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International Journal of Algorithms Design and Analysis

International Journal of Algorithms Design and Analysis is a comprehensive journal that covers all the topics of algorithms designs and analysis. All articles that are included in the journal are peer-reviewed and follow stringent guidelines before publishing. From Divide and conquer to Randomized algorithms. Journal publishes recent advancement in the form of original research articles and review papers.

Focus and Scope of the Journal ! Divide and conquer ! Dynamic programming ! Greedy algorithms ! Back tracking ! Algorithmic languages ! Divide-and-conquer algorithm ! Dynamic programming ! Amortized analysis ! Linear programming ! Linear-time sorting ! Advanced algorithm design ! Randomized algorithms

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EDITORIAL BOARD MEMBERS Dr. Devi Priya Rangasamy Department of Information Technology, Kongu Engineering College, Erode (Tamil Nadu), India

Ziyan Wu Research Scientist, Vision Solutions and Technologies, Siemens Corporate Research, Princeton (NJ), U.S.

Dr. Praveen sam Rachapudy Professor, Department of Computer Science & Enginering, G. Pulla Reddy Engineering College, India

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Dr. Sunil Kumar Department of Computer Science & Engineering, College of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh (Rajasthan), India

Dr. Sasmita Mishra Associate Professor Department of Comp. Sc. & Applications IGIT, Sarang Talcher, Dhenkanal, Orissa, India

Mr. S Sivakumar Periyar University, Salem, India, India

Dr. R G Jeyakumar Amrita School of Engineering, Coimbatore., India

Dr. T Abirami Kongu Engineering College, Perundurai, Erode Tamilnadu, India

Dr. Manish M Kayasth UCCC & SPBCBA & Udhna Academy College of Computer Application and IT, India

Prof Saurabh Rai Dhirendra Prasad DKTE Society's Textile and Engineering Institute, Ichalkaranji, India

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Kalyan Kumar Jena Dept. of CSEA, IGIT, Sarang, Odisha, India.

Dikes Shadap Department of Mathematics, Shillong College, Shillong, Meghalaya, India.

Mr. Sanku Sinha Assistant Professor, R.V.S. College of Engineering and Technology, Jamshedpur.

From the Editor's Desk Dear Readers, We would like to present, with great pleasure, the inaugural volume of a new scholarly journal, International Journal of Algorithms Design and Analysis. This journal is part of the Computer Science and Engineering, and is devoted to the scope of present Engineering issues, from theoretical aspects to application-dependent studies and the validation of emerging technologies. This new journal was planned and established to represent the growing needs of Algorithms Design and Analysis as an emerging and increasingly vital field, now widely recognized as an integral part of scientific and technical investigations. Its mission is to become a voice of the Computer Science and Engineering community, addressing researchers and practitioners in this area. The core vision of International Journal of Algorithms Design and Analysis in JournalsPub is to propagate novel awareness and know-how for the profit of mankind ranging from the academic and professional research societies to industry practitioners in a range of topics in Computer Science and Engineering in general. Journals Pub acts as a pathfinder for the scientific community to published their papers at excellently, well-time & successfully. International Journal of Algorithms Design and Analysis focuses on original high-quality research in the realm of Divide and conquer, Dynamic programming, Greedy algorithms, Back tracking, Algorithmic languages, Divide-and-conquer algorithm, Dynamic programming, Amortized analysis, Linear programming, Linear-time sorting, Advanced algorithm design, Randomized algorithms. The Journal is intended as a forum for practitioners and researchers to share the techniques of Computer Science and Engineering and solutions in the area. Many scientists and researchers have contributed to the creation and the success of the Computer Science and Engineering community. We are very thankful to everybody within that community who supported the idea of creating an innovative platform. We are certain that this issue will be followed by many others, reporting new developments in the field of Computer Science and Engineering. This issue would not have been possible without the great support of the Editorial Board members, and we would like to express our sincere thanks to all of them. We would also like to express our gratitude to the editorial staff of JournalsPub, who supported us at every stage of the project. It is our hope that this fine collection of articles will be a valuable resource for Computer Science and Engineering readers and will stimulate further research into the vibrant area of Computer Science and Engineering. Puneet Mehrotra Managing Director

Contents 1. A Comparative Analysis of Quasi-Resonant Buck Converter Using PI and Neuro Control Methodology A. Rameshkumar, S. Arumugam

2. Replicants the Tone for Real Images B.M. Alaudeen, G. Tholkappia Arasu

3. Designing for Fingerprint Image Enhancement Mayur Patil

4. Software Analysis and Requirement Specification for HRMS Kshitij Singh Raghav

5. Algorithm and Analysis in Science and Technology Riva Mathur

International Journal of Algorithms Design and Analysis Vol. 2: Issue 1

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A Comparative Analysis of Quasi-Resonant Buck Converter Using PI and Neuro Control Methodology A. Rameshkumar1*, S. Arumugam2 1

Department of Electrical Engineering Surendra Institute of Engineering & Management, Siliguri, India 2 Nanda Engineering College, Erode, India

Abstract A Non-linear controller based on PI (Proportional-Integral) and the other on Neuro-fuzzy integrated system is implemented and their application to the regulation and dynamic control of output voltage of zero current switching quasi-resonant buck converter is comparatively investigated. The algorithm of PI controller involves two main parameters namely Proportional gain (KP)) and Integral time (Kl ) whose manual tuning provides an appropriate control action whereas Neuro-fuzzy hybridization synergizes two techniques by combining the human-like reasoning method of fuzzy systems with the learning and connectionist structure of neural networks wherein structure-learning algorithm is primarily used to find an appropriate fuzzy rule and the parameter-learning algorithm is then utilized to fine-tune the membership function and other parameters. The above topology is modeled using Simulink power system block of the MATLAB software and is operated at 200 kHz to evaluate its closed-loop performance with respect to line and load regulation. It is verified by simulation that the neuro-fuzzy integrated adaptive system modifies itself the characteristics of IF-THEN rules and structure of the system and thus achieves a good transient response characteristics under various operating conditions. Keywords: adaptive network based fuzzy inference system, generalised state space averaging, line regulation and load regulation, proportional-integral controller, quasi-resonant buck converter

INTRODUCTION Aerospace and industrial applications as of today demand stringently specified high power density converter and it is imperative to design such converter to operate at higher frequency to achieve high power density. Pulse Width Modulated (PWM) converter drew prime attention in the last two decades wherein the switch experiences hard switching at switching transitions and it leads to switching loss (Ps) as estimated from Equation (1.0)

Ps = 0.5 Vg Io (t on + t off ).fs

(1.0)

where t on and t off are the ON-period and OFF-period of converter. With Zero Current Switching (ZCS) property, the switch in Quasi-Resonant converter works on the state of zero current during switching ON and OFF to offer advantages such as self-commutation, low switching stress and loss, reduced electromagnetic interference and noise, and faster transient response to line and load variations.[1-] In modeling and analysis of ac and dc behaviour of switched converter, State Space Averaging (SSA) is used because the different state matrix of each switching interval is replaced by an equivalent

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International Journal of Algorithms Design and Analysis Vol. 2: Issue 1

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Replicants the Tone for Real Images B.M. Alaudeen1*, G. Tholkappia Arasu2 Manonmaniam Sundaranar University,Tirunelveli, India AVS College of Technology, Salem, India

Abstract Excessive dynamic range (HDR) is a method that allows a terrific dynamic range of luminance between the lightest and darkest region of a picture. For video compression, the HDR series is reconstructed by using inverse tone-mapping a compressed low dynamic range (LDR) model of the unique HDR content material. We display that the ideal desire of a Tonemapping operator (TMO) can significantly improve the reconstructed HDR quality. It is united state side to compress a large range of pixel luminance in to smaller range that is suitable for display on devices with limited dynamic range. We formulate a numerical optimization problem to find the tone-curve that minimizes the expected mean square error (MSE) in the reconstructed HDR sequence. We also develop a simplified model that reduces the computational complexity of the optimization problem to a closed-form solution. It is also shown that the LDR picture fine due to the proposed techniques suits that produced by using perceptually-primarily based TMOs. Keywords: bit-intensity scalable, high dynamic variety, tone-mapping, video compression. *Corresponding Author E-mail: aladin.avs@gmail.com

INTRODUCTION Natural scene include some distance more seen records that may be captured by most people of virtual imagery and video gadgets .that is due to the fact traditional show devices can simplest aid a restrained dynamic variety and colour gamut. A classic photographic challenge is the mapping of the potentially excessive dynamic range of real global luminances to the low dynamic variety of the photographic print. This tone reproduction problem is also faced by way of pc pictures practitioners who map virtual snap shots to a low dynamic range print or display. For video compression, those advances in display generation have influenced the use of prolonged gamut shade areas. Those include xvYCC (x.v.colour) for domestic theater and the virtual Cinema Initiative coloration area

for virtual theater applications. Yet, even those extended color areas are too limited for the quantity of evaluation that can be perceived by way of the human eye. Excessive dynamic variety (HDR) video encoding goes past the standard color area regulations and attempts to encode all colours that are visible and distinguishable to the human eye [3], and isn't restrained by means of the colour gamut of the display era used. The primary motivation is to create a video format that could be future-proof, independent of a show technology, and restricted best through the overall performance of the human visible gadget (HVS). HDR images hold colorimetric or photometric pixel values (consisting of CIE XYZ) inside the seen coloration gamut and allows for intra-body comparison exceeding 5-6 orders of importance (106:1), without introducing

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Replicants the Tone for Real Images

contouring, banding or posterization artifacts as a result of immoderate quantization. Backward-compatibility may be finished if the HDR video circulation includes 1) a backward-well matched 8-bit video layer which may be without delay displayed on present gadgets, and a pair of) extra facts which in conjunction with this eight-bit layer can yield an excellent best reconstructed version of the authentic HDR content. The sort of move also can include a residual layer to further improve

Alaudeen and Arasu

the high-quality reconstruction.

the

HDR

Figure 1 illustrates the overall coding structure used to offer a backward likeminded HDR video bit circulate. It addresses the trouble of finding a most effective tone-curve for this kind of backward-well matched encoding scheme. To compute the tone-curve, we advise a way that minimizes the distinction within the video exceptional among the unique and the reconstructed HDR video.

Fig. 1. Well Known Structure of the Scalable Technique Used for Backward-Well Matched HDR Video Encoding. The base layer encodes an eight-bit LDR representation of the HDR enters. The enhancement layer encodes the difference (residual) among the inverse tone-mapped base layer and the original HDR source. Minimizing this difference could reduce the size of the HDR residual signal inside the enhancement layer. We additionally obtain the primary intention of tonemapping that's to supply an LDR photo with a visible reaction as comparable as possible to the unique HDR photograph. . The rest of this paper is organized as follows: an outline of associated paintings is presented in segment II. In segment III, the proposed tone-mapping approach that considers tone-mapping collectively with

compression is discussed in detail. Segment IV demonstrates and analyzes the overall performance of the proposed techniques. Sooner or later, we draw our conclusions in phase V. LITERATURE SURVEY Backward likeminded HDR video encoding has received sizable hobby currently. A color space of encoding HDR content based at the luminance threshold sensitivity of the human visible gadget. They concluded that 10–12 bit luma encoding is enough to encode the whole range of visible and bodily plausible luminance degrees.

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International Journal of Algorithms Design and Analysis Vol. 2: Issue 1

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Fig. 2. Machine Evaluation of the Proposed Tone-Mapping Technique. (a) Demonstrates the Appropriate Scenario wherein the Real H.264/AVC Encoding is Employed, (b) Suggests the Practical Scenario which is addressed by way of this Paper. The number one intention of tone-mapping is to produce the fine low-dynamic variety rendering of an HDR scene this is visually close to the visible excessive evaluation sign. An evaluation of such operators may be taken first taken into consideration tone-mapping explicitly with a purpose to optimize photograph compression. They used forward and inverse waveletprimarily based tone-mapping (compressing and commanding) in an iterative optimization loop to reduce HDR

PROPOSED WORK Solution on this phase, we present the demanding situations of obtaining a great nice reconstructed HDR illustration in a backward-well suited HDR video encoding machine and describe in element the approach we advocate toward overcoming those demanding situations. The overall performance of a backwardwell matched HDR video and image encoding gadget relies upon on the coding performance of the LDR base layer and the HDR enhancement layer.

The performance of several tone-mapping operators in terms of excellent loss due to ahead and inverse tone-mapping turned into in comparison. Local tone-mapping operators (spatially variant) were found to be greater at risk of excellent loss than international operators (spatially invariant).

A. Tone-Mapping Curve The global tone-mapping curve is a feature that maps HDR luminance values to both the display’s luminance variety, or directly to LDR pixel values. The tone-mapping curve is usually continuous and nondecreasing.

It examines the effects of our observation with the 2 tone-mapping operators that completed the fine in this study: the photographic TMO and the adaptive logarithmic TMO.

The two maximum common shapes for the tone curves are the sigmoidal (ï¿½Sshapedï¿½) or a compressive power function with an exponent 1 (gamma correction).

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Replicants the Tone for Real Images

Alaudeen and Arasu

The tone-mapping curve can then be uniquely precise with the aid of a fixed of slopes. V  Vk (1) S k  k 1



Which paper work a vector of tonemapping parameters. The use of this parameterization, the forward tonemapping function is defined as V(l) = (l-lk).Sk +Vk (2)

Fig. 3. Parameterization of a Tone-Mapping Curve and the Notation. The bar-plot within the historical past represents an image histogram used to compute p(l). Wherein is the LDR pixel price, is the section similar to HDR cost, this is l k  l < l k+1 . The inverse mapping function is then v vk l (v; S k )    l. pL(l ) for Sk<0,Sk=0 lS0 where Sk   S1…SN  . (3) When the slope is zero (Sk=0), l (v; S k ) is assigned an expected HDR pixel value for the entire range S0 in which the slope is equal zero. PL(l) is the probability of HDR pixel value. B. Statistical Distortion Model As mentioned earlier in Section III, accurately computing the distorted HDR values would be too computationally demanding. Instead, we estimate the

2 E  l l =  2 

   l (v; s k )  l     l lmin v  0  lmax vmax

 pc(v(l )  v / v(l ))  p L (l )

(4)

Where pc(v  v / v) is the probability that the encoding error equals v  v . Note that (2) and (3) show that both and v and l are uniquely determined by the values of and, respectively. Therefore, the conditional probabilities for these two variables and their corresponding summations have been removed from the calculation of the expected value of the error above. It simplify the expression above by removing the dependency of on .Consequently, the continuously relaxed objective function is written as:

   ( S K ) =    l (v; s k )  l   l lmin v  0  pc(v  v ). p L (l ) lmax vmax

(5)

error l  l assuming that the compression 2

distortions follow a known probability distribution PC. Under this assumption, the expected value of the error l  l

2 2

The only unknown variable is the probability distribution of the compression error pc(v  v ), which can be estimated for any lossy compression scheme. In Appendix A we model such distribution for the H.264/AVC I-frame coding.2

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International Journal of Algorithms Design and Analysis Vol. 2: Issue 1

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However, we will show in Section III-D that the distribution of the compression scheme error is not necessary to calculate a good approximation of the encoding error. C. Optimization Problem The optimum tone curve can be found by minimizing the  ( S K ) function with respect to the segment slopes (6) S K arg min  (S K ) where s1 …. s N Subject to : s min  s K  s max for k=1….N N

s k 1

.  v max .

(7)

The first constraint restricts slopes to the allowable range, while the second ensures that the tone curve spans exactly the range of pixel values from 0 to v max .The minimum slope s min ensures that the tonemapping function is strictly increasing and thus invertible and l (v; s K ) can be computed. we can write: (8) l (v  1; sk )  l (v; sk ) >log10(1.01) 1 s  (log ( 1 . 01 )) 10 So that K (9) D. Closed-Form Solution The distortion model in (5) gives a good estimate of compression errors, but poses two problems for practical implementation in an HDR compression scheme: 1) it requires the knowledge of the encoding distortion distribution pc , and 2) the optimization problem can only be solved numerically using slow iterative. l in the distortion model (5) using the inverse mapping function in (3), this gives

 (S k )  p L (l ).(

 (S k )  

l l min v  0

pL(l ) . Var v  v  S k2

(11)



k 1

Where p k  l l p L (l ) and l k and l k 1 l 1 k

Define the lower and the upper bounds of a segment, respectively. This problem can be solved analytically by calculating the first order Karush-Kuhn-Tucker (KKT) optimality conditions of the corresponding Lagrangian, which results in the following system of equations:

 2 p1  0 s13  2 p2  0 s 23  2 pN  0 s N3 N v  s K  max  0 k 1

l l min v  0

(10)

  pc(v  v ).

Since the variance of (v  v ) does not depend on the slopes, it does not affect the location of the global minimum of  ( S k ) . The tone-curves found using the accurate model from (5) and a simplified model from (11) were almost the same. The constrained optimization problem defined in (6) can now be re-written as follows: N p arg min  2k s1.......sN k 1 s k N v Subject to  s k  max (12)

  pc(v  v ).

After reorganizing we get



l lmin

l max vmax

vv 2 ) Sk

lmax

l max vmax



(13)

Where  is the Lagrange multiplier. The solution to the above system of equations results in the slopes s k given by

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Replicants the Tone for Real Images

sk 

/3 v max . p 1 K N

 . p 1k / 3

Alaudeen and Arasu

(14)

k 1

Note that the expression derived in (14) does not consider the upper bound constraint imposed on s k in (7). Let be the set of the index of a segment with a slope that exceeds the upper bound. We overcome the upper bound violation using the following adjustment:

Sk=

  v max    max  iX  N

 . p 1j / 3

   . p 1 / 3 for S k

k I ,

jX

RESULTS We first validate the proposed strategies: optimization using the statistical model proposed in phase III-B and the closed-

shape solution primarily based on a simplified model derived in segment III-D. Then, our models are in addition analyzed primarily based on the generated tone curve and the distortion of the reconstructed HDR content. A. Model Validation We validate that the statistical version of section III-B effects in a tone curve that actually reflects the ground-fact results. Ground-fact outcomes are carried out using the proper scheme illustrated in Figure 2(a), where the actual H.264/AVC encoder and decoder are hired to find the really foremost piecewise linear tone curve. To make the test computationally viable, we divided the tone curve into four segments of equal width. Figure4(b) suggests the fee-distortion result in phrases of bit price vs HDR MSE.

Fig. 4. Validation of the Proposed Fashions Via Evaluation With the Floor-Truth Solution. The Top Discern, (a) Shows the Tone Curves Computed Using the Statistical Model, the Closed-Form Answer and the Ground-Truth Optimization for the Picture ï¿½Memorialï¿½. The x Axis Denotes the HDR Luminance Within the Log-10 Scale, and y Axis Is the LDR Pixel Fee, (b) Demonstrates the End Result of HDR MSE (in log10 Scale) as Opposed to Bit Price (Bits/Pixel). The Lower the MSE Value, the Better the Photo First-Class. IJADA (2016) 16–25 © JournalsPub 2016. All Rights Reserved

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International Journal of Algorithms Design and Analysis Vol. 2: Issue 1

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B. Dependence of the Tone Curves on QP We confirm that the proposed statistical model may be properly approximated by the closed-shape solution which produces a tone curve that is unbiased of QP. The probability distribution of the H.264/AVC compression errors, that's a function of QP, is covered within the statistical version proposed in section IIIB. The figures show that the tone curves are not substantially affected by the variant of QP.

C. Similarly Analysis Of The Closed-Shape Solution The tone curve because of the closedshape answer given by (14) can be generalized as follows: sk 

vmax . p1k / t N

(16)

 . p1k / t k 1

is identical to the histogram equalization operation. A particular first-rate metric, Figure 6 shows the resulting common performance over 40 HDR photos. On this experience, the outcomes demonstrated in Figure 6 suggest that our closed-shape solution ensures top overall performance.

Fig. 5. Tone Curves Generated Using the Statistical Model with Extraordinary QP Values for the Images“Atriumnight” and “Desk”. The notation of the axis is the same as Figure 4(a). The smaller the cost of QP, the higher the compression best. 87 and 88

segments are used “AtriumNight” and “Desk ”.

Fig. 6. Price-distortion Curves, Tone Curves and Tone-Mapped Photos for the Image “Atrium Night”.

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Replicants the Tone for Real Images

The first row demonstrates the ensuing tone-curves with unique TMOs, observed through the outcomes for MSE and SSIM as opposed to bit fees; the second row indicates tone-mapped LDR pics the use of the proposed statistical version and the closed-form answer. The 0.33 row indicates the tone-mapped pics using the present tone-mapping methods. All of the tone-mapped pictures shown are compressed. The compression quantization parameters used for “Atrium Night” is 10. The number of segments used for the histogram is 87.

Alaudeen and Arasu

Figures 6,7 and 8 show the tone curve, rate-distortion curves and tone-mapped LDR images for three snap shots. Additional outcomes for greater pix are protected in the supplementary cloth. The LDR images shown in those figures exhibit that the pics tone-mapped using our method additionally provide good great. To in addition exhibit the quality of the LDR pix generated by the proposed fashions, Figure 11 suggests the distortion maps of the LDR pix in comparison with their original HDR opposite numbers.

Fig. 7. Charge-Distortion Curves, Tone Curves and Tone-Mapped Photographs for the Photograph “Coby”. The Notation is Similar to Fig.6. The Compression Quantization Parameters Used for “Coby” Is 22. The Variety of Segments Used for the Histogram Is 36.

Fig. 8. Distortion Maps of the LDR Pics Relative to the Unique HDR Images.

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The linear aggregate of the tone-curves produced by using our approach and these algorithms. Moreover, our look at shows that the overall pleasant of pix tone-mapped with our technique is comparable to different tone-mapping algorithms and none of the tone-mapped images we generated became taken into consideration as unacceptable. This means that for the programs that don't require a finely adjusted backward-well matched layer, our approach can be used at once. The LDR pictures evaluated have now not been compressed. In every of the distortion maps, 3 colors denote three unique styles of distortions: inexperienced for loss of visible evaluation; blue for amplification of invisible evaluation; purple for reversal of seen comparison. The higher depth of a colour correlates with higher distortion of that type.

CONCLUSION We showed that the appropriate choice of a tone-mapping operator (TMO) can extensively improve the reconstructed HDR pleasant. We evolved a statistical model that approximates the distortion on account of the mixed approaches of tonemapping and compression. The use of this version, we formulated a limited optimization hassle that reveals the tonecurve which minimizes the predicted HDR MSE. The ensuing optimization trouble, but, suffers from excessive computational complexity. Therefore, we presented a few simplifying assumptions that allowed us to reduce the optimization problem to an analytically tractable shape with a closedshape answer.

The closed-form answer is computationally efficient and has a performance compatible to our advanced statistical version. Furthermore, the closed-form answer does no longer require the knowledge of QP, which makes it suitable for instances in which the compression strength is unknown. although our fashions are designed to reduce HDR MSE, the vast overall performance evaluations display that the proposed methods provide fantastic performance in terms of SSIM and the LDR photograph pleasant, similarly to an first rate overall performance in MSE. REFERENCES 1. Seetzen H., Heidrich W., Stuerzlinger W. et al., “High dynamic range display systems,” ACMTrans. Graph. (Proc. SIGGRAPH) 2004; 23(3): 757–65p. 2. IEC61966-2-4, Colour Measurement and Management—Part 2–4: Colour Management—Extended-Gamut YCC Colour Space for Video Applications— xvYCC 2006. 3. Mantiuk R., Krawczyk G., Myszkowski K. et al. “Perceptionmotivated high dynamic range video encoding,” ACM Trans. Graph. (Proc. SIGGRAPH)2004; 23(3): 730–738p. 4. Ward G., “Real pixels,” Graphics Gems II 1991, 80–83p. 5. Bogart R., Kainz F.,Hess D., “Openexr image file format,” in Proc. ACM SIGGRAPH 2003, Sketches Applicat. 2003. 6. Larson G. W., “Logluv encoding for full-gamut, high-dynamic range images,” J. Graph. Tools 1998, 3(1) 15–31p. 7. Gao Y., Wu Y., “Applications and Requirement for Color Bit Depth Scalability,” 2006; Tech. Rep., ISO/IEC JTC1/SC29/WG11 and ITUT SG16 Q.6, JVT-U049.

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BIOGRAPHIES Dr. G. Tholkappia Arasu was born in Salem, Tamil Nadu in 1974. He has received Masters Degree from M.K University and Ph.D from Anna University Chennai. In the Academic year 2009–2010 he was a Professor in the Department of Computer Science and Engineering and he was the Principal of Jayam College of Engineering and Technology, Dharmapuri. He was the Principal of A.V.S Engineering College, Salem. At present he is working as Principal of A.V.S College of Technology, Salem. He guides various research scholars, students etc. in Computer Science and Engineering.

Alaudeen and Arasu

B.M.Alaudeen (LM’09, LM’09, and LM’09) was born in Salem; Tamilnadu in 1980. He has received Distinction in Masters of Computer Applications from Jamal Mohamed College, Bharithidasan University, Trichy and also he was a research scholar from Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu under the guidance of Dr. G. Tholkappia Arasu, Principal A.V.S. Engineering College Salem,Tamilnadu.He is also the Life Member in Computer Science Teacher Association (CSTA), Life Member in International Association of Engineers (IAE), Life Member in Indian Journal of Science &Technology (IJST). At present he is the Assistant Professor in the PG Department of Computer Studies & research at AVS College of Arts & Science, Salem, Tamilnadu.

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Designing for Fingerprint Image Enhancement Mayur Patil* Department of Information Technology, SSBT’s COET, Bambhori, Jalgaon (M.S.), India

Abstract In this paper UML diagrams of fingerprint image enhancement are shown. The Unified Modeling Language is a standard visual modeling language. There are various types of UML diagrams such as Class diagram, Use Case diagram, Sequence diagram, Collaboration diagram. The data flow diagrams are also shown. The software engineering process model i.e. Waterfall model is also described. Keywords: data flow diagrams waterfall model, unified modeling language

UML DIAGRAM UML is a common language for business analysts, software architects and developers used to describe, specify,

design, and document existing or new business processes, structure and behavior of artifacts of software systems.

CLASS DIAGRAM

Fig. 1. Class Diagram. A Class diagram shows a set of classes, interfaces and collaborations and their relationships. These diagrams are the most common diagram found in modeling object oriented systems. Class diagram

address the static design view of a system. Figure 1 shows the classes, attribute, operations and relation between them.

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Software Analysis and Requirement Specification for HRMS Kshitij Singh Raghav* Department of MCA, Guru Gobind Singh IP University, New Delhi, India

Abstract Before deciding to develop any system, it is imperative that the existing system is thoroughly studied and understood so as to arrive at a decision. Identification of the role of Software must be specified in making its use into the study of a larger system like the Border Roads. For it is necessary to take a harder look at software’s role to understand the specific requirements that must be achieved to build high quality software. This knowledge is generally acquired by analysis the existing system. Keywords: Human Resources Management System, software, system analysis

INTRODUCTION Hence, the ‘System Analysis’ is the process of identifying the current problems, and gathering and interpreting the facts to improve the system’s performance. It is a phase of carrying out a detailed study of the user’s requirements. Or in other words, System Analysis means a detailed explanation or description. Before computerized a system under consideration, it has to be analyzed. We need to study how it functions currently, what are the problems, and what are the requirements that the proposed system should meet. Analysis is a detailed study of the various operations performs by a system and their relationships within an outside of the system.[1] System Analysis is conducted with the following objectives in mind:  Identify the customer’s need.  Evaluate the system concept for feasibility.  Perform economic and technical analysis.  Allocate functions to hardware, software people, database and other system elements.

 

Establish cost and schedule constraints. Create a system definition that forms the foundation for all the subsequent engineering work.

SOFTWARE REQUIREMENT SPECIFICATION Requirement specification is the activity during which requirements are recorded in one or more forms, usually in a Software Requirement Specification (SRS) Document. SRS is the starting point of the software development activity. The requirements may be in natural language, a formal language or in a graphical form. The purpose of the software requirement specification is to bridge the communication gap between developer and client. SRS is the medium through which the client and user needs are accurately specified. A good SRS should satisfy all the parties – something very hard to achieve and involving trade off and persuasions.[2] Some of the goals accomplishes by SRS are: 

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Algorithm and Analysis in Science and Technology Riva Mathur* Nanoscience and Technology Consortium, Noida, India

Abstract By this paper I would like to cascade upon the advancement in the computational techniques for science and technology. Mostly people will acquire a strong computer science background and thus gain access to a wide range of opportunities in the information technology industry only but now-a-days in science and medical background scope of opportunities also getting higher because without algorithm and analysis results cannot be achieve. Keywords: algorithm, coding, design

INTRODUCTION Algorithm is more than a branch of computer science. It is the core of computer science, and, in all fairness, can be said to be relevant to most of science, business, and technology (this is state by

Har92, p. 6). Now-a-days every channel needs computational help to analyses and design the results. Science branch 80% cannot be able to end up with results without algorithm and analysis.

ALGORITHM AND ANALYSIS USES IN SCIENCE AND TECHNOLOGY

Fig. 1. Algorithm Design and Analysis Process.[1]

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