transistor size is varied as shown in Fig.4, the mirror transistor M1 is set to 7Lmin to reduce channel length modulation and reduce variation of the threshold voltage. The width of transistor M2 is same to the sum of the widths of the nMOS transistors of the PDN. The width of the keeper transistors of the gates, which are simulated using the LCR keeper, are varied to get the desired delay.
Fig.2 Conditional Keeper Domino [3]
This circuit has a few drawbacks such as decreasing delay of the inverters and the NAND gate has a few restrictions. Robustness can be achieved by using the
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6. Diode Footed Domino (DFD): Diode-footed domino [12] is reduced leakage current, enhanced performance and better strength as shown in fig.6 . In this circuit,M1 is the diode footer, which is in sequence with the evaluation network. Leakage current is reduced by M1 due to the stacking effect. Also M1 increases the switching threshold voltage of the gate and there by improves noise immunity. The mirror transistor and current feedback improve the robustness of the circuit against sub-threshold leakage and input noise in the deep submicron range.
Fig.4 Leakage Current Replica Domino [5]
5. Controlled Keeper by Current-Comparison Domino (CKCCD): The reference circuit [12] of the leakage current consists of transistors M5, M6, M7 and M8. The transistor M5 is off inactive mode and will be on in standby mode to decrease standby power. The size of the mirror transistor M3 is selected based on the leakage of the pull down transistors. The mirror current should be greater than the pull down leakage and minor than the lowest PDN discharge, current with at least one in put at the high logic level to make sure correct operation. Since the reference circuit is a replica circuit of the PDN, the reference current varied switch temperature presently similar to the PDN leakage current. Thus, the design is almost insensitive to temperature variations.
Fig6. Diode Footed Domino[2]
7. Diode-Partitioned Domino (DPD): In the DPD according to [7] each partition Consists of 4 legs to get the most excellent results as shown in fig.7. All inverters and keepers of the partitions are set to least size and the length of the major keeper transistor MK . The desired delay is achieved by varying the size of the precharge and keeper transistors.
Fig.5 Controlled Keeper by Current-Comparison Domino[6] Fig.7 Diode-Partitioned Domino
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8.Wide FAN-IN or gate Current comparision comparision Domino: This circuit is similar to a replica leakage circuit [7], in which a series diode-connection transistor M6 similar to M1 is added. In fact, as shown in Fig.8,.The circuit has five extra transistors and a shared reference circuit compared to standard footless domino (SFLD).
MEval are off. Also, the output voltage is raised to the high level by the output inverter. B. Evaluation Phase: In this phase, clock voltage is in the high level [CLK = “1”, CLK = “0” in Fig. 8] and input signals can be in the low level. Therefore, transistors Mpre and MDis are off, transistor M1, M2, Mk2, and MEval are on, and transistor Mk1 can become on or off depending on input voltages. Thus, two states may occur. First, all of the input signals remain high. Second, at least one input falls to the low level. In the first state, a little amount of voltage is established across transistor M1 due to the leakage current. Even if this leakage current is mirrored by transistor M2, the keeper transistors of the second stage (Mk1 and Mk2) compensate this mirrored leakage current. It is obvious that upsizing the transistor M1 and increasing the mirror ratio (M) increase the speed due to higher mirrored current at the expense of noiseimmunity degradation. In the second state, when at least one conduction path exists, the pull-up current flow is raised and the voltage of node A is decreased to nonzero voltage, which is equal to gate-source voltage of the saturated transistor M1.
Fig 8.Wide FAN-IN or gate Current comparision comparision Domino
The circuit can be well thought-out as two stages. The first stage reevaluation network includes the PUN and transistors MPre,MEval, and M1.The PUN.The second stage looks like a footless domino with one input [node A in fig5],without any charge sharing, one transistor M2 regardless of the Boolean function in the PUN,and a controlled keeper consists of two transistors. Only one pull-up transistor is connected to the dynamic node instead of the n-transistor in the n-bit OR gate to decrease capacitance on the dynamic node, yielding a higher speed. The input signal of the second stage is prepared by the first stage. In the evaluation phase, thus, the dynamic power consumption consists of two parts: one part for the first stage and the other for the second stage. A. Predischarge Phase: Input signals and clock voltage are in high and low levels, correspondingly, [CLK = “0”, CLK = “1” in Fig.8] in this phase. Then, the voltages of the dynamic node (Dyn) and node A have fallen to the low level by transistor MDis and raised to the high level by transistor Mpre, in that order. Hence, transistors Mpre, MDis, Mk1, and Mk2 are on and transistors M1, M2, and
9. SIMULATION RESULTS AND COMPARISONS These circuits are simulated using TANNER Tools TSPICE in the high-performance 180 nm, 130nm, and 90nm predictive technology. The supply voltage used in the simulations is 2 V in the wide fan-in (4, 8, 16, input)
OR-gate,circuit.The simulated dynamic circuits results of Power,Delay,Energy,Energy Delay Propagation as shown in given tables
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TABLE-I Comparisons of power, delay, energy, E.D.P of SFLD Wide FAN-IN Gates(4,8,16 in puts)
IN PUTS 4 in
8 in 16in
180nm
130nm Propaga tion Delay
Energy
E.D.P
3.5x -4 10 w
31ns
10.8x -13 10
3363.5 x10-22
3.43X 10-4w 3.52X 10-4w
31n
106x 10-13 109x 10-13
3296x 10-22 3382x 10-22
Averag e
Power
31n
90nm Propaga tion Delay
Energy
E.D.P
A.P
P.D
Ener gy
E.D.P
4.7x 10-4w
31ns
145.7x 10-13
4516x 10-22
6x 10-4w
30n
36x 10-13
1080x 10-22
4.59x 10-4w 4.6x 10-4w
31n
142.2x 10-13 142.6x 10-13
4410x 10-22 4420x 10-22
6.38x 10-4w 6.69x 10-4w
30n
191x 10-13 207x 10-13
5742x 10-22 6429x 10-22
Averag e
Power
31n
31n
TABLE-II Comparisons of power, delay, energy, E.D.P of CKD Wide FAN-IN Gates (4, 8, 16 in puts)
IN PUT
180nm Average
4 in
8 in 16 in
Power
1.34X 10-4w 1.3X 10-3w 1.17X 10-3w
130nm Propag ation Delay
Energy
31n
41.5x 10-13 39x 10-12 36.2x 10-12
30n 31n
E.D.P
Average
1287x 10-22 1170x 10-21 1124x 10-21
1.6x 10-3w 1.67x 10-3w 1.53x 10-3w
90nm
power
Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
31n
49.6x 10-12 50.1x 10-12 47.4x 10-12
1537x 10-21 1503x 10-22 1470x 10-21
9.6x 10-3w 6.38x 10-4w 2.13x 10-3w
30n
288x 10-12 191x 10-13 63.9x 10-12
8640x 10-21 5742x 10-22 1917x 10-21
30n 31n
30n 30n
TABLE-III Comparisons of power, delay, energy, E.D.P of HSD Wide FAN-IN Gates (4, 8, 16 in puts)
IN PUT 4in
8in 16in
180nm
130nm
90nm
Average Power
Propaga tion Delay
Energy
E.D.P
Average power
Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
3.6x 10-4w 3.59X 10-4w 3.65X 10-4w
31n
111.6x 10-13 111x 10-13 113x 10-13
3459x 10-22 3449x 10-22 3507x 10-22
4.65x 10-4w 4.69x 10-4w 4.73x 10-4w
31n
144x 10-13 145.3x 10-13 146.6x 10-13
4468x 10-22 4507x 10-22 4545x 10-22
6.5x 10-4w 6.5x 10-4w 6.77x 10-4w
30 n 30 n 30 n
195x 10-13 195x 10-13 203x 10-13
5850x 10-22 5850x 10-22 6093x 10-22
31n 31n
31n 31n
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TABLE-IV Comparisons of power, delay, energy, E.D.P of LCR Keeper Wide FAN-IN Gates (4, 8, 16 in puts)
IN PUT
180nm Propaga tion Delay
Energy
3.51x 10-4w 3.52X 10-4w
31n
3.57X 10-4w
31n
Average 4 in
8 in
16 in
130nm
Power
31n
90nm Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
4.5x 10-4w 4.60x 10-4w
31n
4309x 10-22 4420x 10-22
6.37x 10-4w 6.37x 10-4w
30 n 30 n
191x 10-13 191x 10-13
5733x 10-22 5733x 10-22
4.64x 10-4w
31n
139x 10-13 142.6 x 10-13 143.8 x 10-13
4459x 10-22
6.62x 10-4w
31 n
205x 10-13
6361x 10-22
E.D.P
Average
108x 10-13 109x 10-13
3373x 10-22 3382x 10-22
110x 10-13
3430x 10-22
Power
31n
TABLE-V Comparisons of power, delay, energy, E.D.P of CKCCD Wide FAN-IN Gates (4, 8, 16 in puts)
IN PUT
180nm
130nm Propag ation Delay
Energy
2.3x 10-4w
30n
8 in
2.41X 10-4w
16 in
2.46X 10-4w
Average
4 in
Power
90nm Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
3.2x 10-4w
31n
99.2x 10-13
3075x 10-22
6.06x 10-4w
30n
181x 10-13
5454x 10-22
2316x 10-22
3.19x 10-4w
31n
3065x 10-22
114x 10-13
3536x 10-22
3.2x 10-4w
31n
3065 x 10-22 3.7x 10-4w
31n
2364x 10-22
98.89 x 10-13 99.2x 10-13
31n
114x 10-13
3555x 10-22
E.D.P
Average
69x 10-13
2070x 10-22
31n
74.7x 10-13
31n
76.2x 10-13
power
3075x 10-22
TABLE-VI Comparisons of power, delay, energy, E.D.P of DFD Wide FAN-IN Gates (4, 8, 16 in puts)
IN PUT
180nm Average
4 in
8 in 16 in
Power
3.54X 10-4w 3.67X 10-4w 3.71X 10-4w
130nm Propag ation Delay
Energy
30n
106x 10-13 113x 10-13 115x 10-13
31n 31n
E.D.P
Average
3186x 10-22 3526x 10-22 3565x 10-22
4.71x 10-4w 4.79x 10-4w 4.86x 10-4w
power
90nm Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
30n
141.1x 10-13 148.4x 10-13 150.5x 10-13
4239x 10-22 4603x 10-22 4670x 10-22
6.07x 10-4w 5.84x 10-4w 6.02x 10-4w
30 n 30 n 30 n
182x 10-13 175x 10-13 180x 10-13
5463x 10-22 5256x 10-22 5400x 10-22
31n 31n
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TABLE-VII Comparisons of power, delay, energy, E.D.P of DPD Wide FAN-IN Gates(4,8,16 in puts)
IN PUT
180nm Average
4 in
8 in 16 in
Power
3.70X 10-4w 3.67X 10-4w 3.66X 10-4w
130nm Propag ation Delay
Energy
30n
111x 10-13 113x 10-13 113x 10-13
31n 31n
E.D.P
Average
3330x 10-22 3526x 10-22 3517x 10-22
6.12x 10-4w 4.79x 10-4w 6.42x 10-4w
90nm
power
Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
30n
183.6x 10-13 148.4x 10-13 192.6x 10-13
5508x 10-22 4603x 10-22 5778x 10-22
6.16x 10-4w 5.84x 10-4w 6.0x 10-4w
30 n 30 n 30 n
184x 10-13 175x 10-13 180x 10-13
5544x 10-22 5256x 10-22 5400x 10-22
31n 30n
TABLE-VIII Comparisons of power, delay, energy, E.D.P of CCD Wide FAN-IN Gates (4,8,16 in puts)
IN PUT
180nm Average
4 in
8 in 16 in
Power
1.07X 10-4w 8.34X 10-4w 0.92X 10-4w
130nm Propag ation Delay
Energy
1.53 n 1.29 n 12n
1.63x 10-13 10.7x 10-13 132x 10-22
E.D.P
Average
2.50x 10-22 13.8x 10-22 132x 10-22
2.25x 10-4w 1.39x 10-4w 1.57x 10-4w
90nm
power
Propaga tion Delay
Energy
E.D.P
A.P
P.D
Energy
E.D.P
0.9n
20.25x 10-13 1.807x 10-13 1.86x 10-13
18.2x 10-22 2.34x 10-22 2.22x 10-22
4.19x 10-4w 2.3x 10-4w 2.5x 10-4w
0.3 n 0.1 n 83 n
1.25x 10-13 0.23x 10-13 207x 10-13
0.3771x 10-22 0.023x 10-22 1722x 10-22
1.3n 1.19 n
4 3 2 1 0
4 in 8 in 16 in SFLD
CKD
HSD
LCR keeper
CKCCD
DFD
DPD
CCD
Fig.9. Comparison of power supply with same delay
10. CONCLUSION: The leakage current of the evaluation network of dynamic gates was considerably increased with technology scaling, particularly in wide domino gates, yielding reduced noise immunity and improved power consumption. So, new designs were required to get preferred noise robustness in wide fan-in circuits. Also, rising the fan-in not only reduced the worst case delay.
The main aim is to make the domino circuits extra robust and with low leakage without significant performance degradation or increased power consumption. This was done by comparing the evaluation current of the gate with the leakage current. Keeper size of very high fan-in gates. Using the highperformance 180 nm [3] at a power supply of 2 V, wide
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fan-in 4 to 16-bit OR gate circuit. The proposed design plus several existing circuit designs were simulated and compared, imulation results verified significant progress in leakage reduction and satisfactory speed for high-speed applications. moreover, they verified that. Thus, the CCD was above all suitable for implementing wide fan-in Boolean logic functions with high noise
immunity, lower area consumption, time delay, Energy, Energy, Delay Propagation and power consumption. Moreover, a normalized FOM, previously proposed by the authors, was modified to include standard deviation of delay.
REFERENCES: [1] J. M. Rabaey, A. Chandrakasan, and B. Nicolic, Digital Integrated Circuits: A Design Perspective, 2nd ed. Upper Saddle River, NJ:Prentice-Hall, 2003. [2] H. Mahmoodi and K. Roy, “Diode-footed domino: A leakage-tolerant high fan-in dynamic circuit design style,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 51, no. 3, pp. 495–503, Mar. 2004. [3] Sumit Sharma “ A Novell High-speed low-power domino logic technique for static output in evaluation phase for high frequency inputs” IJERA march 2014.. [4] M. H. Anis, M. W. Allam, and M. I. Elmasry, “Energyefficient noise-tolerant dynamic styles for scaled-down CMOS and MTCMOS technologies,” IEEE Trans. Very Large Scale (VLSI) Syst., vol. 10, no. 2, pp. 71–78, Apr. 2002. [5] Y. Lih, N. Tzartzanis, and W. W. Walker, “A leakage current replica keeper for dynamic circuits,” IEEE J. Solid-State Circuits, vol. 42, no. 1,pp. 48–55, Jan. 2007. [6] A. Peiravi and M. Asyaei, “Robust low leakage controlled keeper by current-comparison domino for wide fan-in gates, integration,” VLSI J., vol. 45, no. 1, pp. 22–32, 2012. [7] H. Suzuki, C. H. Kim, and K. Roy, “Fast tag comparator using diode partitioned domino for 64-bit microprocessors,” IEEE Trans. Circuits Syst., vol. 54, no. 2, pp. 322–328, Feb. 2007. [8] K. Roy, S. Mukhopadhyay, and H. MahmoodiMeimand, “Leakage current mechanisms and leakage reduction techniques in deep sub micrometer CMOS circuits,” Proc. IEEE, vol. 91, no. 2, pp. 305–327,Feb. 2003. [9] N. Shanbhag, K. Soumyanath, and S. Martin, “Reliable low-power design in the presence of deep submicron noise,” in Proc. ISLPED, 2000, pp. 295–302. [10] M. Alioto, G. Palumbo, and M. Pennisi, “Understanding the effect of process variations on the delay of static and domino logic,” IEEE Trans. Very Large Scale (VLSI) Syst., vol. 18, no. 5, pp. 697–710, May 2010. [11] H. Mostafa, M. Anis, and M. Elmasry, “Novel timing yield improvement circuits for high-performance lowpower wide fan-in dynamic OR gates,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 58, no. 10, pp. 1785– 1797, Aug. 2011. [12] Ali Peiravi , Mohammad Asyaei “CurrentComparison-Based Domino: New Low-Leakage HighSpeed Domino Circuit for Wide Fan-In Gates” VOL. 21, NO. 5, MAY 2013
M.CHENNAKESAVALU, He completed B.Tech in 2003 at JNTUHyderabadand He completed M.Tech in 2010 at JNTUAnantapur.He got lecturership in UGCNET-2013.He has 10 years of teaching experience. He is working as ASSOC.professor in Dept of ECE, in RGMCET,Nandyal. He published Nearly12 papers in Various publication. His research areas are low power VLSI design and interconnects in NOC.He has professional memberships in MIETE. He guided 5 projects at master level.
K.SUBRAMANYAM received the B.E degree in electronics and communications engineering from the JNT University of Anantapur in 2008 to 2012. He is currently pursuing the M.E. degree in digital systems and computer electronics engineering from the JNT University of Anantapur. His current research interests include low-power, high-performance, and robust circuit design for deep-sub micrometer CMOS technologies.
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Development of Sensing Device to Detect Persons Hiding in a Car 1
Mohammed Sanaullah Shareef.M.Y, 2Mohammed Tharique.PM Department of Electronics and Communication Engineering, C.Abdul Hakeem College of Engineering and Technology 1 sanaullahshareef786@gmail.com, , 1 +919944738419.
1,2,3
and places a heavywork load on border officers, resulting in high costs in view ofthe thousands of vehicles that cross the borders. Alternatives tothis manual method have been considered, such as the X-rayequipment used to check vehicles and their cargo [4] and theuse of a mobile inspection robot [5]. However, these methodsrequire a high initial cost as well as a large setup area. Moreover, danger of using the X-ray method for finding illegal immigrants is indicated because the illegal immigrants would beexposed to X-ray [6]. A simpler method, which uses microvibrations to detect a concealed person, has been proposed [7].We also presented a similar method that detects heartbeat signals by means of a piezoceramic device set under the tire of avehicle [8]. These methods, however, are sensitive not only tohuman micro vibrations but also to external disturbances such asground vibration and wind force acting on the vehicle. This paper describes a novel pneumatic method that uses silicon air tubes and a low-frequency condenser microphone asa pressure sensor highly sensitive to the heartbeat signals of aperson hiding in a vehicle but robust against external disturbances such as ground vibration and wind force.
Abstract—This paper describes a novel method for detecting the presence of a person hiding in a car. One of the important strategies of homeland security is border control. In particular, strict and effective monitoring to control illegal immigration is a key strategy for maintaining public safety and a healthy local economy, and is essential for preventing the entry of terrorists. Here, we focused on
developing a method to detect a person trying to illegally cross the border by hiding in a car. The proposed method is based on pneumatics. A silicon tube (inner diameter 4 mm) with one end plugged by a highly sensitive pressure sensor and the other end capped is sandwiched between two rigid boards and placed on the ground at the entrance gate of the border. When one wheel of the car is on the board and the engine is stopped, the pressure sensor can detect human vital signs such as the heartbeat, which cannot be concealed. Due to the high sensitivity of the pressure sensor, consideration was given to the effect of external disturbances such as ground vibration and wind force acting on the car. Here, we propose a heartbeat detection filter robust against disturbances but sensitive to the heartbeat signal and an index to discriminate between the presence and non-presence of a person, and we present the experimental results obtained using the proposed method under various disturbance conditions. Index Terms—Automobile, condensermicrophone, security, silicon tube.
II. DESIGN OF SENSING DEVICE A. Principle of the Sensing Device Using Silicon Tube andCondenser Microphone Fig. 1 shows the principle of the sensing device used to measure themicro vibrations. The silicon tube is set between flexiblespacers on the base board. At one end of the tube is the low-frequency condenser microphone used as the pressure sensor, andthe other end of the tube is capped. A cover board is placed ontop of the tube and flexible spacers. When one wheel of the caris on the board and the engine is stopped, and if there is a personhiding inside the car, the vibration from the person’s heartbeatwill be transmitted through the car chassis, wheel, tire, coverboard, and finally to the silicon tubes and spacers. Displacementcaused by the vibrating cover board compresses the silicon tubesand spacers, which decreases the crosssection area of the tubeand increases the air pressure in the tube. The pressure sensordetects the change in pressure. The pressure change is producednot only by the heartbeat but also by the resonance frequencycomponent of the car chassis fromthe dynamic pressure of windacting against the car and the movement of a person changingposition.
I. INTRODUCTION ILLEGAL immigration from neighboring countries is a serious problem from the viewpoint of homeland security and crime prevention. In the United States, the number of illegal immigrants exceeds 1,20,00,000 [1]. In bordering countries, illegal immigration is frequently attempted via ground vehicles such as cars and trucks in which one or more persons are hidden under the seats, in the engine compartment or in spaces carved out of the dashboard [2]. In one case, an officer of the Arizona State Border Control found 13 people hiding in a van disguised as a transport truck [3]. Accurate devices to quickly and easily find people hiding in vehicles are necessary to maintain strict border control as well as make the legal immigration procedure more effective. Generally, a border officer checks the inside and/or outside of a vehicle to determine if anyone is hiding there. The inspectionis a visual search, which is time-consuming
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S[m2]surface upon which the force acts; K[N/(m2)]spring constant of the silicon tube γratio of specific heat; Ththreshold for discriminating between presenceand non-presence of person; The total force acting on the surface of the tube is given by
(1)
Fig. 1. Principle of the microvibration sensing device using silicon tubes.
Fig. 3. Signal processing flow for extracting fh(t) andfb(t)components. As the change in the state of air in the tube is adiabatic, the relationship between the pressurep(t) and the volume v(t)is given by Poisson’s law as follows: (2) Total differentiation of (2) forp(t) is given by
Fig. 2. Mathematical model of air tube with condenser microphone.
(3) By lettingP(t)=P0,V(t)=V0,dv(t)=Sx(t),f(t)=Sdp(t)then (2) can be rewritten as
B. Mathematical Model of the Silicon Tube and CondenserMicrophone We considered a mathematical model for the sensing device depicted in Fig. 1, Fig. 2 shows a schematic model of the device. The variables and constants for themodel are defined as follows: t[s]continuous time; K discrete time by sampling interval ; T[s]total measurement time; Po[Pa]steady-state pressure in the tube; p(t)[Pa]change in pressure P(t)[Pa]pressure with change in pressure ; V0[m3]air volume of the tube under steadystatepressure; v(t)[m3] total air volume in the tube with change involume fh(t)[N] force acting on the tube due to human heartbeat; fb(t)[N] force acting on the tube due to body movement; fc(t)[N]force acting on the tube due to resonancevibration of the car chassis; n(t)[N]force acting on the tube due to random noise f(t)[N]total force acting on the tube; x(t)[m]surface displacement due to total force e(t)[V]output voltage from the pressure sensor
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(4) Equation (4) shows the characteristics of the air spring of thesilicon tube. On the other hand, the silicon tube itself has stiffness with spring constant K. Thus, from (3) or (4), the changein pressure in the tube due to the total force is given by (5) Thus, from (5) above, the coefficient * which corresponds to thecross-sectional area that relates the external force to the tubeand the internal pressure, is a function of the contactingarea S. Thecoefficient increases in proportion to S forthe range , whereas for the range , it decreases in proportion to S. Thesilicon tube is flexible and thus the spring constant K issmall, smaller than that of the air spring. Then, the coefficientincreases in proportion to S the contacting area . Thus, toobtain a larger coefficient, we must make the contacting area S wider and reduce the spring constant of the tube and spacer. Torealize these conditions, the spacer must be as soft as possibleto
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make the contacting area wider; the tube is pressed not onlyby the upper plate but also by the side spacer, as shown in thefront view in Fig. 1.
sensor. Sensitivity is 10 mV/Pa withflat-gain characteristics for the frequency range of 0.1 Hz to 10kHz, allowing the detection of heartbeat and body movement.
C. Signal Processing Flow The pressure expressed in (5) is detected by the condensermicrophone acting as a highly sensitive pressure sensor. Outputvoltage e(t) from the sensor includes various vibration forces asgiven by (1). Fig. 3 shows how to extract the force components fh(t)andfb(t), which are the vital signs of a person hiding inthe car. The fundamental frequency of the adult human heartbeat is around 0.7 to 3.0 Hz. However, the frequency range alsocovers part of the mechanical vibration component fc(t)of thechassis, which must be distinguished from the heartbeat. Theheartbeat includes higher harmonic components of up to severalhundred Hz.We monitored the higher harmonic components of8 to 12 Hz with a relatively high spectrumand less noise and discriminated the components by means of a band pass filter. Thecomponent is rectified and smoothed using a band pass filterwitha cutoff frequency of 0.5 and 2.0 Hz, which yields a shift backto the fundamental frequency range. The other vital sign,fb(t) for body movement, is much greater than the heartbeat and thuscan be directly detected. The output voltage from the pressure sensor is an A-D converted with the sampling time andexpressed by e(k) for the discrete time .
Fig. 4.Sensing device for verification experiment.
D. Index for Judging Human Presence Here, we define the index for judging whether or not a personis present in the car. In the filtering process in Fig. 3, the disturbance componentsfc(t) and n(t) are filtered out. Thus, the standard deviation of the output signal becomes greater whena person is present compared to when no one is present. Thus,the simple standard deviation of can serve as the index:
(6) The presence of aperson is discriminated by comparing the index with thethreshold .
Fig. 5. Experimental conditions for Case 1. The cover board is made of polycarbonate with a thickness of 5 mm.
III. VERIFICATION EXPERIMENT A. Measurement System for Verification Experiment Fig. 4 shows the sensing device used in the verification experiments. Forty-eight rectangular shaped spacers with dimensions 400*30*10 mm are set on the base board. The spacersare made of a soft, sponge-type material. Six silicon tubes, each with a pressure sensor, are set among the spacers. The tubescover the entire area of the board and are compressed by verticaland horizontal forces, as shown in the front view in Fig. 1. Sincesilicon tubes are softer and more flexible and durable than vinylchloride tubes, the spring constant K of the tube and spaceris smaller and the sensitivity becomes high. A low-frequencymicrophone (MX-E4758) was selected as thehighly sensitive pressure . As shown in Fig. 5, we placed a blower 1 m away from a sedan-type car to blow air at different strengths: strong 132
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B. Measurement Procedures In order to verify the validity of the sensing method, we carried out the experiment for two cases using the sensing device shown in Fig. 4. We let T=2.56s and ∆t=10ms and acquired data 40 times and calculated index F for each experimental condition. (Case 1) Verification experiment for robustness against wind and vibration. In practical case, this system might be used outdoors. In that case, the chassis is shaken by thewind and itmight cause a factor of the error for judgment. Hence, we verified the robustness ofthe system against the wind
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m3/min, medium 102 m3/min, weak 72 m3/min and none. We compared how index F changed depending on whether or not a person wasin the car. (Case 2) Ability to detect a person in a camper with varioushiding places.
Fig.
6.
Experimental conditions for Case 2. Fig. 7.Heartbeat vibration from a person in the car (no wind). (a) Output from the sensing device. (b) Result of FFT.
As shown inFig. 6, we checked for the presence of peoplein a camper,where there are various hiding places. The sensing plate was set under the front left-side wheel. One or two persons were hiding in the driver’s seat and the assistant driver’s seat, rear cabin, or roof.With no one in the vehicle, we measured the output signal when there were no people around the car and when there were many people in the vicinity of the car.
calculated is F=0.81.Fig. 8(a) for the wave whenno one is in the car, shows a smaller signal level than that of Fig. 7(a). Furthermore, Fig. 8(b) has no conspicuous spectrum as seen in Fig. 7(b) but a small spectrum extends over a wide range. The value of index F=0.012 in this wave. Index Fwhen a person was in the car is 25 times greater than when no one was in the car. Fig. 9 shows the histogram for index Funder four different wind conditions when a person was in the car. Fig. 10 shows the histogram under the same wind conditions but with no one in the car.
IV. RESULTS OF THE EXPERIMENT A. Robustness Experiments in Case 1 Fig. 7 shows the output signal e(k) and its Fourier transform by FFT when a person was in the car and there was no wind. Fig. 8 shows the resultswhen no one was in the car and there was no wind. Fig. 7(a) shows the periodic wave with the same period as the heartbeat of the person. Fig. 7(b) shows thefundamentaland higher components of theheartbeat. For the signal in Fig. 7, the index
Fig. 8.Car vibration (no one in the car, no wind). (a) Output from the sensing device. (b) Result of FFT.
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camper is greater than that for the sedan. Fig. 11(b) shows the histogram when a person is in the assistantdriver’s seat. The distribution range is almost the same as thatof Fig. 11(a), but the distribution fluctuation range is narrower than in the case of a person sitting in the driver’s seat. This is because the sensing device board is just under the assistant driver’s seat, where the person was sitting. Fig. 11(c) shows the results when two people are sitting in the rear cabin.
Fig. 9. Histogram of index F when a person was in the car. In Fig. 9, when a person was in the car, despite the strength of wind blowing, index F is distributed in the range from 0.20 to 0.40; whereas in Fig. 10 with no one in the car, index F is distributed from 0.015 to 0.21. The frequency in the histogram increases in medium wind of 102 m/min and strong wind of 132 m /min due to the vibration caused by the wind. These figures show the clear difference in the histograms of the index F between the presence and the absence of persons inthecar, despite the wind blowing at different strengths. Therefore, we consider that this system is robust over the wind and can be used outdoors.
Fig. 10. Histogram of index Fwhen no one was in the car. B. Results of Case 2 Fig. 11(a)–(d) shows the histogram of the index when one person or two persons are (a) in the driver’s seat of the camper,(b) in the assistant driver’s seat, (c) in a seat in the back cabin, and (d) on the roof. Fig. 11(e) is the histogram of the index when no one was in the car and no one was walking near the car, and when many people were walking in the vicinity of the car, as shown in the photo in Fig. 11(e). In the histogram in Fig. 11(a), index F is distributed from 0.02 to 0.40, which are lower values compared to that of the sedan-type car. This is because the distance between the sensing device board and the seating position in the
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Fig. 11.Histogram of index for Case 2. (a) Driver’s seat. (b) Assistant driver’s
seat. (c) Seat in rear cabin. (d) On the roof. (e) No person in the car.
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The histogramdistribution of the index ranges from 0.15 to 0.20, which is lowerthan that in Fig. 11(a) and (b). This is due to the greater distance between the sensing device and the seats in the rear cabin. Fig. 11(d) shows the results when a person is on the roof. The distribution is similar than that in Fig. 11(c), but more widely spread because the roof is an unstable location resulting in frequent body movements. Fig. 11(e) shows the results when no one was in the car and there were either weak or strong disturbance vibrations. The distribution is similar to that shown in Fig. 10 when no one was in the sedan-type car. The most frequent distribution is concentrated around F=0.015. From the histograms in Fig. 11(a)–(e), index F decreases in proportion to the distance between the person’s position and the sensing device board. This is because the signal attenuates in proportion to the distance. However, when a person is in the car, even a slight body movement provides a greater value of index .Thus, the distribution is more widely spread than when no one is in the car. Even with the difference in histogram distribution due to the different cars and hiding positions, there was still a clear difference in distribution when people were in the car and when no one was in the car. Index F can be used to discriminate between the presence and nonpresence of people in a car being inspected.
Fig. 12. Cumulative frequency distribution of index F for Case 1 (presence) and Case 2 (non-presence). In this system, we used heartbeat signal. Actually, there are other bio-signals such as breath, but the frequency of the heartbeat and those of breath are different. As shown in Fig. 3, we extract only the band width of the heartbeat frequency by signal processing for the index. In general, humans cannot stop heartbeat consciously regardless of the breathing status. Therefore, breathing and other biosignals whose frequencies are different from that of heartbeat do not affect the judgments of thesystem.Furthermore, even if animals are hidden in the vehicle, this system is capable of detecting their presences if the forces of the heartbeats or the motions of the animals are as strong as human heartbeats. This system can detect if at least one person is hiding or notin the vehicle. So the system does not detect how many persons are hiding in the vehicle. In a real scene at border security, border officers require the drivers and all fellow passengers to get out of the vehicles. In this condition, if the system finds at least one person hiding in the vehicle, the vehicle and the parties including the drivers and all fellow passengers should be investigated more strictly by the border officers. Hence, we consider that role of this system is not to find how many persons are hiding in the vehicles, but to find out atleast one person hiding in the vehicle. Regarding detection time, the X-ray method requires shortertime than this system, but [6] indicates the danger of using the X-ray method for finding illegal immigrants because of their exposure to X-ray. This system needs more detection time than X-ray because the system requires the drivers and all fellow passengers to get out of the vehicles, but compared with the hands-on searching by border officers, the system can reducethe detection time without the dangers such as the exposure to radiation.
V. DISCUSSIONS From the histograms in Figs. 9–11, we considered how to set a threshold Thfor judging the presence or non-presence of a person hiding in the car. Fig. 12 shows the cumulative frequency distribution of index F for Case 1 (presence) and Case2 (non-presence). Both the sedan-type car and the camper show a similar tendency when no one was in the car even under conditions of blowing wind and external ground vibration. The distribution when a person was in the sedan begins to increase fromF=0.12.Here, we decided the threshold so that the cumulative frequency distribution for non-presence would be over 90%. Then, for the camper, the threshold valueTh=0.08,i.e., ifthere might be people hiding in the vehicle, and forthe sedantype car Th=0.12, i.e., if F>0.12, there might be people hiding. For both the sedan and camper, if index F is less than 0.12, there is a 90% probability that no one is hiding inthe vehicle. The results did not perfectly discriminate between the presence and non-presence of a person due to the disturbance from the dynamic pressure of wind and ground vibration. If the inspection was conducted in a closed area with less vibration, the judgment accuracy would be improved. Nonetheless, by identifying the high probability of a concealed person, a more detailed inspection could be carried out and vice versa, which would make the inspection procedure more efficient.
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VI. CONCLUSION This paper describes a novel method for detecting the presence of a person hiding in a car. This pneumatic method uses silicon tubes and highly sensitive pressure sensors to monitor the vibrations from human vital signs. The employment of a low frequency condenser microphone as the pressure sensor provides sufficient sensitivity to detect the signals from human vital signs transmitted to one of the wheels of the car. From the filtered sensing signal, an index using the standard deviation of the signal is presented to discriminate
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between the presence and non-presence of a person in the car. The validity of the proposed method was examined using a sedan-type car and a camper. For both vehicles, when no one was in the car, distribution of the indexwas concentrated in the lowrange. For the sedan, the index when a person was in the car was clearly greater than that when no one was in the car. For the camper with a concealed person, the signal level decreased in proportion to the distance between the position of the person and the sensor location. Furthermore, the body movements of a concealed person enhanced index. REFERENCES [1] M. Zargham, “Obama to tackle immigration reform this year: Report,” REUTERS, Sep. 9, 2009. [Online]. Available: http://www.reuters.com/article/GCABarackObama/idUSTRE5380MU20090409 [2] D. Williams, “The immigrants stuffed into car seats and under bonnets trying to get into Europe,” Daily Mail, Dec. 29, 2007. [Online]. Available: http://www.dailymail.co.uk/news/article-504994/Theimmigrants-stuffed-car-seats-bonnets-tryingEurope.html [3] T. Gaynor, “U.S. border cops nab migrants in fake beer truck,” REUTERS, Nov. 25, 2008. [Online]. Available: http://www.reuters.com/article/domesticNews/idUSTR E4AO9RS20081125 [4] “The border security buildup: True border security requires reforming our broken immigration laws,” National Immigration Forum, 2010. [Online]. Available: http://www.immigrationforum.org/images/uploads/2010 /Border_Security_Fact_Sheet.pdf [5] “Robotic ferret will detect hidden drugs and weapons,” Engineering and Physical Sciences Research Council (U.K.) Jun. 12, 2009. [6] J. Doyle and L. Sorrell, “French ban X-ray scans for illegal immigrants as radiation makes them ‘too dangerous’,” Mail Online, May 14, 2010. [Online]. Available: http://www.dailymail.co.uk/news/article1278565/French-ban-X-ray-scans-illegal-immigrantsradiation-makes-dangerous.html [7] W. Dress, T. Hickerson, and R. Pack, “Enclosed space detection system,” in Proc. IEEE Security Technol., 30th Annu. Int. Carnahan Conf. 2–4, Oct. 1996, pp. 26–28. [8] Y. Kurihara et al., “Measurement of micro vibration of car by piezoelectric ceramics—Detection of bioinformations in the car and application for security,” IEEJ Trans. EIS, vol. 130, no. 5, pp. 844–851, 2010.
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EXPLODING SPACE TRAVEL A fresh approach to space travel to achieve higher speeds The energy imparted to one fragment will be E= 1/2*m*v2 m= mass of the fragment v=velocity of the fragment If there are ‘n’ equally sized particles, the energy is multiplied by the number of particles n*E = n*(1/2*m*v2 ) Since the energy is fixed, the velocity of the fragment and hence the distance traveled by it can also be determined. About 75% of the energy of an explosion is converted to heat or sound and only 25% of useful energy can be used for generating the kinetic energy. An explosion can be controlled by controlling the causes of explosion. Factors or causes may be materials used for creating the explosion, the atmosphere around the explosion etc.
K.Rohith Sharma Department of E&I SRM University Chennai,India. rohithstriker@gmail.com Abstract: Space travel is one of the fields in which even the byproducts of the inventions made are very much useful to the world. This concept strives to approach space travel with a new perspective that might be contradictory to the conventional thinking process. Index terms: physics of explosion,space, concept of space explosions.
INTRODUCTION Space travel,from ages has been one of man’s prime areas of interest. His quest to explore the universe has begun but has not reached his targets yet. Technology that he gets is limitless but he is limited by his speed at which he cruises through the heavenly bodies.The intention of this idea proposed is to have a fresh look at space travel which may be contradictory to the conventional methods. This method may be the answer for achieving high speeds as well as distances traveled.
Control over an explosion is needed because : a.To prevent damage or action that is not necessary. b.To control the amount of debris during the explosion. c.The debris control is a key feature to this proposal. The Munroe effect (classical definition: conical space at the forward end of a blasting charge to increase the explosive's effect and thereby save powder) can be used to obtain shaped charge to obtain a focused explosion on a fragment and thereby transferring more energy to focused fragment.
I.PHYSICS OF EXPLOSIONS A.Explosion: An explosion is a rapid increase in volume and release of energy in an extreme manner.All this energy released is equally distributed into nature either by sound or heat or imparting kinetic or potential energies to particles associated with the explosion. B.Energy: when a bomb explodes, the energy is distributed as follows Total energy =heat energy+sound energy+ energy imparted to the fragments associated with the explosion. The energy associated with the fragments is a very small fraction when compared to the total energy. This energy is in the form of kinetic energy.
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1: Aerodynamic cover 2: Air-filled cavity 3: Conical liner 4: Detonator 5: Explosive 6: Piezo electric trigger
shuttle).
The above figure is a simulated graphical model of Munroe’s model for shaped charged The explosion preferred for this application is by nuclear detonation as the energy is very high. Such high values of energy are needed to provide sufficient kinetic energies.
II.SPACE Space is a perfect podium for the demonstration the Newton’s laws of gravitation. First law: A body continues to be in a state of rest or of uniform motion until or unless compelled by an external force. Space has the minimum friction and hence a body continues to move with uniform velocity in a straight line when a force acts on it. Second law: The force exerted on the body is equal to the product of mass and acceleration gained by the body. Third law: Every action has an equal and opposite reaction.
On earth or any planetary surface, the newton's law cannot be perfectly demonstrated because of various factors mainly friction. Friction occurs due to relative motion between two objects and hence tends to bring the relative speed between the objects to zero On earth, atmosphere and land are the biggest sources of friction for any moving object In space, There is no land or atmosphere. It is mostly vacuum the only elements of friction in outer space can be other heavenly objects irrespective of sizes. In space,if any object is applied with a force then it will accelerate to a certain level as per the Newton’s second law which states that F=m*a. Now, after it has gained that acceleration,it will move with a constant velocity forever in a straight line until another force tries to change it's course. Hence no more fuel is required is outer space in accelerating the object (in this case, a space
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III.CONCEPT OF SPACE EXPLOSION The whole universe is believed to be formed by the big bang theory which implies that a big explosion created the universe. The energies and thereby the velocities of heavenly bodies were distributed during the big bang. The idea is to reinstall the same big bang theory but on a smaller scale when compared to itself. A controlled explosion can produce not only debris as per calculation but can also achieve velocity of the focused part of explosion. The Idea is simple. By focusing an explosion in outer space, we can achieve better speeds for space travel. Consider an analogy of a hand grenade. A certain time after the pin is removed from the hand grenade, it explodes. The above explosion is not focused. A few modern grenades have focused explosion (hollow charge) for maximum damage. An explosion that can be shaped by Munroe’s effect and can be focused on a specific fragment which can be a satellite or any space travel vehicle.
Consider the explosion of a focused grenade. Rather than considering the damage caused by the focused fragment, consider the speed achieved and distance traveled by it. This can be calculated by using laws of energy and kinematics. The fragments from normal grenade can travel as far as 200m from the site of explosion with a damage range upto 6m.. Bigger the focused fragment, more will be the energy gained by it and hence more will be the acceleration gained and distance traveled by it. Consider a space shuttle as a focused part on a huge grenade i.e. whose explosion is focused on the space shuttle.
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Consider an explosion to occur in such a way that proper protection is offered to the shape shuttle. The space shuttle along with its protection gear is arranged as a single fragment. Hence, the space shuttle fragment will get the energy out of the explosion. The source of explosion to be nuclear is explained as follows:
As told earlier, E=1/2*m*v2 Let m=1000kg v= 50,000kmph (say) Substituting the ‘m’and ‘v’ values E= 1/2*(103)*(5*104)2 =25/2* 1011 =12.5*1011 Joules =1.25TJ (TeraJoules) (1 Joule= 1 N*m2/s2) (1TJ=1012J)
(2.5 percentage of 88TJ =2.2TJ)
Hence the required velocity is achieved. Going by the analogy, a focused grenade is to be built so that it can be exploded by a remote control. In our case, a space shuttle is the focused fragment of the grenade. The remote control here is a ground station. The grenade is built entirely in space.
Similarly, S.No.
Velocity to be attained (x*10,000kmph)
1 2 3 4 5
6 7 8 9 10
After the explosion: Enormous amounts of matter gets destroyed Since all of this is in space, Newton's space can be applied peacefully. The unfocused fragment gets its part of energy and departs from the grenade. A part of the energy is focused on the focused fragment and hence is delivered to it. The energy as per the law will provide a acceleration to the fragment with which it will move in a predefined straight line. Hence the path of shuttle is set.
Energy to be supplied (TJ) 1.8 2.25 3.2 4.05 5
The blast yield of the “fat-guy” nuclear bomb (That destroyed Nagasaki during the world war-II ) is 88TJ. From the figure (weapon energy distribution) it can be seen how the energy is distributed. Hence the yield energy is distributed into three parts and energy more than the required (1.25TJ) is obtained even if the energy usable is 2.5%. This is for one fragment of the explosion. For ‘n’ fragments the total usable energy gets distributed. The energy used by the remaining fragments = total energy - energy imparted to the focused fragment.
Problems faced with this method and possible solutions: 1.Material: A. The material to make,not the bomb, but to construct the shaped charge model can be collected from the space debris. B. If space debris is not a viable options,the resources from the heavenly bodies can be utilized. 2.Survival of the temperatures: A. Bigger shell which will act as a protective measure to the space vehicle. B. Starlite. The product of plastic that can survive upto 10000 degrees centigrade. Coating the space vehicle with multiple layers of starlite should suffice the need. 3.Secure location for the explosion: A. There are lot of empty spaces between
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planets. Detonating a nuclear bomb in these spaces will do either no damage or no considerable amount of damage to the planets or other celestial bodies. Advantages : 1.Since this entire explosion takes place in space, minimal damage is done to the planet. 2.Velocities that are greater than that achieved by the existing propulsion systems can be attained. 3.More number of shuttles can be sent for space exploration at a single go. 4.The above can be done by increasing the number of focused parts. (setting up a pipeline from the source of energy and the shuttle(s)) 5.Can be used to travel farther distances in space.
Acknowledgment: I would like to thank my University for supporting me to undertake this project. I would also like to express my gratitude to my department faculty for their undenying support when ever asked for.
References : 1.https://www.llnl.gov/str/pdfs/06_98.3.pdf 2. Kissane, Karen (2009-05-22). "Fire power equalled 1500 atomic bombs". The Age (Melbourne). 3. Maurice on Tomorrow's World". YouTube. March 29, 2009 4.George, Rose (Apr 15, 2009). "Starlite, the nuclear blast-defying plastic that could change the world". The Daily Telegraph (London). Retrieved June 11, 2011 5.Stull (1977). Fundamentals of fire and explosion 6.wikipedia
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GPS TRACKING SYSTEM WITH LICENSE DETECTOR AND SPEED CONTROL 1.
P.NAVEEN HONEST RAJ
2.
V.VIGNESH
ELECTRICAL AND ELECTRONICS ENGINEERING KCG COLLEGE OF TECHNOLOGY CHENNAI EMAIL : kaushik19011995@gmail.com
ABSTRACT:
INTRODUCTION: Nowadays transport plays a very important role in our day to day life. In transportation, roadways play a very important role. The vehicles of private organisation and institution hold a very important place in the roadways transportation. The government points to statistics that underscore the safety of school buses, which transport 23 million children a day. According to the NHTSA, about 800 school-aged children are killed in motor vehicle accidents during normal school travel hours each year. Only about 20 of those deaths are school-bus related — an average of five school bus passengers and 15 pedestrians, often students hit inadvertently by the school bus, according to the NHTSA statistics. And also approximately 17,000 children are treated in emergency rooms
In this paper we have discussed to develop a speed controller, license detector and also a GPS tracking system in vehicles of any private transport or institution. So in this we have used a RFID reader, a GPS device and a GSM. We have also discussed about each device’s working in detail and its advantages.
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annually, having been injured in school buses, with 42 percent of those injuries involving crashes. So to make the travel safer and secure and easier we can fix this device. COMPONENTS: The components used in this device are RFID, GPS and GSM. 1. Radio Frequency Identification (RFID). 2. Global positioning System (GPS). 3. Global System for Mobile communication (GSM).
An Electronic Product Code (EPC) is one common type of data stored in a tag. When written into the tag by an RFID printer, the tag contains a 96-bit string of data. The first eight bits are a header which identifies the version of the protocol. The next 28 bits identify the organization that manages the data for this tag; the organization number is assigned by the EPC Global consortium. The next 24 bits are an object class, identifying the kind of product; the last 36 bits are a unique serial number for a particular tag. These last two fields are set by the organization that issued the tag. Rather like a URL, the total electronic product code number can be used as a key into a global database to uniquely identify a particular product.
RFID: The RFID works with the help of radio waves. The abbreviation RFID stands for Radio Frequency Identification. This can be used in our device to detect license. This also acts as a key. Only after detecting the license the driver can start the vehicle. It can also be used for security purpose. We can store some license ID’s in RFID so that; the persons with those licenses can only start the vehicle. If the person with wrong unidentified ID tries to operate the vehicle it will not start. If he tries more than three times there will be a buzzer. The image of a sample RFID tag.
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The RFID reader will check the numbers in each tag. It compares with the registered numbers by the owner or organisation. If it matches only, the driver can start the bus.
At present we have speed limits for each region. Like we have speed limit of 60kmpr in city and 80kmpr in highways. The speed limit varies according to the area. It is difficult know the changing speed limit while driving. So in this we are going to use GPS to find the area in which the bus is travelling.
GPS SPEED SENSOR: The Global Positioning System (GPS) is a satellite-based navigation system made up of a network of 24 satellites placed into orbit by the U.S. Department Defence. GPS was originally intended for military applications, but in the 1980s, the government made the system available for civilian use. GPS works in any of weather conditions, anywhere in the world, 24 hours a day. There are no subscription fees or setup charges to use GPS.
The GPS device is used to locate the exact position of the vehicle with the help of latitude and longitude. The latitude and the longitude are received from the GPS in the form of (4.4). We have to program the controller in such a way that if the vehicle crosses particular latitude or longitude then the limit should change. A set of latitude and longitude represent only a point like a graph. When we draw a imaginary line across a road which acts as a boundary for changing speed limit, either the latitude or longitude will the same for all the points drawn on that line. So we can use that as the condition for switching the speed limit. Two LEDs can be used to represent the city and highway limit. If the driver crosses the speed limit there will be buzzer until the driver slows down the speed below the limit. If then also he increases the speed, the GSM will send a message to the register that can be kept in the
GPS satellites transmit two low power radio signals, designated L1 and L2. Civilian GPS uses the L1 frequency of 1575.42 MHz in the UHF band. The signals travel by line of sight, meaning they will pass through clouds, glass and plastic but will not go through most solid objects such as buildings and mountains.
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organisation or institution. The register will register it and the management can action against the driver and warn him.
the bus. So that the respective driver can be warned or fined by the institution or organisation. GSM: GSM, which stands for Global System for Mobile communication, reigns as the world’s most widely used cell phone technology. It is approximated that 80 percent of the world uses GSM technology when placing wireless calls, according to the GSM Association (GSMA), which represents the interest of the worldwide mobile communication industry. This amounts to nearly 3 billion global people.
The above picture is the map of shollingnallur junction which is outer to Chennai. The highways start from this junction. A set of latitude and longitude present only a single point. So we have considered the black line drawn in the map as a boundary separating city and highway. In that line any one, latitude or longitude remains same. In the above map longitude remains constant. So we can program in such a way that if the vehicle crosses the longitude (e.g.: 8013.9282) then the speed limit will change from city to highways.
For practical and everyday purposes, GSM offers users wider international roaming capabilities than other U.S. network technologies and can enable a cell phone to be a “world phone”. More advanced GSM incorporates the earlier TDMA standard. In this we use the GSM device used to send SMS. The private transport or institution pickup the passengers of the bus at the boarding point at a particular time. The GSM will send the report of the bus to the passengers via SMS. When the vehicle starts it will send SMS to the phone numbers which had been already stored. And also when the
If the driver crosses the speed limit there will be buzzer. If he further increases the speed it will be registered through GSM. The details about the driver are received from the license detected while starting
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vehicle comes nearer to a boarding point it will send SMS to those persons in that boarding point that the “the bus will arrive shortly”. This helps the passengers to get the bus on time and to know the location of the bus.
Therefore this project will provide a secure and safety road travel for all people. This will also make people to follow traffic rules. REFERENCE: 1.http://cellphones.about.com/od/ph oneglossary/g/gsm.htm
DISADVANTAGE:
2. http://abcnews.go.com/blog
The can be used for single route only. As there will be change in the latitude and longitude we can use the device for a particular route. We have to program it each time to work in different routes.
3. http://www8.garmin.com/
CONCLUSION:
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GREEN BUILDING
SUBMITTED BY R.LOGARAJA, K.SURESHKANNAN B.E.CIVIL ENGINEERING FINAL YEAR, NPRCET.NATHAM DINDIGUL.DT TAMILNADU EMAIL ID:logaraja.npr@gmail.com
ABSTRACT: Adoption of Green Building Technology has become mandatory for ensuring sustainable development. However, its wide acceptance would require critical examination of various aspects like energy conservation, cost, strength and durability. Use of modern electronic controls and smart devices can enhance the functionality and performance of green
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building. Architects should get equipped with technical know-how for planning and designing intelligent green buildings to meet the future needs.
INTRODUCTION Green building (also known as green construction or sustainable building) expands and complements the building design concerns of economy, utility, durability, and comfort A Green Building is one which uses less water, optimizes energy efficiency, conserves natural resources, generates less waste and provides healthier space for occupants as compared to conventional buildings. OBJECTIVES OF GREEN BUILDING
Green Buildings are designed to reduce the overall impact on human health and the natural environment by the following ways. Using energy, water and other resources efficiently. By reducing waste, pollution, and environmental degradation.
FUNDAMENTAL PRINCIPLES Structure design efficiency Energy efficiency Water efficiency Materials efficiency Waste and toxic reduction
Need of Green Concept: Increasing urbanization has given a boost to building industry and number of bungalows, building complexes and multi storied buildings are being designed by architects and engineers with innovative concepts and enhanced features. However, it is observed that in many cases, environmental aspects are ignored leading to uncomfortable habitat and increased maintenance and energy requirements. Green building concept needs to be realized by all concerned with building activity and studied in detail by designers and planners for better built environment. Green or eco-friendly building is often considered as the luxury of wealthy people which is not correct. In fact, it is the necessity of common middle class man who cannot afford to provide air conditioning or pay exorbitant electricity bills.
Critical Appraisal of Green Technology:
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Green Technology is mandatory for ensuring sustainable development. However, in spite of all out awareness campaigns and promotional efforts, the adoption of this technology has remained marginal and restricted to environmentally conscious or wealthy people with main motive of value addition rather than economy and energy conservation. In case of building industry, the green tagging of building enhances the value and supersedes other nongreen competitors in attracting customers. The cost aspect of green building and loss of other functional requirements like security and durability are often concealed by green builders leading to a failure in convincing common man to adopt green building design. The main functions of the building are, to give shelter from environmental factors like Sun’s heat, rain, wind and extreme vagaries of nature like flood and hurricanes. Provide security to life and property. Provide privacy.
Durability and Strength. Aesthetic beauty Comfortable living Civil engineering considers strength, durability and cost as the main criteria while selecting materials, planning and construction. Architecture gives more stress on aesthetics and comfort and pays attention to fulfilling psychological needs of occupants. Green building techniques seek to conserve and utilize natural resources, emphasize eco-friendly construction and think of minimum carbon footprint on global scale.
Integration of IT and Green Technology: Green building is need of tomorrow’s future and old methods of construction and use of natural materials though more eco-friendly cannot provide for the needs of tomorrow like space economy, security, modern gadgets and amenities and flexibility in energy management. Fortunately advances in IT and automation controls can make the green building intelligent providing security, energy conservation and weather responsive automated operations. Use of solar energy, LED lighting, Rainwater harvesting, preservation of indoor air quality and thermal comfort zone and waste minimization can be made highly effective and economical through sensor based controls and appliances.
Panorama of smart home appliances: Numerous smart control devices are available in market which can be deployed easily for ensuring security, comfort and for energy and water conservation. Restricted access
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control, intruder alarm, dimmer light controls, Bluetooth, WI-Fi and/or net enabled smart devices, smoke detector, sensor based plumbing fixtures, automatic controls for opening and closing of Venetian shutters or blinds etc. Siemens, Tata Honeywell and ABB are some of the world’s largest producers of such controllers. Coupled with it, there are varieties of innovative products like wind powered fans, waterless urinals, dual flush latrines, Light tubes, LED lighting systems which can help in achieving green objectives.
Intelligent Green Buildings of Tomorrow: Energy, buildings, and the environment are interlinked in a symbiotic relationship, and the results of this interaction can be optimized for maximum gains by convergence of Green and Intelligent building concepts. An intelligent building is one in which the building fabric, space, services and information systems can respond in an efficient manner to the initial and changing demands of the owner, the occupier and be in harmony with the environment. Intelligent buildings not only have energy efficiency but also have safety and telecommunications systems and other required automations propelled by innovations.
Design of Intelligent Green Building: The ability to perform accurate whole building energy, water, and carbon emission analysis early during the design phase is essential for green building design. Simulating a building's energy use is a hard problem, requiring not only a model of the building and the materials that make it up but also a model of the building's location, with the path of the sun through the year and weather data that is accurate and detailed, including humidity, wind, simple daytime-nighttime temperatures, and a host of other location specific parameters. There are many modeling and simulation tools for design of green building Simplified Whole Building Energy simulation tools include: ASEAM, Carrier HAP, Energy-10 and TRACE 600. Detailed simulation tools include: BLAST, DOE-2, and ESP-r. Lighting and Day lighting Simulation - ADELINE, RADIANCE, SUPERLITE
Solar System Simulation - TRNSYS Ecotect is a whole-building simulator that combines an interactive building design interface and 3D modeler with a wide range of environmental analysis tools for a detailed assessment of solar, thermal, lighting, shadows & shading design, energy & building regulations, acoustics, air flow, and cost & resource performance of buildings at any scale. It can be used as plug-in in AutoCAD. A computable Revit design model is a great fit for the analyses needed for sustainable design — even during schematic design. As soon as the layout of a building's walls, windows, roofs, floors, and interior are established, a Revit model can do whole building analyses. Green Building Studio Web service meets ASHRAE Standard 140 and is certified by the U.S. Department of Energy. Built specifically for architects and using gbXML for data
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exchange across the Internet, the GBSWeb service can be used for building designs and sophisticated energy analysis. Selection of a simulation program should consider the project requirements, time and cost, availability of computer system and experience of the user. Once the building completed and people use it, it should be monitored for the actual performance.
Role of Green Architects: The architects should learn the basic concepts of green and eco-friendly building and make appropriate changes in methods and materials they are using for the building design. In order to achieve desired green rating, they should use modeling and simulation tools after necessary customization to suit client’s specific requirements and site constraints. One Earth can be counted as among the largest green building projects in India. LEED certified it as „PLATINUM‟ and it is built on an area of 10.13acres. Several accolades continue to shower upon Suzlon‟s global headquarter in Pune “One Earth”.SUZLON ENERGY LIMITED,PUNE: This green building ensures optimal use of natural light and minimal wastage of electricity or energy consumption. First airport in Asia to be certified with SILVER rating. India’s first Greenfield airport is undeniably among the top 10 green buildings in India. RAJIV GANDHI INTERNATIONAL AIRPORTHYDERABAD: Its smart lighting, heat recovery wheel and high efficiency chillers make this office stand out from the rest. GOLD rated building by LEED. NOKIA-GURGAON:
Role of Dnyandeep: Dnyandeep Education & Research Foundation has launched a website www.envis.org to act as an effective medium for creating awareness about environmental protection and for popularization of green building/green city concept. The website includes all technical papers presented in the earlier workshops and seminars on this topic. It is proposed to have networking of architects and builders for information exchange and transfer of technical know-how. The foundation is planning to provide all necessary guidance and training to architect members with the help of experts in Green Building Design, IT, Instrumentation and Control though this website. Let us hope that with active participation from all concerned we will be able to usher a new era of intelligent green buildings affordable to all in future.
DIFFERENT FROM OTHER BUILDINGS The design, maintaince and construction of buildings have tremendous effect on our environment and natural resources.
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Green Building is different from the other buildings because it use a minimum amount of nonrenewable energy, produce minimal pollution, increases the comfort, health and safety of the people who work in them. It also minimizes the waste in construction by recovering materials and reusing or recycling them.
AFFECT ON NATURAL RESOURCES According to surveys conducted in 2006, 107.3 million acres of total land area is developed, which represents an increase of 24 percent land covering green buildings over the past 3 years. In terms of energy, buildings accounted for 39.4 percent of total energy consumption and 67.9 percent of total electricity consumption. Reduce operating costs Create, expand, and shape markets for green product and services Improve occupant productivity.
GREEN BUILDING PROJECT IN INDIA Suzlon Energy Limited-Pune Biodiversity Conservation India-Bangalore Olympia Technology Park-Chennai ITC Green Centre-Gurgaon The Druk White Lotus School-Ladakh Doon School-Dehradun Raintree Hotels-Chennai Nokia-Gurgaon Rajiv Gandhi International Airport-Hyderabad Hiranandini-BG House, Powai ABN Amro Bank, Chennai Palais Royale at Worli, Mumbai Punjab Forest Complex,Mohali
CONCLUSION
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This research identified the exciting developments taking place on the technology front and analyzes their implications for intelligent and green buildings, highlighting examples of “best in class” buildings employing green and intelligent technologies. These buildings are dynamic environments that respond to their occupants‟ changing needs and lifestyles. This research provided documented evidence to educate and influence end-users, building owners, architects, and contractors that a “greener building” can be achieved using intelligent technology and that this “greening” will provide a tangible and significant return on investment.
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This calls in the need to apply engineering principles in the bio-medical field. Computer aided diagnosis (CAD) systems are automated systems which are capable of identifying the tumor cells from the mammograms without the intervention of radiologists or oncologists. [6] The mammograms are analyzed for the presence of malignant masses, thickening of the tissue and deposits of calcium known as microcalcification.[7] The masses often occur in dense areas of the breast and have different shapes and spotted by the bright spots in the mammograms. The micro-calcification is generally identified in the classifier stage as either benign or malignant. Malignant micro- calcification generally has a diameter of less than 0.5 mm and is fine and stellar structured.
EARLY DETECTION OF BREAST CANCER USING CAD SYSTEM EMPLOYING SVM CLASSIFIER Authors S.DURGALAKSHMI ;nan_naned@yahoo.co.in, SHREYA REDDY ;shreyareddy.7676@gmail.com, Fourth year Panimalar Institute of Technology
ii.
DETECTION USING CAD SYSTEM:
ABSTRACT: Mammography is a low radiation process used to detect breast cancer. It is of two types – screening mammography and diagnostic mammography. Screening mammography is the procedure used to detect cancer in asymptotic population while diagnostic is used to analyze the patients with abnormal conditions. Usually diagnostic is used as a follow up to the screening method in abnormal patients. Interpretation of the mammography can be difficult due to the poor contrast and less difference between a healthy and abnormal breast. Due to which high rate of false positives and false negatives are seen owing to some women undergoing surgery unnecessarily. To increase the efficiency and accuracy, image analysis and classification are done with the help of a CAD system.[8][9]
Breast cancer is the most common cancer pathology detected in women. It is the second leading cause for morbidity and high mortality rates. The cause of the cancer is still unknown thus early detection of it is very important. This paves way to the need for early detection systems for breast cancer. Currently the most prevalent system of detection is mammogram. But often detection from the mammogram is not accurate which calls for a second opinion which is a costly affair. This paper presents a computer aided diagnosis system for early detection of the cancerous tissue. It employs an efficient image enhancement system using and classification using the support vector machine. KEYWORDS: mammogram, microcalcification, CADsystem, breast cancer. i.
The CAD process mainly uses 3 distinct phases which are the (1) image processing, (2) sedimentation and (3) classification. The first step is where the mammogram is processed and the contrast and quality of the image is enhanced for easy identification. In the second step, the separation of the suspicious cells from the background parenchyma is done. While in the third step the classification of the cells as either benign or malignant is done.
INTRODUCTION:
Breast cancer is the abnormal growth of cancerous cells in the mammary glands of the woman. It is reported that 1 in 12 woman are affected with breast cancer. The symptoms of which are formation of lumps, lesions, or abnormal skin texture or areolar swelling and change in size of the breasts.[1]The detection of these is often difficult as the density and shape of the breasts change from woman to women. This is done with the help of screening using a low radiation this technique is known as mammography. [2] Most common diagnosis of the cancerous growth is the biopsy, which is an invasive procedure by which a piece of tissue or a sample of cells is removed from your body so that it can be analyzed. [3]The drawback of such an approach is a high number of non-productive biopsy examinations with high economic costs. Statistics show that only 15-34% of breast biopsies are proved cancerous and that 10-30% of all cases of breast cancer goes undetected by mammography.[4] [5]
iii. IMAGE PROCESSING: The image processing phase is further split into steps as shown in the figure 1. [10][11]
Figure 1.Image processing phases. The original mammogram is taken as the input image to the CAD system in which it is passed through the Gaussian
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soothing filter. The filter smoothens the image by removing noise and is used to blur the image to obtain a smooth gray scale.[12] In the contrast stretching step, which is also known as the full scale histogram stretching, the image is stretched over the entire gray scale area. A well distributed is one which has a good visibility and high contrast. [13] Then the top hat operation is performed on it. It is a morphological method using mathematical operations. It divides the image into two sets – the object and the structuring elements. The top hat performs the difference of the original image and its opening points which are the collection of the foreground points. This highlights the bright spots in the image.[14]The DWT reconstructs the decomposed image back. Thus a reconstructed image is available at the end of the image processing by the CAD.
iv. SEGMENTATION:
Figure 5.Process from segmentation to classification In the segmentation phase in the CAD, the suspect cells are split from the background image using global and local thresholding as micro-calcification look brighter than the surrounding. In global thresholding is a technique in which the histogram reveals peaks corresponding to the background and the object. In the case of micro-calcification extra peeks are raised. Local thresholding is used to refine results obtained in the previous stage as the pixels need to have an enhanced intensity values. This is followed by feature extraction where the abnormalities are detected and extracted. The selection process in the CAD involves an optimum set of features from those available in the image after segmentation. The feature space can be divided into subspace based on intensity and texture. The scale space method is used to extract the abnormalities; it is based on Laplacian scale-space representation.[15] The possible micro-calcification are identified by local maxima on a range of scales in the filtered image. For finding the size, contrast, the response is used. The finding is demarcated as a micro-calcification if the contrast exceeds a predefined threshold value.
Figure 2.a)original b)filtered image
v. SUPPORT VECTOR CLASSIFIER: Support vector machine is a learning model with algorithm for analyzing data and recognizing patterns in them. It classifies the inputs given to it based on the pattern recognized. It is based on a decision plane model. [16]
Figure 3.a)original b)enhanced image
The SVM divides the inputs into two classes and builds a model which assigns the value to either one of the classes. It is a mapping of the values as points in space such that they can be divided into two distinct set by a wide margin between them. They points are identified to belong to a category based on which side they fall on. The SVM intakes a test set data which its interprets and a data set which it categories.[17] The SVM does linear classification using hyper planes while non-linear cluster classification using kernel functions. (1)LINEAR METHOD: Figure 4 a) reconstructed mammogram b) segmented output
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In the linear method, the inputs are clearly spread out in a linear manner and thus finding a plane that separates them properly is the only concern.
Figure 8.thehyperplane
The margin on one side is 1 / |w|, thus the total margin is 2*(1/|w|). Thus minimizing this |w| values will increase the separability.
Figure 6.a hyper plane dividing two data sets The decision plane separates the assets of different classes. For proper classification, the margin of the decision plane from the class objects must be greater. This would allow a better categorizing of the samples.
The width can be increased by the use of Lagrange multipliers. Using karush-kush-tucker conditions,
The best hyper plane is thus one with maximum margin from both the classes.
Applying this in the width equation, the width of the plane will be the dot product of the data objects. L= xi .xj where xi and xj are data objects. (ii) NON LINEAR METHOD: For the nonlinear data set, the classification cannot be done using linear SVM. This requires the special kernel function. In this case, the SVM introduces an extra set of dimension where the points can be projected on to in a better space.
Figure 7.Twohyper planes with varying margins
This space is calculated using the kernel functions There are number of kernels that can be used in SVM models. These include linear Polynomial, RBF and sigmoid:
xi*xj - linear (γxixj+coeff) - polynomial Exp (-γ|xi-xj|2) - RBF Tanh (γxixj+coeff) -sigmoid
The Radial Base Function (RBF) kernel is the most suitable method with SVM. [18]
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For Gaussian radial basis function: K (x, x’) =exp (-|x-x’|2/ (2σ2)).
[7] M. Rizzi, M. D’Aloia and B. Castagnolo, “A fully automatic system for detection of breast microcalcification clusters,” J. Med. Biol. Eng., 30: 181-188, 2010.
vi. RESULT: [8] L.J.W. Burhenne,”potential contribution of computer aideddetection to the sensitivity of screening mammography”,Radiology, vol.215, pp.554-562, 2000.
The proposed method involves the mammogram image being filtered by a Gaussian filter based on standard deviations and the resultant image is context stretched. The unwanted background is removed using top hat approach. The output is decomposed into two scales which are reconstructed back using DWT. The image is then segmented and the features from the selected area are extracted in the fragment extraction phase and then the tumor classification is done using the SVM classifier.
[9] T.W.Freer and M.J.Ulissey, “Screening mammography with computer aided detection: prospective study of 2860 patients in a community breast cancer”, Radiology, vol.220, pp.781-786, 2001. [10]Howard Jay Siegel, Leah.J, “PASM: A Partitionable SIMD/MIMD System for Image Processing and Pattern Recognition” IEEE trans. [11]SumanThapar, ShevaniGarg, “Study and implementation of various morphology based image constrast enhancement techniques”, International Journal of Computing & Business Research
vii. CONCLUSION: The computer aided diagnostic system has tremendously helped the radiologists as they do not need a second opinion from doctors. Moreover the hit ratio is comparatively higher. This reduces the negative reports about the existence of breast cancer and helps identify cancer in early stages. Thus the treatment process can be more effective. It is also reported that the linear method of classification proves more efficient because of the linearity in the data set. The classifier was able to vividly demarcate the different cancer cells as benign or as malignant.
[12]Ahmed Elgammal, “Digital Imaging and Multimedia Filters”,Rutgers [13]Kumar, jagatheswari, “Contrast Stretching Recursively Separated Histogram Equalization for Brightness Preservation and Contrast Enhancement”,Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference [14]Jiang Duan, Chengdu, Wenpeng Dong, “Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)”, 2011 IEEE Symposium
REFERENCES:
[15] T. Netsch and H. O. Peitgen, “Scale-space signatures for the detection of clustered microcalcifications in digital mammograms,” IEEE Trans.
[1]Mammography[Online] http://www.radiologyinfo.org/en/pdf/mammo.pdf [2] American college of radiology, Reston VA, Illustrated Breast imaging Reporting and Data system (BI-RADSTM) , third edition, 1998.
[16] C. Cortes, V. N. Vapnik, “Support vector networks”, Machine learning Boston, vol.3, Pg.273-297, September 1995
[3]E.C.Fear, P.M.Meaney, and M.A.Stuchly,”Microwaves forbreast cancer detection”, IEEE potentials, vol.22, pp.1218,February-March 2003.
[17]Zhang Xinfeng, ZhaoYan “Application of Support Vector Machine to Reliability, telkomnika ,2014 [18]JooSeuk Kim, Ann Arbor, ”Performance analysis for L2 kernel classification”
[4] Homer MJ. “Mammographic Interpretation: A practical Approach”, McGraw hill, Boston, MA, second edition, 1997. [5]Maria Rizzi*MatteoD’AloiaBeniaminoCastagnolo, “Review: Health Care CAD Systems for Breast Microcalcification Cluster Detection”,Journal of Medical and Biological Engineering,2012. [6] S.M.Astley,”Computer –based detection and prompting of mammographic abnormalities”, Br.J.Radiol, vol.77, pp.S194S200, 2004.
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Modelling & simulation of solar PV array field incorporated with solar irradiance and temperature variation to estimate output power of solar PV field Vishwesh Kamble#1, Milind Marathe*2 , Rahul Rane#3 1# 2*
Student, Dept. of Electronics, K. J. Somaiya collage of engineering, Vidyavihar, Mumbai, India. Faculty, Dept. of Electronics, K. J. Somaiya collage of engineering, Vidyavihar, Mumbai, India. Address 1
vishwesh.k@somaiya.edu milind.marathe@somaiya.edu
2
*
Second Company Head, Technology centre, E&A, L&T limited, Mahape, Navi Mumbai, India. ³rahul.rane@lntebg.com Abstract— Photovoltaic systems are designed to feed either to grid or direct consumption. Due to global concerns, significant growth is being observed in Grid connected solar PV Plants. Since the PV module generates DC power, inverter is needed to interface it with grid. The power generated by a solar PV module depends on surrounding such as irradiance and temperature. This paper presents modelling of solar PV arrays connected to grid-connected plant incorporated with irradiance and temperature variation, to design simulator to study and analyse effect on output power of solar PV arrays with irradiance and temperature variation, also to estimate the output power generated by PV arrays. The mathematical model is designed implemented separately on simulator for each PV components connected in PV systems, which are PV cell, Module, sting, array and field of arrays. The results from simulation based on model are verified by the data collected from power plants and experiments done on solar PV cell.
To create a model for solar PV system and implement it on simulation software we made some assumptions regarding atmospheric conditions effect on PV array. With the model for simulating PV arrays, we also wanted simulator to implement graphical representation of Arrays, therefore we used some basic design models for graphical design of PV system in simulator. The assumptions we made for simulation are as follows:
Keywords— Photovoltaic, Irradiance, Temperature, array, model, simulator
I. INTRODUCTION Photovoltaic power generation is gaining acceptance today as source of clean and pollution free energy. These power generators shows significant growth in grid connected as well as stand-alone applications. Our objective was to create mathematical model for solar PV array connected across these generators, incorporated with irradiance and temperature variation and implement the model using simulation for study and analyse the effect on output of field of solar PV arrays and also to estimate the power output of a solar PV arrays connected across power plants. We implemented our mathematical model using iVisionMax[1] PC suit to create simulation. We verified results from simulator based on our model by comparing it with data collected from power plants and results from experiment on solar cell.
Fig. 1 Illustration of solar array field Figure 1 shows the illustration of solar array field which shows the order in which solar field is designed, we can see that field of arrays are made by solar array connected together, array is made up of strings connected in parallel, string is made up of modules connected in series and module is made up of cells connected in series. We designed software to simulate whole field, each array, each string and module connected in the field separately. We designed dedicated windows for every level. A. Assumptions 1) Input Inputs to simulator are as follows: Climate conditions= Sunny and Cloudy, Solar irradiance= 0 W/m² to 1000 w/m², Module temperature = -5ºC to 80º, [veu7] Shadow density = 1% to 100%.
II. LOGICAL THINKING
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2) Effect of other atmospheric conditions All other atmospheric conditions such as humidity, wind speed, edging of sunrays, dust and snow are already imposing their effect on solar irradiance and module temperature as well. Therefore,
(1) Where, b0, b1, b2, b3, b4, b5 are regression coeff. Which are may vary according to location. It means that module temperature is already affected by other conditions and it’s the same in case of irradiance. 3) Climatic conditions Climate conditions are depends upon the seasons of particular locations therefore we are assuming following conditions: Sunny Conditions: In sunny conditions, we are assuming that the location where the PV array is set up is free from all kinds of shadows including cloud shadow as well as other monumental shadows (clear sky). Solar PV modules are directly coming across Sun with no shadow effect and output of PV array is clearly dependent on solar irradiance and Module temperature. The tilt angle of Solar PV modules should be equal to latitude and the orientation of the Array should be always facing South (True south). Cloudy conditions: In cloudy conditions, we are introducing shadow over modules which will affect irradiance as well as module temperature. We are inputting shadow density of a cloud in simulator which will change the given irradiation and module temperature as input before and accordingly output will be changed.to simulate cloudy conditions we are assuming that cloud shadow is covering all the PV modules in PV array field with same amount of shadow density and whole sun is being covered by cloud (for Eg. Rainy day).
Fig. 2
Fig. 3 Change in Irradiance and module Temp. due to shadow density (based on experiments and observation) Since the purpose of designing the simulator is to study and analyse effect of atmospheric conditions on solar PV module and its fields of arrays therefore we are not considering any other effect on the output of module. We are just concerned about how much power required from solar Array field and how it will get affected by change in atmospheric conditions. III. MATHEMATICAL MODELLING 1) PV Module mathematical modelling Assumptions: the following terms we are using in the programming while designing the simulation software in iVisionMax. Voltc = Vmax of a PV cell. Crin = Imax of a PV cell. Refchng = change in Vmax due to irradiance. I0 = Diode saturation current (f (i0) i.e. base value of I0 of a cell ) Ir = Solar irradiance. Temperature = Module Temperature in ºC. Tambient = Ambient temperature in ºC. Chngeintemp = change in ambient temperature. Equation 2 derives the base value of diode saturation current which is denoted as i0, it is already present in silicon solar cell due to its material characteristics. but Base i0‘s presence can be neglected till module temperature is 25º C. if the module temperature is increased than diode saturation
Assumption of cloudy condition for simulator [4]
When shadow comes over PV module or an array, what happens is that shadow will resist solar insolation and irradiance. As much as the shadow density (SD)[10], darker the shadow hence, irradiation reaching to the module is reduced by amount of shadow present due to cloud as shown in figure 3(a). Similarly, module temperature also gets affected due to shadow over module, but it doesn’t happen
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rapidly as irradiance, it will need some time to see reduction in temperature. Therefore we are considering time for shadow to remain over PV array, to give the change in Module temperature. To simulate or mimic change in module temperature we are using Newton’s law of cooling, which we will enlighten in next sub-topic. From experimenting and studying we can say that change in irradiation due to shadow is linear in nature and change in module temperature is logarithmic and exponential. Studies show that module temperature is always 20 to 30% [5] more than ambient temperature. While designing the equations for simulation we are considering ambient temperature 80 % approx. of a module temperature. Which is calculated in cloudy condition after the module temperature is specified. [7][6]
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current will rise and gets added with output current. Equation 3 illustrates the change in diode saturation current. i0 = (q*A*Dn*ni²/(Ln*Na))
[8]
TABLE I CHANGE IN MODULE TEMPERATURE IN CLOUDY CONDITIONS
Date: 20/05/14
(2)
where, q = charge in a cell, A = surface area in mm²,Dn = Depletion width, ni= intrinsic concentration, Ln = length if n region, Na = Doping density I0 = i0*(exp(0.09672*(temperature-25))) [2]
Time: 10.40 AM 11.20 AM
(3)
12.02 PM
a) Application of Newton’s law of cooling for change in module temperature due to cloud: Newton's Law makes a statement about an instantaneous rate of change of the temperature [9]. Newton’s law of cooling
Ambient Temperature Initial AT
After time(t)
Initial M.Temp .
M. Temp after time(t)
25.5ºC
27.6 ºC
60 ºC
28.2 ºC
28.0º C
27.2 ºC
60 ºC
27.6 ºC
28.0 ºC
27.8 ºC
60 ºC
28.7 ºC
Time (min) 34.21 min 36.47 min 34.47 min
It is seen that module takes approximately 35.05 minutes to cool down to ambient temperature. We repeated the same procedure 3 times on same module to see the change in module temperature with reference to ambient temperature. To find out change in module temperature due to shadow of a cloud, we have to find change in ambient temperature due to shadow, which is illustrated by equation 6 and 7.
Solution, To apply newton’s law of cooling we have to find value of k, which is the relation between change in temperature of module with respect to time. To find k, we have to perform experiment as shown in figure 4. For experiment what we did is we heated the solar PV cell module up to 60º C in laboratory with no sunlight spectrum, we can assume that with no irradiance. We forcefully heated the module and then we observed and recorded time taken by the module to cool down to ambient temperature the table below shows us reading of the experiment, initially module temperature is 26.5 ºC and ambient temperature is 25.5 ºC. We used hair dryer as heating source from which we heated the module to 60 ºC and we measured the temperature on multichannel RTD device as shown in figure 4. We used multichannel RTD for measuring ambient temperature as well as module temperature at same time so that we can measure exact time for cooling down of module, table 1 shows the results from the experiment:
b) Newton's law of cooling regression coefficient Now let’s see, how to find K? When shadow is over module, 1st we have to measure change in ambient temperature. Since we are feeding module temperature directly we are using property of relation between ambient temperature and module temperature. Ambient temp is always 75% to 80% of module temperature. [5] Tambient = (temperature*0.8)
[5]
(6)
chngeintemp=(Tambiant-Tambiant*(exp((time/0.0545))))* exp(-(shd_dnsity)/100 (7) In equation 7, we are using heat transfer equation for calculating change in module temperature due to shadow and presence of shadow over module. Change in ambient temperature is function of shadow density in % and time of presence of shadow over module. Here, equation 8 is derived from solution of Newton’s law of cooling. To find k we solved the equation and also did experiment on module to acquire the data for calculation, below equation 8 we can see the calculation for k using experiment results. We used the value of k as constant in equation 9. k = ((log10 ((M.Temp-Tambient)/(changedM.TempTambient))*2.303)/Time(t))
(8)
= 0.08124.
Fig. 4:
From table 1, we took values to solve the equation 8. Change in module temperature is function of change in ambient temperature and time required to cool down to ambient temperature with time and constant k . Equation 9 ,shows the calculation for changed module temperature based on change in ambient temperature. Here we are calculating effect of ambient temperature only not shadow effect because we have
Experiment setup for change in module temp due to cloud
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already calculated ambient temperature over shadow. Equation 10, shows the changed irradiance due to shadow of a cloud over module. Figure 3 shows the relation of irradiance with shadow and module temperature with shadow.
Condition 1- Effect of module temperature on module current if > 25º C As we seen earlier in equation 13 and 14 are independently related to voltage and current respectively. Here, we are relating module temperature with current. It states that if module temperature increased over 25º C, Carrier concentration is changed due to which band gap of PV module decreases, so I0 (i.e. Diode saturation current) increases, and eventually module voltage decreases.I0 gets introduced in Module output current. We are already defining decrease in voltage from equation 14 and we are also calculating I0 from equation 3. Therefore we add I0 with module current. As we can see in figure 5 Isc is slightly increase with decrease in VOC. Change in output current due to change in module temperature is shown in equation 16.
Changed module temp. = (chngeintemp) + ((temperaturechngeintemp)*(exp(-k*(temp)))) (9) Changed. Solar irradiance = ((100-shd_dnsity)/100)*ir (10) [2] c) Output Equations As we explained mathematical modelling of the input side, now let’s see equations and formulations from the output side. Equation 11, 12 and 11a are representing regression coefficients or factors which relets the change in input to change in output, voltc gives voltage of a cell from module specified by user. Crin is regression coefficient between current and solar irradiance and refchnge gives the relation between changes in irradiance to change in voltage. Voltc = Vmax/No. of cells
(11)
Crin = Imax/1000
(12)
Refchnge (irradiation coeff.)= (11mv-VOC of cell) / ln(1000-ir) (11a) Where, ir = 10 W/m2, VOC = 600mv, 11 mv [ according to real diode characteristics].[3] Equation 13 gives the voltage of a module dependent upon module temperature without effect of irradiance over it, for this equation we always assume that except change in temperature other parameters are at ideal state, but we are compensating effect of other conditions such as irradiance separately which we will see in the following conditions we made. The temperature coefficient we took is always specified in specification sheet provided by manufacturers. Here we are taking it as constant but it may vary with manufacturer to manufacturer. Module current equation 14 is based in the principle states that, current directly proportional to irradiance. Where, crin is regression coefficient which always gets changed with module to module.as illustrated in equation 12. Equation 15 is product of voltage and current obtained from 13 and 14 i.e. power of a module. Module voltage = (voltc*(1+ (shadw_den1-25)*(- 0.34/100)))* No. of Cells on module. (13)
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Imp = Imp*(1+(M.temp.-25)*(0.1%))-I0
(16)
Condition 2- Effect of solar irradiance on module voltage if irradiance is < 1000 W/m² As we know that, irradiance is not stable in nature and there are rapid variations. Solar irradiance does not reach to its maximum value every time i.e. 1000 W/m² throughout the day. The variations in irradiance not only will affect the current but also voltage of a module. To simulate change in module voltage we introduce regression coeff. refchnge (β) as we explained earlier in equation 11a. The module voltage will face exponential or logarithmic change due to change in solar irradiance. We illustrated this scenario by equation 17. It states that if solar irradiance is less than 1000 W/m² and varies with time then effect on module voltage will be deduction in module voltage with product of irradiance regression factor refchnge and natural log of 1000 - solar irradiance. Module voltage’ = module voltage - (ln(1000-ir)*refchng) (17) 2) PV String mathematical modelling
Where, (-0.34 % is temperature coeff. provided by manufacturer.) Module current = crin*ir Module power = Module voltage* Module current
Fig. 5: The effect of temperature on the IV characteristics of solar cell.[3]
a) Design formulation for string
(14) (15) String is nothing but No. of PV modules connected in series connection. The string in PV array decides output voltage of
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PV array field of a power plant. For example, if want to generate 70 V dc voltage and we are having 50 W PV module with Vmax of 35V so we have connect two PV module in series to make a string which will generate 70 V at standard atmospheric conditions. In case of simulator, to design string it is important to find No. of modules needed in a string based on given estimated voltage and module specified. ( for eg.290 w module.) No. of modules in a string = estimated voltage/ Vmax Vmax = voltc*no. of cells in a module. Or No. of modules in a string = estimated power/Pmax
since it is type of energy harvesting therefore we use the term PV array field to pronounce solar power plant. Solar array field is nothing but No. of PV arrays connected together across solar inverter or to grid inverter to generate the power. In here Field voltage and Field Current are defined by No. of modules in a string and total No. of strings in a field respectively. No. of arrays are in a field are also important here while designing.
(18) No. of modules in a string = estimated voltage/ Vmax of module. (18a) Total module = (estimated power)/type of module (19) Total strings= total module/no of module
(25) (26) (27)
b) Simulation formulas for string No of array = total strings/no of strings All other simulation formulas used are same as module simulator except module voltage, module voltage will be multiplied by total number of modules connected in a string.
(28)
b) Simulation formulas for field
We are already calculating total number of modules and total String voltage = Module voltage*total module in a String (20) number of strings connected in field of arrays, as we can see it in equation 26 and 27. Therefore, simulation formulas will 3) PV Array mathematical modelling remain same for field simulations. a) Design formulations for Array
5) Calculations for constants
Array is made by connecting strings of PV module in series or parallel connection to meet our power requirement from PV array field. Mostly in solar arrays all the strings are always connected in parallel. Array defines current output required from PV field. In case of Solar array now there are two factors are important Array voltage and array current. Array voltage is equal to String voltage i.e. string decides the voltage of array as well as field. Array current is defined by no of string connected in parallel mostly dependent on no. of channels in array junction box (AJB or SMB). No. of module = ((estimated power)/(Pmax))/noofstrings (21) Or No. of module = estimated voltage/(voltc*noofcell) (22)
a)
For time constant (τ)
Changed ambient T = Tambient – Tambient*exp(-time/τ) Based on experiment: Tambient = 60º C Changed ambient T = 59.6ºC Time = 3 sec (i.e. 0.05) Sol: 59.6 = 60– 60*exp(-0.05/τ) -0.4/-60 = -exp(-0.05/τ) ln(0.0066) = -0.05/τ τ = - 0.05/- 5.0206
b) Simulation formulas for array
τ = 0.00995.
Simulation formulas are same as module and string simulation except array voltage and array current.
b) For refchng:
Array voltage = String voltage
(23) We observed that for typical (156X156) Poly crystalline solar cell there is 11mv change in VOC per 10 W/m² [3]. Studies Array Current = String current * total no. of strings of an show that a cell generates 600 mv against the full sun array. (24) spectrum. Using above information we can find refchng coeff. 4) PV Field mathematical modelling
11mv = 600mv – (ln(1000 - ir) * refchng) -589 mv = -ln (1000- 10)* refchang
a) Design formulation for field
Refchng = 0.085776
The term field is not used prominently in the industry because PV arrays are built for generating power in power plant but
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TABLE II COMAPARISION RESULTS BETWEEN EXPERIMENT RESULTS AND SIMULATION RESULTS Actual Time
Irradiance (W/m²)
M. temp.
power (W)
(ºC)
Simulated power
Error (%)
(W)
Cell 1 10:45
310 w/m²
42 ºC
1.19 W
1.26 W
-5.88%
10:59
252 w/m²
53 ºC
0.92 W
0.99W
-7.6%
11:53
296 w/m²
55 ºC
1.02 W
1.06W
-3.7%
12:33
317 w/m²
45.2 ºC
1.14 W
1.19W
-4.3%
12:36
278 w/m²
46 ºC
0.97 W
1.04W
-7.2%
1:02
252 w/m²
51.4 ºC
0.91 W
0.93W
-2.1%
12:50
167 w/m²
50.9 ºC
0.60 W
0.61W
-1.6%
13:18
287 w/m²
51 ºC
0.97W
1.00W
-3%
13:26
266 w/m²
54 ºC
0.92 W
0.92W
0%
13:45
272 w/m²
56ºC
0.88W
0.93W
-1.6%
Cell 2
Fig. 6: PV field simulator using iVisionmax IV. RESULTS AND VALIDATION To verify the simulation and mathematical model, we studied couple of power plants and also we performed experiment on Solar cells, so that we can validate our model and simulation results by comparing them with the results from experiment and data collected from power plant.
Cell 3
a) Experiment done over solar cells We performed experiment on 3 kinds of solar cells; all three solar cells are top end polycrystalline solar cells available in market. They differed from each other based on cell efficiency. We used 16.10%, 17% and 18% efficiency solar cells and performed test on them with effect of solar irradiance and temperature variation. The set up for solar cell experiment is shown in figure 7.
Structural design: Type of module: 180W (bp) Pmax = 160 W Vmax = 35.2 v Imax = 4.7 A No. of cells in module = 60 units. Total No. of modules in Power plant = 300 units; Inverter efficiency = 96 % In a Power Plant, it is observed that there are two types of Array boxes are used, they are all 5 channel Array boxes classified as “Array junction Box” ( AJB’s) and “Main junction Box”. First small String are made of 4 No. of modules. 5 strings are connected to each(parallel) array junction box. And 3 AJB’s are connected to Main Junction Box so eventually 12 modules are making on big string connected to Main Junction box’s one channel. Therefore, Total no of small strings: 75 units Total no of big strings: 25 units Modules in small string: 4 units Modules in big string: 12 units Main array Junction Box: 5 units
Fig 7: setup for solar cell experiment Specifications: Cell 1: 4.38 W (cell eff. 18 %) Vmax = 35 V, Imax = 9.01 A Cell 2: 4.13W (cell eff. 17 %) Vmax = 35 V, Imax = 8.49 A Cell 3: 3.91W(cell eff. 16.10 %) Vmax = 35 V, Imax = 8.04 A
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From table II, we can see that, we got error within ± 10% which is allowable in case of simulator. Therefore we can say that our system is near to accurate and within the permissible range right from solar cell to solar array field, Which validates our simulator as we compared the simulated results with actual results from the field. b) Case study 1- 50 KW Power Plant (L&T)
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Table III shows comparison of results from simulator and L&T power plant, we can see the error of the comparison result is within the allowable range of error. We compared power generated by 50KW power plant with our simulated results for the same weather conditions as we can see it in table.
these results we can say that our simulation model for studying the PV solar Array field incorporated with solar irradiation and temperature variation is near to accurate. Finally, based on the analyses performed for this work, we can help to inform one of the most problematic issues of project acceptance from a commercial perspective – that of the appropriate allowable Error margin to attach to a system rating result. While a full uncertainty analysis is outside the scope of this paper, we can point out the largest source of error in this type of test. They include: model uncertainty, which we estimate at -7.7 to 4.5% Solar Irradiance measurement uncertainty, which ranges between 2-5% Module temperature measurement uncertainty, which ranges between 2-4% Therefore, we estimate that total Error in the comparison results of power output of Solar array field studied and tests done on solar module with the simulated results will be -4.95 to 5 %.
TABLE III COMPARISION RESULTS OF L&T P/W PLANT AND SIMULATION RESULT Power Date & Solar Module Power Error (Simulat time irradiance Temp. (actual) (%) or) 10/06/14 198.6 63.2 ºC 9.49 KW 9.42 KW 0.73% 2.15 PM W/m² 5/08/14 10.52 126 W/ m² 31 ºC 6.687 KW 6.57 KW 1.7% AM 5/08/14 10.52 147 W/m² 31 ºC 7.449 KW 7.66KW -2.8% AM 5/08/14 10.85K 10.53 208 W/m² 31 ºC 10.14 KW -7.00% W AM 5/08/14 10.48K 10.53 201 W/m² 31 ºC 9.792 KW -7.02% W AM 5/08/14 10.59K 10.53 203 W/m² 31 ºC 9.887 KW -7.1% W AM 5/08/14 10.69K 205 W/m² 31 ºC 9.918 KW -7.7 % 10.53 W
V. CONCLUSIONS From above study and research we can conclude that, change in atmospheric conditions such as humidity, wind, dust, wind, and edging will lend us on showing change in temperature of array and irradiance of sun. So if we consider effect of temperature and irradiance parameters, we can obtain power output solar cell of solar field, we can able to estimate and predict output power of whole solar field. Our main goal is to model and simulate the PV solar array field to study and analyse the output change in the PV Solar array according to solar irradiance and temperature variation. With solar irradiance and module temperature we also added shadow density as another input which will mimic cloudy condition effect with time of shadow present over module. We hope, we validated our simulation model by comparing the results from PV Power plants and results from our PV simulator. Hence we achieved our objective of modelling and simulation of PV solar array which can helps us to study and analyse change in nature of PV array fields output with respect to change in atmospheric conditions.
c) Case study 2- 1 MW power plant, Enrich energies, Solapur. We studied 1MW power plant situated in Solapur, from Enrich energies LTD. the comparison results of 1mw power plant and simulation results are shown in table IV. First two readings from the table are from same day dated 26/5/2014 time 13.43 pm and 16.14 pm respectively. Next three reading are of same power plant taken earlier dated 26/11/2013 time 16.41 pm, 29/11/2013 time 12.15 pm and time 12.15.33 pm respectively. TABLE IV COMPARISION BETWEEN 1 MW PLANT & SIMULATION RESULT Irradiance (w/m²)
Ambient
Module
Actual
Simulator
temp.
temp.
Output
Output
(KW)
(KW)
(ºC)
(ºC)
Error
REFERENCES
(%)
(Enrich)
[1]
804 W/m²
38.04 ºC
51.49ºC
703.00KW
704.39KW
-0.2%
190 W/m²
38.75 ºC
51.25ºC
173 KW
165.11KW
4.5%
142.2W/m²
31.50 ºC
42.00ºC
117.80KW
126.42KW
-7.3%
464.19W/m²
26.14 ºC
35.00ºC
395.60KW
422.93KW
-6.9%
469.97W/m²
26.24 ºC
35.20ºC
419.50KW
427.29KW
-1.1%
[2] [3] [4] [5] [6]
From table we can see that by comparing results from our simulator with actual Power plant output results, we achieved accuracy within ±10% error which is within the allowable margin of error. The error after comaprision between experiment results, collected data from power plants and simulated results is varies from – 7.7 % to + 4.5 %. From
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[7] [8] [9] [10]
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L & T automation, Mahape, iVisionPMSmaxTM Vision A Complete Power Management System http://www.lapsys.co.jp/english/ http://pveducation.org/pvcdrom/solar-cell-operation/ http://homeguides.sfgate.com/effects-temperature-solar-panel-powerproduction-79764.html http://www.earthsolar.co.uk/page9.html https://www.grc.nasa.gov/www/K12/problems/Jim_Naus/TEMPandALTITUDE_ans.htm http://mentalfloss.com/article/49786/how-much-does-cloud-weigh “Introduction to semiconductor materials and devices”, M.S.Tyagi. John Wiley & Sons. http://www.ugrad.math.ubc.ca/coursedoc/math100/notes/diffeqs/cool.h tml “Vue-7: from the ground up” by Ami Chopine and Vladmire Chopine
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RTOS Based Electronics Industrial Temperature and Humidity Monitoring using ARM Processor A. Prasath
P. Dhakshinamoorthi
PG Scholar M.E - Embedded System Technologies Nandha Engineering College Erode, Tamilnadu, India sivaprasath15@gmail.com
PG Scholar M.E - Embedded System Technologies Nandha Engineering College Erode, Tamilnadu, India dhakshinamoorthi08@gmail.com
patching to kernel. So the system updates also possible when it required. The ARM Controller is latest one and it support higher end applications now a days. In here we can choose the ARM controller ported with real timeoperating system(RTLinux).The system connected through the wireless sensor network for monitoring remote area. RTLinux is a preemptive, hard real-time deterministic multitasking kernel for ARM Controller. RTLinux running with Linux command and ANSI C source code for system and compilation.RTLinux can runs on more number of tasks by patching as our requirement. RTLinux performs inter-process communication (like semaphores, message queuesand mailbox), timer management and memory management. It supports for monitoring and controlling the environment by getting information from sensed value of electronics manufacturing industriesby guidance of the Engineers. The complete architecture of this paper has beendivided into 3 parts: the system hardwaredetails, software detail and finally the conclusion.
Abstract--The main of this paper is to monitor the temperature and humidity values in manufacturing electronic plants and assembling environment. The temperature and humidity are the key issues in manufacturing electronics plant and it leads to loss in production. This paper aims to provide a solution to this problem by remote monitoring of the temperature and humidity levels of different area of the plant with the help of the Wireless Senor Network Module and thesystem implemented withARM processor with porting of Linux based real time operating system. Here in addition, thebuzzerfacility is there tointimationsoundswhen over limit and SD cardfor further reference and to stores the data instantly and contiguous. These things make the electronic industries to manufacture the device ideal. Keywords--ARM Processor,RTLinux, Communication Protocol, Gateway, SD card.
WSN,RTC(DS1307),
I. INTRODUCTION Recently the electronics industries and others facing major production fault due to temperature and humidity, thus temperature cause more defects like improper soldering joints, extra oxidation of boards and bridging, solder components. Even though the environment, particularly some machines like solder paste refrigerator and desiccators for storage of paste and bare PCB respectively also have to control over these parameters. Also some factory looking to control their machineâ&#x20AC;&#x2122;s temperature with certain level to improve the efficiency of the particular machine, such a case some time lead unexpected accident, low product quality and more. Hence now the monitoring and control system established with LabView tool and interfaces of microcontroller. But the authors have coming to share their ideas in this paper, to implement the monitoring and allotment the temperature and humidity system with ARM with porting of RTLinux. The system which is implemented with RTOS is having multitasking capability to monitor multiple tasks and controlling it. Also the system can add more applications by
II. HARDWARE DETAILS Real-Time Module includes support for USB storage devices, such as thumb drives and external USB hard drives, for RT targets that have onboard USB hardware refer Fig 1.
Fig 1.Block diagram of industrial monitoring system
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The wireless gateway function in here is to receive the task from sensors and pass the task to ARM Processor.
A.ARM PROCESSOR ARM designs microprocessor technology that lies at the heart of advanced digital products, from mobile phones and digital cameras to games consoles and automotive systems, and is leading intellectual property (IP) provider of highperformance, low-cost, power-efficient RISC processors, peripherals, and system-on-chip (SoC) designs through involvement with organizations such as the Virtual Socket Interface Alliance (VSIA) and Virtual Component Exchange (VCX). ARM also offers design and software consulting services.
E.RTC (DS1307) The DS1307 Serial Real time clock(RTC) counts seconds, minutes, hours, day of the week, date, month and year.The purpose of an RTC or a real time clock is to provide precise time and date which can be used for various applications. RTC is an electronic device in the form of an Integrated Chip (IC) available in various packaging options. It is powered by an internal lithium battery. As a result of which even if the power of the system is turned off, the RTC clock keeps running. It plays a very important role in the real time systems like digital clock, attendance system, digital camera etc. In applications where time stamp is needed, RTC is a good option. Using RTC for designing such application has always been a good designer’s choice although the beginning might be a bit difficult.While designing any real time system which deals with time, there are two ways of handling the time factor. One is to generate the time internally which is done by programming the timers of the controller and the other is to use an RTC. The RTC is low power, 56 bytes of non-volatile RAM for data storage, 2 serial interface wire in bi-directional and 8 pin Dual Inline Package. The battery backup mode is less than 500nA and it has automatic power switching to battery when power fails at 2 5 °C. The RTC operates in industrial temperature range from 4 0 °C to +85°C. It is used in TV, VCR and phonenumber recall. The DS1307 RTC is connected to ARM controller using I²C bus with time counters refer Table 1.
ARM7 processor family continues to be used today for simple 32-bit devices, newer digital designs are increasingly making use of the newer, more powerful and feature-rich ARM processors which offer significant technical enhancements over the ARM7 family. System designers wishing to upgrade from ARM7 benefit from a robust ARM processor roadmap providing multiple upgrade options, including the latest Cortex processors.In most cases migration is straightforward, and brings significant benefits in PPA, features and efficiency. ARM's architecture is compatible with all four major platform operating systems: Symbian OS, Palm OS, Windows CE, and Linux. As for software, ARM also works closely with its partners to provide optimized solutions for existing market segments. B. TEMPERATURE SENSOR The LM35 series are precision integrated-circuit temperature sensors, whose output voltage is linearly proportional to the Celsius (Centigrade) temperature. The LM35 has an advantage over linear temperature sensors calibrated in Kelvin, as the user is not required to subtract a large constant voltage from its output to obtain convenient Centigrade scaling. The LM35 does not required any external calibration or trimming to provide typical accuracies of ±¼°C at room temperature and ±¾°cover a full -55 to +150°C temperature range. Low cost is assured by trimming and calibration at the wafer level
COUNTING CYCLE
CARRY TO NEXT UNIT
Seconds Minutes Hours(24) Hours(12)
00 to 59 00 to 59 00 to 23 12AM 01 AM to 11 AM 12PM 01 PM to 11 PM
59 to 00 59 to 99 23 to 00 -
Date
C. HUMIDITY SENSOR Humidity is the presence of water in air. The amount of water vapor in air can affect human comfort as well as many manufacturing processes in industries. The presence of water vapor alsoinfluences various physical, chemical, and biological processes. Humidity measurement in industries is critical because it may affect the business cost of the product and the health and safety of the personnel. Hence, humidity sensing is very important, especially in the control systems for industrial processes and human comfort. Controlling or monitoring humidity is of paramount importance in many industrial & domestic applications. In semiconductor industry, humidity or moisture levels needs to be properly controlled & monitored during wafer processing
Months Years Weekdays Timer
01 to 31 01 to 30 01 to 29 01 to 28 01 to 12 01 to 03 0 to 6 00 to 99
CONTENT OF THE MONTH COUNTER -
11PM to 12AM 31 to 01 30 to 01 29 to 01 28 to 01 12 to 01 6 to 0 No carry
1,3,5,7,8,10,12 4,6,9,11,
-
Table 1.Cycle length of the time counters, clock modes
F.SD Card The SD-memory card is non-volatile flash memory, portable device used in mobile, computer and other consumer appliances. It gives high security, memory size can vary depends on cost, used in audio and video recording. The SD-memory card is a Secure Digital Input Output (SDIO) card, it support data protection, avoid the duplication sensed value in same timing and security systems based on identification cards in International standard ISO7816. An embedded version of MMC is eMMC, according to the JESD84-A43. The interfacing of SD card with ARM using Serial Peripheral Interface (SPI) bus and operates in 3.3volts in Table 2. It offers up to 8-bit wide interface and can be applied in SD-memory card compatible hardware interfaces. While the SD-memory card adds an advanced data storage functions to an application and easily accessible.
D. GATEWAY The wireless gateways from Comcast provide the functionality of a Wi-Fi router and voice modem in a single device. The wireless gateway functions such as firewall, port forwarding, port blocking, diagnostic tools and WI-FI Protected Setup. It gives a secure wireless home network and connects your computers, laptops, and other Wi-Fi electronic products (such as game systems, tablets, or mobile phones). International Conference on Advancements in Engineering Research
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assembler and linker operation of embedded product in real time in fig 2.
PIN NUMBER
NAME
TYPE
DESCRIPTION
1
NCS
I
SPI card select (CS) (negative logic)
2
DI
I
SPI serial (MOSI)
3
VSS
S
Ground
4
VDD
S
Power
5
CLK
I
SPI serial (SCLK)
6
VSS
S
Ground
7
DO
O
SPI serial data out (MISO)
8
NC nIRQ
O
Unused
9
1
O
Unused
data
in
clock
Fig 2.RTLinux Operating System Table 2.SPI Interfacing pins for SD memory card
RTLinux design objective is that the system should be transparent, modular, and extensible. Transparency means that there are no unopenable black boxes and the cost of any operation should be determinable. Modularity means that it is possible to omit functionality and the expense of that functionality if it is not needed. The base RTLinux system supports high speed interrupt handling and no more. And extensibility means that programmers should be able to add modules and tailor the system to their requirements. It has simple priority scheduler that can be easily replaced by schedulers more suited to the needs of some specific application. When developing RTLinux, it was designed to maximize the advantage we get from having Linux and its powerful capabilities available.
G. BUZZER Buzzer is an audio signaling device and it is mechanical, electromechanical or piezoelectric. It is used as alarm device in timing manner and confirmation of user input from personnel computer or other devices by making sound. Buzzer is connected to ARM using one wire connecting wire. H.LED Light emitting diodes (LEDs) are semiconductor light sources and it has two terminals. The light emitted from LEDs varies from visible to infrared and ultraviolet regions. They operate on low voltage and power. LEDs are one of the most common electronic components and are mostly used as indicators of circuit. LED display the monitoring value of temperature and pressure in real time environment.
RTLinux functions schedule a priority scheduler that supports both a "lite POSIX" interface described below and the original V1 RTLinux API, which controls the processor clocks and exports an abstract interface for connecting handlers to clocks. It posix IO supports POSIX style read/write/open interface to device drivers, FIFO connects RT tasks and interrupt handlers to Linux processes through a device layer so that Linux processes can read/write to RT components. RTLinux is a semaphore contributed package by Jerry Epplin which gives RT tasks blocking semaphores and POSIX mutex support is planned to be available in the next minor version update of RTLinux. RTLinuxMemorybuffer is a contributed package written by Tomasz Motylewski for providing shared memory between RT components and Linux processes
III. SOFTWARE DETAIL It is a written description of a software product, that a software designer writes in order to give a software development team overall guidance to the architecture of the software project. An SDD usually accompanies an architecture diagram with pointers to detailed feature specifications of smaller pieces of the design. Practically, a design document is required to coordinate a large team under a single vision. A design document needs to be a stable reference, outlining all parts of the software and how they will work.
A. RTLinux RTLinuxis open source hard real-time RTOS microkernel.The function of the RTLinux is mostly depends on kernel. The programming of RTLinux is written in Linux command and C coding. It is portable, scalable, preemptive, high-performance interrupt handling and multitasking kernel. It is developed for commercial purpose by FSM Lab andWind River System and it has connectivity with GUI and File Systems. It is multi-environment real time kernel running in core environment and supports multiple porting of devices. It is easy to implement and highly secure real time system. It supports processors and controllers embedded applications in real time. RTLinux program coding supports compiler,
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And the other application supporting codes are can develop with C and C++ language which are generally used for development of usual general purpose and special purpose system. IV. COMMUNICATIONS PROTOCOL All communications between devices require that the devices agree on the format of the data. The set of rules defining a format is called a protocol. Communication protocols cover authentication, error detection and correction, and signaling. They can also describe the syntax, semantics, and synchronization of analog and digital communications.. There are thousands of communication protocols that are used 71
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everywhere in analog and digital communications.It supports both wired and wireless communication.
1-Wire bus. The 8-bit family code, a subset of the 64-bit ID, identifies the device type and functionality. Typically, 1-Wire slave devices operate over the voltage range of 2.8V (min) to 5.25V (max). Most 1-Wire devices have no pin for power supply; they take their energy from the 1-Wire bus (parasitic supply). It is a unidirectional bus and it is connects the LED display to ARM processor.
A. SPI BUS SPI (Serial Peripheral Interface) bus is a low power, full duplex, master-slave interfacing bus. It is solid role in embedded systems whether it is system on chip processors, both with higher end 32-bit processors such as those using ARM, MIC or Power PC and with other microcontrollers such as the AVR, PIC etc. These chips usually include SPI controllers capable of running in either master or slave mode. In-system programmable AVR controllers can be programmed using an SPI interface. Chip or FPGA based designs sometimes use SPI to communicate. So, SPI is a common technology used nowadays for communication with peripheral devices where we want to transfer data speedily and within real time constraints. There are many serial interfaces right from Morse code telegraphy, RS232, USB, Fire wire, Ethernet and many more. Each serial interface offers advantages or disadvantages for many designs, depending on criteria such as needed data rate, space availability, and noise considerations. It is simple 4 wire serial communication bus and it operates on 10MH. In SPI data is shifted in /out one at a time and transmit data from master device to/from one or more slave devices over short distances. It is high speed data transferring bus and no limit upto 8 bit transfer.The SPI bus is straightforward and versatile, enabling simple and fast communication with a variety of peripherals. A high speed multi-IO mode host adapter and some invaluable tool in debugging as well as adding SPI communication capabilities to any test system.
V. CONCLUSION In this paper, the authors are develops the idea to monitor the temperature and humidity value using wireless sensor in Real time. In this paper the existing model has to monitor the temperature and humidity value using microcontroller. The result of this paper is more secure to keep the monitoring data in real time RTLinux. In future, ability to add some more tasks to monitor, such as employee authentication checking, data logging of cctv camera and etc. The values of the monitoring data in real time are displayed on the LED and Buzzer for intimation of warning. VI. REFERENCES
[1].Prasath.A,”PC based Data acquisition system with Interfacing of I2C RTC (DS1307) & SPI ADC (MCP3201) with 8051”, project done at 2011. [2].Kollam.M,”Zigbee Wireless Sensor Network for Interactive Industrial Automation” published Year 2011.
Better
[3].Agrawal,”Complete industrial solution for automation in temperature and humidity monitoring using labview” published on IEEE conference at year 2012. [4].Tamilselvan.K,“SD card based Data Logging and Data Retrieval for Microcontrollers to using μc/os- II” International Journal of Engineering Research & Technology (IJERT) Vol.2 Issue 11, November 2013.
B. I²C BUS Two wires: serial data (SDA) and serial clock (SCL). All I2C master and slave devices are connected with only those two wires. Each device can be a transmitter, a receiver or both. Some devices are masters – they generate bus clock and initiate communication on the bus, other devices are slaves and respond to the commands on the bus. In order to communicate with specific device, each slave device must have an address which is unique on the bus. I2C master devices (usually microcontrollers) don’t need an address since no other (slave) device sends commands to the master .It supports both Multi-master and Multi-slave, so it can detect the collision easily. It supports 7 and10-bit addressing and each device connects to the bus using software with unique address. The maximum speed of the I²C bus is 3.4Mbits/sec and it varies depends on the modes of application. I²C bus is simple and flexible used in many applications.I2C bus is transferred in 8-bit packets (bytes). There is no limitation on the number of bytes, however, each byte must be followed by an Acknowledge bit. This bit signals whether the device is ready to proceed with the next byte. For all data bits including the Acknowledge bit, the master must generate clock pulses. If the slave device does not acknowledge transfer this means that there is no more data or the device is not ready for the transfer yet. The master device must either generate Stop or Repeated Start condition.
[5].Prasath.A,
Dineshbabu.N,”CompleteIndustrialSolution for Automation in Temperature andHumidity Monitoring using Microcontroller”presented at NCICC-2014,SNS College of Technology at 2014.
[6].Tamilselvan.K, Dhakshinamoorthi.P,”An Efficient Data Acquisition System for Microcontrollers with RTOS “presented at PCID-2014, BannariammanInstitute of Technology.
C. ONE WIRE BUS One –wire bus is a makes connection to one master and multiple slaves. 1-Wire technology is a serial protocol using a single data line plus ground reference for communication. A 1-Wire master initiates and controls the communication with one or more 1-Wire slave devices on the International Conference on Advancements in Engineering Research 72
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SMART VEHICLE TRACKING SYSTEM USING GSM, GPS AND RC5 Shreya Kaushal1, Kavita Rahega2 IET Bhaddal Technical Campus, Ropar1 Gurukul Vidyapeeth Institute of Engineering and Technology2
Abstract Currently almost of the public having an own vehicle, theft is happening on parking and sometimes driving insecurity places. The safe of vehicles is extremely essential for public vehicles. Vehicle tracking and locking system installed in the vehicle, to track the place and locking engine motor. The place of the vehicle identified using Global Positioning system (GPS) and Global system mobile communication (GSM). These systems constantly watch a moving Vehicle and report the status on demand. When the theft identified, the microcontroller automatically sends SMS to the authorized person, while as the microcontroller also stops the engine motor. Authorized person has nothing to do in such a smart system. The whole work is done by the microcontroller itself. Once the Car is locked using a remote, on theft attempt an automatic message is sent to the car owner along with the car location. The GPS/GSM Based System is one of the most important systems, which integrate both GSM and GPS technologies. It is necessary due to the many of applications of both GSM and GPS systems and the wide usage of them by millions of people throughout the world.
tracking of the theft vehicle and various other applications. The system is microcontroller based that consists of a global positioning system (GPS) and global system for mobile communication (GSM). This project uses only one GPS device and a two way communication process is achieved using a GSM modem. GSM modem, provided with a SIM card uses the same communication process as we are using in regular phone. This system is user friendly, easily installable, easily accessible and can be used for various other purposes. The system allows to track the target anytime and anywhere in any weather conditions. The applications include monitoring driving performance of a parent with a teen driver. Vehicle tracking systems accepted in consumer vehicles as a theft prevention and retrieval device. If the theft identified, the system sends the SMS to the vehicle owner, once the car has been locked. 2. The two modules of the GPS based navigation system The project can be divided into two basic modules: 1. The GPS reception system: This is the main module. This consists of building the hardware for reception of data from satellites through a GPS receiver, synchronizing the receiver with the satellite through G-monitor software, extracting appropriate data from GPS receiver once it has been synchronized, calculating Indian Standard Time (IST) and displaying the data on a LCD screen. 2. Tracking of a target location: This consists of designing the Visual Basic Interface, extracting relevant positional data from microcontroller, comparing target location and current location and tracking by a vehicle as a result of this comparison.
1. Introduction GSM and GPS based vehicle location and tracking system will provide effective, real time vehicle location, mapping and reporting this information value and add by improving the level of service provided. A GPS-based vehicle tracking system will inform where your vehicle is and where it has been, how long it has been. The system uses geographic position and time information from the Global Positioning Satellites. The system has an "On- Board Module" which resides in the vehicle to be tracked and a "Base Station" that monitors data from the various vehicles. The On-Board module consists of GPS receiver, a GSM modem Various problems that we face: 1. In critical condition (when vehicle is stolen), one is confused what to do, 2. If one has something expensive and he wants to check it regularly , 3. To find the shortest path available.
2.1 Requirements of the system The hardware elements used for the two modules are: GPS receiver, GPS antenna, UART 16C550, PIC 16F73, LCD Screen, A toy vehicle. 2.2 The software used is: 1. MPLAB IDE for the assembly language coding of PIC16F73 2. PicKit programming software The hardware setup for synchronizing the GPS receiver is very simple. We have to connect a GPS antenna to the receiver and interface the receiver to a computer through a MAX 232 chip. Then we use
This system has Global Positioning System (GPS) which will receive the coordinates from the satellites among other critical information. Tracking system is very important in modern world. This can be useful in soldier monitoring,
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the Gmonitor software to synchronize the GPS receiver with the satellites.
programmable and erasable read only memory (PEROM). It has 256 bytes of RAM, 32 input/output (I/O) lines, three 16-bit timers/ counters, a six-vector two-level interrupt architecture a full-duplex serial port, an on-chip oscillator and clock circuit. The system clock also plays a significant role in operation of the microcontroller. An 10 MHz quartz crystal connected to pins 18 and 19 provides basic clock to the microcontroller. Power-on reset is provided by the combination of electrolytic capacitor C3 and resistor R1. Port pins P2.0 through P2.7 of the microcontroller are connected to data port pins D0 through D7 of the LCD, respectively. Port pins P0.5, P0.6 and P0.7 of the microcontroller are connected to Register-select (RS), Read / write (RW) and enable (E) pins of the LCD, respectively. All the data is sent to the LCD in ASCII format for display. Only the commands are sent in hex form. Register-select (RS) signal is used to distinguish between data (RS=1) and command (RS=0). Preset RV1 is used to control the contrast of the LCD. Resistor 10k limits the current through the backlight of the LCD. Port pins P3.0 (RXD) and P3.1 (TXD) of the microcontroller are used to interface with the RFID reader through RS232 and GSM Modem are used to interface through Max232. Port pins from P1.0 to P2.7 of the microcontroller are connected to keyboard. The GPS and GSM are used to connect through RXD and TXD pins of the microcontroller for further processing. Using RC5, the car is locked and GSM module is used to send SMS if a theft attempt is detected. If unauthorized person enter into the car, the microcontroller checks whether the car is still in locked condition, if yes it senses the theft attempt and locks the car then the controller issues the message about the location of the vehicle to car owner or authorized person. To open the door or to restart the engine authorized person needs to unlock the car. In this method, tracking of vehicle location easy and also engine(s) are locked automatically thereby thief cannot get away from the car.
Figure 1. Block Diagram of Transmitting unit
Figure 2. Block Diagram of Receiver unit. The antenna used for reception of information from GPS satellites is the “GPS Smart Antenna”. This antenna is used to establish contact by the GPS receiver with the satellites. It has a magnet base which helps in easy mounting. Once the antenna has been positioned properly and connected with the GPS receiver, we can start synchronizing the GPS receiver with the satellites through Gmonitor software. The GPS receiver used is the “GPS-MS1E”- a fully self controlled receiver module for Global Positioning System manufactured by SiRF technology, Inc.Nowadays, data is relayed according to NMEA-0183 specification. NMEA has several data sets. In our application, we make use of the RMC- Recommended Minimum Specific data set. RMC data set: the RMC data set contains information on time, latitude, longitude, height, system status, speed, course and date. This data set is relayed by all GPS receivers.Now that the GPS receiver is receiving valid data, we proceed to connect the GPS receiver to the PIC18F452microcontroller through UART16C550. Once this is accomplished, we then proceed to display the appropriate data on an LCD. 3. Proposed Circuit Diagram The circuit diagram of the vehicle tracking and locking embedded system using GPS and GSM technology is shown in Fig. The compact circuitry is built around PIC 16F73 Microcontroller. The PIC16F73 is a low power; high performance CMOS 8-bit microcomputer with 8 kB of Flash
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Figure 3. circuit diagram of vehicle tracking and locking embedded system using GPS and GSM technology
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4. Hardware Design
Incorrect logic or errors in computations can analyze by stepping through the code in simulation. Simulators run at speeds 100 to 1000 times slower than the actual micro controller hardware and, thus, long time delays should avoid when simulating a program. Micro controller-based systems usually have interfaces to various external devices such as motors, I/O ports, timers, A/D converters, displays, push buttons, sensors and signal generators, which are usually difficult to simulate. Some advanced simulators, such as the Proteus from Lab center Electronics allow the simulation of various peripheral devices. Inputs to the simulator can come from files that may store complex digital I/O signals and waveforms. Outputs can be as form of digital data or waveforms, usually stored in a file, or displayed on a screen. Some simulators accept only the assembly language of the target microcontroller. It has become necessary to simulate a program has written in a high-level language. The software program has been written in C or assembly language and compiled using MPLAB 8.0 software. After compiler operation, the hex code generated and stored in the computer. The hex code of the program should be loaded into the PIC16f73 micro controller by using Top win Universal programmer.
PIC16F73 microcontroller is the heart of the project that is used for interfacing. Two pins are VCC pins and other two pins are at ground. Pin 9 is reset pin. A crystal oscillator of 10 MHz is connected to the microcontroller. RS-232 protocol is used as serial communication between the microcontroller, GPS and GSM modem. A serial driver MAX232, 16 pin IC is used for converting RS-232 voltage levels into TTL voltage levels. There are four electrolytic capacitors which are used with MAX232. A 12V battery is used to power the circuit. A 7805 regulator is used to convert 12V into 5V. The microcontroller and MAX232 are powered by 5V. LED indicates the presence of power supply.
6. Hardware Assembling and Testing: First step, we need to make single side PCB layout for the given circuit diagram. After made the PCB the following process is required to complete the project:1. Assemble all the components on the PCB based on circuit diagram. 2. This will Include Power Supply, Controller circuit, 16×2 LCD, TSOP 1738, IC L293D, DC geared motor and the required components like Chassis, Chester wheel and dummy motor 3. TX and RX pins of the GSM modem to RX and TX Pins of Micro controller respectively. 4. L293D is 16 pin IC whose pins are connected to +5V Ground (0V) and different pins of the micro controller while a few a left behind or not connected. 5. The various Pins of PiC 16f73 are connected to power supply, IC L293D, LED‟s ( to check the various responses), TSOP 1738 (used to receive signals from a Remote control), LCD (16×2 i.e., 16 characters in 2 lines each) and to the two modules viz., GSM and GPS modules. 6. Insert a valid SIM in the GSM modem. 7. Connect the GPS module according to circuit diagram. 8. The power supply consists of three 4V, 1 Amp rechargeable batteries which provide supply to the whole circuit and can be recharged by connecting the output of the batteries to the input of 12V regulator (7805). Positive output of the battery to
Figure 3.Block diagram of Vehicle tracking and locking system based on GSM, GPS and RC5 5. Debugging and Testing Process A microcontroller-based system is a complex activity that involves hardware and software interfacing with the external world. Doing well design of a microcontroller-based system requires skills to use the variety of debugging and testing tools available. The debugging and testing of microcontroller-based systems divided into two groups: software-only tools and software-hardware tools. Software-only tools come as monitors and simulators, which are independent of the hardware under development. Software-hardware tools are usually hardware dependent, more expensive and range from in-circuit emulators and in-circuit simulators to in-circuit debuggers. In general, the higher the level of integration with the target hardware, the greater the benefit of a tool, resulting in a shorter development time, but the greater the cost as well. The factors to consider when choosing a debugging tool are cost, ease of use and the features offered during the debugging process. The user program operated in a simulated environment where the user can insert breakpoints within the code to stop the code and then analyze the internal registers and memory, display and change the values of program variables and so on.
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the 12V input of the 7805 regulator and negative output to the ground of 7805 regulator. The project is implemented and tested successfully. This system is very useful and secure for car owners.
once it is implemented in all vehicles, then it is possible to track anytime from anywhere. It has real-time capability, emerges in order to strengthen the relations among people, vehicle and road by putting modern information technologies together and able to forms a real time accurate, effective comprehensive transportation system. In this paper, we have proposed a novel method of vehicle tracking and locking systems used to track the theft vehicle by using GPS and GSM technology. When the car is locked using a remote, engines are automatically locked. In case a theft is identified by the microcontroller, it sends the exact location of the vehicle to the owner and also engines remain in the same condition i.e., Locked. When the theft identified, the responsible people does not have to anything as the system is pretty smart that it automatically sends SMS to the authorized person, then issue the control signals to stop the engine motor. After that all the doors locked. To open the doors or to restart the engine authorized person needs to unlock the car using the remote. In this method, one can easily track the vehicle anytime and anywhere in any weather
Figure 4. Hardware Design of the System
References:1. Asaad M. J. Al-Hindawi, Ibraheem Talib, “Experimentally Evaluation of GPS/GSM Based System Design”, Journal of Electronic Systems Vol. 2 No- 2 pp-230-233, June 2012.
7. Objective and Future Scope The objective of the project is to build an additional feature to the present security system that will warn the owner of the vehicle by sending SMS when there has been an intrusion into the vehicle and to provide a solution to avoid car stolen in the lower cost than advance security car system. The project is all about controlling theft of a vehicle. The system is about making vehicle more secure by the use of GPS and GSM technology. The other objectives may include:1. Developing Automatic Vehicle Location system using GPS for positioning information and GSM/GPRS or information transmission with following features. 2. Acquisition of vehicle’s location information (latitude longitude) after specified time interval. 3. Transmission of vehicle’s location and other information (including ignition status, door open/close status) to the monitoring station/Tracking server after specified interval of time. This project can be further enhanced by the use of camera and by developing a mobile based application to get the real time view of the vehicle instead to check it on PC, which would be more convenient for the user to track the target and to provide a solution to avoid car stolen in the lower cost than advance security car system
2. Albert Alexe, R.Ezhilarasie, “Cloud Computing Based Vehicle Tracking Information Systems”, IJCST Vol. 2, Issue 1,pp 432-446 March 2011. 3. Chen Peijiang, Jiang Xuehua, “Design and Implementation of Remote monitoring system based on GSM,” IOSR Journal of Electronics and Communication Engineering, vol.42, pp.167-175. 2008. 4. Yusnita Rahayu and Fariza N. Mustapa A Secure Parking Reservation System Using GSM Technology International Journal of Computer and Communication Engineering, Vol. 2, No. 4, July 2013. 5. Albert Alexe, R.Ezhilarasie, “Cloud Computing Based Vehicle Tracking Information Systems”, IJCST, ISSN: 2229-4333, Vol. 2, Issue 1, March 2011. 6. Kai-Tai Song, Chih-Chieh Yang, of National Chiao Tung University, Taiwan, “Front Vehicle Tracking Using Scene Analysis”, Proceedings of the IEEE International Conference on Mechatronics & Automation 2005.
8. Conclusion Tracking system is becoming increasingly important in large cities and it is more secured than other systems. It is completely integrated so that
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Experimental Investigation and predictive modelling for Surface Roughness of drilling on GFRP composites Prasanna Ragothaman Department of Mechanical Engineering, Sri Ramakrishna Engineering College, Coimbatore, India prasse1994@gmail.com Abstract: Glass Fiber Reinforced Plastics composites have an increased application in recent days, due to its enhanced structural properties, Mechanical and thermal properties. Drilling of holes in GFRP becomes almost unavoidable in fabrication. The heterogeneous nature of this kind of materials makes complications during machining operation. However, drilling is a common machining practice for assembly of components. The quality of holes produced in the GFRP material is severely affected by surface roughness, Circularity and Delamination . The objective of the study is to apply the full factorial design, ANOVA and Fuzzy logic model to achieve an improved hole quality considering the minimum surface roughness through proper selection of drilling parameters. The regression method is employed in the Experimental investigation and Mathematical modelling of drilling of GFRP material using HSS drill bits and the fuzzy logic model for the validation of the mathematical model. Keywords: GFRP, ANOVA, Fuzzy logic, Full factorial Method, Drilling, Surface Roughness. 1. Introduction Glass fiber Reinforced Plastics (GFRP) are widely being used in the automotive, machine tool industry, aerospace components, sporting equipmentâ&#x20AC;&#x2122;s [1] because of their particular mechanical and physical properties such as specific strength and high specific stiffness. An aircraft fuselage structure around 100,000 holes is required for joining purpose [2, 3]. About 60% of the rejections are happening in aircraft industry due to the defects in the holes [4]. Many of these problems are due to the use of non-optimal cutting tool designs, rapid tool wear and cutting parameters [5, 6]. Among the defects caused by drilling with tool wear, Delamination appears to be the most critical [7].The surface finish of the work piece is an important attribute of hole quality in any drilling operation. During machining many factors affect the surface finish. Many theoretical models have concluded that the effect of spindle speed on surface roughness is minimal [8]. In practice, however spindle speed has been found to be an important factor [9]. The quality of drilling surfaces depends on the cutting parameters and tool wear, while changing the cutting parameters causes to tool wear [10].Researchers have attempted to model the surface roughness prediction using multiple regression, mathematical modeling based on physics of process, fuzzy logic [11].Machining operation being highly nonlinear in nature, soft computing techniques have been found to be very effective for modeling [12].The influence of process parameters such as spindle speed, lubrication and feed rate on surface finish were investigated during the experimentation of Metal matrix composites. The experiments were conducted according to the full factorial design [13] .The percentage of contribution of highest influential factors can be determined using analysis of variance(ANOVA) a statistical tool, used in design of experiments[14,15]. Fuzzy logic is a mathematical formalism for representing human knowledge involving vague concepts and a natural but effective method for systematically formulating cost effective solutions of complex problems [16]. A model was developed for surface roughness on drilling of GFRP composites using fuzzy logic [17]. The primary objective of this study is to quantify the influence of process input parameters on surface roughness by formulating a mathematical model and validating using Fuzzy logic model.
2. Design of experiment Design of experiment is the design of all information -gathering exercises where variation is present, whether under the full control experimenter or not. The cutting speed, feed rate and thickness of GFRP plate are the three parameters under investigation in the present study. A full factorial experimental design with a total number of 27 holes drilled into the GFRP specimen to investigate the hole quality on Surface Roughness. The full factorial design is the most efficient way of conducting the experiment for that three factors and each factor at three levels of experiments is used. Hence as per Levelsfactor (factors to power of levels) formula = Levelsfactors ,N = 33 = 27, N- number of experiments.
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Table 1Assignment levels of process parameters Factors Speed, s(rpm) Feed, f(mm/rev) GFRP Plate thickness, t(mm)
Levels 1 2 280 900 0.18 0.71
3 1800 1.40
5
15
10
Fig 1: Fabricated GFRP plate 3. Specimen Preparation The Glass fiber reinforced composite used is fabricated using hand lay-up technique [12]. The composition type is Glass fibers (fiber length=20-30mm) reinforced with isopathalic resin with 30% reinforcement .The material was fabricated and then cut into pieces of 22cm x 11cm for all the three thicknesses of plate (Fig.1). 3.1 Methodology
Fig 2.Surface Roughness tester
Fig 3.Experimental setup
Experiments were carried out in high speed radial drilling machine using HSS drill of 10mm diameter. Experiments were carried out according to full factorial design. It provide a powerful and efficient method for designing processes that operate consistently and optimally over a variety of conditions. The selected levels of process parameters were given in Table 1.Fig. 3 shows the photographic view of the experimental setup. Further, the hole quality characteristics surface roughness measured using roughness tester [Mitutoyo TR-200].Fig. 2 shows the measurement of hole quality characteristics using roughness tester. Point angle was measured before every drill for 27 experiments using Digital Profile Projector [OPTOMECH, 10x magnification]. 4. Results and Discussion 4.1. ANOVA The Analysis of variance is extensively used to analyze the experimental results. ANOVA tests the significance of group difference between two or more groups. The normal probability plot represents that all the points on the normal plot lie close to the straight line (main line) or not. Versus fits plots represents that how far deviation occur from the normal distribution. An interaction plot is occurs when the change in response from the one level of a factor to another level differs from change in response at the same two level second factor. A main effect plot is present when different levels of an input affect the responses directly. 4.2. ANOVA for Surface Roughness Fig.4 Represent that all the points lie closer to the regression line, this implies that the data are fairly normal and there is a no deviation from the normal. Histogram graph shows the skewness. The Equation No. 1 represents that feed has much effect on Ra. The main effect plot for Surface Roughness has been shown in the Fig 5. The plot shows that Ra decrease with low cutting speed and low feed rate for 15 mm plate, as well as the initial (without wear in drill bit ) point angle has less effect on Ra. Table 3. Shows that the analysis of variance of second order model with 95% confidence interval for the Surface roughness experiments. Parameter A gives 44.2% contribution to the Ra.
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Fig.5 Main Effect Plot for Ra Fig.4 Residual plot for Ra
Fig.6 Interaction Plot for Ra
4.3 Mathematical model for Surface Roughness The models were based on the Box-Behnkn design method. The developed second order mathematical model for surface roughness. Surface Roughness = 4.87 - 0.00086 (s) + 2.49 (f) + 0.249 (t) (1) Table 3Analysis of variance Predictors Coef Constant 4.873 S 8.56e-4 F 2.494 T 0.2487 S = 3.54808 R-Sq = 21.1% R-Sq(adj) = 10.8% Source Regression Residual Error Total
DF 3 23 26
SE Coef 2.352 1.09e-3 1.367 0.1673
SS 77.44 289.54 366.99
T 2.07 -0.78 1.49 2.07
MS 25.81 12.59
F 2.05
P 0.05 0.442 0.151 0.017
P 0.135
Fig.6 Represent that high feed rate and low speed have less effect on Ra while drilling on 5mm thickness plate. When drilling on 10mm thickness of plate with cutting parameters of low speed and feed rate shows surface roughness is
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minimized. For 15mm plate high speed and high feed rate has less effect on Ra. From Fig.5 shows that when decreasing the point angle, the effect of surface roughness is increased. Decreasing the point angle is causes tool wear. Fig.7 shows the predicted and measured hole characteristics at different drilling process parameter conditions. The result significantly shows that the values relatively follow the similar trend pattern of the measured value and predicted values from the developed regression model. 4.4 Fuzzy logic model Fuzzy logic refers to a logical system that generalizes the classical two-value logic for reasoning under uncertainty. It is a system of computing and approximate reasoning based on a collection of theories and technologies that employ fuzzy sets, which are classes of objects without sharp boundaries. Fuzzy logic is the best captures the ambiguity in the input. Fuzzy logic has become popular in the recent years, due to the fact that it is possible to add human expertise to the process. Nevertheless, in the case where the nonlinear model and all the parameters of a process are known, a fuzzy system may be used. 4.4.1.Devolopment of fuzzy logic model The surface roughness in drilling of GFRP is assumed as a function of three input variables viz.plate thickness, spindle speed, and feed rate. The Fuzzy logic prediction model is developed using Fuzzy Logic Toolbox available in Matlab version 7.10(R2010a).In this work Mamdani type Fuzzy Inference Systems(FIS) is used for modeling. The steps followed in developing The fuzzy logic model are described below. 4.4.1.A.Fuzzification of I/O variables: The input and output variables are fuzzified into different fuzzy sets. The triangular membership function is used for simplicity yet computationally efficient. It is easy to use and requires only three parameters to define.The input variables plate thickness [5-15 mm] ,spindle speed [280-1800 rpm] and feed rate [0.18-1.4 mm/rev] are fuzzified into three fuzzy sets viz.Low (L),Medium(M),and High(H) as shown in the Fig.8 (a,b,c).The output variable i.e. The surface roughness is divided into nine fuzzy sets as Very Very Low(VVL),Very Low(VL),Low(L),Medium1 (M1),Medium2 (M2), Medium3 (M3),High (H),Very High (VH),Very Very High (VVH) as shown in Fig.8 (d) to increase the resolution and accuracy of prediction.
Fig.8 Fuzzification of I/O variables 4.4.1.B. Evaluation of IF-THEN rule The three input variables are fuzzified into three fuzzy sets each, the size of rule base becomes 27(3x3x3).For generating the Fuzzy rules, the level of the variable having more membership grade on a particular fuzzy set is considered. With appropriate level of all the input variables representing the corresponding fuzzy set, the surface roughness values are used for 27 data sets of fuzzy rule base. Since all the parts in the antecedents are compulsory for getting the response value, the AND (min) operator is used to combine antecedents parts of each rule. The implication method min is used to correlate the rule consequent with its antecedent. For example, the first rule of the FIS can be written as
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Rule 1: ‘if Thickness is Low and Speed is Low and Feed rate is Low then surface roughness is Very Very low (VVL)’. 4.4.1.C.Aggregation of Rules The aggregation of all the rule outputs is implemented using max method, the commonly used method for combining the effect of all the rules. In this method the output of each rule is combined into single fuzzy set whose membership function value is used to clip the output membership function. It returns the highest value of the membership functions of all the rules. 4.4.1.D.Defuzzification The aggregate output of all the rules which is in the form of fuzzy set is converted into a numerical value (crisp number) that represents the response variable for the given data sets. In the present work, the centroid defuzzification method is used for this purpose. It is the most popular method used in most of the fuzzy logic applications. It is based on the centroid calculation and returns center of area under the curve. The predicted values of surface roughness are compared with the experimental output, prediction model output and fuzzy output. The comparison of prediction performance in fuzzy logic output, prediction model output with the experimental results is given in the Table 4. Table 4.Surface roughness values for Experimental output, Predicted output and Fuzzy output Surface Roughness, Ra (µm) Plate S. Speed Feed Point angle Experimental Predicted thickness No s(rpm) f(mm/rev) θ (◦) output output t (mm) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Fuzzy output
5
280
0.18
107º32'07"
3.19
6.3224
2.15
5
280
0.71
107º29'47"
11.98
7.6421
10.00
5
280
1.40
107º25'32"
6.49
9.3602
5.75
5
900
0.18
107º20'44"
4.09
5.7892
3.62
5
900
0.71
107º17'58"
9.16
7.1089
7.87
5
900
1.40
107º15'20"
8.79
8.8270
7.87
5
1800
0.18
107º19'31"
12.96
5.0152
12.1
5
1800
0.71
107º12'25"
7.27
6.3349
5.75
5
1800
1.40
107º08'28"
3.33
8.0530
2.50
10
280
0.18
107º02'57"
5.64
7.5674
3.62
10
280
0.71
106º47'11"
8.98
8.8871
7.87
10
280
1.40
106º39'47"
11.42
10.6052
10.00
10
900
0.18
106º30'09"
1.76
7.0342
2.19
10
900
0.71
106º28'42"
7.12
8.3539
5.75
10
900
1.40
106º24'50"
8.58
10.0720
7.87
10
1800
0.18
106º21'20"
7.72
6.2602
5.75
10
1800
0.71
106º19'26"
10.07
7.5799
10.00
10
1800
1.40
106º16'32"
8.97
9.2980
7.87
15
280
0.18
106º32'07"
8.37
8.8124
7.87
15
280
0.71
105º58'10"
10.48
10.1321
10.00
15
280
1.40
105º52'37"
15.75
11.8502
14.30
15
900
0.18
105º44'12"
5.43
8.2792
3.62
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Plate thickness t (mm)
Speed s(rpm)
Feed f(mm/rev)
Point angle θ (◦)
Surface Roughness, Ra (µm) Experimental Predicted output output
Fuzzy output
15
900
0.71
105º36'04"
18.25
9.5989
16.4
15
900
1.40
105º14'39"
11.43
11.3170
10.00
15
1800
0.18
105º22'42"
4.74
7.5052
3.62
15
1800
0.71
105º08'35"
6.64
8.8249
5.75
15
1800
1.40
104º58'49"
8.55
10.5430
7.87
Fig.10 Correlation between Experimental Ra with predicted Ra and Fuzzified Ra The variation of surface roughness with different combinations of input variables is studied using the output surface FIS.Fig.14,Fig.15,Fig 16,and Fig 17 shows the Functional dependence of surface roughness(Ra) with Plate thickness,feed rate and Spindle speed.It can be observed that the surface roughness increase with increase in plate thickness or increse in spindle speed or increase in feed rate.And it is also observed that surface roughness is decreases with small plate thickness, medium spindle speed and small feed rate. Fig.10 indicates that the outputs from the experiments, Prediction model, Fuzzy are in good correlation with each other.
Fig.11 Rule viewer
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Fig .12 Surface Viewer
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Fig .13 Surface roughness vs Thickness
Fig.14 Surface roughness vs Speed
Fig .15 Surface roughness vs Feed rate
5. Conclusion This experimental investigation presents the surface roughness characteristics of drilling on GFRP composites. A simple regression prediction model was developed based on the function of process variables and the following conclusions were made
Surface Roughness was analyzed as a function of process input variables. Validation was done with a developed fuzzy –rule based model. The results obtained from experiments, Prediction model and the fuzzy model are in good correlation with each other.
From analysis of variance and from the fuzzy model, the results indicated that low feed rate, high spindle speed and 5mm thickness of GFRP plate gives better Surface Roughness.
It was observed that the surface roughness increases with the decreasing of point angle.
Further investigations are needed to enhance the hole quality characteristics considering different tool materials and tool diameters, considering machine vibration, etc during drilling of GFRP composites.
6. References [1] Park, J. N., Cho, G. J.A Study of the Cutting Characteristics of the Glass Fiber Reinforced Plastics by Drill Tools, International Journal of Precision Engineering and Manufacturing, vol. 8 (2007) 11-15 [2] VijayanKrishnaraj, Member, IAENG, Effects of Drill Points on Glass Fiber Reinforced Plastic Composite While Drilling at High Speed, Proceedings of the World Congress on Engineering 2008 Vol II, WCEE 2008, July 2-4(2008) London, U.K. [3] Sonbatry El, Khashaba U.A, Machaly T, Factors affecting the machinability of GFRP/epoxy composites, Comp Structures, 63 (2004) 329-338. [4] Montgomery, D.C.,. Design and Analysis of Experiments: Response Surface Method and Designs. John Wiley and Sons. New York, USA, 2005.
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[5] Konig W, WulfCh, GraĂ&#x; P and Willercheid H, Machining of fiber reinforced plastics, Annals CIRP, 34 (2) (1985) 537-548. [6] Komaduri R, Machining of fiber-reinforced Composites, Mechanical Engineering, 115 (4), (1993) 58-66. [7] A.M. Abrao et.al,. Drilling of fiber reinforced plastics: A review, Journal of Materials Processing Technology 186 (2007) 1â&#x20AC;&#x201C;7. [8] Abrao A M, Faria PE, Campus Rubio J., C., Reis P, PauloDavim J Drilling of fiber reinforced plastics: A Review. J Materl. Process Technology 186 (2007) 1-7. [9] CaprinoG, Tagliaferi V Damage development in drilling glass fiber reinforced plastics. Int J Mach tools Manuf (6): (1995) 817-829. [10]Hocheng, H.and H. Puw. On drilling characteristic fiber reinforced Thermoset &Thermoplastics. Int J Mach tools Manuf ,32 (1992)583-592. [11]M.Chandrasekaran, M.Muralidhar, C.M.Krishna and U.S.Dixit, Application of soft computing techniques in machining performance prediction and optimization:a literature review,Int J Adv Manuf Technol,Vol.46(2010) 445-464. [12]M.Chandrasekaran and D.Devarasiddappa ,Development of Predictive Model For Surface Roughness in End Milling of Al-SiC Metal matrix Composites using Fuzzy logic, Engineering and Technology 68 (2012) 1271-1276 [13]Sureshkumar ManickamShanmugasundram et.al, Experimental Investigation of Prediction of Hole quality Characteristics of Aluminum Matrix Composite (AMC225xe). Advanced Materials Research Vols.622-623 (2013) 1305-1309. [14] C.Y.Hsu,C.S.Chen,C.C.Tsao,Free abrasive wire saw machining of ceramics, Int J Adv Manuf Technology 40 (2009) 503-511. [15] Bala Murugan Gopalsamy,Biswanath Mondal,Sukamal Ghosh,Optimisation of machining parameters for hard machining:grey relational theory approach and ANOVA,The International journal of Advanced Manufacturing Technology 45 (2009) 1068-1086. [16]Vikram Banerjee et.al,Design space exploration of mamdani and sugeno inference systems for fuzzy logic based illumination controller, International journal of VLSI and Embedded system-IJVES (2012) 97-101. [17]B.latha and B.S.Senthilkumar, Modeling and Analysis of Surface Roughness Parameters in Drilling GFRP Composites Using Fuzzy Logic, Materials and Manufacturing Processes 25(8) (2010) 817-827.
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“Wireless Ad hoc Networks” C:LATAH SOUNDARYA E-mail:jesuslvu94@gmail.com
M.SREEKAR PRANAV E-mail:sreekar.pranav4@gmail.com
Wireless Ad-hoc Networks Abstract Deployed in 1990’s, Mobile Ad-hoc networks have been widely researched for many years. Mobile Ad-hoc Networks are a collection of two or more devices equipped with wireless communications and networking capability. These devices can communication with othernodes that immediately within their radio range or one that is outside their radio range. For the later, the nodes should deploy an intermediate node to be the router to route thepacket from the source toward the destination. The Wireless Ad-hoc Networks do not have gateway, every node can act as the gateway. Although since 1990s’, lotsof research has been done on this particular field, it has often been questioned as to whether the architecture of Mobile Ad-hoc Networks is a fundamental flawedarchitecture. The main reason for the argument is that Mobile Ad-hoc Networks are almost never used in practice, almost every wireless network nodes communicate to base-station and access points instead ofco-operating to forward packets hopby-hop.We take the position that Mobile Ad-hoc Networks (MANET) are a fundamentally flawed architecture. As argument, we try to clarify the definition, architecture and the characters of MANET, as well as the main challenges
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of constructing the MANET. Although many works have been done to solve the problem, we will show in this paper that it is very difficult to solve these limitations which made the Mobile Adhoc Networks a flawed architecture. After giving many evidences and analysis, we could see that the key technologies of Wireless Ad-hoc Networks were not implemented as well as we expect. That is to say, many problems are inherently unsolvable. Thus, we could explain why we take the position that Mobile Ad-hocNetworks are flawed architecture.
Introduction Research on Wireless Ad Hoc Networks has been ongoing for decades. The history of wireless ad hoc networks can be traced back to the Defense Advanced Research ProjectAgency (DAPRPA) packet radio networks (PRNet), which evolved into the survivable adaptive radio networks (SURAD) program [11]. Ad hoc networks have play an important role in military applications and related research efforts, for example, the global mobile information systems (GloMo) program [12] and the near-term digital radio (NTDR) program [13]. Recent years have seen a new spate of industrial and commercial applications
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for wireless ad hoc networks, as viable communication equipment and portable computers become more compact and available. Since their emergence in 1970’s, wireless networks have become increasingly popular in the communication industry. These networks provide mobile users with ubiquitous computing capability and information accessregardless of the users’ location. There are currently two variations of mobile wireless networks: infrastructure and infrastructureless networks. The infrastructured networks have fixed and wired gateways or the fixed Base-Stations which are connected to other Base-Stations through wires. Each node is within the range of a Base-Station. A “Handoff” occurs as mobile host travels out of range of one Base-Station and into the range of another and thus, mobile host is able tocontinue communication seamlessly throughout the network. Example applications of this type include wireless local area networks and Mobile Phone.The other type of wireless network, infrastructurelessnetworks, is knows as Mobile Ad-hoc Networks(MANET). These networks have no fixed routers, every node could be router. All nodes are capable of movement and can be connected dynamically in arbitrary manner. The responsibilities for organizing and controlling the network are distributed among the terminals themselves. The entire network is mobile, and the individual terminalsare allowed to move freely. In this type of networks, some pairs of terminals may not be able to communicate directly with each other and have to relay on some terminals so that the messages are delivered to their destinations. Such networks are often refereed to as multi-hop or store
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andforwardnetworks. The nodes of these networks function as routers, which discover and maintain routes to other nodes in the networks. The nodes may be located in or on airplanes, ships, trucks, cars, perhaps even on people or very small devices. Mobile Ad-hoc Networks are supposed to be used for disaster recovery, battlefield communications, and rescue operations when the wired network is not available. It canprovide a feasible means for ground communications and information access. 2. Characters and Fundamental Challenges of Wireless Ad-hoc Networks Since Wireless Ad-hoc Networks are inherently differen from the well-known wired networks, it is an absolutely new architecture. Thus some challenges raise from the two key aspects: selforganization and wireless transport of information [4], [5].First of all, since the nodes in a Wireless Ad-hoc Network are free to move arbitrarily at any time. So the networks topology of MANET may change randomly and rapidly at unpredictable times. This makes routing difficult because the topology is constantly changing and nodes cannot be assumed to have persistent data storage. In the worst case, we do not even know whether the node will still remain next minute, because the node will leave the network at any minute. Bandwidth constrained is also a big challenge. Wireless links have significantly lower capacity than their hardwired counterparts. Also, due to multiple access,fading, noise, and interference conditions etc. the wireless links have low throughput.Energy constrained operation. Some or all of the nodes in a MANET may rely on batteries. In this scenario, the most
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important system design criteria for optimization may beenergy conservation. Limited physical security: Mobile networks are generallymore prone to physical security threats than are fixed cable networks. There are increased possibility odeavesdropping, spoofing and denial-of-service attacksin these networks. 3. The Argument It is debated in academic as whether the Mobile Ad hoc Networks are a fundamentally flawed architecture. The reason for the debate is that Mobile Ad hoc networks are almost never used in practice, the wireless networks weuse now is still Base-station or Access Point related. If we could proof that, technically, the Mobile Ad-hoc is unrealizable, then we could say it is a flawed architecture. We take the position that MANET is a flawed architectureand will prove our position in section 5. In section 4, we will explain the counterclaim argument in favor that MANET is a novel architecture which is technical correctand could be realized and put into practice. Section 5 refutes this argument and explains why we think MANET is a flawed architecture using some detail examples. Conclusion and implication of our position are presentedin Section 6. 4. Counter Argument It is claimed that Mobile Ad-hoc networks is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. It is great importance in situation where it is very difficult to provide the necessary infrastructure. Furthermore, ad-hoc networks have been recognized as an important form of wireless network. MANETs are internetworks formed by mobile wireless routers, with each router
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having one or more associated host devices (e.g., computers and sensors). A MANET’srouter implements routing protocols that—unlike conventional routing techniques—tolerate rapid changes in connectivity among nodes. MANET’s routing algorithms organize the network by automatically discovering the topology of the connectivity among constituent nodes. The collection of interconnected nodes serves as the network’s communications infrastructure. MANETs are nonhierarchical systems, with each node (mobile router) serving identical roles as asource, sink, and passthrough for data. Thus, the MANET is not tied to an existing or static communications infrastructure (as is a cellular telephone network). The ability to independently self-organize and serve as its own infrastructure makes MANETs particularly attractive for the industrial communications requirements in harshmanufacturing environments. Many researches have been done on all aspects of the Mobile Ad-hoc Networks to make it more suitable for wireless communications. People develop lots of routing protocols to fit the mobility of the Wireless Ad-hoc Networks. The routing algorithms become more and more fit the rapid changing network topology of Wireless Adhoc Networks.The Wireless Ad-hoc Networks itself is not hierarchy. In order to manage all the nodes and make Routing Protocols as well as Collision Detection mechanism easier, Peoplebring out the idea of constructing the Wireless Ad-hoc Networks into a hierarchic architecture. Thus we have thedefinition of Cluster. The networks is divided into clusters, each cluster has its own cluster head. The clusterhead willcontain the information of the other nodes in this
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cluster. This idea is great, by using cluster, we avoid the flooding process when doing routing and fault diagnoses. And also the selforganization method was explored. Selforganizationnetworks are improved Mobile Ad-hocnetworks. They distinguish themselves from traditional mobile ad-hoc networks, based on the traditional internet two level hierarchy routing architecture, by emphasizing their self-organization peculiarities. Selforganized networks can act in an independent way from any provider. Self-organized networks are also potentially very largeand not regularly distributed. For example, one single network can cover the entire world. Also, self-organized networks are highly co-operative, the tasks at any layer are distributed over the nodes and any operation is the results of the cooperation of a group of nodes. People believe that MANET will be the main architecture of the future wireless networks where the normal wireless networks are impossible to build, especially in military usage or emergency. They think the most important characteristic which sets Wireless Ad-hoc networks apart from cellular networks is the fact that they do not rely on a fixed infrastructure. They also think Mobile Ad-hoc networks are very attractive for tactical communication inmilitary and law enforcement. Again, they believe that Wireless Ad hoc Networks will play an important role not only in military and emergency application, but also can be applied in civilian forums such as convention centers, conferences, and electronic classroom. However, we do not agree with the above statements. Ourpoint of view is that when we talk about the Mobile Adhoc networks, we
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think they are a flawed architecture, because first, until now, we havenâ&#x20AC;&#x2122;t seen any practice of the Wireless Ad-hoc Networks, are the routing protocols, selforganization, security solutions are all theories based on simulation. Second, today, almost every wireless network nodes communicate to base-stations and accesspoints, instead of cooperating to forward packets hop-byhop. In the following section, we will discuss in detail the major technical topics about the Wireless Ad-hocnetworks. The results show us that even consider for the basic technical topics, the Wireless ad-hoc networks arefundamentally flawed architecture. 5. Wireless Ad-hoc Networks Issues Even the most zealot supporters of MANET have to admit that it is a challenging task to enable fast and reliable communication within such a network. The inherent characters of MANET make it a flawed architecture no matter what we have done or will do to improve theperformance of the networks. Below are the factors that prevent the mobile ad hoc networks to be an in-flawedarchitecture. 5.1 Security in Wireless Ad-hoc Networks Security is an important thing for all kinds of networks including the Wireless Ad Hoc Networks. It is obviously to see that the security issues for Wireless Ad HocNetworks are difficult than the ones for fixed networks. This is due to system constraints in mobile devices as well as frequent topology changes in the Wireless networks. Here, system constraints include low-power, small memory and bandwidth, and low battery power. Mobility of relaying nodes and the fragility or routes turn Wireless Adhoc Network architecture into highly hazardous architectures. No entity is
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ensured to be present at every time and it is then impossible to rely on a centralized architecture that could realize network structure or even authentication. The people who consider the Mobile Ad hoc Networks are not a flawed architecture, while we cannot see it used in practice is only because most of its applications are in military are totally wrong. Itis true that Mobile Ad hoc Networks come from the military. But perhaps those persons forgot one of the most important things: the Security! Everybody knows that the core requirement for military applications dealing with trust and security! That is to say,security is the most important issue for ad hoc networks, especially for those security sensitive applications. As we have mentioned before, in Mobile Adhoc Networks, security is difficult to implement because of the networks constrains and the rapidly topology changes.After investigation, we found that there are two kinds of security related problems in the Mobile Ad-hoc Networks. One is the attacks based on the networks which are justsimilar to the Internet, the other is Fault Diagnoses. Fault Diagnoses algorithm is used to pick out the faultynodes and at the same time remove the node from the whole networks. This process should be realtime as to guarantee the performance of the whole networks. In order to solve the fault diagnoses problem, many fault diagnoses algorithms [6] were bring out. After carefully surveying the existing algorithm today, we found that they cannotcorrectly diagnose faulty node with the presence of the changing of the network topology during the process of diagnosis, and these algorithms are analyzed with repetitious diagnosis for all the mobile hosts and cause the great system overhead due to the
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transmission of diagnosis messages by means of flooding throughout the whole networks. While the topology of Mobile Ad-hoc Networks changes from time to time, then we cannot use this kind of Fault Diagnoses Algorithm to solve the questions.Therefore, we can see that the current fault diagnosis algorithms cannot solve the fault diagnosis problem. As for the networks attacks, there are several factors ofsecurity that we should consider. First, Availability ensures the survivability of network services despitedenial of service attacks. Confidentiality ensures that certain information is never disclosed to unauthorized entities. Integrity guarantees that a message being transferred is never corrupted. Authentication enables anode to ensure the identity of the peer node it is communicating with. Yet, active attacks might allow the adversary to delete massages, to modify messages, and to impersonate a node, thus violating availability, integrity, authentication, and non-repudiation. Although that many security-related researches have been done to this problem[7], [8], we could see that Mobile Ad hoc networks are inherently vulnerable to security attacks. While, on the other hand, it is said that the main applications of MANETare in military and emergency, all these applications are security-sensitive. MENAT can not satisfy the security requirement of the applications, so this makes that MANET is a flawed architecture. 5.2 Routing Protocol in Ad-hoc Networks Wireless Ad-hoc Networks operates without a fixed infrastructure. Multi-hop, mobility, large network size combined with device heterogeneity and bandwidth and battery power
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ISBN NO : 378 - 26 - 138420 - 8
limitations, all these factors make the design of routing protocols a major challenge. Lots of researchers did tremendous work on the Wireless Adhoc Routing Protocols. Two main kinds of Routing Protocols are existed today: one is called table-driven protocols (including distance vector and link state), another is on-demand protocols.In table driven routing protocols, the protocols consistent and up-to-date routing information to all nodes is maintained at each node whereas in on-demand routing the routes are created only when desired by the sourcehost. While for the on demand Routing protocols, “on demand” means that it builds routes between nodes only as desiredby source nodes. It maintains these routes as long as they are needed by the sources.If we look up the key words “Wireless Ad hoc Networks Routing Protocols” in Google, we could find tons of millions of all kinds of routing protocols, as LAR (LocationAided Routing), DSDV (DestinationSequenced Distance-Vector Routing), AODV (Ad-hocOn-Demand Distance Vector Routing), and DSR (Dynamic Source Routing Protocol)…… However, after survey various types of routing strategies proposed for wireless ad-hoc networks, we find the truth is all these routing protocols are all have inherent drawbacks and cannot be considered as good routing protocols for Wireless ad hoc Networks. Just like Windows operatingsystems need patch at all the time, the Wireless Ad hoc networks routing protocol are all needs patches too. The main problems about the routing protocols are as following: First of all, consider the rapid passing pattern. Wedefine the rapid passing pattern to be one node passing through
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the whole network very quickly. Such a rapid passing node will generate the following affects to the whole network. First, the topology of the network changed rapidly, which will lead to the lost of packets. Second, we have to modify every node’s routing table that within the communication distance of the rapid-passing node, that will greatly improve the consumption of the bandwidth and the overhead of the networks. Third, obviously there will be tremendous delay of the data sending to the rapid-moving node. Transmission between two hosts over a wireless network does not necessarily work equally well in both directions. Thus, some routes determined by some routing protocols may not work in some environments. Many routing protocols may create redundant routes, which will greatly increase the routing updates as well as increase the whole networks overhead. Periodically sending routing tables will waste network bandwidth. When the topology changes slowly, sending routing messages will greatly waste the bandwidth of Wireless Ad-hoc Networks. This will add additional burdens to the limited bandwidthof the Ad-hoc Networks. Periodically sending routing tables also waste the battery power. Energy consumption is also a critical factor which prevents Wireless Ad-hoc Networks to be a non-flowed architecture. We will discuss this in 5.3. We all understand that a stable network routing protocols is essential for any kinds of networks. However, for the Wireless ad hoc Networks, we could not find a stable routing protocol even after we have done research on it more than 10 years. Needless to say that it is the
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Wireless Ad hoc Networks itself is flawed. 5.3 Energy Consumption of Wireless Ad-hoc Networks Energy consumption is also one of the most importantperformance metrics for wireless ad hoc networks, it directly relates to the operational lifetime of the networks. Mobile elements have to rely on finite source of energy.While battery technology is improving over time, the need for power consumption will not diminish. This point will have a harmful effect on the operation time as it will haveon the connection quality and bandwidth.In the Wireless Ad-hoc Networks, battery replacement may not be possible. So as far as energy consumption concerned, we should try to preserve energy while maintaining high connectivity. Each node depends on small low-capacity batteries as energy sources, and cannot expect replacement when operating in hostile and remote regions. For Wireless Adhoc Networks, energy depletion and reduction is the primary factor in connectivity degradation and length of operational lifetime. Overall performance becomes highly dependent on the energy efficiency of the algorithm. Energy consumption is one of the most important performance metrics for wireless ad hoc networks becauseit directly relates to the operational lifetime of the network. Most research efforts are focused on performance comparisons and trade-off studies between various lowenergy routing and self-organization protocols, whilekeeping other system parameters fixed. As a result, very little has been revealed about the relationship between the aggregate energy consumption and non-protocol parameters such as node density, network coverage area,and
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transceiver power characteristics. We emphasis energy consumption not only because that it is the key problem in the research of Wireless Ad-hoc Networks, but also, we find that Energy consumption problem also affects the routing protocols and the QoS of the whole networks. Let’s assume that each source randomly selects one of the possible routes and asks the intermediate nodes on the route to relay traffic. Sinceenergy is a valuable resource, intermediate nodes may not wish to consume their energy to carry the source’s traffic. This is called “Selfish” of the node. However, if every node behaves ‘Selfish’ and refuse to cooperate, networkthroughput may be drastically reduced.Also, there are many works have done to solve the energy consumption problem. However, unfortunately, little practical information is available about the energyconsumption behavior of wireless ad hoc network interfaces and device specifications do not provide information in a form that is helpful to protocol developersThis, again, prove that the Wireless Ad Hoc Networks cannot be put into practice. Further, we can hold our position that the Wireless Ad Hoc Networks are a fundamentally flawed architecture. 6. Conclusion Mobile Ad hoc Networks are an ideal technology to establish in an instant communication infrastructureless for military application or a flawed architecture has been bought out in this position paper. As we have proved using the three main technical topics of the Wireless AdhocNetworks, We hold the position that the Wireless Ad hoc Networks are a flawed architecture for the following technical reasons:
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The most important thing for the networks is security. It is even important for Wireless Ad hoc Networks because its applications are in military. The MANET can not appropriately solve the problem of the security. Routing is also a big problem. All the routing protocols for Wireless Ad hoc Networks are need patches. No suitable and stable routing protocols until now. Energy consumption problem still cannot be solved even much of efforts have been done to it. All these prove that the Wireless Ad hoc Networks is a flawed architecture. Not only because it is almost never used in practice but also because there are several technical difficulty that cannot be conquered.Besides, all the Wireless Adhoc Networks are expected to be selfconfiguration. Self-configuration are referring to two aspects, one is during the first construction of thenetwork, the self-configuration network is supposed to be forming the network itself. The other problem is when one host moves in or moves out the Wireless Ad-hoc networks, the network should have the ability to reconfiguration the topology of the whole networks. Again we could see that although many works have been done on this topic, but unlucky, all the discussions do not give us a satisfied answer to the self-configuration question. The question is never tackled in systematic way. That again prove out argument that the Wireless Ad-hoc Networks is a fundamental flawed architecture, or else we should find the appropriated answer to the problems. However as the wireless and embedded computing technologies continue to advance, I do hope later, one day, we could build our wireless networks rely on
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some kinds of the Wireless Ad hoc Networks. References: [1] IEEE Std 802.11 – 1999: Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications, Inst. Elec. Electron. Eng., New York, USA, 1999.ISBN 0-7381-1658-0 [2] IPN Progress Report, August 15, 2002, Analysis of Energy Consumption for Ad Hoc Wireless Sensor Networks Using a Bit-Meter-Per-Joule Metric, J.L.Gao [3] A Distributed Light-Weight Authentication Model for Ad-hoc Networks [4] M. Satyanarayanan. Fundamental challenges in mobile computing.submitted paper. [5] M. Haardt W. Mohr R. Becher, M. Dillinger. Broadband wireless access and futurecommunication networks.Proceedings of the IEEE, 89(1), 2001. [6] S.Chessa, P.Santi, “Comparison Based System-Level Fault Diagnosis in Ad-Hoc Networks”, Proc. IEEE20th Symp. on Reliable Distributed Systems (SRDS), New Orleans, pp. 257-266, October 2001 [7] Erik Skow, Jiejun Kong, Thomas Phan, Fred Cheng, Richard Guy, RajiveBagrodia, Mario Gerla, and Songwu Lu, “A Security Architecture for Application Session Handoff” [8] Lidong Zhou, Zygmunt J. Haas, “Securing Ad Hoc Networks” [9] David B. Johnson, “Routing in Ad Hoc Networks of Mobile Hosts”, Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications, December 1994. [10] LjubicaBlazevic, LeventeButtyan, SrdanCapkun, Silvia Giordano, JeanPierre, Hubaux and Jean-Yves Le Boudec, “Self-Organization in Mobile
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