I&M Magazine - April 2016 by Alessandro Ferrero

Vol. 19, No. 2 April 2016

Instrumentation & Measurement is Worldwide: Highlighting IEEE Region 10

contents table of

April 2016 VOL. 19, NO. 2

Instrumentation & Measurement I&M society web site

http://imm.ieee-ims.org

I&M magazine web site

features

http://ieee-ims.org/publications/im-magazine

—Sergey Kharkovsky, Paritosh Giri, and Bijan Samali

editor-in-chief

Wendy Van Moer University of Gävle Department of Electronics, Mathematics and Natural Sciences SE-801 76 Gävle, Sweden wendy.w.vanmoer@ieee.org

Non-contact Inspection of Construction Materials Using 3-axis Multifunctional Imaging System with Microwave and Laser Sensing Techniques

Real-Time NIR Imaging of Palm Dorsa Subcutaneous Vein Pattern Based Biometrics: An SRC Based Approach

—Sandip Joardar, Amitava Chatterjee, and Anjan Rakshit

associate editor-in-chief Simona Salicone simona.salicone@polimi.it

senior editor

June Sudduth j.sudduth@ieee.org

administrative assistant Kristy Virostek virostek5@verizon.net

Wind Turbine Condition Monitoring and Fault Diagnosis in China —Xuefeng Chen, Ruqiang Yan, and Yanmeng Liu

Research Activities on Sensing, Instrumentation, and Measurement: New Zealand Perspective

—Subhas Mukhopadhyay

I&M editorial board Ruth A. Dyer Alessandro Ferrero Mark Yeary Salvatore Baglio Zheng Liu Ruqiang Yan Veronica Scotti Bryan Kibble Charles Nader Lee Barford Kevin Bennet

Higher Education of Measurement Control and Instrumentation Specialty in China

—Aiguo Song and Lihui Wang

managing editor

Spectrum Sensing Challenges: Blind Sensing and Sensing Optimization

—Mohamed Hamid, Slimane Ben Slimane, Wendy Van Moer, and Niclas Björsell

Beverly Lindeen blindeen@allenpress.com

advertising sales manager Onkar Sandal +1 800 627 0932 x218 Fax: +1 785 843 1853 osandal@allenpress.com

on the cover:

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columns

Editorial 4 Letter to the Editor 4 Guest Editorial 5 Basic Metrology 20

Future Trends in I&M 29 Life After Graduation 43 Calendar 58

departments New Products

IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE: (ISSN 1094-6969) (IIMMF9) is published bimonthly by The Institute of Electrical and Electronics Engineers, Inc. Headquarters: 3 Park Avenue, 17th Floor, New York, NY 10016-5997 +1 212 419 7900. Responsibility for the contents rests upon the authors and not upon the IEEE, the Society, or its members. Individual copies: IEEE members $20.00 (first copy only), nonmembers $25.00 per copy. Subscriptions: $6.00 per member per year (included in Society fee) for each member of the IEEE Instrumentation and Measurement Society. Nonmember subscription prices available on request. Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limits of U.S. Copyright Law for private use of patrons: 1) those post-1977 articles that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA; 2) pre-1978 articles without fee. For other copying, reprint, or republication permission, write Copyrights and Permissions Department, IEEE Service Center, 445 Hoes Lane, Piscataway, NJ 08854 USA. Copyright © 2015 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Postmaster: Send address changes to IEEE Instrumentation & Measurement Magazine, IEEE, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331 USA. Canadian GST #125634188

Printed in the U.S.A.

IEEE Instrumentation & Measurement Magazine

April 2016

editorial Wendy Van Moer

IM Around the World

nstruments and measurements, we use them all day long, in our professional as well as our daily life, everywhere around the world. From now on, each April issue of Instrumentation and Measurement Magazine will be dedicated to a specific IEEE region. It will allow the different regions to show to the rest of the world their work in the field of I&M. What kind of I&M research is going on in that particular region? What are the difficulties? Where do they put the focus?

This April issue is dedicated to IEEE Region 10. Our guest editor is Prof. Ruqiang Yan, from the School of Instrument Science and Engineering, Southeast University, China. He is also a member of the Administrative Committee of the IEEE IM Society. It was a great pleasure to work with him on this issue, and I would like to take the opportunity to thank him for his dedication and valuable time. Welcome to Region 10! Groetjes,

lettertotheeditor Bryan Kibble

Reply from Bryan Kibble

ear Dr. Buckmaster, Thank you for your interest in my Oct. 2015 Basic Metrology column â&#x20AC;&#x153;Where has all our helium gone?â&#x20AC;? I am encouraged by the ability of scientists and engineers to solve their own problems, for example, by their increasing use of individual closed-cycle helium refrigerators.

Nevertheless, we need to treat as urgent the economic recovery of helium from less helium-rich oil and natural gas wells, and ultimately from the atmosphere. Are there any IEEE members who are politically influential out there who could help with this, and similar issues? Bryan Kibble

IEEE Instrumentation & Measurement Magazine

April 2016

guesteditorial Ruqiang Yan

Instrumentation and Measurement around the World: Region 10

nstrumentation and measurement have made significant contributions to various science and engineering domains. With the rapid advancement of electronics, communication, and computer technology, we are also witnessing new development of instrumentation design and measurement methods around the world. As a young faculty member working in the field, it is a great honor for me to organize this special issue of Instrumentation & Measurement Magazine with the goal of introducing research activities related to instrumentation and measurement in the IEEE Region 10. This Region is also referred to as the Asia Pacific Region and covers a geographical area stretching from South Korea in the north-east to New Zealand in the south to Pakistan in the west. In this special issue, five papers from researchers in Region 10 were invited to present various aspects of instrumentation and measurement. The paper written by Professor Aiguo Song (China) introduces the curriculum of higher education of measurement control and instrumentation specialty in China. He uses the Southeast University as an example to illustrate the education of instrumentation and measurement for undergraduate students. The paper written by Professor Xuefeng Chen (China) provides an overview of wind turbine monitoring and

April 2016

diagnosis activities in China, where the hardware structure and software implementation of the monitoring and diagnosis systems are discussed. The paper written by Professor Subhas Mukhopadhyay (New Zealand) talks about some of the research and development activities on instrumentation and measurement happening in New Zealand, especially the research activities of the Smart Sensing and Intelligent Systems Group of Massey University. The paper written by Professor Amitava Chatterjee (India) elaborates, with extensive examples, the development of a novel real-time biometric identification system utilizing Near-Infrared (NIR) imaging of Palm Dorsa Subcutaneous Vein Pattern (PDSVP) as a physiological biometric feature. The paper written by Professor Sergey Kharkovsky (Australia) describes the design of a 3-axis multifunctional imaging system with microwave and laser sensing techniques and demonstrates its applicability to noncontact detection of metal and dielectric targets embedded in layered structures and to cracks on the surface of tilted and cylindrical specimens. I hope the readers will find this special issue interesting and informative. I would like to express my sincere appreciation to the Editor-in-Chief, Professor Wendy Van Moer, for her support and valuable advice. You may contact Dr. Yan at ruqiang@seu.edu.cn. His bio is available at http://ieee.ims.org/ contacts/ruqiang-yan. Ruqiang Yan, Southeast University, P.R. China

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Non-contact Inspection of Construction Materials Using 3-axis Multifunctional Imaging System with Microwave and Laser Sensing Techniques Sergey Kharkovsky, Paritosh Giri, and Bijan Samali

icrowave imaging techniques have been applied in a wide variety of commercial and scientific applications such as non-destructive testing and evaluation, material characterization and medical applications [1]-[3]. Microwave non-contact techniques have demonstrated the ability to detect flaws in various dielectric and composite materials using relatively simple microwave reflectometers [3]-[8]. These materials include construction materials and composites such as cement-based materials and concrete structures strengthened by carbon fiber reinforced polymer (CFRP) laminates possessing delamination and debonds [3]-[7], and layered dielectric materials with a hidden crack [8]. The assessment of cracks in concrete structures is crucial for their safety and cost effective maintenance since they not only affect their appearance but also the load-carrying capacity and durability [9]-[11]. Several non-invasive testing techniques have been under investigation for the purpose of crack detection in concrete. They include acoustic testing, ultrasonic techniques, optical methods, and microwave techniques. However, if compared with non-invasive methods for metal structures, the non-invasive methods for concrete structures are at a relatively early stage of development [9]–[11]. Microwave techniques have a great potential for the inspection of construction materials including concrete, since microwave signals can penetrate inside generally lossy dielectric materials and can interact with their inner structure [1], [4], and [6]. Unlike ultrasonic testing methods, microwave methods do not require contact between the microwave antennas and the specimen under test. Typically, microwave imaging is performed by scanning a single antenna over an object [4], [5] at some distance between the antenna and the specimen under test (referred to as the standoff distance). Microwave images are produced using reflected signal data obtained over a two-dimensional (2-D) scanned area and, if necessary, signal and image processing. The increasing utility of new composite materials and structures in many critical applications (e.g., infrastructure, transportation, aerospace, etc.) has also necessitated the need for dedicated and robust imaging techniques capable of inspecting these structures [3], [4],

and [12]. One of the important issues to be considered is how to scan the antenna with respect to the part being inspected. When the specimen being inspected has a flat surface and is not tilted, a simple raster-scan can be performed. Thus, scanning systems come in a large variety of configurations to meet specific inspection needs [3], [4], [6], [7], and [12]. In practice, the standoff distance may change due to a local relative tilt between the surface of the specimen under test and the antenna, specimen surface roughness and bulging, etc. Indications associated with the change in the standoff distance may mask desired indications of flaws in generated images of a specimen [5], [6], and [12]. Several techniques may be used to reduce the undesired influence of standoff distance variations. One method involves the use of a mechanical (i.e., roller) system that can keep the standoff distance constant during the scan. However, this method is ineffective when the sample under test possesses local surface roughness/bulging that may be smaller spatially than the inspection area of the open-ended probe. Moreover, this is no longer a non-contact measurement [6]. Another method involves measuring the standoff distance variation during a scan and then removing its undesired influence by post processing (e.g., subtracting the effect of the standoff distance variation from the reflected signal). One way to accomplish this would be to have a spring-loaded potentiometer that is in contact with the surface of the sample; during the scan, a voltage proportional to the potentiometer resistance is generated, indicating the variations in the standoff distance [5], [6]. There are two basic disadvantages associated with this system: ◗◗ the potentiometer is in contact with the surface of the specimen, and ◗◗ the potentiometer is attached to the side of the openended waveguide antenna and, therefore, does not measure the standoff distance exactly under the antenna. To overcome these disadvantages, a microwave inspection system with a dual-polarized microwave reflectometer was proposed in [6]. This system uses an open-ended square waveguide antenna that is capable of simultaneously producing

April 2016

height of inclination and tilt angle. According to this data, the positioning platform provides the motion of the sensing unit in such a way that it follows a surface profile of the specimen (i.e., standoff distance is kept constant throughout the scan). In addition, LDS data is used to generate 2-D images and a Fig. 1. Proposed system testing a specimen with flaws. (a) Schematic and (b) A picture of laboratory prototype. 1-D profile of the specimen surface. The obtained mitwo orthogonally polarized signals and a conditioning cir- crowave data is used to generate 2-D magnitude and phase cuit with a compensation algorithm that are used to remove images as well as 2D images using a synthetic aperture radar the undesired influence of the standoff distance variation au- (SAR) technique [12]. tomatically [6]. However, a disadvantage of this system is that The system uses an Aglient N5225A PNA to generate and the reflectometer and the compensation algorithm are rela- measure continuous wave microwave signals with the output tively complex. power of not greater than 10 mW, as shown in Fig. 1b. An obAnother method of microwave imaging includes the use ject-oriented LabVIEW program controls the PNA via a GPIB of a 3-axis multifunctional scanning system proposed in [12] interface and drives the stepper motors using a 3-axis motion for optimization of standoff distance and contour following. controller card and individual current amplifiers. Linear enFor this purpose, the antenna was moved by a three-axis posi- coders along each axis provide position feedback [12]. tioning platform using the third Z-axis motion in addition to a raster scan over the XY-plane. The software allowed manual Application of the System control of the antenna location and calculated the path to scan, based on the start position, end position, and step size for each Microwave Imaging axis. In this paper, design of this system has been modified by In this investigation, an X-band (8.2 GHz â&#x20AC;&#x201C; 12.4 GHz) openadding a laser displacement sensor (LDS) and software to pro- ended rectangular waveguide antenna (OEWA) with the vide automated control of the antenna location. Capability of the output aperture dimensions of 22.86 mm by 10.16 mm was system to generate images of the specimen under investiga- used. The calibration of the setup at the output aperture of the tion using microwave data and displacement data separately, microwave sensor was performed using an Agilent X-band and microwave images of the specimen using a combination of waveguide calibration kit [12]. To illustrate the ability of the system to detect targets in microwave data and displacement data, is demonstrated. The results of application of this system for imaging of construc- composite materials under conditions of constant and varied tion materials and structures, in particular for the purpose of standoff distances, we used a layered construction foam struca non-contact detection of cracks in tilted and complex structure backed by a metal plate with four embedded metal disks tures such as a cement-based cylindrical specimens and blocks (each has diameter of 15 mm and thickness of 1 mm). The dimensions of the foam structure were 180 mm by 180 mm by 90 and a metal profile, are presented. mm. Figs. 2a and 2b show two views of the schematic of the Imaging System and Measurement foam specimen tilted with respect to the raster scan plane (reSetup ferred to as scan plane 1) of the sensing unit. At tilt angle q = Fig. 1a shows the schematic of the proposed imaging system 0 (non-tilted specimen), the standoff distance is constant, and with indications of its major components, while a laboratory it is equal to the standoff distance at an initial position (home prototype of the system is shown in Fig. 1b. The sensing unit position) of the sensing unit, do, while at q > 0 the standoff disincludes the microwave antenna (MA) and LDS. The antenna tance d varies, as shown in Fig. 2a. In this investigation, we and LDS radiates microwave and optical signals, respectively, scanned the part of the specimen with one disk located at the into the specimen, picks up the reflected signals, and sends depth of ~15 mm (Fig. 2b). Figs. 2c and 2d show 8.2-GHz raw them to control center. The control center includes the per- images of the metal disk obtained at q = 0 (Fig. 2c) and q = 6o formance network analyzer (PNA), power supply for LDS, (Fig. 2d). We used an open-ended waveguide antenna movcontrol box, and a microprocessor for control and data processing over scan plane 1 with do = 38 mm, with the scanned area of ing. The control box houses the power control and protection 90 mm by 90 mm and steps of 3 mm by 3 mm. The gray-scale devices and the motor controller. LDS measures the displace- levels correspond to different values of magnitude of reflecment values along the entire range of the tilt of the scan area tion coefficients. Indication of the disk is clearly visible for the in real-time using the LabVIEW platform that indicates the non-tilted specimen, i.e., at q = 0 (Fig. 2c). However, when the April 2016

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arbitrary standoff distance. Then, a few spot-by-spot measurements were performed using a linear scan along the Z-axis to determine an optimal standoff distance, which provided the largest difference between magnitudes of reflection coefficients measured at places with and without a rod. Finally, a SAR image was generated, and it clearly demonstrated the location of the rod and its important features such as its tip, as Fig. 3e shows [12].

Laser Imaging To demonstrate the capability of the system for Fig. 2. Schematic of the (a) side and (b) top view of a construction foam specimen with embedded metal disks tested by non-invasive testing of the sensing unit with an open-ended waveguide antenna (OEWA) moving over scan plane 1, and images obtained at tilt complex metal structures angle, q, of (c) 0o and (d) 6o. using the LDS, the system specimen is tilted, the image shows a gradual intensity change was set to perform a raster scan and generate 2-D images and from right to left, representing the standoff distance change 1-D profiles of the structure under investigation. The paramover the scanned area due to the specimen tilt. This change eters given in the LabVIEW program were sampling rate, masks the indication of a relatively strong target, i.e., the metal resolution, and the scanned area. Obtained displacement data disk. Overall, imaging of the metal disk embedded in a tilted were saved as a 2-D or 1-D matrix, and the image as â&#x20AC;&#x153;displaceconstruction foam specimen is a challenging task though the ment vs. coordinatesâ&#x20AC;? was generated using this matrix in real disk is a strong target, as shown in the case of the non-tilted time. The smoothness of the image was further improved by specimen. performing spatial averaging. The raw 2-D image, as well as To illustrate the capability of the imaging system to inspect a smooth image, were displayed in the LabVIEW graphic user construction materials and structures, the following experiment was performed in [12]. It relates to the detection and evaluation of cables and metallic pipes on walls covered by plasterboard sheets, as shown in Figs. 3a and 3b. The schematic of the specimen with a 12-mm diameter steel rod located on a concrete surface under a plasterboard sheet is shown in Figs. 3c and 3d. It can be seen that the specimen is a layered structure of air-plasterboard-air-concrete with a hidden steel rod. First, a preliminary detection Fig. 3. A picture of a wall with cables and pipes: (a) before and (b) after covering by plasterboard sheets. A schematic of of the bar was conducted the specimen with a 12-mm diameter steel rod under a plasterboard sheet: (c) the side and (d) top view, and (e) its image [12]. by using imaging at an 8

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April 2016

Fig. 4. A 1.5-mm width through-cut in the metal profile: (a) Picture of the metal profile. (b) A 2-D image. (c) A 1-D profile of the through-cut.

displacement) is relatively wide. In addition, the estimation of the depth of displacement in the 1-D profile gave the value of ~42 mm, which is very close to the distance between the top and bottom parts of the metal profile (Fig. 4a). The results show that the LDS is capable of transmitting the signal (i.e., light), which may penetrate through transparent structural features of a metal structure such as through-cracks and -holes and pick up signals partially reflected from the metal surface, and from interior features of complex structures. Another specimen was a cylindrical concrete specimen with a nominal diameter of 100 mm and height of 200 mm. This specimen was loaded using a standard testing machine until multiple cracks with different widths occurred due to the loading effect, as shown in Fig. 5a. The purpose of this measurement includes the investigation into the influence of curvature (i.e., the incident angle, ď Ą, as shown in Fig. 5b) on the crack detection. The incident angle is defined as the angle between a laser beam incident on the surface of the specimen and a tangent that touches a cylindrical curve of the specimen at the incident point. The position of the same crack was changed from location A to location B by rotating the cylindrical specimen to change the incident angle, as shown in Fig. 5b. The scan path was set to be 100 mm to cover the entire diameter of the specimen. Fig. 5c shows the mean curve of a 1-D profile (Displacement vs position of laser spot) scan at two incident angles (i.e., locations of crack). The resultant curves at locations A and B (Fig. 5d) show similar characteristic responses, which were

interface (GUI) in real time, while these data were saved for further processing in MATLAB. In MATLAB, the image was further smoothened by giving proper color representation, and we enhanced the image further by performing interpolation. One of the specimens was a metal profile with a 1.5 mm width through-cut on its top part, as shown in Fig. 4a. The thickness of the profile and the distance between the top and bottom parts were 1.5 mm and 41 mm, respectively. Fig. 4a also shows that the scan area (5 mm x 10 mm) included the through-cut. Fig. 4b shows the 2-D image of the scanned area and corresponding color bar where different colors represent different values of the displacement. Indication of the throughcut can be clearly seen in the image. Moreover, Fig. 4b and a 1-D profile of the through-cut shown in Fig. 5. Cylindrical concrete specimen with cracks: (a) A picture. (b) A schematic of the top view of semi-cylindrical part of Fig. 4c also demonstrate the specimen with two locations A and B of a 2-mm width crack. (c) The displacement reading (Disp.) vs. position of laser spot along the scan path around (left) crack location A, (right) crack location B and (d) Corresponding indication of the crack. that the range of color (i.e., April 2016

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presented in Fig. 2. Scan plane 1 refers to the raster scan with variable standoff distance, d, at q > 0 (Fig. 2a and Fig. 6b), while scan plane 2 indicates scanning at constant distance, do, as shown in Fig. 6b. As was shown in Fig. 2d, the image obtained at scan plane 1 demonstrated a gradual intensity change which masked the indication of the metal disk. Fig. 6c shows a raw image of the metal disk obtained at scan plane 2. Figs. 2d and 6c clearly demonstrate that when scanning at constant distance do, the gradual change was removed, and the indication of the metal disk became prominent and similar to that obtained for the nontilted specimen shown in Fig. 2c. Another tilted specimen was a mortar block with dimensions of 235 mm by 100 mm by 100 mm which possessed a natural through crack, as shown in Fig. 6d. The block was located on a tilted wooden plate with tilt angle q = 4o. In this investigation, we foFig. 6. (a) Picture of the sensing unit with the open-ended waveguide antenna (OEWA) and LDS testing the tilted construction foam specimen with the embedded metal disks. (b) Its schematic showing two scan planes. (c) A raw image cused on the indication of of a metal disk obtained at scan plane 2. (d) A picture of the tilted mortar block with a through crack. (e) Raw images of the the crack on the surface of crack showing its indication on the surface of the block obtained at two scan planes. the mortar block with relatively strong reflection obtained by subtracting crack and no crack condition data (∆ and scattering of electromagnetic waves from its edges in conDisp. vs position of laser spot). trast to investigating the foam specimen where the influence of The result showed that it is possible to get the reading of edges of the specimen was negligible due to low dielectric perdisplacement and its change, indicating crack at a wide range mittivity of foam. Therefore, a lower standoff distance and a of  from ~0° to ~180°. This result highlights the fact that the higher operating frequency have been used in this case. proposed method provides effective crack detection at differFig. 6e shows 11.5-GHz raw images of the scanned area (90 ent incident angles. mm by 45 mm) over a 2-mm width crack obtained using the magnitude and phase of reflection coefficient at the two scan Imaging of Tilted Specimens planes. The initial standoff distance do was set to be 6 mm. Both Microwave imaging capability of the proposed system with the magnitude and the phase raw images obtained at scan integrated microwave and laser techniques was tested by plane 1, i.e., at variable distance d (Fig. 6e), show a gradual raster scanning of a few tilted specimens using two scan intensity change from right to left, representing the standoff planes. First, the construction foam specimen with embed- distance change over the scanned area due to the block tilt. ded metal disks was tested using the sensing unit (Fig. 6a) There is a very weak indication of the crack that is masked by at these two planes as a continuation of the investigation the gradual intensity change. When scanning at scan plane 2, 10

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the gradual intensity change is removed, and there are prominent indications of the crack in the raw images, as shown in the two lower images in Fig. 6e.

[6] S. Kharkovsky, A. C. Ryley, V. Stephen, and R. Zoughi, “Dualpolarized near-field microwave reflectometer for noninvasive inspection of carbon fiber reinforced polymer-strengthened structures,” IEEE Trans. Instrum. Meas., vol. 57, no. 1, pp. 168–175,

Conclusions

Jan. 2008.

In this paper, the design of a 3-axis multifunctional imaging system with microwave and laser sensing techniques is described, and the results of its application to non-contact testing of construction materials are presented. This system can provide automated control of the movement of sensing units including a microwave antenna and a laser displacement sensor for contour following, optimization of standoff distance, and two imaging capabilities. First, the system is capable of generating images of the specimen under investigation using microwave data and displacement data, separately. In this case, the displacement data is used to generate 2-D images and a 1-D profile of the specimen surface while microwave images may provide information on interior structure of the specimen. Second, a combination of microwave and laser displacement sensor techniques can generate microwave images of the specimen by providing an optimal standoff distance and removing automatically, at hardware level, the change of the standoff distance. The applicability of the system to non-contact detection of metal and dielectric targets embedded in layered structures and flaws such as cracks on the surface of tilted and cylindrical specimens has been demonstrated.

[7] R. Zoughi and S. Kharkovsky, “Microwave and millimeter wave sensors for crack detection,” Fatigue Fract. Eng. Mater. Struct., vol. 31, pp. 695-713, 2008. [8] M. Maazi, O. Benazaim, D. Glay, and T. Lasri, “Detection and characterization of buried macroscopic cracks inside dielectric materials by microwave techniques and artificial neural networks,” IEEE Trans. Instrum. Meas., vol. 57, no. 12, pp. 28192826, Dec. 2008 [9] T. Yamaguchi and S. Hashimoto, “Fast crack detection method for large-size concrete surface images using percolation-based image processing,” Mach. Vision Appl., vol. 21, pp. 797-809, 2010. [10] S. Park, S. Ahmad, C.-B. Yun, and Y. Roh, “Multiple crack detection of concrete structures using impedance-based structural health monitoring techniques,” Experimental Mech., vol. 46, pp. 609-618, 2006. [11] J. Nadakuduti, G. Chen, and R. Zoughi, “Semiempirical electromagnetic modeling of crack detection and sizing in cement-based materials using microwave methods,” IEEE Trans. Instrum. Meas., vol. 55, pp. 588-597, 2006. [12] S. Kharkovsky, R. Ratnayake, M. T. Ghasr, and B. Percy, “Microwave imaging with a 3-axis multifunctional scanning system,” in Proc. Int. Instrum. Meas. Technology Conf. (I2MTC), pp.

Acknowledgments

1572-1575, 2014.

The authors express their appreciation to Professor Reza Zoughi and Dr. Mohammad Tayeb Ghasr (the Applied Microwave Nondestructive Testing Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA) for their help and support of this work. The authors also acknowledge Professor Brian Uy, the Foundation Director of Institute for Infrastructure Engineering, Western Sydney University, Australia (currently the Director of Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, the University of New South Wales, Sydney, Australia), for his support of early stages of this work.

References [1] M. Pastorino, Microwave Imaging. Hoboken, NJ, USA: John Wiley

Sergey Kharkovsky (M’01–SM’03–F’11) (S.Kharkivskiy@ westernsydney.edu.au) is an Associate Professor of Sensor Technologies with the Institute for Infrastructure Engineering at Western Sydney University, Australia. Prior to joining Western Sydney University, he was a Research Associate Professor with the Applied Microwave Nondestructive Testing Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA, from 2003 to 2011. His current research interests include nondestructive evaluation and imaging of composite materials, structural health monitoring, and material characterization. He is a recipient of the 2013 Harold A. Wheeler Applications Prize Paper Award of the IEEE Antennas and Propagation Society.

& Sons, Inc., 2010. [2] S. Ahmed, A. Schiessl, F. Gumbmann, M. Tiebout, S. Methfessel and L. Schmidt, “Advanced microwave imaging,” IEEE Microw. Mag., vol. 13, no. 6, pp. 26 - 43, Sept.- Oct. 2012. [3] R. Zoughi, Microwave Non-Destructive Testing and Evaluation. Dordrecht, The Netherlands: Kluwer, 2000. [4] S. Kharkovsky and R. Zoughi, “Microwave and millimeter wave

Paritosh Giri received his M.S. degree in Aerospace Engineering from Chonbuk National University, South Korea, in 2013. He is currently a Ph.D. candidate in the Institute for Infrastructure Engineering at Western Sydney University, Australia. His research focuses on the development of wireless imaging sensory system for infrastructure health monitoring.

nondestructive testing and evaluation – overview and recent advances,” IEEE Instrum. Meas. Mag. vol. 10, no. 2, pp. 26-38, Apr. 2007. [5] N. Qaddoumi, T. Bigelow, R. Zoughi, L. Brown, and M. Novack, “Reduction of sensitivity to surface roughness and slight standoff distance variations in microwave testing of thick composite structures,” Mater. Eval., vol. 60, no. 2, pp. 165–170, Feb. 2002. April 2016

Bijan Samali received his D.Sc. degree in Structures and Dynamics from George Washington University, USA, in 1984. Professor Samali is the Director of Institute for Infrastructure Engineering at Western Sydney University, Australia. Prior to joining Western Sydney University, he held a Personal

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Chair in Structural Engineering at University Technology, Sydney since 1999. He is the author or co-author of over 450 scholarly publications (including over 120 journal publications), on a wide range of topics in the areas of structural

engineering, structural dynamics, vibration and motion control, wind and earthquake engineering, bridge engineering, damage detection and health monitoring of structures, including keynote addresses and invited papers.

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April 2016

Real-Time NIR Imaging of Palm Dorsa Subcutaneous Vein Pattern Based Biometrics: An SRC Based Approach Sandip Joardar, Amitava Chatterjee, and Anjan Rakshit

ith the advancement in biometric recognition techniques, Palm Dorsa Subcutaneous Vein Pattern (PDSVP) [1] has emerged as a reliable and promising physiological biometric feature. Consequently, PDSVP has been extensively used as a biometric feature in recent research works on biometrics. However, the procedure of automatic data acquisition and PDSVP extraction is quite challenging. This article shows how this problem can be addressed in practice where a real life biometric identification system has been developed utilising near infrared (NIR) imaging of PDSVP of humans, where no fixed setup is employed for data acquisition. We highlight, in this article, three key ideas: automatic data acquisition, PDSVP extraction and, subsequently, biometric person identification using sparse representation based classification. Finally, we highlight the research challenges associated with implementing a Biometric Identification System (BIS) using PDSVP as a physiological feature.

Implementing PDSVP as a Biometric Feature One of the most distinguished advantages of using PDSVP as a biometric feature is its inherent high degree of immunity to forgery by impostors. The vein pattern lies underneath the skin of the palm dorsum and can only be altered by surgical intervention. Moreover, quite a few research works [1] [2], have analyzed and concluded that PDSVP remains stable and unchanged over a long tenure of one’s lifetime. However, a prominent challenge corresponding to PDSVP being used as a biometric feature is that of an automatic data acquisition system. This remains a challenging, relevant research problem in developing PDSVP based biometric systems for real life applications.

Data Acquisition There have been several research works [1], [3]-[8] in recent times that have utilized PDSVP as a biometric feature but most of them implemented a manual fixed-setup based data acquisition method. However, in this article we propose an automatic data acquisition procedure wherein a Near-Infrared (NIR) Pan-Tilt camera (FOSCAM® FI8918W [9]) is used to April 2016

Fig. 1. Flow Diagram representing the automatic data acquisition procedure.

Algorithm 1 – Palm dorsum auto-locating algorithm 1. A Raspberry® Pi 2 Model B [10] module sends a command (through wireless transmission using Netgear® JNR1010 N150 [11] router) to the NIR camera FOSCAM® 8918W. 2. On receiving the signal, the NIR camera starts the initialization process. 3. Initialization: i. Move pan-tilt camera to the initial position (rotate to the extreme-left position horizontally and tilt to the extremedown position vertically). ii. Switch ON NIR LEDs. 4. After initialization, locate the palm dorsum by implementing the following Seek-&-Freeze algorithm. Seek-&-Freeze: i. Initialize flag: match_found = False ii. WHILE (horizontal_position <= horizontal_extrema) - WHILE (vertical_position <= vertical_extrema) a) FOSCAM® FI8918W captures image, test_img, and sends it to the Raspberry® Pi 2 Model B module; b) test_img is compared with an already existing reference image, ref_img, of the palm dorsum; c) IF (test_img matches with ref_img) - Set match_found = True; - BREAK; END IF d) Increment vertical_position by one step; END WHILE - Increment horizontal_position by one step; END WHILE 5. Once the palm dorsum has been automatically located a total of 10 images per person are acquired with an intermediate delay of 2 minutes between two consecutive images acquired.

automatically locate the palm dorsum of the subject and, subsequently acquire the NIR images of the PDSVP. The entire procedure is described in Algorithm 1, and the flow diagram of data acquisition is represented in Fig. 1. In this algorithm, the test_img is compared with ref_img by extracting the SIFT keypoint descriptors [12] of both images and then comparing the descriptors using the brute force matching algorithm. In this research, the brute force matching has been performed by implementing kNN algorithm. If the match is a good match (based on distance calculated by kNN brute force matching), then the image acquired is considered to be that of a palm dorsum, the position is locked and data acquisition by the NIR camera is activated. On closely observing Fig. 1, we can notice that image 1 shows the first test_img, and finally, image 8 shows the image in which the Seek-&-Freeze algorithm could satisfactorily locate the palm dorsum and hence locked the camera position for further data acquisition. We can easily infer from the images 1-8 of Fig. 1 that, although there are many other background objects in the images, the Seek&-Freeze algorithm locked the position and stopped scanning only when it found the palm dorsum. This is because when matching the test_img with ref_img , we have directly not employed a brute-force matching algorithm, and instead, we have first extracted the Keypoint Descriptors using SIFT transform and then employed the brute-force matching method for comparing one descriptor with another. Therefore, the test_img 14

is compared with ref_img based on their keypoint descriptors which are invariant to image scaling and rotation [12] and also partially invariant to illumination and 3D displacement of camera viewpoint [12]. This establishes the robustness of the Seek-&-Freeze algorithm utilized in this work. The position of the palm dorsum and the camera after the Seek-&-Freeze algorithm locks position is shown in Fig. 2. Fig. 3 shows the hardware setup employed in our research work. The most significant components of the integrated hardware setup are: ◗◗ a low-cost Raspberry® Pi 2 Model B with an ARMv7 processor and 1 GB RAM (which greatly enhances the computational ability even at such small size of the module) [10], ◗◗ a Netgear® JNR1010 N150 (used for wireless communication between the data acquisition device and the Raspberry® Pi 2 Model B that greatly enhances the range of area that the data acquisition sensor can cover without having the overhead constraint of being close to the CPU) [11], and ◗◗ an NIR camera FOSCAM ® 8918W (for near-Infrared image acquisition of the PDSVP) [9].

Database Creation For the last few years, the Electrical Instrumentation and Measurement Laboratory of Department of Electrical Engineering,

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April 2016

Fig. 2. Position of the palm dorsum and the camera after position lock.

Jadavpur University, Kolkata, India has been actively involved in research on biometrics. The focus has been both on development of state-of-the-art biometric authentication and recognition algorithms utilizing image processing and computer vision based techniques. The human physiological features under investigation include palm print, face, ear, hand shape, palm dorsa vein pattern, etc., as well as development of real life system solutions for such biometric systems that employ both visual and NIR and far infrared (FIR) cameras. In this context, [20] showed how such a real biometric system can be developed for NIR imaging of PDSVP based biometrics, utilizing collaborative representation based classification (CRC). However, this article proposes a more sophisticated and improved version of NIR imaging of PDSVP based biometrics utilizing the Seek&-Freeze algorithm (utilized for sophisticated auto-locating of palm dorsum) and sparse representation based classification (SRC) (for authentication and recognition, described later). The database utilized in this work, named the JU-NIR-V2: NIR Vein Database is a much expanded version compared to the database JU-NIR-V1: NIR Vein Database utilized in [20]. The JU-NIR-V2: NIR Vein Database consists of 600 images of the PDSVP acquired from 60 different subjects/persons, in the age group of 20-59 years. There are 25 female and 35 male subjects under consideration here. Images of the palm dorsum of the right hand were acquired in a laboratory environment with temperature of about 27 °C. Out of those ten images per subject acquired, one image is chosen, with complete randomness, for the training dataset creation. The remaining nine images per subject are utilized for testing the recognition algorithm by forming a well-structured test dataset. This form of recognition is popularly known as Single Sample Per Person (SSPP) based training and is one of the most severe and difficult cases of pattern recognition and identification. Consequently, when we test the proposed recognition algorithm, it is tested with such a Small Sample Size (SSS) training dataset that it is intentionally subjected to a tough challenge and, therefore, results of extensive experimentation will be a direct reflection of the robustness and stability of the algorithm discussed in this article. April 2016

Fig. 3. Integrated hardware setup developed for PDSVP based biometric system.

To make such a system really useful in real life situations, each image acquired by the NIR camera FOSCAM® 8918W [9] undergoes a few steps of image processing before it is incorporated into the JU-NIR-V2: NIR Vein Database, as shown in Fig. 4. Initially, the raw NIR PDSVP image of the right hand of a person is acquired after the Seek-&-Freeze algorithm determines the position, as explained in Algorithm 1. The image in step 1 of Fig. 4 depicts the raw NIR image of the PDSVP of a certain subject. On observing the raw image, we can infer that the PDSVP is concentrated only on a small area, and there might be other detailing in the background, which might in some way affect the recognition process by contributing to the image feature matrix. Consequently, the first step is the background removal by cropping only the PDSVP region, carried out using window-based cropping technique. Binary thresholding of the raw image, raw_img, considered in step 1 is accomplished based on the global mean of raw_img. Subsequently, the left-toright (and right-to-left) and top-to-bottom (and bottom-to-top) scanning are performed to determine the initial and final rows and columns for the cropping window. Thereafter, implementing the cropping window, we obtain the image shown in step 2. However, the PDSVP is not quite discernible from the image of step 2, and therefore, we employ Contrast Limited Adaptive Histogram Equalization (CLAHE) for enhancing the PDSVP. One significant advantage of Adaptive Histogram Equalization (AHE) is that, rather than considering the whole image matrix at a time, like Global Histogram Equalization (GHE), AHE concentrates on small neighbourhoods called “tiles or blocks” (8 × 8 in our case). Further, Contrast Limiting greatly enhances the distribution of the histogram bins, so when there is a histogram bin over and above the specified contrast limit (40 in our case), those pixels are clipped (clip limit here chosen as 2) and distributed in a uniform fashion to other histogram bins, and after that, the local histogram equalization

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is applied. The resultant image is shown in step 3, comprising additional information over and above the PDSVP which might aid or affect the recognition process. Consequently, we need to extract only the PDSVP, which has been achieved utilizing the Local Adaptive thresholding technique and the image so obtained is shown in step 4. Finally, in step 5, we perform morphological closing (disk structural element, neighborhood=1) followed by opening (disk structural element, neighbourhood = 3) to fuse the narrow breaks between Fig. 4. Flow Diagram representing the step-by-step image pre-processing carried out for each raw NIR PDSVP image long stretches of the veins acquired. and to eliminate small dotnoise. The final image is . In the training atoms of class i incorporated into the JU-NIR-V2: NIR Vein Database as shown i is given by are present, the dimension of each training atom is m and the in step 6. total number of training atoms for class i is ni. The SRC based Biometric Identification Algorithm biometric identification method utilized in this work is given In the last few years, sparse representation based classifica- in the following Algorithm 2. tion (SRC) [13]–[15] has emerged as one of the most successful In this work, the l1 minimization of expression (2) has been strategies of pattern recognition using compressive sensing carried out using the well known l1_ls solver package [16]. techniques. SRC requires the linear representation of the test sample as a sparse combination of the training samples. A Experimental Results and Inferences is formed where In this section, we elaborately discuss the results and intraining dictionary each column vector is a training atom (training image references drawn from a series of extensive experimentation shaped into a column vector), and the local dictionary for class performed on the JU-NIR-V2: NIR Vein Database. The database

Algorithm 2 – Biometric identification using sparse representation based classification

1. The query sample, , is coded over the dictionary using the representation vector obtained by the minimization of the objective function given by the following expression (1). (1)

The aforementioned expression (1) can be transformed into a Regularized formulation given by expression (2) [14]. (2)

where,  ∈ (0,1) is a positive regularization constant. 2. Then the representation residual for the ith class is given by the following expression (3). (3)

where,

is the representation sub-vector for the ith class.

3. Finally, the query sample,

, is classified to that class ‘i’ for which the reconstruction residual , ri, is minimum.

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April 2016

Table 1 – Noise employed for synthetic corruption of raw PDSVP images Noise

Parameters

Gaussian

Mean = 0.001, Variance = 0.01

Salt and Pepper

Noise Density = 0.01

Poisson

Specific to Poisson distribution of Signal Data available

Speckle

Variance = 0.05

has been created with sample images, each of size 450 × 350. The creation of the training and testing dataset has already been discussed. In total, the SSPP based biometric identification system was executed for 35 experimental trials, and then the average recognition rate of 98.42 ± 0.42% (mean ± SD) was obtained. The average computational burden of one identification cycle of a single test sample is 1.29 seconds. The 98.42% mean recognition rate reiterates the high robustness of the proposed algorithm with a randomly selected training dataset in each one of the 35 experimental trials. The standard deviation of 0.42% clearly shows that the proposed recognition algorithm is highly stable. The worst recognition rate obtained among the 35 experimental trials was 97.78% (12 wrong classifications out of 540 test samples), and the best recognition rate obtained was 99.07% (five wrong classifications out of 540 test samples). Noise corruption during data acquisition is a significant issue and a real matter of concern, and therefore, in this section, we synthetically corrupt the raw PDSVP images acquired with 4 different noises that are commonly known to corrupt images during data acquisition, as shown in Table 1. Fig. 5 shows the subsequent image processing steps. The synthetically corrupted database, as shown in Fig. 5, was subjected to the aforementioned experimental conditions for biometric identification. In fact, the data acquisition for the JU-NIR-V2: NIR Vein Database was carried out in a normal laboratory environment without any external constraints in the

Table 2 – Mean recognition rate with noise corrupted databases Database

Recognition Rate (%)

Uncorrupted

99.07 (best) 97.78 (worst) 98.42 ± 0.42 (mean ± S.D.)

Gaussian

98.83 (best) 97.66 (worst) 98.30 ± 0.41 (mean ± S.D.)

Salt and Pepper

99.02 (best) 97.46 (worst) 98.44 ± 0.42 (mean ± S.D.)

Poisson

99.22 (best) 97.27 (worst) 98.35 ± 0.59 (mean ± S.D.)

Speckle

99.22 (best) 97.27 (worst) 98.45 ± 0.54 (mean ± S.D.)

process of data acquisition, and therefore, there was already ample scope for noise corrupting the database naturally. Eventually, the results of extensive experimentation tabulated in Table 2 for noisy images explained the robustness and stability of the proposed algorithm. Finally, we performed a comparative analysis of our proposed method with other existing PDSVP based biometric identification methods. It would have been ideal if all of the methods could have been compared using the same database and images acquired under identical environmental conditions. Unfortunately, for the available, state-of-the-art published results, the databases are not readily available in public domain and the methodologies for dataset creation are also different and add to the difficulty. Nevertheless, Table 3 compares our results with the results of different contemporary published works. The comparative analysis shows the usefulness and robustness of the algorithm discussed in this article.

Conclusion

Fig. 5. Sample corrupted raw images and the subsequent image processing steps. April 2016

This article is an elaborate discussion, with extensive exemplification, of the development of a novel real-time biometric identification system utilizing NIR imaging of PDSVP as a physiological biometric feature. This system can locate the palm dorsum without any manual intervention required in most of the existing PDSVP based biometric identification systems proposed so far, to the best of our knowledge and belief. The automatic data acquisition system developed greatly enhances convenience for the subject whose PDSVP NIR image is being acquired, and it greatly helps in maintaining easy portability of the data acquisition setup. The results obtained from extensive experimentation show that the accuracy, robustness, and stability of the proposed algorithm is extremely high. However, one of the significant drawbacks of NIR imaging of the PDSVP is that it is sensitive to

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Table 3 – Comparative analysis Method Wang et al. [17]

Recognition Rate (%) Point-based Hausdorff method = 58 (best) Line Edge Map method = 66 (best) Gabor Feature based method = 80 (best)

Soni et al. [18]

99.26 (best)

Yuksel et al. [4]

97.33 (mean)

Badawi et al. [19]

99.888 (mean)

Joardar et al. [20]

100 (best) 97.34 ± 0.56 (mean ± S.D.)

Proposed in this article

99.07 (best) 97.78 (worst) 98.42 ± 0.42 (mean ± S.D.)

illumination conditions during data acquisition, and at times, this can affect the quality and discernibility of the PDSVP in the raw NIR images acquired. Therefore, this illumination variation can pose a tough challenge for PDSVP based biometrics. The Biometrics Research Team at the Electrical I&M Laboratory, Department of Electrical Engineering, Jadavpur University, Kolkata, India is presently considering this challenging problem as one of their primary future scopes of research. They intend to work towards effective, real life implementations of multi-modal data acquisition systems, which can be, as far as possible, insensitive to the effects of variation in illumination while acquiring the NIR images. They will also work to develop associated useful machine learning and pattern recognition algorithms in conjunction with such data acquisition systems.

based dorsal hand vein recognition through random keypoint generation and fine-grained matching,” in Proc. 2015 Int. Conf. on Biometrics (ICB), pp. 326-333, May 2015. [9] “User Manual Model: FI8918W Indoor Pan/Tilt Wireless IP Camera,” [Online]. Available: http:// foscam.us/downloads/ FI8918W%20User%20Manual.pdf. [10] “Introducing the Raspberry Pi 2 Model B,” [Online].Available: https://learn.adafruit.com/downloads/pdf/introducing-theraspberry-pi-2-model-b.pdf. [11] “N-150 4-Port Wireless Router JNR 1010 User Manual,” [Online]. Available: http://www.downloads.netgear.com/files/GDC/ JNR1010/JNR1010_UM_10Aug12.pdf. [12] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, vol. 60, issue 2, pp. 91-110, Nov. 2004.

References

[13] M. Elad, Sparse and Redundant Representations-From Theory to

[1] J. M. Cross and C. L. Smith, “Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification,” in Proc. IEEE 29th Int. Carnahan Conf. on Security Technology, pp. 20-25, Oct. 1995.

Applications in Signal and Image Processing. New York, NY, USA: Springer, 2010. [14] L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation: which helps face recognition?” in

[2] A. K. Jain, R. Bolle, and S. Pankanti, Eds., Biometrics: Personal Identification in Networked Society. New York, NY, USA: Springer US, 2006.

Proc. IEEE Int. Conf. on Computer Vision (ICCV), pp. 471-478, Nov. 2011. [15] L. Zhang, M. Yang, X. Feng, Y. Ma, and D. Zhang, “Collaborative

[3] C. L. Lin and K. C. Fan, “Biometric verification using thermal images of palm-dorsa vein patterns,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 2, pp. 199-213, 2004. [4] A. Yuksel, L. Akarun, and B. Sankur, “Hand vein biometry based on geometry and appearance methods,” IET Computer Vision, vol. 5, no, 6, pp. 398-406, Nov. 2011.

representation based classification for face recognition,” arXiv preprint arXiv:1204.2358, 2012. [16] “l1_ls: A Matlab Solver for Large-Scale l1-Regularized Least Squares Problems,” [Online]. Available: https://web.stanford. edu/~boyd/l1_ls/l1_ls_usrguide.pdf. [17] Z. Wang, B. Zhang, W. Chen, and Y. Gao, “A performance

[5] J. Liu, J. Cui, D. Xue, and X. Jia, “Palm-dorsa vein recognition based on independent principle component analysis,” in Proc. Int. Conf. on Image Analysis and Signal Processing (IASP), pp. 660-664, Oct. 2011. [6] J. Liu, and Y. Zhang, “ Palm-dorsa vein recognition based on two-dimensional fisher linear discriminant ,” in Proc. Int. Conf. on Image Analysis and Signal Processing (IASP), pp. 550-552, Oct. 2011. [7] Y. Tang, D. Huang, and Y. Wang, “Hand-dorsa vein recognition

[8] R. Zhang, D. Huang, Y. Wang, and Y. Wang, “Improving feature

evaluation of shape and texture based methods for vein recognition,” in 2008 Congress on Image and Signal Processing, pp. 659-661, May 2008. [18] M. Soni, S. Gupta, M. S. Rao, and P. Gupta, “A new vein patternbased verification system,” Int. J. of Comput. Sci. and Inform. Security, vol. 8, no. 1, pp. 58-63, 2010. [19] A.M. Badawi, “Hand vein biometric verification prototype: a

based on multi-level keypoint detection and local feature

testing performance and pattern similarity,” in Proc. 2006 Int.

matching,” in Proc. 21st Int. Conf. on Pattern Recognition (ICPR),

Conf. on Image Processing, Comput. Vision, & Pattern Recognition,

pp. 2837-2840, Nov. 2012.

vol. 1, pp. 3-9, Jun. 2006.

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[20] S. Joardar, A. Chatterjee, and A. Rakshit, “A real-time palm dorsa subcutaneous vein pattern recognition system using collaborative representation based classification,” IEEE Trans. Instrum. Meas., vol. 64, issue 4, pp. 959-966, Dec. 2014.

Sandip Joardar is pursuing his doctoral research at the Electrical Engineering Department, Jadavpur University, India and is also currently working as an instrumentation engineer with the Haldia Petrochemicals Ltd., Haldia, West Bengal, India. His research interests include multi-dimensional signal processing, biometrics, and process instrumentation and control. Amitava Chatterjee (SM’13) (cha_ami@yahoo.co.in) presently serves as a Professor in the Department of Electrical

Engineering, Jadavpur University, Kolkata, India. He also serves as an Editor of IEEE Transactions on Vehicular Technology and as an Associate Editor of IEEE Transactions on Instrumentation and Measurement and IEEE Sensors Journal. His current research interests include intelligent instrumentation, computer vision, image processing and pattern recognition, nonlinear control, robotics and signal processing. Anjan Rakshit is a Retired Professor with the Department of Electrical Engineering, Jadavpur University. His current research interests include digital signal processing, smart instrumentation, intelligent control, robotics and design of real-time systems.

For additional information and further details, please refer to our sister publication, the IEEE Transactions on Instrumentation and Measurement. S. Joardar, A. Chatterjee, and A. Rakshit, “A real-time palm dorsa subcutaneous vein pattern recognition system using collaborative representation based classification,” IEEE Trans. Instrum. Meas., vol. 64, no. 4, pp. 959-966, Dec. 2014. G. Betta, D. Capriglione, M. Corvino, C. Liguori, and A. Paolillo, “Face based recognition algorithms: A first step toward a metrological characterization,” IEEE Trans. Instrum. Meas., vol. 62, no. 5, pp. 1008–1016, May 2013. A. Chatterjee, R. Fournier, A. Nait-Ali, and P. Siarry, “A postural information-based biometric authentication system employing S-transform, radial basis function network, and extended Kalman filtering,” IEEE Trans. Instrum. Meas., vol. 59, no. 12, pp. 3131–3138, Dec. 2010.

April 2016

IEEE Instrumentation & Measurement Magazine 19

basicmetrology Bryan Kibble

Is the Universe Shaking?

sually whenever we want to measure something, be it the red shift of an immensely distant galaxy or the diameter of a proton, if we extend the abilities of our senses with appropriate instruments, we can see it. And if we can see it, we stand a good chance of being able to measure it. But if the very existence of the phenomenon is in question, then we must first look for it. Before Maxwell, scientists were puzzled how an electromagnetic wave could propagate in space when there was nothing for it to propagate in. Now, we have become accustomed to the idea that both electric and magnetic fields can be created in otherwise empty space and that a varying electric field can create a varying magnetic field, which in turn creates a varying electric field, and so these fields can propagate. But a hundred years ago Einstein went further and asked us to understand that the very structure of space itself can be distorted. This distortion can also propagate and carry energy to displace masses encountered along the way. Because of frequent repetition and experimental observation, we have come to accept that mass distorts the space around it, but there are no masses in the space the gravitational wave is passing through. So a hypothetical cosmological metrologist could be asked to measure this property of space even though there is nothing there to measure, until that is, the wave encounters test masses. Mind-bending! Gravitational waves have still not been directly observed, although there is indirect evidence for them from the energy lost through their radiation from an orbiting pair of neutron stars, one of which is also a pulsar. But circumstantial evidence, no matter how strong, is not totally convincing for the human mind. There is always the possibility that we are the victims of a coincidence in that the energy loss could have some other cause, which just happens to be the right amount. It is therefore gratifying that several efforts are under way to detect directly the effect of a gravitational wave on separated test masses as it passes through our solar system. But these efforts involve pushing the limits of present observational technology to an incredible extent.

This is because even the cataclysmic energy released by the collision of two stars not too far away in the universe is so diluted by spreading out over the vast distance the event would be from us, and that the wavelength is so great compared with the dimensions of an earth-bound detector that the energy intersecting our detector is too small to register. Until now, that is. The five detectors scattered over the earthâ&#x20AC;&#x2122;s surface are interferometers whose principle is the same as the rotatable apparatus constructed by Michelson to show that the velocity of light is the same in all horizontal directions, thus verifying a key prediction of the special theory of relativity. The only difference is one of size. Whereas Michelsonâ&#x20AC;&#x2122;s was of table-top dimensions, earth-bound gravitational wave detectors have arms kilometres long, and because fluctuations in air density would cause far too much noise, the light paths have to be in evacuated tubes. The mirrors at the end of the arms are so flat and reflect so well that laser beams can be sent around the interferometer four hundred times without too much loss of intensity, thus increasing the detection sensitivity by this factor when the beams are recombined to interfere. All this is needed to have any hope of detecting stretching of the arms by less than 10-18 metres as a gravitational wave goes by. The massive mirrors must also be suspended on spring systems so that they are isolated to this extent from ground vibrations in the audiofrequency range. To put this tiny distance in perspective, it is a thousand times less than the diameter of a proton and will only produce a shift in the interferometer of 10-9 of an optical fringe. To experimentalists like me who have used interferometers as a length-measuring tool, 10-5 of a fringe seems very much like pushing your luck, and we must admire the courage of these would-be detectorists not to have been deterred from even beginning their work. Even more ambitious plans are being implemented, to send the apparatus into space with the interferometer parts mounted in three satellites 106 kilometers apart. That solves the vacuum and ground vibration problems nicely, and these dimensions match the wavelength expected. The European Space Agency has just taken the first step by sending up a preliminary satellite to test whether a test mass within a satellite can be kept still enough to monitor its distance from a similar mass in another satellite 106 kilometers distant.

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April 2016

All in all, it is not too much to expect some news of the universe shaking from mighty collisions within it sometime this year, or shortly after. In some ways a null result would be even more interesting, being mind-shaking for relativistic theoreticians, but Einstein couldnâ&#x20AC;&#x2122;t have been wrong, could he? The inevitable question of why should we devote considerable economic resources to this work deserves a convincing answer. Unlike Faradayâ&#x20AC;&#x2122;s new-born child, the knowledge gained is unlikely to be of practical benefit to the human race, maybe ever. But we are more than practical animals. Those privileged few of us who have sufficient financial resources

not to have to spend all our energy on merely having enough food and housing to exist can afford the time for our insatiable curiosity about our home, the universe. That proportion needs to increase, and then maybe less effort will be expended trying to exhaust the earth of its resources or to kill one another in the name of some supposed religious edict and more on realising that the earth is very finite in the vast universe, and we could very easily exploit it to our extinction. You may contact Dr. Kibble at b_kibble@sky.com. His bio is available at http://ieee-ims.org/contacts/bryan-peter-kibble.

Footnote

ear Readers, No sooner than the ink for this column was dry on the page and sent off, the news broke about the actual claimed observation of gravitational waves. So it seems that we must contemplate as correct the model that the abstract space-time coordinates of the universe

April 2016

are capable of distortion, but relative to what? Apparently, in physics, we are not entitled to ask how things work; they are what they are and we just do our best to model them. Bryan

IEEE Instrumentation & Measurement Magazine 21

Wind Turbine Condition Monitoring and Fault Diagnosis in China Xuefeng Chen, Ruqiang Yan, and Yanmeng Liu

ith the advent of a more severe energy crisis and environment contamination, wind energy, as one of the green and renewable energy resources, has attracted more and more attention worldwide [1], [2]. According to statistics issued by the Global Wind Energy Council (GWEC), by the end of 2014, the global installed capacity had reached 360,000 MW, while the installed capacity in China accounted for 114,763 MW, nearly one third of the total, as illustrated in Fig. 1. Meanwhile, it is envisioned in 2050 Blueprint of Wind Power Development in China that by the years 2030 and 2050, the scale of installed capacity will exceed 4 × 105 MW and 106 MW, respectively, so as to meet 8.4% and 17% of the demand for electricity nationwide, enabling wind energy to be one of five main energy resources. Since the first installation of a 1.5 MW wind turbine in 2005, China has witnessed increasing installment of wind turbines, adding up to over 70,000 wind turbines now. However, highspeed development can hardly deny low operation reliability, and the operation reliability of wind turbines in service is low. It is known that downtime caused by faults accounts for 25.6% of the rated generation time. For a wind turbine with a twentyyear service life, its operation and maintenance cost consumes 10%-15% of a wind farm’s revenue. Besides, for offshore wind farms, 20% to 25% of the revenue is spent on operation and maintenance. High operation and maintenance costs increase the running cost of wind farms, decreasing economic benefits accordingly. As a result, the industry is in urgent need of research and development of condition monitoring and fault diagnosis systems for wind turbines with the purpose of decreasing operation costs and lowering wind turbines’ operation risks, like disastrous accidents caused by early failure. In addition, Guidelines on Vibration Condition Monitoring of Wind Turbines issued by the National Energy Administration of China in November 2011 states that offshore wind turbines (≥2 MW) should be stationary mounted. For wind turbines with power lower than 2 MW, semi-fixed installation systems or portable systems should be applied. Although 1.5 MW wind turbines are popular in China, most manufacturers are investigating and promoting wind turbines with power above 2 22

MW, which means that more and more manufacturers need to add vibration monitoring into their integrated systems. Correspondingly, developing an on-line condition monitoring fault diagnosis system for wind turbines will help increase availability, maintain equipment, and improve utilization of wind turbines.

A Wind Turbine Monitoring and Diagnosis System There are many organizations that conduct research and development in the area of wind turbine condition monitoring and fault diagnosis in China, including Tsinghua University, Beijing University of Chemical Technology, North China Electric Power University, Chongqing University, etc. Their research results have been applied in the wind turbine industry, as by the Goldwind Group, and they have advanced wind energy utilization in China. In particular, a holistic approach of combining theoretical analysis, technical development, and practical verification has addressed some key issues in developing wind turbine condition monitoring and fault diagnosis systems. It is the HET-P system, which has been widely installed on more than one hundred wind farms. Based on the HET-P system, an integrated wind turbine drive-train vibration analysis and diagnosis model, named “Primary diagnosis-precision diagnosis-remote diagnosis,” was established. Primary assessment of fault components is realized through a two-grade vibration amplitude alarm strategy. Then, vibration data of those observed key components are analyzed in detail with the help of precision diagnosis to identify the location of fault sources. In the last step, doubts in fault diagnosis are reconfirmed by experts or diagnosis teams remotely so that mistakes can be prevented and the accuracy of fault diagnosis can be improved. This HET-P system takes component structure, variable speed operating conditions, and severe environmental temperature differences into consideration. Stable and reliable data acquisition and modern fault diagnosis techniques used in this system not only provide a supporting platform for reliable operation of wind turbines, but also identify conditions

April 2016

Fig. 1. Accumulative installed capacity of wind turbines in China and worldwide from 2001 to 2014 [3].

of key components online, realizing remote and real-time monitoring, fault alarm and operating conditions diagnosis of the drive-train in the wind turbines. They provide scientific evidence for preventative maintenance, which lowers the maintenance cost of wind turbines. The HET-P system mainly includes three key parts: Information Acquisition Module, the Software Module, and the Remote Monitoring and Diagnosis Module.

at both the turbine cabin and tower bottom; therefore, the data acquisition unit can transfer the data to the tower bottom through a fiber-optic transmission line. Fig. 2 shows the hardware for data acquisition and transmission, which features the functions of real-time data sampling, transmission, storage and management. Fig. 3 illustrates how data are collected for wind turbines.

Software Module Information Acquisition Module

There are two key techniques built into the software module. To effectively monitor the wind turbine drive train in real- One is that a peak index is constructed based on large data time, a data acquisition (DAC) system is built to continuously mining using a support vector machine. This index is able to capture its information. The system is made of an input in- provide an alarm threshold for identifying the operating staterface, preamplifier, filter, follower, A/D converter, buffer, a tus of wind turbines. The other is a sparse diagnosis method, core processor unit and an external interface. The signals are which is proposed to process vibration data and is able to measured by the sensors, which are connected to the DAC reflect fault features through energy concentration in the specsystem through the input interface. Then, the signals are am- trum. As a result, it can trace back and locate faults of the plified by a preamplifier, and a filter is used to remove some drive-train system in wind turbine equipment. unwanted noise. After that, the signal is sent through the folBased on established alarm standards, the software module lower with high driving capability before it enters the A/D is constructed to evaluate the operation status of the wind turconverter for A/D conversion and then stays in the buffer. The bines, which is executed through extraction and analysis of the data in the buffer are analyzed with corresponding algorithms test data. If it is approaching the alarm threshold, the module in the core processor unit. Finally, the processed data are packed, transferred through an external interface and are then displayed on a computer. The online data acquisition unit, power supply unit, and communication signal exchange unit are all set up in the protective unit of the data acquisition system, where wires connect the interface of the protective unit and the sensors at measurement points. Fig. 2. Data acquisition and transmission hardware. Switchboards are installed April 2016

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Fig. 3. Illustration of data collection for wind turbines. FOT stands for fiber optic transmitter.

will first identify the fault of key components with reference to quantitative diagnosis standards. Then, more attention will be paid to those key components whose vibration amplitude exceeds the alarm threshold. The monitoring interface is shown in Fig. 4. When the status information of the wind turbine exceeds the alarm threshold, fault location identification occurs. The reason why wind turbines are degraded is examined by both classical signal analysis and specialized signal analysis techniques. Then, key components are confirmed to guide corresponding strategies for wind farm maintenance. As one of the major classical signal analysis techniques, time domain statistics are implemented first, including

minimum/maximum vibration amplitude, mean value, variance, skewness, kurtosis, etc. Then, some specialized signal analysis techniques, such as sparse decomposition, are adopted to analyze non-stationary and nonlinear vibration signals. The inherent signal components are extracted, and their instantaneous frequency information is displayed to reflect the dynamics of the signal. With these specialized signal analysis techniques, the non-stationary signal component featuring faults can be effectively separated and identified. As an example, Fig. 5 shows the software analysis interface.

Fig. 4. Primary diagnosis interface based on alarm standards.

Fig. 5. Analysis interface of wind turbine vibration data.

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Fig. 6. Illustration of an Internet-based remote monitoring service.

Remote Monitoring and Diagnosis Module By utilizing the HET-P system and web technology, vibration data collected from single or multiple wind farms can be transmitted to a remote monitoring and diagnosis center, as Fig. 6 illustrates, where an internet-based remote monitoring and diagnosis service is provided. This can help to store vibration data with the potential to utilize large amounts of them to set up an alarm threshold for monitoring wind turbines. Furthermore, it can provide remote technical support with the help of fault diagnosis experts for solving difficult problems encountered in wind turbine condition monitoring. In such a way, information about the wind turbine status can be fused together to improve the accuracy of the fault diagnosis and secure safe operation of wind turbines. Based on the three modules described above, Fig. 7 shows the developed wind turbine monitoring and diagnosis system. This system has received certification of electromagnetic compatibility (EMC) according to instructions from the international Community European (CE) â&#x20AC;&#x201C; GB/T Std 17799.2-2003, the General Standard of EMC, and safety supervision of the testing center in the China Electrical Power Research Institute. Currently, the system has been widely installed on more than one hundred wind farms in the Gansu, Inner Mongolia and Xinjiang Uygur Autonomous Regions. April 2016

The Key Technique in Monitoring Wind Turbines In general, monitoring and diagnosing wind turbines in an effective way are quite challenging. They are different from those large-scale rotating machines running under constant speed because wind turbines feature variable-speed operations. Therefore, traditional methods used for rotating machines with constant speed are not qualified for this application, which leaves a big issue in the field of vibration monitoring and diagnosis of wind turbines.

Fig. 7. HET-P system for wind turbine monitoring and diagnosis.

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Table 1 – FAG6326C3 bearing defect characteristic frequency at a speed of 1791 r/min Outer ring fault frequency (Hz)

Inner ring fault frequency (Hz)

Roller fault frequency (Hz)

Holder fault frequency (Hz)

93.5

145.3

65.6

11.7

There are three main difficulties. First, it is difficult to identify the overall vibration thresholds with variable speed. Thus, real-time monitoring and an alarm system for wind turbines are hard to realize because the vibration amplitude fluctuates in complicated working conditions, such as variable-speed operating conditions. Second, with variable speed, fault diagnosis of the drive-train system is also difficult. Since the planetary gearbox and bearings inside the drive system run with variable speed, it causes structural defect-related response signals to be low in time-frequency aggregation, leading to difficulty in fault feature extraction. Third, failure modes of large-scale composite blades are clearly different from those of metal structures. Variable speed causes fluctuation of the stress and acoustic signals, making damage difficult to identify. To tackle this challenge, a key technique called “Sparse Decomposition” has been developed [4]. Specifically, recent advances in signal processing have focused on the use of sparse representation in various applications. as a Sparse representation aims to represent a signal linear combination of a few elements from a given dictionary

. In particular, we can write , where has nonzero entries. Moreover, we are interested in the case where m < n. Elementary linear algebra tells us that  is not uniquely recoverable from x by linear algebraic means, as the equation x = D may have many solutions. However, we are seeking a sparse solution, and for a certain dictionary D, sparsity will prove a powerful constraint. The simplest way to pose a recovery algorithm is using the optimization

. (1)

Relying on the prior information that faulty features with a similar spectrum have different oscillating waveforms, apparently intractable problems could be reformulated into a sparse optimization problem with appropriate regularization terms, which enforce structured sparsity constraints over the subcomponents of the vibration signals. Moreover, harmonic components can be sparsely represented in the redundant harand impulsive components could monic dictionary be sparsely approximated through an over-complete Gawith an appropriate window length. bor dictionary Introducing a union of the two redundant dictionaries, strikingly, extracting multiple components from compound signals could be formally expressed as the following sparse optimization problem:

(2)

counts the nonzero entries of a vector. The variable x is an approximation of harmonic components x1, and  is the coefficient of impulsive components under the Gabor dictionary D. Moreover, 1 and 2 are the regularization parameters which balance the sparse degree of every component. The constraint in this optimization problem accounts for the presence of noise and model imperfection, thus ≤ 0 depends upon the noise variance  and is usually set as for practical applications. The solution to (2) can be solved using an orthogonal matching pursuit algorithm, and the details can be referred to in [4]. As an example, when one wind turbine is inspected on a wind farm, it is found that the peak Fig. 8. Time domain waveform and decomposed components of vibration signals in a wind turbine gearbox. 26

where

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consistent with the physical examination by putting a hose endoscope into the gearbox to check the bearing. Findings indicate that the high-speed end bearing inside the gearbox had a serious scratch, as Fig. 10 shows.

Summary With the advance of scie n c e a n d t e c h n o l o g y, China is promoting the development of wind energy. However, with the high demand for energy efficiency, the size of wind turbines has been increasing, leading to higher operational costs, and a higher risk of failures. To lower the operational risk to wind turbines and decrease the corresponding costs, developing condiFig. 9. An envelope spectrum of the vibration signal in a wind turbine gearbox after decomposition. tion monitoring and fault value of the bearing vibration signal near to the high speed end diagnosis tailored to wind turbine technology is becoming of the gearbox fluctuates seriously, and the root mean square a direction of long-term focus. In this article, we introduced value is much higher than that of other measuring points, high some of the research activities regarding the development above the normal value. To ensure safe operation of the wind of wind turbine condition monitoring and fault diagnosis turbine, features are extracted from vibration signals of the systems at a collaborative innovation center of high-end mangearbox, and the corresponding health condition evaluation ufacturing equipment in China. In particular, a key technique is conducted. It is known that the model of the rear bearing is based on sparsity theory has been developed to monitor and FAG6326C3. Since the rotating speed is 1971 r/min, the cor- diagnose wind turbines running in variable speed conditions. responding bearing defect characteristic frequencies can be It is envisioned that with the increased maturity in technolanalytically calculated, as Table 1 shows. ogy, including monitoring and diagnosis techniques for wind The sparse decomposition technique is adopted to process the vibration signal as shown in the graphs in Fig. 8, with the discrete cosine transform (DCT) extracting the harmonic waves and the discrete wavelet transform (DWT) extracting the impact components. The processed data length is 8,192 with 12,800 Hz sampling frequency, leading to 1.5625Hz frequency resolution. Fig. 8 shows the time domain waveform of each signal component after decomposition, and Fig. 9 shows the corresponding envelope spectrum. Due to the influence of interfering signals, the defect-related information can hardly be observed in the envelope spectrum of the original signal, as shown in Fig. 9. From the composition of spectrum peaks with the frequency of 144 Hz in the envelope spectrum of the impact signal after decomposition, there is an indication that regular vibration impact with the frequency of 144 Hz exists in the original signal. The frequency of 144 Hz corresponds to the defect characteristic frequency of the bearingâ&#x20AC;&#x2122;s inner ring, indicating that the abnormal vibration of the bearing is caused by the defect in the inner ring. This is Fig. 10. Scratch on the inner ring of a high-speed end bearing in the gearbox. April 2016

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turbines, wind energy will be available for the Chinese people’s daily lives more and more.

References [1] S. Chu and A. Majumdar, “Opportunities and challenges for a sustainable energy future,” Nature, vol. 488, pp. 294-303, 2012. [2] W. Liu, B. Tang, J. Han, X. Lu, N. Hu, and Z. He, “The structure

2007, the Second Awards of Technology Invention of China in 2009, the China National Funds for Distinguished Young Scientists in 2012 and was named a chief scientist of the National Key Basic Research Program of China (973 Program) in 2015. Dr. Chen is the chapter chairman of the IEEE Xi’an and Chengdu Joint Section Instrumentation and Measurement Society.

healthy condition monitoring and fault diagnosis methods in wind turbines: a review,” Renewable and Sustainable Energy Reviews, vol. 44, pp. 466-472, 2015. [3] “Global Wind Report 2014: Annual Market Update,” IEA Wind, G. W. E. Council, vol. 149, 2015. [4] D. Zhaohui, C. Xuefeng, Z. Han, and Y. Ruqiang, “Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis,” IEEE Trans. Ind. Electron., vol. 62, pp. 6594-6605, 2015.

Xuefeng Chen (chenxf@mail.xjtu.edu.cn) received his Ph.D. degree from the Xi’an Jiaotong University, Xi’an, China in 2004. He is a Professor of Mechanical Engineering with Xi’an Jiaotong University. His research interests include ﬁnite-element method, mechanical systems and signal processing, diagnosis and prognosis for complex industrial systems, smart structures, aero-engine fault diagnosis and wind turbine system monitoring. Dr. Chen was a recipient of the National Excellent Doctoral Dissertation of China in

Ruqiang Yan (ruqiang@seu.edu.cn) received his Ph.D. degree from the University of Massachusetts Amherst in 2007. He was a Guest Researcher at the National Institute of Standards and Technology (NIST) from 2006 to 2008. He joined the School of Instrument Science and Engineering at the Southeast University, China as a Full Professor in October 2009. He is also affiliated with the Collaborative Innovation Center of High-End Manufacturing Equipment at Xi’an Jiaotong University as a researcher. His research interests include nonlinear time-series analysis, multi-domain signal processing, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Yanmeng Liu received her M.S. degree from Xi’an Jiaotong University, Xi’an, China, in 2015. She joined the Collaborative Innovation Center of High-End Manufacturing Equipment at Xi’an Jiaotong University, Xi’an, China in June 2015.

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futureI&M trends in

Jenny Wirandi

Future Measurement Technology is Not Always about New Technology

ear readers, My guest author for this issue is Jenny Wirandi, from Sweden. I am happy that her contribution comes immediately after Mohammed Khalil’s one (February 2016 issue), when he highlighted, with his own experience, the gap between research and industry. In fact, Jenny is showing us an example where this gap has been filled, providing also enthusiastic economical results. Curious readers, I immediately leave you to her experience! Until the next issue, When Simona asked me to write a piece on my thoughts about future measurement technology, I immediately thought about a project that is currently in progress at my workplace, OKG AB, a Nuclear Power Plant in the south of Sweden. This particular project is not so much about new technology. However, I do believe it is about the future of measurement techniques. In my point of view, I do not think future measurement technology always has to be about new technology. It could be about developing further existing techniques, since it shortens the time from research to industrial impact. Our project is about reducing the measurement uncertainty of an important parameter by combining existing methods and novel research. By doing this we have been able to increase the power output, the thermal energy. And why do I think this is the future? This would naturally lead to additional income for the company. But it also leads to energy efficiency, which is a hot topic in Europe right now, since it involves preservation of natural resources and energy conservation. However, the best part is that we have been able to do this without any reconstruction at the plant. Please observe that our project is not unique for us only; it could be applied to many large power plants such as nuclear, gas and coal, for instance. April 2016

Measurement in Power Plants Before I explain our project more in detail, I will give you some background data. In power plants, the feed water flow rate is the most important parameter when the thermal energy is determined. The flow rate is almost the same all the time. Nevertheless, it is hard to measure, although it varies a little during normal operation. The main reasons for this are: ◗◗ The lack of traceability – It is hard to find a suitable laboratory that could offer a mock-up for this size and environment (pipe diameter >400 mm, media >200 °C, pressure >60 bar). ◗◗ The installation effects – Flow meters for hot water are sensitive to the flow velocity disturbance generated in the pipe. The flow standard says that an extra uncertainty should be added, where the amount depends on the kind of installation effect and the upstream distance to the actual measuring device. ◗◗ The medium temperature – Water temperature in the flow is not homogeneous and affects the output signal of the flow meter.

Measurement of Feed Water Flow at OKG AB In my company, OKG AB, a Nuclear Power Plant with a thermal power output of 3900 MW in the south of Sweden, we use two Venturi flow meters to measure the feed water of the nuclear power plant. There are two flow meters since the feed water is divided between two pipes. The choice was Venturi is since they are robust, reliable, and have the right safety class. As in most power plants, our measurements are complex. The pipe diameter is 400 mm, the water temperature is about 218 °C at 76,5 bar, and the flow maximum is 1077 kg/s in each pipe. Before the flow meters there are two bends of 90 degrees (Fig. 1). The bends create swirls, which also affect the measurement result. (The swirls in our pipes are confirmed by a Computational Fluid Dynamics CFD analysis). However, the bends in the pipe are necessary to mix the hot and cold water and must not be straightened. Despite the bends in the pipe, there is a change of the temperature in the media, which also affects the output signal. The flow meters were of course calibrated according to the current standard before installation. However, it was done

futureI&M continued trends in

Despite the fact that the tracer measurement was considered to be success, it could not be done regularly. It is extremely tricky to perform, expensive, and limited to one flow rate per test.

Third Action

Fig. 1. The pipes before the Venturi flow meter.

once, more than thirty years ago, and the full installation effect was not included. As you can understand, with the pipe installation shown in Fig. 1, combined with the discussion above, this is not an ideal condition for measuring one of the most important parameters in our plant. However, it could of course, be a delightful challenge for a measurement engineer! To gain control over the measurement uncertainty of our feed water flow, we have developed workarounds systematically.

First Action Within the power plant, we make regular calculations of the reactor core thermal power (heat balance). These calculations have shown each year that the measured levels of the flow were too low, a systematic error. Also, the physicists who calculate the fuels and their planned burnout cycle have indicated that, according to their calculations, the measured levels of the flow were probably too low.

Second Action A few years ago, my company, together with an accredited laboratory, made flow tracer measurements. They were done at two different times, with about a year in between. The tracer confirmed what we had thought: there was a systematic error of the Venturi. Since it is known that tracer measurements are reliable tests – ours were done twice – and have a low uncertainty, we were comfortable adjusting our Venturi flow meters to reduce the systematic error. By doing this, the plant could increase the production of energy by an extra ~67 MW. 30

My company has been following and supporting a European research project, where one goal was to find a way to predict the output of different flowmeters at higher temperatures than those used in their calibration. This was done by developing a model that described their temperature dependence mathematically. A spin-off effect of this project was that the researchers developed an asymmetric swirl generator, which simulates the installation effect of double 90° bends out of plane which normally generates the most serious systematic errors in flow metering, in other words, a swirl generator for the “worst case.” This swirl generator opened the possibility for us to make calibrations that include the installation effect without building a mock-up, and this convinced us to put their research into practice. Two years ago, in the summer of 2014, we installed two parts of 11-beam ultrasonic flow meters. The flow meters were calibrated in an accredited flow laboratory, using the same pipe diameters and the same flow rate as we do. The calibrations were made with both straight pipe length and with the swirl generator of the “worst case.” At present, we have two ultrasonic flow meters that control our Venturi in-situ, with a specified measurement uncertainty of ±0.25 % (compared to the initial uncertainty of ~±1%). This project has enabled us to soon make another adjustment of our Venturi and get an extra ~8MW thermal effect. As a side effect, both the tracer measurement and the measurement of the ultrasonic flow meters have resulted in an improvement of our temperature measurement of the feed water. To sum up, the three different methods above: calculation of the reactor core thermal power; flow tracer measurements; and in-situ measurement by ultrasonic flow meters have made us comfortable knowing that we were working in the right direction towards our goal to reduce the measurement uncertainty of the feed water flow. In total, all the adjustments of systematically reducing the systematic error have resulted in an extra income of ~6 million Euro per year. And at the same time, we have increased our safety margins and this without any reconstruction of the power plant! To achieve this “only” by reducing the measurement uncertainty must be in the future of measurement technology. Jenny Wirandi (jenny.wirandi@okg.uniper.energy) received the B.Sc. degree in Electrical Engineering from the University of Kalmar, Kalmar, Sweden, in 1997 and the Ph.D.

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degree in Electrical Measurement from the Lund University, Lund, Sweden, in 2007. After her B.Sc. degree in 1997, Jenny became an Instrument Engineer at the consulting firm Benima Sydost AB, Kalmar, Sweden, and in 2007, after the Ph.D. degree she joined the Blekinge Institute of Technology, Karlskrona, Sweden as a post-doc. In 2008, she joined Oskarshamn Nuclear Power Plant, Oskarshamn, Sweden, as a Systems Engineer in electrical and I&C engineering, and in 2011, she become the head of that subsection. Since 2013, she

April 2016

has been the Assistant Manager of Production Unit 3, in the same company. Jenny received the IEEE Instrumentation and Measurement Society’s “Outstanding Young Engineer Award” and the “Andy Chi Best Paper” in 2009 and 2010, respectively. Her research interests are modern measurement concepts and their applications to industry, including traceable calibration, measurement uncertainty, and the role of the operator in the measurement system

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Research Activities on Sensing, Instrumentation, and Measurement: New Zealand Perspective Subhas Mukhopadhyay

he research activities on sensors, instrumentation, and measurement in New Zealand are vibrant and dynamic. This report provides a glimpse of some of the research and development activities happening recently. The report’s basis is the papers presented at workshops of the IEEE IMS New Zealand chapter over the last few years, along with special emphasis on the activities of the Smart Sensing and Intelligent Systems Group (S2IS) of Massey University, New Zealand.

Introduction New Zealand has a land area of 267 710 km2 and is the home of only 4.7 million people. It has eight tertiary universities and a few crown research institutes. The country is quite vibrant in terms of research and development activities in the area of sensors, instrumentation, and measurements (SIM). Though the emphasis of research and development activities is on agriculture and environment, there are still many other research activities ongoing in the country. Sensors and instrumentation are the core of today’s engineering curriculum, because they are strongly cross-disciplinary and hence provide ideal subjects for today’s environmentally-aware students, and allow them to receive and contribute up-to-date knowledge of applications, technology, and solutions. There is also a need for interactions among researchers, scientists, engineers, and practitioners to discuss their research findings and activities. These research results became more visible to the wider community after the formation of the IEEE Instrumentation and Measurement Society (IMS) New Zealand Chapter in 2010 [1], [2]. The IEEE IMS New Zealand Chapter provides an exciting opportunity for the New Zealand researchers to report SIM related research activities in different technical workshops organized under the chapter.

I&M Research Activities in New Zealand Agriculture The population of the world is 7.3 billion (United Nations) and is expected to reach between 8.3 and 10.9 billion in 2050 32

(United Nations). The population of the world was ~1.6 billion in 1900 and ~6.0 billion in 2000. The total land area of the world being unchanged, more and more urbanization is reducing the effective land area for agriculture. To feed the growing population living on this planet, the productivity of high-quality food must increase with reduced waste. On top of that, the current agricultural system is too dependent on climate and too sensitive to climate change. To avoid loss of agricultural products and to adapt to changing climate, environmentally-friendly, energy-saving, reduced-waste, science based precision agricultural systems need to be developed and improved. The current agricultural system uses large amounts of water and many chemical fertilizers, which lead to wasted water and chemical contamination that leads to pollution of the environment. To address these issues, it is crucial know the optimum conditions, including water use and the nutrients needed for maximum growth of crops to increase productivity. To provide the right environment, there is a need for accurate and reliable measurement of many parameters and the ability to control those parameters. Thus, smart sensing technologies for precision agriculture have significant roles in achieving these objectives. The agricultural, forestry, and horticultural industries play a fundamentally important role in New Zealand’s economy, particularly in the export sector and in employment. Overall, the primary sector directly accounts for 6.0% of real GDP and contributes over 50% of New Zealand’s total export earnings. Currently, the irrigation system in New Zealand does not include high performance sensors. The net effect of employing smart sensors in the agricultural field will be a significant increase in New Zealand’s primary sector productivity and sustainability. For example, if implemented, a soil moisture sensor in variable rate irrigators may reduce water usage by at least 12%, resulting in productivity gains of US$109M per annum. Savings from avoiding nitrogen leaching could amount to US$300/ha per annum per year. Many universities and research centers are working on the development of sensors to address agriculture, especially precision agriculture.

April 2016

Fig. 1. Sensors developed at Massey University, New Zealand. (a) FR4 based; (b) Silicon MEMS based; (c) Glass based.

Auckland University The sensors and instrumentation research activities in Auckland University has spread over different departments such as Electrical Engineering, Mechanical Engineering, Bioengineering Institute, and so on. A few research activities are listed here: ◗◗ Sensing and measurement techniques to detect and monitor trace gases. ◗◗ Detection and measurement of biological molecules, life function, and real time behaviour of biomolecules. ◗◗ 3D motion sensing for sports. ◗◗ Measurement of bone quality using nanoindentation. ◗◗ Health impacts from vehicle emissions (http://www. engineering.auckland.ac.nz/en/about/our-research/ iande-research-theme/vehicle-air-pollution.html). ◗◗ Monitoring of life function. ◗◗ Wearable hand power assistive devices. ◗◗ Flexible strain sensor for air muscles using polypyrrole coated rubber. ◗◗ Characterization of ionic polymer metallic composites as sensors in robotic finger joints. ◗◗ Electronics for artificial muscle energy harvesting. ◗◗ Soft sensing for underwater applications. ◗◗ Shear strain sensor for improved robot and human dexterity. ◗◗ Wireless Sensor Networks and Internet of Things. ◗◗ Electrochemical investigation of surface attachment chemistry via carbodiimide coupling. ◗◗ Irradiation angle sensors. ◗◗ Semiconductors oxide as gas sensors. ◗◗ Assessment technique for measuring ankle orientation and stiffness. ◗◗ Robotic wrist orthosis for joint rehabilitation.

Lincoln Agritech Ltd. Lincoln Agritech Ltd. – Science for Agriculture, Industry and the Environment – has researchers who are working on different moisture sensors to determine the right amount of water use during farming. They are also working on the development of different biosensors to determine nutrients April 2016

levels in soils and alternative pH measurement. A few other research activities are time domain reflectometry for moisture measurement and imaging, structural sensing using optical fibers and radar applications for remote sensing, stochastic modelling for use in digital signal processing applications and so on.

The Auckland Univ. of Technology Research The researchers at the Auckland University of Technology (AUT) are working on different aspects of sensors and instrumentation, namely wireless sensor networks based monitoring of crop growth, characterizing and quantifying vegetation biophysical and biochemical features using remote sensing, software systems design and development, numerical computing and data modelling and so on. The research activities of AUT are quite wide in depth and coverage, and a few of them include wearable hand power assistive devices, semiconducting oxides as gas sensors, machine learning including fuzzy logic, neural networks and decision trees, distributed software agents and intelligent environment applications, embedded sensor and ad-hoc networks and human-computer interaction.

The University of Otago The University of Otago focuses on routing protocols and scheduling polices for reliable real-time industrial communication, low-power reliable data communication in body sensor networks, efficient routing in delay tolerant networks, and traffic classification and scheduling in software-defined networks.

The University of Canterbury Research activities of the University of Canterbury includes micro and nano-fabrication, microfluidics, bio-nanotechnology, lab-on-a-chip, wireless communications/radio communications, precision agriculture, enhanced primary sector productivity and food, security, emergency response, assisted living, technology to support home care for the aged and improved health, smart cities, sensors, body area networks,

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Fig. 2. Molecular Imprinted Polymer-based detection of DEHP molecules. The polymer is synthesized. Target molecules are extracted from the polymer; sensor is dipped in contaminated water to test DEHP.

animal tracking and trap monitoring, protection of the natural environment and biodiversity.

Victoria University of Wellington Victoria University of Wellington research involves detecting harmful molecules in the environment, wireless sensor networks powered by ambient energy harvesting, wireless communication networks in extreme environments, and applied cognitive systems, essentially how to use inspiration from natural intelligence to enable computers/machines/ robots to behave usefully. This includes: cognitive robotics, learning classifier systems (a branch of evolutionary computation), modern heuristics for industrial applications, nanofabrication, micro- and nano-scopy, micro-and macroelectrical and mechanical interfaces.

Smart Sensing, Instrumentation, and Intelligent Systems The Smart Sensing and Intelligent Systems Group (S2IS) of Massey University, New Zealand is engaged in the design and development of different types of sensors and intelligent systems towards augmenting the wellness of humans. Fig. 1 34

shows the research works on sensors to develop novel types for different applications such as the quality of dairy products and leather [3], monitoring nitrate content in water [4], phthalate contamination in water [5], quality of saxophone reeds [6], etc. Other ways of making intelligent systems is by using sensors available in the markets, where developing a smart sensing system requires the design of proper instrumentation circuits that have a major function [3]. The fabricated sensors shown in Fig. 1 can be made very sensitive with state of the art microelectromechanical systems (MEMS) based fabrication techniques and have been used to determine many parameters such as contamination in water [4], [5]. The challenging part is to induce a selective property to the sensor such as a molecular imprinted polymer (MIP) based coating material that has been developed to trap Di-(2-ethylhexyl) phthalateÂ (DEHP) molecules contaminated in water [5], as shown in Fig. 2. The phthalate molecules contaminate water due to leaching from plastic bottles. Developing a Wireless Sensor network (WSN) based smart home for assisted living is an important area of research to provide a safe, sound and secured living environment for residents, especially people living alone at home. With the help of

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April 2016

Fig. 3. Wireless sensor network-based smart home for assisted living.

smart sensors and instrumentation, as shown in Fig. 3, it is possible to convert an old home to a smart home [7], [8]. A home built in 1938 has been converted to a smart home using intelligent sensors and instrumentation technology. The smart home can provide an analytical value of the wellness level of the person living alone at home [7]. The wearable sensors may also be added to the home monitoring system if the person can benefit from monitoring physiological parameters [9]. Environmental monitoring needs attention on not only the design and development of smart sensors and instrumentation, but also on energy harvesting, installation of sensing systems and accessibility of data through wireless communication. An Internet of Things (IoT) and WSN based fire and gas leakage monitoring system has been developed as shown in Fig. 4. The system can be very useful to tourists if IoT based monitoring systems are made available with all of the latest information.

Research on Applications of Sensors and Instrumentation Some other works include different applications of sensors and instrumentation to measure properties and performance of different systems [9], [10]. We provide a short description of a few research projects currently ongoing.

Early Detection of Osteoporosis Research on the design and development of smart sensors and associated instrumentation related to the detection of osteoporosis is under consideration. A label-free, non-invasive sensing system for the detection of C-terminal telopeptides of Type-I April 2016

collagen (CTX-I) was developed. The sensing system is based on smart, interdigital sensors functionalized by immobilizing streptavidin agarose on the sensing area to introduce selectivity for the antigen-antibody solution. The current investigation is on the development of a molecular imprinted polymer (MIP) to detect Type-I collagen in blood and then in urine for domestic use.

Smart Homes for Elder Care Requirements of the sensors for making a smart sensor network have been investigated. The aim of this research is to develop a smart home for elderly people who need to be constantly monitored for health and safety reasons. Many elderly people dread the idea of being forced to live with their adult children, or in a rest home, or in other sheltered living arrangements. They want to live independently and keep control of their own lives. Yet, at the same time, they know there is a high risk of injury or even death because of a fall or stroke. With the population aging in most developing countries, there will be more and more elderly people living alone in the future. Such people need to be monitored continuously and provided with immediate medical help and attention when required. The cost of hospitalization is ever increasing, and so is the cost of rehabilitation after a major illness or surgery. Hospitals are looking to send people back home as soon as possible to recuperate. During this recovery period, several physiological parameters need to be continuously measured. Hence, telemedicine and remote monitoring of patients at home are gaining added importance and urgency. The current research is to develop a model to predict the wellness of the person.

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Fig. 4. With the help of wireless sensors networks, it is possible to detect smoke and fires as in the first two photos, and they can provide early warning. The bottom middle photo shows the sensor node to detect smoke and fire. The bottom left shows the structure of one network using the Internet. The top right diagram is of a wireless sensor network-based environmental monitoring system.

A wearable physiological monitoring device has been developed as part of a smart home to monitor physiological parameters (such as temperature, heart rate, and falls) of a patient. The system consists of an electronic device, which to be worn on the wrist and finger by an elderly or at-risk person. The system can be used by normal person as well for the monitoring of physiological parameters. Using several sensors to measure different vital signs, the person is wirelessly monitored within his own home in a smart home. An accelerometer has been used to detect falls. The device can monitor the stressed condition of the person and sends an alarm to a receiver unit that is connected to a computer. This sets off an alarm, allowing help to be provided to the person. Since no vision sensors (camera or infrared) are used, the system is non-invasive, respects privacy, and will find wide acceptance. The system can be used in combination with the bed sensor (part of the home monitoring system) to monitor the person during the night. The goal of wellness sensors networks research is to develop a new protocol of wireless sensor networks, which will 36

be applicable in a smart home environment to provide a safe, sound and secured living environment for the inhabitant. The developed protocol will quantitatively provide (forecast) wellness of the inhabitant. The protocol will be a reliable, flexible, efficient, and real-time system and will have improved performance over the current existing IEEE 802 standard-based protocols. The preliminary work has provided exciting results.

Selective Electrochemical Sensing of Phthalates A real time, non-invasive system to detect phthalates (that are known to cause birth defects or reproductive harm in test animals) in spiked aqueous samples is under consideration. It will employ an electrochemical impedance spectroscopy (EIS) technique that incorporates a novel interdigital capacitive sensor with multiple sensing thin film gold micro-electrodes fabricated on a native silicon dioxide layer grown on a semiconducting single crystal silicon wafer. The sensing surface is functionalized by a self-assembled monolayer of 3-aminopropyltrietoxysilane (APTES) with embedded MIP to introduce

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selectivity for the DEHP molecule. Various concentrations (1 to 100 ppm) of DEHP in deionized MilliQ water were tested using the functionalized sensing surface to capture the analyte. A frequency response analyzer (FRA) algorithm was used to obtain impedance spectra to determine sample conductance and capacitance for evaluation of phthalate concentration in the sample solution. A spectrum analysis algorithm interpreted the experimentally obtained impedance spectra by applying complex nonlinear least square (CNLS) curve fitting to obtain an electrochemical equivalent circuit and corresponding circuit parameters describing the kinetics of the electrochemical cell. Principal component analysis was applied to deduce the effects of the surface immobilized molecular imprinted polymer layer on the evaluated circuit parameters and its electrical response. The results obtained by the testing system were validated using a commercially available high performance liquid chromatography diode array detector system [5].

In mirror therapy, the patient is seated comfortably with the robot positioned on the hand side of their sagittal plane. The base station for the motion capture system (Polhemus Patriot) is placed in the mirror position to the robot, to the right of the participant’s sagittal plane. A computer monitor with webcam, for the mirror feedback, is placed on a desk in front of the participant. Mirror therapy rehabilitation has been in development since the early 1990s and is proving to have a significant effect on the recovery of stroke patients. These systems also reduce the required input from therapists, thus allowing a single therapist to treat several patients at a time.

Multi-Axis Robotic Plasma Cutting This project will develop the required kinematics and a control algorithm for a multi-axis robotic head. A five-axis plasma cutting machine provides value by reducing the number of steps in the fabrication process of the head. This improves throughput and provides potential to improve quality and precision.

Detection of Nitrates in Natural Water Sources A novel sensor based on the combination of meander and interdigital planar electromagnetic sensors has been developed for monitoring the level of contamination in water sources. Two nitrate forms, namely sodium nitrates and ammonium nitrates of different concentrations between 5 mg and 20 mg dissolved in one liter of distilled water, were used to observe the sensor response. Initial results show that the sensor can acceptably detect the presence of nitrate in any samples as shown from the calculation of complex relative permittivity. Furthermore, the sensor response seems to be independent of pH in the pH range of 2 and 9. Water samples taken from various sources and locations have been tested with the sensor, and the results, when compared with the results obtained using nuclear magnetic resonance (NMR), show a good correlation of the interdigital output with the total amount of organic materials where the ionic strength of the water sample was also estimated. The work and improvements are under consideration.

Alternative Energy

Tree Pruning Robots

Conclusion

This research will study robotic systems design and kinematics of anti-falling mechanism for tree pruning, identify the problems and propose a possible solution for a tree pruning robotic system design.

This report provides a glimpse of some of the current research in the area of sensors, instrumentation and measurement in New Zealand, especially focusing on the research activities of the S2IS group at Massey University. It is not easy to describe all of the research activities, but it provides some useful information of the overall outline of different activities currently ongoing. Detailed research outcomes are reported in various publications of the IEEE and other organizations sharing knowledge with wider communities.

Robotic Rehabilitation Systems This research proposes a novel system for stroke rehabilitation, combining proven robotic therapy and mirror therapy techniques. Stroke is the result of a sudden disruption of the flow of blood in the brain, due either to a blood clot or hemorrhage, which damages the neural pathways in the brain of the sufferer. Stroke is a major cause of disability throughout the world. Mirror therapy is used to treat complex regional pain syndrome in amputees and stroke patients, and has also proven effective in the recovery of motor function in stroke patients. April 2016

Multi-Input power electronics circuitry for integration of renewable energy is in demand. The need for more electric power has increased significantly in recent years. To keep the demand and supply chain at a balanced position, generation of more power is required. The conventional methods of power generation using fossil fuels have lost their utility due to the emission of greenhouse gases, depletion of fossil fuels and increase in fuel prices. Deregulation on the part of power authorities has also played a vital role in the identification and utilization of Distributed Energy Resources (DER). DER has the potential to accommodate both renewable and non-renewable forms of energy, such as Photo voltaic, wind, bio gas, micro turbines and fuel cells to improve power system stability, energy efficiency, capability, reliability and security. Current research investigates alternate energy generation sources and integration of their multi source operation.

References [1] I. Woodhead, “IMS in New Zealand,” IEEE Instrum. Meas. Mag., vol. 17, no.5, pp. 48–50, 2014. DOI: 10.1109/MIM.2014.6912203. [2] A. Taberner, “IEEE IMS New Zealand chapter report,” IEEE Instrum. Meas. Mag., vol. 18, no. 5, pp. 42 - 43, 2015. DOI: 10.1109/ MIM.2015.7271227.

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[3] S. C. Mukhopadhyay, Intelligent Sensing, Instrumentation and Measurements, Smart Sensors, Measurement and Instrumentation Series, vol. 5. Berlin, Germany: Springer-Verlag Berlin Heidelberg, Apr. 2013. DOI: 10.1007/978-3-642-37027-4. [4] X. Wang, Y. Wang, H. Leung, S. C. Mukhopadhyay, M. Tian, and J. Zhou, “Mechanism and experiment of planar electrode sensors in water pollutant measurement,” IEEE Trans. Instrum. Meas., vol. 64, no. 2, pp. 516-523, Feb. 2015.

for assisted living,” IEEE Sensors Journal, vol. 15, no. 12, pp. 73417348, Dec. 2015. [9] K. Kaur, S. C. Mukhopadhyay, J. Schnepper, M. Haefke and H. Ewald, “A ZigBee based wearable physiological parameters monitoring system,” IEEE Sensors Journal, vol. 12, no. 3, pp. 423430, Mar. 2012. [10] C. Ranhotigamage and S. C. Mukhopadhyay, “Field trials and performance monitoring of distributed solar panels using a low

[5] A. I. Zia, S. C. Mukhopadhyay, P.-L. Yu, I. H. Al-Bahadly, C. P. Gooneratne, and J. Kosel, “Rapid and molecular selective

cost wireless sensors network for domestic applications,” IEEE Sensors Journal, vol. 11, no. 10, pp. 2583-2590, Oct. 2011.

electrochemical sensing of phthalates in aqueous solution,” Biosensors and Bioelectronics, vol. 67, pp. 342-349, May 2015. [6] S. C. Mukhopadhyay, J. D. M. Woolley, G. S. Gupta and S. Demidenko, “Saxophone reed inspection employing planar electromagnetic sensors,” IEEE Trans. Instrum. Meas., vol. 56, no. 6, pp. 2492-2503, Dec. 2007. [7] N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless sensor network based home monitoring system for wellness determination of elderly,” IEEE Sensors Journal, vol. 12, no. 6, pp. 1965-1972, Jun. 2012. [8] H. Ghayvat, J. Liu, S. C. Mukhopadhyay and X. Gui, “Wellness sensor networks: a proposal and implementation for smart home

Dr. Subhas Chandra Mukhopadhyay (M’97, SM’02, F’11) currently is working as a Professor of Sensing Technology with the School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand. His fields of interest include sensors and sensing technology, instrumentation, wireless sensor networks, etc. He is a FIEEE (USA), a FIET (UK) and a FIETE (India). He is a Topical Editor of IEEE Sensors Journal, an Associate Editor of IEEE Transactions on Instrumentation and Measurements. He chairs the IEEE IMS TC 18 on Environmental Measurements.

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April 2016

Higher Education of Measurement Control and Instrumentation Specialty in China Aiguo Song and Lihui Wang

n China, attention to the education of measurement control and instrumentation has increased since 1997, when the Measurement Control and Instrumentation Specialty was redefined and approved by the Ministry of Education of China. The Measurement Control and Instrumentation Specialty is a broad-range discipline involved with optics, mechanics, electronics and computer technology. In this paper, we introduce the specialty-training goal and curriculum for undergraduate students. We selected the Southeast University in China as an example to illustrate the education of measurement and instrumentation for undergraduate students.

History of Specialties Related to Instrumentation Instrument science and technology is the basis of scientific research and industry, agriculture, building, traffic, medicine, astronomy, military affairs, etc. In 1953, Chinaâ&#x20AC;&#x2122;s government made a large scale adjustment to its university system. Some universities and colleges in China began to set up instrumentation specialties from 1953 to 1960, such as the Precision Machinery Instrumentation Specialty in Tianjin University, Tsinghua University, Harbin Institute of Technology, and Hefei University of Technology; Optical Instrumentation Specialty in Zhejiang University, Changchun University of Science and Technology, Beijing Institute of Technology; Navigation Instrumentation Specialty in Southeast University, Shanghai Jiao Tong University; and the Aerospace Instrumentation Specialty in Beijing University of Aeronautics and Astronautics and Nanjing University of Aeronautics and Astronautics. Since 1966, thirty universities and colleges have developed departments that focus on more than eleven kinds of instrumentation specialties. These instrumentation specialties are classified into four types of instruments: mechanical instrumentation, optical instrumentation, electronic measuring instrumentation, and control instrumentation [1], [2]. Along with the rapid development of electronics and computer technology since 1990, however, the measuring approach and instrumentation design have changed greatly, which has resulted in fundamental changes in the instrumentation specialty. April 2016

Since 1997, the Ministry of Education of China reformed all of the specialties in universities and colleges under the principle that these areas should be general education rather than professional education and redefined this area as one specialty, called the Measurement Control and Instrumentation Specialty [3]. It was developed into a broad-range discipline involved with optics, mechanics, electronics and computer technology, and has integrated optics, mechanics, electronics and computer engineering. As shown in Fig. 1, the number of programs offering education in measurement and instrumentation has increased greatly since 1997 in China. In 1996, there were only 36 universities and colleges that had set up the instrumentation specialty for undergraduates in China, increasing to 80 in 1997, 122 programs by 2000, 199 by 2005, and 263 by 2010. The total number of enrolled undergraduate students in this specialty in China increased from 59,800 in 2005 to 86,500 in 2010, with an increase of 44%. Currently, 297 universities and colleges have this specialty, and the total number of the enrolled undergraduate students in this program in China is about 96,000 [4], [5].

Subjects in Measurement Control and Instrumentation Specialty Because the Measurement Control and Instrumentation Specialty is a broad-range discipline involved with optics, mechanics, electronics and computer technology, programs at the

Fig. 1. The development of the Measurement Control and Instrumentation Specialty in China universities since 1996.

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team composed of different roles. ◗◗ Target 3. Have good accomplishments and moral standards. ◗◗ Target 4. Competitively obtain employment in the field of measurement control and instrumentation, or have the ability to enter graduate level studies. ◗◗ Target 5. Be capable of expanding their knowledge and ability through Fig. 2. The core knowledge of the Measurement Control and Instrumentation Specialty. graduate school. various colleges and universities are also different. Some uni◗◗ Target 6. Have the willingness and ability to serve the versities focus on mechanical measurement, some focus on society. optical measurement, and some focus on the electronic inforSpecialty Curriculum at SEU mation, etc. After more than five years of discussion, the Instrumentation The curriculum in the Measurement Control and InstrumentaEducation Steering Committee of China, which is the association tion Specialty at SEU consists of foundation courses for general of universities and colleges who have provided this instrumen- education, basic professional courses, specialized courses and tation education program, described this Measurement Control optional courses. Fig. 3 shows the experimental platform of the and Instrumentation Specialty as an information acquisition Measurement Control and Instrumentation Specialty. Foundation courses for general education mainly include and application specialty. That is to say: no matter what kinds of instrumentation are the focus, they are just tools for information math and physical knowledge, which is necessary learning for acquisition in nature. Therefore, the Measurement Control and subsequent professional courses. Foundation courses for genInstrumentation Specialty belongs to information technology. eral education that total 60.5 credits include: ◗◗ ideology, foreign language, math and physics, computer, The Instrumentation Education Steering Committee of China art and social science, for example, and recommends core knowledge of the education of instrumenta◗◗ courses titled: Morals and Ethics and Fundamentals of tion based on the information flow shown in Fig. 2. Law, Physical Education, College English, Advanced Training Goals at SEU English, Programming and Arithmetic Language, CalcuSoutheast University (SEU) is one of the national key univerlus (A) and general education in arts and social sciences. sities administered directly under the Central Government Professional basic courses that total 30 credits include the and the Ministry of Education of China. It is also one of the common theory base of the measurement control and instruuniversities of Project 211 and Program 985 that are financed mentation specialty. Professional basic courses include the by the Central Government to build a world-class univer- following: sity. Southeast University has become a comprehensive and ◗◗ Instrument Science and Technology Studies (seminar), research-oriented university that features the coordinated de◗◗ Fundamentals of Circuit Analysis, velopment of such multi-disciplines as science, engineering, ◗◗ Computer Architecture and Logic Designing (Bilingual), medicine, literature, law, philosophy, education, economics, ◗◗ Engineering Mechanics B, management, art, etc., with engineering as its focus. ◗◗ Fundamentals of Electronic Circuits, Signals and Systems, Expected goals of the graduates in the Measurement Con◗◗ Micro-computer System and Interfaces (Bilingual), trol and Instrumentation specialty who have earned the ◗◗ Engineering Optics, bachelor degree in approximately five years are as following: ◗◗ Automatic Control Principles, and ◗◗ Target 1. Proficiency in measuring information acquisi◗◗ Introduction to Information Communication Networks. tion and preprocessing, analysis and design of the control system and related hardware and software development Specialized courses total 18.5 credits and include: skills. Be able to design systems of measuring and control ◗◗ Engineering Graphics and CAD, technology and instruments. Be capable of putting ◗◗ Sensor Technology (English), forward instrument system solutions and application ◗◗ Elementary Precision Machinery Design, systems according to the requirements of the project. ◗◗ Signal Analysis and Processing (Bilingual), ◗◗ Target 2. Can be a core member or leader and undertake ◗◗ Intelligent Instrument Design, a certain professional field of work independently in a ◗◗ Theory of Errors and Data Processing (English), 40

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Fig. 3. The experimental platform of Measurement Control and Instrumentation Specialty.

◗◗ Navigation Positioning Control and its Application, ◗◗ Advanced Control Theory, and ◗◗ Virtual Instrument Technology (seminar). Ten credits in optional courses that aim to broaden the scope of knowledge of students in one area include: ◗◗ Mechatronics Systems Design, ◗◗ E l e c t ro m a g n e t i c C o m p a t i b i l i t y P r i n c i p l e s a n d Applications, ◗◗ Image Processing, ◗◗ Fundamentals of VLSI Design, ◗◗ Power Electronic Technology, ◗◗ Data Structure, ◗◗ Communication Theory, ◗◗ Navigation Systems, ◗◗ Database Technology and its Application (seminar), ◗◗ Virtual Reality and Data Visualization (Bilingual) (seminar), ◗◗ Pattern Recognition (Bilingual) (seminar), ◗◗ Intelligent Vehicle Technology (seminar), April 2016

◗◗ Computer Aided Design and Simulation Technology (Bilingual) (seminar), ◗◗ Foundation of Computer Networks and Applications (Bilingual) (seminar), ◗◗ Photoelectric Detection Technology (seminar), ◗◗ Mechanical and Electrical Integration (seminar), ◗◗ MEMS (seminar), ◗◗ BioMEMS (seminar), ◗◗ GNSS Receiver Technology Development (Bilingual) (seminar), ◗◗ Satellite Navigation Positioning Technology and its Application (Bilingual) (seminar), ◗◗ Information Navigating Systems (seminar), ◗◗ Control Technology and Systems (Bilingual) (seminar) , ◗◗ Innovative Design of Robots (seminar), ◗◗ Robot Vision Measurement and Control Technology (seminar), ◗◗ Wireless Sensor Networks and Application (seminar), and ◗◗ Innovative Design of Modern Instruments (seminar).

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[3] Catalogue of Engineering Specialties, The Ministry of Education of China. Beijing, China: China Higher Education Press, 1997. [4] “Summary of instrumentation education steering committee of China (2006-2010),” Higher Education Department, The Ministry of Education of China, Part 1, 2011. [5] “Report of instrumentation discipline development study,” Instrumentation Education Steering Committee of China, 2015. [6] A. Song, J. Wu, and J. Cui, “Engineering training and innovation education of undergraduates in measurement control and instrumentation specialty,” China University Teaching, no. 1, pp. 41-43, 2012.

Fig. 4. Sensor course experiments of students majoring in Measurement Control and Instrumentation at Southeast University, China.

Extracurricular Practice Training Extra engineering practice increases the abilities of students majoring in Measurement Control and Instrumentation. SEU has developed scientific research projects (3 compulsory credits) in the process of talent training, which not only cultivates practical creative ability, but also promotes the scientific spirit, scientific attitude, scientific morality and scientific methods of students, even though some students will not do research work after graduating [6]. Figs. 4 and 5 show students at SEU involved in classroom extracurricular projects related to the coursework in the specialty.

Summary Education of Measurement Control and Instrumentation in China has developed quickly, owing to its wide application in different fields. In this paper, we introduce the specialty training goals and curriculum system for undergraduate students of the Measurement Control and Instrumentation Specialty. We used the program at Southeast University in China as an example to illustrate the education structure for undergraduate students, which is according to the national development demand and requires a solid professional foundation, a strong practical ability, and moral, intellectual, physical, and allround development. The program at SEU aims to cultivate students to become highly trained professionals and technical personnel. The program trains students who can engage in scientific research, technology development, engineering design, operational management and teaching, in sensor and testing technology, intelligent instruments, measurement and control systems and other technical fields.

Aiguo Song (song@seueducn) received the B.S. degree in Instrument Control in 1990, the M.S. degree in Measurement and Control in 1993 from Nanjing Aeronautics and Astronautics University, Nanjing, China, and the Ph.D. degree in Measurement and Control from Southeast University, Nanjing, China in 1996. From 1996 to 1998, he was a researcher with the Intelligent Information Processing Laboratory, Southeast University, China. He was an Associate Professor from 1998 to 2000 and a Professor and Director of the Robot Sensor and Control Lab, Southeast University, China from 2000 to 2003. From 2003 to 2004, he was a visiting scientist with the Lab for Intelligent Mechanical Systems, Northwestern University, Evanston, IL, USA. He is currently the Dean of the School of Instrument Science and Engineering, Southeast University, a member of the Chinese Instrument and Control Association, and a member of the Instrumentation Education Steering Committee of China. Lihui Wang received the B.S. degree, the M.S. degree in Automatic Control and the Ph.D. degree in Precision Instrumentation from Harbin Engineering University, Harbin, China in 2002, 2005, and 2009, respectively. From 2009 to 2011, he held a post-doctorate position with the School of Electrical Engineering, Southeast University, China. He is currently the Vice Dean of the School of Instrument Science and Engineering, Southeast University, China.

References [1] “Report of instrumentation discipline development study,” Instrumentation Education Steering Committee of China, 2004. [2] A. Song and Y. Kuang, “Research on the cultivating system reformation of measurement control and instrumentation specialty for undergraduates,” Research in Higher Education of Engineering, no. 1, pp. 48-51, 2005. 42

Fig. 5. Extracurricular robot research projects of students majoring in Measurement Control and Instrumentation under teachers’ guidance at Southeast University, China.

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lifegraduation after

J. Max Cortner

Who’s the Boss?

xcept for those engineers who are self-employed, everyone has a boss. Most institutions have a welldefined reporting structure consisting of numerous layers of managers with titles indicating their level. Engineers report to supervisors, supervisors report to managers, managers to directors, directors to vice presidents, and so on. From the 1950s when Peter Drucker pioneered management science, corporations have shifted the size and shape of this triangle of management to fit the best available theory. The number of people in each reporting layer is called the “span of management,” and each corporation attempts to keep it within a range that indicates the number of people a given individual can manage. The functions of management include vision and mission communication, goal setting, and performance evaluation. An engineer should expect the boss to explain her role in the department and how it relates to the corporate vision and mission. Often, this local interpretation is indistinct as it is mixed with the functional role of the department. For example, if the company manufactures weather radar systems, there may well be an engineering team whose job is to design digital integrated circuits to control the units. The skills of a digital IC designer don’t seem to relate to radar specifications such as range and resolution directly. Some organizations practice matrix management in which project managers assemble cross functional teams of engineers who report to different functional managers. Project managers assign tasks to match skills and monitor progress. They provide the functional manager with feedback on the performance of team engineers. Then (typically once a year) the functional manager rates the engineers on the team based on how well they meet the goals. In most cases, there are other subjective criteria such as teamwork, innovation, and initiative. The ratings result in salary raises and promotions. The annual review meeting between manager and engineer is a an awkward one way discussion in which the boss tries to rationalize the rating and the

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engineer hears all the negatives. In more than 40 years of experience, I have worked as an engineer and as a manager. I’ve been on both sides of this table. What does an engineer want from the boss? They want a clear definition of success. What are you expected to do? How fast are you expected to do it? What defines good results for which you can expect to be rewarded? You want development support to constantly improve your performance and you want opportunities. Everyone enjoys a raise, but it’s the opportunity to perform interesting engineering that really motivates. There are certainly other professions that have higher average annual incomes, and engineers are smart enough to have chosen one of those if they were solely interested in monetary reward. So when the annual review discussion with the boss happens, you as an engineer should participate as fully as your manager. Get feedback from your boss including examples of the performance he or she used to rate you. Ask what you should have done, and ask for the development support to improve your skills. If your manager doesn’t talk about your next assignment and your career direction, ask! Tell the manager what you like to do, and ask for assignments that use your skills as much as possible. Although the focus of most managers is directing work and rating their employees, the best managers seek long term results by addressing the real needs of their engineers. The best bosses get to know their engineers’ interests as well as their talents. For any given project, the assignments of all team members can’t be optimized. But with an understanding of your interest, it is more likely that the boss will assign you to the task you want. In a matrix organization, your boss can guide you to projects that match your interest. These opportunities will allow you to shape your own career as well as set you up for performance success and a promotion. It’s up to you to be the boss of your career! You may contact Mr. Cortner at Max.Cortner@bsci.com. His bio is available at http://ieee-ims.org/contacts/max-cortner.

Spectrum Sensing Challenges: Blind Sensing and Sensing Optimization Mohamed Hamid, Slimane Ben Slimane, Wendy Van Moer, and Niclas Björsell

y any measure, wireless communications is one of the most evolving fields in engineering. This, in return, has imposed many challenges, especially in handling the hunger for higher data rates in the next generation wireless networks. Among these challenges is how to provide the needed resources in terms of the electromagnetic radio spectrum for these networks. In this regard, cognitive radio (CR) based on dynamic spectrum access (DSA) has been attracting huge attention as a promising solution for more efficient utilization of the available radio spectrum. DSA is based on finding and opportunistically accessing the free-of-use portions of spectrum. To facilitate DSA, spectrum sensing can be used. However, spectrum sensing faces many challenges in different aspects. Such aspects include blind sensing and sensing optimization, which are both to a great extent measurement challenges. We discuss different contributions in addressing these two challenges in this article.

Introduction Since their appearance in the beginning of the 19th century, wireless communications have been continuously evoving and advancing. As of today’s daily life, communicating wirelessly is one of the essential needs. Today’s wireless landscape is ranging from networks that cover thousands of kilometers to networks used to transfer signals within human bodies. Within all of these wireless networks, huge amounts of information are exchanged as different measurements and predictions show an exponential growth for the mobile data traffic. Therefore, a fundamental question presents itself, that is: How much longer will we be able to handle this growth? On the abstract level, this is an issue of a growing demand that has to be met with more resources. One of the essential resources when considering wireless networks is the usable electromagnetic radio spectrum. Currently, different portions of the spectrum are exclusively licensed for specific wireless service providers. However, this exclusive access licensing has led to an inefficient utilization of the radio spectrum since most 44

of the licensed bands remain underutilized [1]. Hence, better utilization of our radio spectrum can contribute to boosting the data rates in wireless networks. Among the efforts taken by regulators worldwide to achieve better usage of the spectrum is the introduction (promotion) of secondary operation in the licensed spectrum [2]. This promotion of secondary markets, together with the rapid evolution of the software defined radio (SDR) techniques, have paved the road towards cognitive radio (CR) systems [2]. CR systems were first introduced by J. Mitola in 1999 [3]. Generally, CR refers to a radio device that has the ability to sense its radio frequency (RF) environment and modify its spectrum usage based on what it detects [2]. One of the CR features is dynamic spectrum access (DSA). With DSA, a license exempt user called the secondary user (SU) opportunistically accesses a portion of spectrum that is not used by its licensed user, known as the primary user (PU) [1]-[5]. To adopt DSA, an SU needs to locate the usable spectrum that is called a spectrum hole [2]. According to the literature, one of three approaches can be used to find spectrum holes [4]: spectrum sensing (which is also called signal detection), geo-location databases, and beacon signals. Throughout this paper, sensing and detection are used interchangeably [2]. Geo-location databases and beacon signals are out of the scope of this article. With spectrum sensing, the SU scans across the usable spectrum to identify the spectrum holes using one of the spectrum sensing techniques [2]. Some of those techniques are briefly introduced in this article.

Challenges in Spectrum Sensing Challenges in the DSA arena can be categorized into business, regulatory, and technical challenges [5]. Regarding business and regulatory challenges, the model of DSA still lacks a lot of quantitative evaluation methodologies for many factors which include technology availability, infrastructure modifications, and deployment costs. Moreover, motivating the licensee operators to share their spectrum is a fundamental challenge [2]. All of these uncertainties have led to some skepticism from industry and regulators regarding DSA viability.

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Many aspects are involved in the technical challenges that are discussed the most in the literature. First, the impact of secondary operation on the PUâ&#x20AC;&#x2122;s performance is a challenge faced by DSA. Another technical challenge is the extent of scalability of the deployed secondary systems. In addition, developing sharing mechanisms that guarantee acceptable quality of services for not only PUs but also coexisting SUs is a big technical challenge in DSA [2]. Fetching and disseminating spectrum availability knowledge is a challenge that attracts most of the research within DSA. A preliminary challenge is to decide which approach Fig. 1. Challenges in DSA [2]. among spectrum sensing, geo-location database, or beacon signals to use and in which band [2]. Generally, DSA technical challenges branch in many directions. This paper focuses on the challenges in spectrum sensing as shown in Fig. 1 [2]. Even though this paper primarily addresses blind sensing and sensing optimization, the other examples that are included fall in the overlapping areas with other challenges either fully or partially, as depicted in Fig. 1. This article also summarizes the contributions included in [2].

Blind Sensing Either a PU signal bearing noise or noise only components can be present in a specific band. Therefore, a binary hypothesis framework can be stated as: : The null hypothesis denoting noise only existence. : The positive hypothesis that denotes signal bearing noise existence. The main task of spectrum sensing is to declare either or from the received signal [2]. To evaluate a sensing technique, two statistical performance measures are used, namely the probability of false alarm and the probability of detection. The probability of false alarm pfa is the conditional probability of wrongfully detecting a signalâ&#x20AC;&#x2122;s existence when only noise is present [6], [7]. On the other hand, the probability of detection pd is defined as the conditional probability of truly detecting an existing signal [6]. An extensive survey on proposed spectrum sensing techniques is available in [8]. Table 1 summarizes representatives of the spectrum sensing techniques with their requirements. April 2016

Different sensing techniques provide different degrees of reliability, which are proportional to their complexity. (Reliability is used here to reflect sensing accuracy in terms of the probabilities of detection and false alarm. Complexity can be used for either hardware or computational complexity [2]. In this article, sensing time is used as a quantitative measure for the computational complexity [2]). Moreover, from Table 1, it can be observed that different techniques require different amounts of information about the signal that may exist in the band under sensing. Hereafter, we call the amount of needed information about the existing signal the degree of blindness, where the less the needed information for a technique,

Table 1 â&#x20AC;&#x201C; Some proposed spectrum sensing techniques and their requirements [6] Sensing technique

Requirements

Energy detection (ED)

Noise variance.

Waveform sensing

Signal pattern in terms of preambles, pilot patterns, spreading sequences, etc.

Feature detection

Cyclic frequencies of the signal.

Matched filtering

PU signal features such as modulation scheme and pulse shaping.

Eigenvalues based sensing

Nothing.

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keeping in mind that ordinary filters change the Gaussian noise to a colored noise? How does the MME perform depending on the bandwidth of the white Gaussian noise and the whole detection bandwidth? To answer these two questions, descriptions follow of the investigations carried out.

Frequency Domain Rectangular Filtering

Fig. 2. Comparison of different sensing techniques regarding complexity, reliability, and blindness [2].

the blinder the technique is and vice versa. Fig. 2 places different sensing techniques in the complexity, reliability, blindness space. In general, information regarding PU signals is either not available or costly to obtain, especially when considering the frequency agility feature of SUs. Therefore, blind sensing is desirable, and it comes with the price of either less reliability or more complexity.

Issues on Maximum-Minimum Eigenvalue Detection Hereafter, maximum-minimum eigenvalue detection (MME) is considered a representative of eigenvalues based detection techniques. MME employs the minimum eigenvalue of the received sample covariance matrix to extract the noise energy. Then, this noise energy is compared to the highest energy in the signal spectra evaluated using the maximum eigenvalue of the received sample covariance matrix. According to the ratio between the highest and the lowest eigenvalue, signal existence or absence is declared. It is important to note that there are several other techniques used to blindly discern the noise from a measured signal, and the reader is directed to [9]â&#x20AC;&#x201C;[12] for more elaboration on these techniques. For MME, existence of white Gaussian noiseonly components in the frequency domain is a necessity [13]. Regarding these noise-only components, we need to answer these two questions: How do we handle filtering as a pre-process to limit our signal within the band under sensing, 46

With ordinary time domain filtering, the spectrum of received noise-only components is reshaped similarly to the filter transfer function. Therefore, the noise is no longer Gaussian and MME cannot be used [13]. To preserve the signal spectrum shape, a filter with a rectangular transfer function [14] that passes only the in-band signal and completely eliminates the out-of-band components is needed. To attain such transfer function, the use of frequency domain rectangular filtering (FDRF) is proposed in [14]. FDRF preserves the signal frequencyâ&#x20AC;&#x2122;s domain properties by slicing the spectrum into pieces, capturing the components that represent the band under sensing, generating the corresponding time domain signal from that piece by means of the inverse Fourier transform, and finally applying MME to the generated time domain signal. The remaining spectrum pieces are ignored. Fig. 3 illustrates the idea of FDRF [14].

Signal Bandwidth Impact on MME Performance A study derived by the preceding second question regarding the dependency of MME performance on the noise bandwidth is in [15]. This study uses Random Matrix Theory (RMT) to find the optimal ratio between the occupation bandwidth b and the detection bandwidth B. This ratio is denoted as ď ˘ as Fig. 4 shows. The findings of [15] are that an optimal detection is achieved when a signal that occupies half of the observation bandwidth is processed by MME. Fig. 5 shows simulation results for the influence of the ratio of the signal bandwidth to the observation bandwidth on the probability of detection for MME [15].

Fig. 3. Illustration of sub-band spectrum scanning using FDRF. The spectrum of the whole band (black) is divided into three parts (blue, pink and red). Each one represents one sub-band [14].

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spectrum sensing is called a spectrum discriminator (SD) [6], [16].

Tw o - S t a g e B l i n d Sensing

Fig. 4. Illustration of the signal bandwidth b and the detection bandwidth B [2].

Simplicity, Reliability and Blindness Tradeoffs Regarding the simplicity, reliability, and blindness tradeoffs, the contributions of [6], [16] and [17] are summarized by the following question: How can simplicity and blindness be gained simultaneously? Next, we present the responses to this question.

Discriminant Analysis-Based Blind Sensing Discriminant analysis statistical framework is usable to separate a mixture of two statistically independent sets [18]. Accordingly, with discriminant analysis, one can examine whether there exist two sets or only one set of collected observations [19]. Starting from the fact that noise exists everywhere in the electromagnetic radio spectrum, then, discriminant analysis can be used for spectrum sensing as follows. If a power spectra in a specific frequency band is found to contain one group of data, then that is noise only and no signal exists. Otherwise, the positive hypothesis of signal existence is declared, due to the existence of another group of data. Applying discriminant analysis into a power spectrum for the purpose of

Fig. 5. The probability of detection for MME for different values of observation or occupation bandwidth ď ˘ and different values of the SNR [15]. April 2016

As depicted by Fig. 2, reliability and simplicity are contradictory objectives. However, sliding over a range of a received SNR, one observes that the sensing reliability in terms of the probability of detection increases with the increase of the received SNR while the complexity is SNR independent. Hence, we can set an objective of gaining simplicity when high SNR signals are received and assuring reliable detection for the low received SNRs. This objective is not achievable using the same detector all of the time, and instead, one of two strategies can be employed. The first strategy is to switch among multiple detectors connected in parallel, depending on the received SNR [17]. This approach of parallel detectors implies having the SNR estimation phase assembled in the detection process [20]. The second alternative is to pass through a bank of detectors sequentially coupled. Both parallel and sequential approaches are referred to as multi-stage spectrum sensing [2], [17]. If blindness is considered for multi-stage sensing, then, with the unavailability of the information regarding the received signal, SNR estimation increases the complexity of the detection process. Therefore, sequentially connected detectors perform blind detection better concerning the simplicity-reliability objective [17]. In [17], a two-stage detector is proposed by using ED and MME. The detector proposed in [17] is called 2EMC standing for: 2-stages ED-MME Combined detector. ED is used for simplicity, which is the main concern in the first stage [17]. On the other hand, MME is used not only to perform reliable and blind second stage sensing, but also for noise estimation which is fed back to the ED, aiming at having a fully-blind detector. Fig. 6 depicts the flow in the 2EMC process. Besides providing full blindness in 2EMC, the MME ability to provide noise estimation is used for SNR estimation, as explained in [22].

Fig. 6. A schematic of 2EMC detection flow [17].

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Fig. 7. ED, MME, SD, and 2EMC comparison [2].

ED, MME, SD, and 2EMC Comparison Fig. 7 shows a general comparison among ED, MME, SD, and 2EMC. ED and MME represent the lower and upper bounds for the complexity. Concerning the blindness, ED requires a prior knowledge of the noise variance while the rest of the techniques are all fully blind. 2EMC has a higher reliability than SD and largely varying complexity depending on the received SNR. Generally, the choice between using ED, MME, SD or 2EMC depends on the requirements of the application to obtain the most appropriate tradeoffs [2] among sensing reliability, complexity and blindness.

Throughput Driven Sensing Optimization As the main motivation for DSA is to enhance the utilization of the radio spectrum, then spectrum sensing should be performed in a way that guarantees the highest possible throughput for SUs. Therefore, there is a need for answering the following question: How frequently does spectrum sensing need to be performed? To respond to this question, the periodic sensing is optimized with an objective of maximizing the SUs throughput that is explained later.

Opportunistic Channel Access Model

channel is sensed for a time called sensing time and denoted as ts. In the case of the null hypothesis , the SU starts to transmit on the channel, otherwise it senses another channel. The sensing is performed periodically with a period of T. The periodic sensing detects PU transmission reappearance on the channel. Moreover, in the case when no free channel is found, the sensing is resumed periodically, too [23]. As the sensing is performed periodically in discrete points in time, the following situations can be experienced: ◗◗ The sensing results in an occupied channel; however, the channel state changes from ON to OFF state one or more times within a period of T. Meanwhile, the SU misses a fraction of opportunities. ◗◗ The sensing outcome is the null hypothesis, , yet the channel state can change from OFF to ON state once or more within a period of T while the SU is utilizing it. Hence, during a fraction of T, both the PU and SU mutually use the same channel, and they generate mutual interference to each other [23]. Fig. 8 depicts the opportunistic channel access. The higher level of the binary representation is for the ON states, and the lower state represents the OFF states [23].

Generalized Sensing Optimization Framework According to the opportunistic channel access model illustrated by Fig. 8, the SU experiences two classes of throughput: when it is transmitting in the interference free instances and when it experiences interfered transmission due to mutual operation with the PU. Hence, the throughput during the whole operation time, denoted as C [23], is the weighted average of the interfered and interference free throughputs. The mathematical derivations in [23] show the relation between C and the periodic sensing interval T. Therefore, the optimal periodic sensing interval is set to maximize the throughput C.

Case Study: Downlink Cognitive LTE Femto-cells In the context of handling the demands of higher data rates in cellular networks, network operators are moving towards a more distributed network architecture [23]. In this regard, the third generation partnership project (3GPP) framework standards support deployment of low power reduced scale plug and play access points known as femto-cells [24]. Femto-cells are connected to the mobile core network through the internet cloud. The network topology composed of macro and

The available channel for secondary access is modeled as a two state Markov process. These two states are: ON state representing occupied channel state and OFF state when the channel is idle. Both ON and OFF states’ temporal lengths are modeled as random variables with specific statistical distributions [23] and [27]. The SU locates and utilizes the spectrum opportunities by using the Fig. 8. Opportunistic channel access model [23]. following model. The 48

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Fig. 9. Two-tier heterogeneous cellular network [2].

femto-cells is called a two-tier heterogeneous network, as shown in Fig. 9 [2]. From a radio resources perspective, femto-cells can opportunistically use the same radio spectrum assigned for the macro-cell base station (MBS). In this case, MBS and femtocells share the spectrum under DSA framework where MBSs act as PUs and femto-cells with CR capabilities, called cognitive femto-cells base stations (FBS), take the role of SUs. Consequently, higher network throughput is achieved using the same licensed spectrum owned by the network operator [23] and [25]. In [23], periodic sensing intervals for ED are optimized in LTE two-tier heterogeneous networks with the objective of maximizing the FBS throughput in a multi-channel scenario. ED is used to locate the free channels [23]. Moreover, the LTE downlink channel occupancy is empirically modeled as described next.

Empirical Channel Occupancy Modeling For sensing optimization, exponential distributions for both ON and OFF temporal lengths are extensively used in the

Fig. 10. Measurements set up for LTE traffic modeling [2]. April 2016

Fig. 11. Sample of the empirical and fitted CDF using exponentials mixture distributions [27].

literature. However, empirical studies have shown a poor match between the measured data and exponential distributions, mainly due to the heavy tail behavior of the empirical findings [26]. The work reported in [27] explores fitting the empirical data for the LTE channel occupancy into a mixture of exponential distributions combined linearly [2]. Using a distributions mixture is an advantage because of the ease of exponential distributions in solving sensing optimization problems. Moreover, a mixure of distributions offers more degrees of freedom and, therefore, better fitting to the empirical data [28]. The empirical downlink LTE traffic is obtained through a measurement campaign performed in Kista, Stockholm, Sweden. The measurement set up is depicted in Fig. 10 which employs an LTE base station acting as a signal source unit and an antenna connected to a real time spectrum analyzer (RTSA) acting as a data capturing unit. The measurement campaigns are automatized using a PC running MATLAB connected to the RTSA to control its parameters. Moreover, the PC records the data captured by the RTSA for further analysis. The measurement set up shown by Fig. 10 is a part of a generic set up used for performance evaluation, optimization and traffic modeling presented and explained in [2]. Below is a representative case of the results. These representative results are for the measurements carried out for a 1.4 MHz channel that lies between 2650.6 and 2652.0 MHz on Wednesday, October 2, 2013 from 08:00 am to 12:00 am. Fig. 11 shows the

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Fig. 12. Senseless and optimized LTE cognitive femto-cell throughputs [23].

empirical distribution and the fitted exponential distribution mixtures for the OFF periods denoted as x. The figure shows how the fitted mixture approaches towards the empirical distribution when increasing k [27].

empirical statistical model for LTE macro-cell channel occupancy is accomplished [27].

References [1] M. Wellens, J. Wu, and P. Mahonen, “Evaluation of spectrum

Key Results for Sensing Optimization

occupancy in indoor and outdoor scenario in the context of

For benchmarking purposes, the senseless throughput CSL is defined as the FBS throughput when no sensing is performed. Fig. 12 exhibits the senseless throughput in contrast to the optimal throughput achieved Copt when sensing is adopted and optimized with an objective of maximizing the FBS throughput. The results shown in Fig. 12 are obtained using the measurement data taken in different periods of the day on October 2, 2013. As shown, with periodic sensing intervals optimization, the highest gain in the throughput is achieved with the lowest available opportunities. This result reflects the creditability of optimizing the periodic sensing intervals as the necessity of the throughput increases at the peaks of the traffic [23].

cognitive radio,” 2nd Int. Conf. on Cognitive Radio Oriented Wireless

licensed spectrum: blind sensing, sensing optimization and traffic modeling,” Ph.D. dissertation, KTH Royal Institute of Technology, Stockholm, Sweden, March 2015. [3] J. Mitola, “Cognitive radio for flexible mobile multimedia communications,” IEEE Int. Workshop on Mobile Multimedia Commun., pp. 3 –10, 1999. [4] A. Ghasemi and E. Sousa, “Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,” IEEE Commun. Mag., vol. 46, no. 4, pp. 32 –39, Apr. 2008. [5] P. Kolodyz, “Dynamic spectrum policies: Promises and

Concluding Remarks

challenges,” J. Commun., Law and Policy, vol. 22, no. 2, pp. 201 –

This article presents studies on spectrum sensing challenges, mainly blind sensing and sensing optimization. Under the umbrella of blind sensing, MME limitations with ordinary time domain filtering are overcome by using frequency domain rectangular filtering where the signal spectrum shape is preserved [14]. In addition, the optimal ratio between the observation and occupation bandwidths for MME is found to be 0.5 [15]. Moreover, two blind sensing techniques are developed and compared, namely spectrum discriminator and a sequential two-stage combined detector called 2EMC [17]. Concerning sensing optimization, a real-life sharing scenario of LTE cognitive femto-cell is considered for periodic sensing intervals optimization with an objective of maximizing the femto-cell downlink throughput [23]. Prior to solving the optimization problem in LTE cognitive femto-cells, an 50

Networks and Commun., (CrownCom), pp. 420 –427, Aug. 2007. [2] M. Hamid, “On spectrum sensing for secondary operation in

220, Feb. 2004. [6] M. Hamid, N. Björsell, W. Van Moer, K. Barbé, and S. Slimane, “Blind spectrum sensing for cognitive radios using discriminant analysis: a novel approach,” IEEE Trans. Instrum. Meas., vol. 62, no. 11, pp. 2912–2921, Nov. 2013. [7] Z. Aleksic, “Minimization of the optical smoke detector false alarm probability by optimizing its frequency characteristic,” IEEE Trans. Instrum. Meas., vol. 49, no. 1, pp. 37–42, Feb. 2000. [8] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Commun. Surveys Tutorials, vol. 11, no. 1, pp. 116 –130, 2009. [9] D. Rabijns, G. Vandersteen, and W. Van Moer, “An automatic detection scheme for periodic signals based on spectrum analyzer measurements,” IEEE Trans. Instrum. Meas., vol. 53, no. 3, pp. 847–853, Jun. 2004.

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[10] W. Van Moer, Y. Rolain, and J. Schoukens, “An automatic harmonic selection scheme for measurements and calibration with the nonlinear vectorial network analyzer,” IEEE Trans. Instrum. Meas., vol. 51, no. 2, pp. 337 –341, Apr. 2002. [11] S. Schuster, S. Scheiblhofer, and A. Stelzer, “The influence of windowing on bias and variance of DFT-based frequency and phase estimation,” IEEE Trans. Instrum. Meas., vol. 58, no. 6, pp.

[26] R. Guerin, “Channel occupancy time distribution in a cellular radio system,” IEEE Trans. Veh. Technol., vol. 36, no. 3, pp. 89–99, Aug. 1987. [27] M. Hamid, N. Björsell, and S. B. Slimane, “Empirical statistical model for LTE downlink channel occupancy,” Wireless Personal Commun., submitted for publication. [28] A. Feldmann and W. Whitt, “Fitting mixtures of exponentials to long-tail distributions to analyze network performance models,”

1975–1990, Jun. 2009. [12] C. Carobbi and M. Cati, “The absolute maximum of the likelihood

Performance Evaluation, vol. 31, pp. 245-279, 1998.

function of the Rice distribution: existence and uniqueness,” IEEE Trans. Instrum. Meas., vol. 57, no. 4, pp. 682–689, Apr. 2008. [13] Y. Zeng and Y. Chang Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Trans. Commun., vol. 57, no. 6, pp. 1784 –1793, Jun. 2009. [14] M. Hamid and N. Björsell, “Maximum minimum eigenvalues based spectrum scanner for cognitive radios,” IEEE Int. Instrum. and Meas. Technol. Conf. (I2MTC), pp. 2248– 2251, May 2012. [15] M. Hamid, N. Björsell, and S. Ben Slimane, “Signal bandwidth impact on maximum-minimum eigenvalue detection,” IEEE Commun. Letters, vol. 19, no. 3, pp. 395–398, Mar. 2015. [16] M. Hamid, K. Barbé, N. Björsell, and W. Van Moer, “Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: a comparative study,” IEEE Int.

Mohamed Hamid (mohamed.hamid@uia.no) (M’16) received the B.Sc. degree in electrical engineering from Khartoum University, Sudan in 2005, the M.Sc. degree from Blekinge Institute of Technology (BTH), Karlskrona, Sweden in 2009 and the Ph.D. degree from KTH Royal Institute of Technology, Stockholm, Sweden in 2015. From 2010 to 2015, he was with the University of Gävle and the KTH Royal Institute of Technology, Communication System Lab, and Wireless@kth Center, Sweden. In 2016, he joined the University of Agder, Intelligent Signal Processing and Wireless Networks (WISENET) Lab as a post-doctoral researcher. His research interests span over cognitive radio, spectrum management, radio source allocation, heterogeneous wireless networks and traffic modeling.

Instrum. Meas. Technol. Conf. (I2MTC), pp. 2252–2256, May 2012. [17] M. Hamid, N. Björsell, and B. Slimane, “Energy and eigenvaluebased combined fully-blind self-adapted spectrum sensing algorithm,” IEEE Trans. Veh. Technol., vol. PP, no. 99, Feb. 2015, DOI: 10.1109/TVT.2015.2401132. [18] K. Barbé and W. Van Moer, “Automatic detection, estimation, and validation of harmonic components in measured power spectra: All-in-one approach,” IEEE Trans. Instrum. Meas., vol. 60, no. 3, pp. 1061 –1069, Mar. 2011. [19] L. Gonzales-Fuentes, K. Barbé, W. Van Moer, and N. Björsell, “Cognitive radios: discriminant analysis for automatic signal detection in measured power spectra,” IEEE Trans. Instrum. Meas., vol. 62, no. 12, pp. 3351–3360, Dec. 2013. [20] W. Ejaz, N. ul Hasan, and H. Kim, “SNR-based adaptive spectrum sensing for cognitive radio networks,” Int. J. of Innovative Computing, Info. and Control, vol. 8, no. 9, pp. 6095–6105, Sep. 2012.

Slimane Ben Slimane (slimane@kth.se) (SM’12) received his B.Sc. degree in electrical engineering from the University of Quebec in Trois-Rivieres, Quebec, Canada in 1985, his M.Sc. degree from Concordia University, Montreal, Canada in 1988, and his Ph.D. degree also from Concordia University in 1993. In October 1995, he joined the Department of Signals, Sensors, and Systems at the Royal Institute Technology as an Assistant Professor. Since then, he has been involved in teaching modern radio communications and carrying out research projects. He is presently an associate professor in the area of radio communication. His research interest is in the area of wireless communications with special emphasis on digital communication techniques for fading channels, channel coding, access methods, cooperative communications, energy efficiency, and cognitive radio.

[21] S. Geethu and G. Narayanan, “A novel high speed two stage detector for spectrum sensing,” Procedia Technology, vol. 6, pp. 682 – 689, 2012. [22] M. Hamid, N. Björsell, and S. B. Slimane, “Sample covariance matrix eigenvalues based blind SNR estimation,” IEEE Int. Instrum Meas. Technol. Conf. (I2MTC), pp. 718–722, May 2014. [23] M. Hamid, S. B. Slimane, and N. Björsell, “Downlink throughput driven channel access framework for cognitive LTE femto-cells,” CoRR, , 13 Feb. 2015. [Online]. Available: http://arxiv.org/ abs/1502.04044. [24] D. Knisely, T. Yoshizawa, and F. Favichia, “Standardization of femtocells in 3GPP,” IEEE Commun. Mag., vol. 47, no. 9, pp. 68–75, Sep. 2009. [25] O. Gharehshiran, A. Attar, and V. Krishnamurthy, “Collaborative sub-channel allocation in cognitive LTE femtocells: a cooperative game-theoretic approach,” IEEE Trans. Commun., vol. 61, no. 1, pp. 325–334, Jan. 2013. April 2016

Wendy Van Moer (wendy.w.vanmoer@ieee.org) (SM’07) received the M.Eng. and Ph.D. degrees in Engineering from the Vrije Universiteit Brussel (VUB), Brussels, Belgium in 1997 and 2001, respectively. She is currently a visiting Professor at the Department of Electronics, Mathematics and Natural Sciences, University of Gävle, Sweden. She has a teaching assignment at the Thomas More College. Her main research interests are nonlinear measurement and modeling techniques for medical and high-frequency applications. She has published over 100 related conference/peer reviewed journal articles. Dr. Van Moer was the recipient of the 2006 Outstanding Young Engineer Award from the IEEE Instrumentation and Measurement Society. She is currently Editor-in-Chief for IEEE Instrumentation and Measurement Magazine. She was the recipient of the 2010 and 2011 Outstanding Associate Editor Recognition.

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Niclas Björsell (niclas.bjorsell@hig.se) (SM’12) received his Ph.D. in Telecommunication from the Royal Institute of Technology, Stockholm, Sweden in 2007. For more than 25 years, he has held positions in the academy as well as in industry. He is currently Associate Professor at the University of Gävle. He has published more than 60 papers in

international peer-reviewed journals and conferences, and his research interests include radio frequency measurement technology, analog-to-digital conversion, non-linear systems, cognitive radio and automation. Dr. Björsell is an active member of TC10, Instrumentation and Measurement, IEEE.

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newproducts Robert Goldberg

Please send all “New Products” information to: Robert M. Goldberg 1360 Clifton Ave. PMB 336 Clifton, NJ 07012 USA e-mail: r.goldberg@ieee.org

PXI Solid State Multiplexer Pickering Interfaces announces the launch of a new 5 Amp PXI Solid State Multiplexer. This new Solid State Multiplexer (model 40-652) was originally designed for a large defense contractor looking for a multiplexer that could handle large inrush currents. The 40-652 MUX series offers a range of configurations suitable for hot or cold switching signals up to ±100 V at 5 Amps. The use of solid-state relays allows the hot switching of signals without any life degradation, including DC signals that EMR (Electro-Mechanical Relay) designs can only handle with much-reduced service life and power handling. The design is capable of switching inductive loads up to a stored energy of 20 mJ and is capable of withstanding 30 Amp inrush currents for 300 µs when switching capacitive loads. The multiplexer module is available in single pole 48:1, two pole 24:1, single pole dual 24:1 or single pole 24:1 configurations. The use of solid-state relays also ensures a fast, typical operating time of 85 µs, making it ideal for use in applications where the speed of test is critical. This range is also supported by Pickering Interfaces eBIRST switching system test tools. These tools provide an easy to use solution to quickly identify faulty switching systems. Find more information at www.pickeringtest.com.

Functional Tester Supports M2M/IoT Integration Machine-to-machine communications (M2M) and the Internet of Things (IoT) are leading the way into an increasingly April 2016

networked world. The R&S CMW290 functional radio communication tester from Rohde & Schwarz is for users who want to integrate wireless modules into their platforms and test them. Wireless technologies such as LTE, Bluetooth®, and WLAN make it possible to connect devices with the Internet or with each other anywhere and everywhere. Manufacturers who integrate wireless modules into devices must ensure the correct functioning of the target platform and the applications. Rohde & Schwarz has specifically designed its economical R&S CMW290 functional radio communication tester to meet the requirements of integrators with mostly cost-sensitive applications. Users from the M2M/IoT sector typically require only simple functional tests for hardware and applications. The R&S CMW290 therefore offers all essential measurements for RF or hardware testing. For end-to-end testing of applications, the R&S CMW290 can simulate a cellular network and set up a connection between the application on the device or system and the server. This enables users to check the correct functioning of platforms with integrated wireless module in a defined network. The R&S CMW290 supports all cellular and noncellular standards. Find more information about Rohde & Schwarz at www. rohde-schwarz.com.

Mixed Domain Oscilloscope Te k t r o n i x , I n c . i n t r o duces the MDO4000C Series of Mixed Domain Oscilloscopes that can be configured with up to six instruments in a single unit including a full spectrum analyzer. Starting with the highest-performance oscilloscope of any 6-in-1 instrument from Tektronix, engineers can upgrade their MDO4000C instruments over time to meet their most demanding challenges and add functionality as needs change or budgets allow. As with previous generations in the MDO4000 series, the MDO4000C provides a synchronized view of analog and digital waveforms along with RF spectrum traces, making it the

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newproducts continued ideal debug tool for Internet of Things (IoT) and many other embedded engineering applications. Like the 6-in-1 MDO3000 introduced last year, the MDO4000C expands on its core oscilloscope functionality with options to add a spectrum analyzer, arbitrary/function generator, logic analyzer and protocol analyzer. A digital voltmeter (DVM) is available for free with product registration. As an oscilloscope, the MDO4000C features long record length, fast sample rate and fast waveform capture rate to help uncover elusive problems. Compared to standalone instruments, the MDO4000C saves time lost from having to look for and configure instruments and delivers outstanding value without compromising on performance. It also enhances usability through integration. For instance, the built-in arbitrary waveform generator makes it easy to capture signals on the scope, modify them, and then replay them through the generator, enabling margin testing by making it easy for users to add noise to any signal. For more information, go to www.tektronix.com/mdo4000.

Advanced Electronic Warfare Simulation Giga-tronics Incorporated and D-TA Systems Corporation have announced the joint development of an advanced Electronic Warfare signal simulation system. The ability to accurately model the electromagnetic threat environment and key emitter characteristics is the key to testing and evaluating state-of-the-art electronic warfare equipment. In partnership with D-TA Systems, Giga-tronics offers a broadband, wide instantaneous bandwidth solution through a powerful combination of the GT-ASGM18A Advanced Signal Generator and D-TA’s System-95 IF processing system. System-95 features the DTA-9500 ultra-wideband digital IF transceiver and the DTA-5000 RAID Server. The D-TA and Giga-tronics partnership has created a paradigm shift in EW threat simulation. A real-life RF threat environment can be a readily created by playing out pre-recorded and/or simulated data. For more information, please visit www.gigatronics.com and www.d-tacorp.com.

Next Generation Human Vibration Meter Larson Davis, a division of PCB Piezotronics, Inc., announces the release of the new HVM200 Human Vibration Meter that implements the latest measurement technology for industrial hygiene and product testing applications. The HVM200 is used to measure human exposure to vibration in order to prevent injury and better understand 54

workplace hazards and p ro d u c t p e r f o r m a n c e . The HVM200 includes the functionality needed to measure the hand-arm vibration and whole body vibration requirements in support of the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) and directive 2002/44/EC of European Parliament. This meter provides options available for 1/1 and 1/3 octave filters (0.5 Hz to 2000 Hz and 0.4 Hz to 2500 Hz respectively) and raw vibration data recording. Data can then be archived or transferred using USB, Wi-Fi or a removable micro SD memory card. Industrial hygienists and product test engineers can now leverage the power of their phones or tablets by acquiring the free app available on Google Play™ and the Apple App Store. This app can be used in conjunction with the HVM200 to setup a test, make the measurements, and view the results. Vibration data taken with the HVM200 can also be viewed and analyzed on a personal computer using the HVM option for the Larson Davis G4 software. With the G4 HVM software option you can download data and view it in graphical or tabular format. You can also perform “what-if’ analyses by graphically modifying the data and recalculating results. Then a report can be generated and the data can be exported for archival or further processing. For more information about HVM200 visit www. larsondavis.com/HVM200.

Multichannel Streaming and Recording on a Wideband Digital Receiver Keysight Technologies, Inc. introduces multi-module synchronization for its M9703B AXIe (AdvancedTCA Extensions for Instrumentation and Test) high-speed digitizer/wideband digital receiver – increasing the total number of streaming and recording channels available with the M9703B. The new bundles options (-CB1/-CB2) enable multichannel phase coherent digital down conversion (DDC) which has applications in 5G, Radar and Satellite Communications, and Aerospace & Defense. With up to 320 MHz instantaneous bandwidth (IBW) with tunable intermediate frequency, this high-speed digitizer meets the needs for new technology development in 5G

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wireless mobile broadband. Used with the recommended host computer configuration, the new options allow guaranteed recording time, storing all I/Q samples for later analysis. A command line software application is included in the bundle for an easy launch and control of the streaming and recording. As a component of the Keysight solution, the M9703B AXIe high-speed digitizer/wideband digital receiver (bundles -CB1/-CB2) allows customers to quickly characterize the channel behavior in these frequency bands and enables researchers to develop the necessary channel models for designing and validating air-interface alternatives. For other applications where gapless streaming and recording is not required but there is a need to simultaneously read while acquiring, the new signal processing firmware (-TSR option) enables simultaneous capture and transfer of triggered acquisition data to the host computer. Highlights Include: ◗◗ New bundle options allow multichannel digital down conversion streaming and recording capability on a wideband digital receiver ◗◗ Ideal for applications in 5G, Radar and Satellite Communications, and Aerospace & Defense, and ◗◗ Guaranteed recording time, storing all I/Q samples up to full-storage capability of host computer for long-duration frequency post-analysis. Information about product configuration is available at www.keysight.com/find/M9703B.

Bluetooth-Enabled Data Logger Lascar Electronics introduces the latest addition to its EasyLog family of data loggers - the EL-BT-2, its first Bluetooth-enabled temperature and humidity sensor providing users with wireless monitoring via their Android phone or tablet. Available from the Google Play store, the App allows Android users to wirelessly configure the data logger for use, retrieve and view graphed and tabulated data from the device, view alarms as well as email saved data to third parties. This remote monitoring solution is vital in applications where loggers need to be left in situ at all times, and extremely useful for any other users who would rather not physically handle their data logger during set-up or data download. The easy-to-use app p ro v i d e s a n a n i m a t e d walkthrough, enabling users to set the sample rate, temperature scale, temperature and humidity alarms, Bluetooth power-save settings, LCD settings, and variable start times. Data can then be downloaded from the sensor wirelessly April 2016

at any time, for viewing on any Android device. Data stored on the Android device’s memory card and app allows users to send readings to another device or Windows PC for further analysis, via email or another cloud service. The compact EL-BT-2 data logger can store up to 500,000 temperature and humidity readings, with a measurement range of -20 to +60 °C (-4 to +104 °F) and 0-100% RH. The unit’s LCD screen details current, minimum and maximum readings alongside a status indicator for a direct indication of the environment it has been exposed to without using the App. A rechargeable lithium ion battery with a battery life of up to one year powers the device or the unit can be plugged into an AC power source permanently. Accurate monitoring and logging of environmental data is absolutely crucial to the role of the modern Facilities Manager and others, whether monitoring a large-scale cold supply chain, or just simply checking on the general work environment for employees. The EL-BT-2’s wireless function makes environmental monitoring significantly easier, especially for smaller FM teams. Please visit www.easylogbt.com for further information.

Digital Oscilloscopes Siglent Technologies introduces a new line of digital oscilloscopes with Super Phosphor (SPO) technology. This new SDS2000X Series is available in bandwidths of 70 MHz, 100 MHz, 200 MHz and 300 MHz, with real time sampling rates up to 2 GSa/s. Most common functions can be accessed with a single-button control. The advanced technology features of these new scopes include waveform capture rates up to 500,000 wfm/s, record length up to 140 Mpts, and 256-level color and intensity grading display. An innovative trigger mode includes: Edge, Slope, Pulse, Window, Runt, Interval, Dropout, Pattern and HDTV Video. Low background noise supports 1 mv/div to 10 V/div voltage scales. The family of SDS2000X Super Phosphor Oscilloscopes includes an array of measurement and math capabilities, with options for a built-in 25 MHz arbitrary waveform generator, 16 digital channels (MSO), and serial decoding. All scopes include Siglent’s 8-inch TFT-LCD display with 800 x 480 resolution, and supports auto detection of 10X probe with read-out port. I/O interfaces include USB Host, USB Device, LAN, Pass/Fail, Trigger Out and GPIB (optional). The History function can record up to 80,000 frames of waveforms, easily accessible from the control panel. A full range of current and voltage probes are available. For more information, visit www.siglentamerica.com.

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newproducts continued Scientific Camera with High Frame Rates and Megapixel Resolution Princeton Infrared Technologies, Inc. introduces the 1280SCICAM shortw a v e i n f r a re d ( S W I R ) camera that delivers long integration times and the high frame rates at megapixel resolution. Princeton claims the 1280SCICAM provides the longest integration times and the highest frame rates available on the market today. The lattice-matched indium gallium arsenide (InGaAs) sensor features 1280 x 1024 resolution at frame rates greater than 95 frames per second (fps) at full-frame size and operates in the visible to shortwave infrared spectrum from 0.4 µm to 1.7 µm. The camera’s small 12-micron pitch, coupled with the low read noise (<30e-) and high quantum efficiency (> 75% from 1.0 to 1.6 microns) of the imaging array, make the new camera ideal for scientific imaging tasks in the SWIR and visible wavelengths. Other applications include high-speed machine vision and long-range surveillance operations where the small pitch is especially important. A 3-stage thermoelectric cooler (TEC) is integrated into a vacuum package to provide the 1280SCICAM with three temperature setpoints for different conditions, 25 °C (no cooling), 0 °C (fan-cooled), and -50 °C (water-cooled). The on-board array has 14-bit digital output, snapshot exposure with no image lag and features an unprecedented low read noise of <30e-, which is lower than any other cooled SWIR scientific camera currently available. Princeton IR Tech’s advanced SWIR-InGaAs camera has a medium-base Camera Link® to support fast fullframe-rate imaging. The camera’s high dynamic range ratio is greater than 3000:1, with integration times ranging from 50 microseconds to more than 3 minutes. The new 1280SCICAM is available with F- and C-mount lenses. For more information, please visit: www.princetonirtech. com.

New Line of High Efficiency Power Supplies IDEC Corporation announces the PS5R-V line of DIN-rail power supplies, offering high-efficiency in a compact form factor. These power supplies suit a wide range of needs and carry all of the required certifications necessary for use in demanding applications. This next generation of the industry standard PS5R power supply family has updated features and specifications to meet current and future needs. The PS5R-V line of power supplies includes 10 W, 15 W, 30 W, 60 W and 120 W versions, with additional versions coming soon. These power supplies have a very compact form 56

factor with overall dimensions reduced by up to 25% from previous generations. The reduced form factors combine with DIN-rail mounting to free up valuable control panel space and reduce installation costs. Operating temperature ranges from -25 °C to +75 °C offer more versatility. These extended operating temperature ranges often allow these power suppliers to be used in control panels without the need for air conditioning or other cooling devices. In addition, operating efficiencies have been significantly improved, up to 16% from previous generations. The PS5R-V line of power supplies is approved for installation in Class I Division 2 environments in standard control cabinets, making them ideal for use in hazardous location applications such as oil and gas processing and petrochemical facilities. These power supplies add to IDEC’s current line of products approved for installation in hazardous areas. Certifications and approvals include UL 508, UL 1310, SEMI F47 and RoHS. Meeting these stringent industry standards requires the use of very reliable components, resulting in an MTBF of up to 900,000 hours for this line of power supplies. For complete specifications or additional information on the PS5R-V line of power supplies, please visit http:// powersupply.idec.com/.

PC-Based Waveform Acquisition Operates up to the GHz Range For applications requiring extended electronic waveform acquisition and generation, Spectrum has announced a new storage system that works with its PC-based digitizer and generator cards to dramatically increase possible recording and replay times. The new system can be used with any of Spectrum’s M2i, M3i or M4i series of PCIe digitizer or waveform generator cards and delivers data storage sizes from 1 to 32 TB with full support for continuous data streaming at rates up to 3 GB/s. The combination makes it possible to capture high frequency signals up to the GHz range and continuously store them for hours on end or lower frequency signals for even longer periods of time. Each Tera-store system can house up to six digitizer cards, making it possible to configure instruments with 1 to 96 fully synchronous acquisition channels. If more channels are required then a 16 slot

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docking station can be added to expand the system up to 256 channels. At the heart of the platform is a carefully selected, base PC system. This powerful computer includes a Supermicro 4U/ Tower with 8 drive bays and 6 free PCIe slots for digitizer or generator cards. The Tera-store streaming solutions are complete turn-key systems and come factory configured with Spectrum’s Sbench 6 Professional software for digitizer control, data capture, display and analysis. The systems are available with a choice of streaming rates, from 400 MB/s up to 3 GB/s, and storage capacities, from 1 TB to 32 TB. The various options consist of a high performance RAID controller and a number of solid-state or hard-disk drives (SSD/HDDs) that are configured to support the required transfer rates and storage times. Tera-store systems are suited to applications where continuous data acquisition or replay is required or large volumes of data need to be collected, stored and analyzed. For more information, visit www.spectrum-instrumentation. com.

The new C-877.1U11 single axis controller is as small as a deck of cards and priced significantly below the standard single axis controller offered in the past. In addition, a new low-cost 2-axis controller is also available. The digital servo circuit of the new controllers (automatic switchover between static and dynamic PID parameters) takes into account the properties of ultrasonic motors to achieve maximum dynamic performance with settling times as low as a few 10 milliseconds without sacrificing resolution and smooth operation. All ultrasonic motors provide fast acceleration and velocities of 100s of millimeters/second. At the top of the spectrum, the new C-867.262 multi-phase controller allows for extremely smooth motion with velocities as small as 1µm/sec and below. Positioning stages are equipped with an ID chip in the connector, and during start-up the ultrasonic drives identify themselves to the controller, loading the matching operating parameters automatically. The controllers are delivered with extensive software packages, including drivers for LabVIEW as well as dynamic libraries for Windows and Linux. For more information, please visit www.pi-usa.us.

New Motion Controllers for Ultrasonic Piezo Positioners

Robert Goldberg (r.goldberg@ieee.org) has over 35 years’ experience with over 25 years in management of the design and development of hardware and software for a broad range of military electronic products involving digital, RF/Microwave, electro-optical and electromechanical systems. He is retired from ITT Aerospace Communications Division in Clifton, NJ, where he was responsible for Sensor Communication programs utilizing the application of sensor radios developed by ITT as a result of work with DARPA on the Small Unit Operations Situation Awareness System (SUOSAS). Prior to joining ITT, he held positions in systems test and systems engineering with Northrop Grumman in programs related to RF and IR electronic warfare systems. He is a Fellow of the IEEE and is currently chairman of the Fellows Evaluation Committee of the IEEE Instrumentation and Measurement Society.

PI (Physik Instrumente) L.P. now offers a new line of controllers for ultrasonic direct-drive motors and positioning stages. The new controllers offer greater flexibility and responsiveness to user requirements ranging from precision positioning to handling. Positioning systems based on ultrasonic ceramic directdrive motors (PIline®) provide very high dynamics and resolution in a compact, low-profile package. PI now offers a new line of motion controllers for these positioning systems to address the requirements for OEMs and research customers.

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aprilcalendar For more information about the meetings, please go to the I&M Society Web site at www.ieee-ims.org. SAS 2016 / April 20-22, 2016 IEEE Sensors Applications Symposium Catania, Italy

AUTOTESTCON 2016 / September 12-15, 2016 IEEE AUTOTESTCON Anaheim, CA, USA

MeMeA 2016 / May 15-18, 2016 IEEE International Symposium on Medical Measurements and Applications Benevento, Italy

AMPS 2016 / September 28-30, 2016 International Workshop on Applied Measurements for Power Systems Submission deadline: May 30, 2016 Aachen, Germany

I2MTC 2016 / May 23-26, 2016 IEEE International Instrumentation and Measurement Technology Conference Taipei, Taiwan

IST 2016 / October 4-6, 2016 IEEE International Conference on Imaging Systems & Techniques Chania, Crete, Greece

ISPCS 2016 / September 4-9, 2016 International IEEE Symposium on Precision Clock Synchronization for Measurement, Control, and Communication Stockholm, Sweden

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The 2016 IEEE Instrumentation & Measurement Society 2016 Officers President - Ruth A. Dyer, rdyer@ksu.edu Executive Vice President - Max Cortner, max.cortner@bsci.com Vice President of Conferences - Mark Yeary, yeary@ou.edu Vice President of Education - Salvatore Baglio, salvatore.baglio@unict.it Vice President of Finance - Dario Petri, dario.petri@dit.unitn.it Vice President of Membership - Shervin Shirmohammadi, shervin@ieee.org Vice President of Publications - Zheng Liu, zheng.liu@ieee.org Vice President of Technical Committees and Standards - Ruqiang Yan, ruqiang@seu.edu.cn Treasurer - Juan Manuel Ramirez Cortes, jmramirez@ieee.org Junior Past President - Reza Zoughi, zoughi@mst.edu Senior Past President - Jorge F. Daher, j.daher@ieee.org

Administrative Committee (AdCom) 2013–2016 Alessandra Flammini, alessandra.flammini@ing.unibs.it Richard Hochberg, rhochberg@ieee.org Mark Yeary, yeary@ou.edu Mihaela Albu, albu@ieee.org

2015–2018 Salvatore Baglio, salvatore.baglio@unict.it Zheng Liu, zheng.liu@ieee.org Dario Petri, dario.petri@unitn.it Juan Manuel Ramirez Cortés, jmramirez@ieee.org

2014–2017 Lee Barford, barford@ieee.org Max Cortner, max.cortner@bsci.com Ferdinanda Ponci, fponci@eonerc.rwth-aachen.de Shervin Shirmohammadi, shervin@ieee.org

2016–2019 Octavia A. Dobre, odobre@mun.ca Kristen M. Donnell, kristen.donnell@mst.edu Christophe Dubois, cdubois@deltamu.fr Chi Hung Hwang, cchhwang@itrc.narl.org.tw

Other AdCom Members EIC for IEEE Transactions on Instrumentation and Measurement – Alessandro Ferrero, alessandro.ferrero@polimi.it EIC for IEEE Instrumentation & Measurement Magazine – Wendy Van Moer, wendy.w.vanmoer@ieee.org AEIC for IEEE Instrumentation & Measurement Magazine - Simona Salicone, simona.salicone@polimi.it Graduate Student Representative, Mohamed Khalil, mohamedmahmoud.khalil@polimi.it Undergraduate Student Representative, Katelyn Brinker, katelyn.brinker@mst.edu IEEE Young Professionals Program Representative, Erik Timpson, etimpson@kcp.com I&M Society Executive Assistant, Judy Scharmann, j.scharmann@conferencecatalysts.com Region 10 Liaison, Ruqiang Yan, ruqiang@scu.edu.cn Chapter Chairs Liaison, Sergio Rapuano, rapuano@unisannio.it

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Administrative Committee (AdCom) 2013–2016 Alessandra Flammini, alessandra.flammini@ing.unibs.it Richard Hochberg, rhochberg@ieee.org Mark Yeary, yeary@ou.edu Mihaela Albu, albu@ieee.org

2015–2018 Salvatore Baglio, salvatore.baglio@unict.it Zheng Liu, zheng.liu@ieee.org Dario Petri, dario.petri@unitn.it Juan Manuel Ramirez Cortés, jmramirez@ieee.org

2014–2017 Lee Barford, barford@ieee.org Max Cortner, max.cortner@bsci.com Ferdinanda Ponci, fponci@eonerc.rwth-aachen.de Shervin Shirmohammadi, shervin@ieee.org

2016–2019 Octavia A. Dobre, odobre@mun.ca Kristen M. Donnell, kristen.donnell@mst.edu Christophe Dubois, cdubois@deltamu.fr Chi Hung Hwang, cchhwang@itrc.narl.org.tw

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