wireless sensor networks in agriculture: for potato

Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963

WIRELESS SENSOR NETWORKS IN AGRICULTURE: FOR POTATO FARMING KSHITIJ SHINGHAL Department of E&C Engg., Moradabad Institute of Technology, Moradabad, INDIA.

ARTI NOOR Department of M. Tech. VLSI Design Group, C-DAC, Noida, INDIA

NEELAM SRIVASTAVA Department of E&C Engg., Institute of Engineering & Technology, Lucknow, INDIA.

RAGHUVIR SINGH Academic Advisor, Shobhit University, Merrut, INDIA. Abstract : The newly emerged wireless sensor network (WSN) technology has spread rapidly into various multidisciplinary fields. Agriculture and farming is one of the industries which have recently diverted their attention to WSN, seeking this cost effective technology to improve its production and enhance agriculture yield standard. This paper reports on the application of WSN technology to improve potato crop production. By monitoring and understanding individual crop and its requirements, farmers can potentially identify the various fertilizers, irrigation and other requirements. The sensor node, which is small in size and low in power consumption, shows significant potential in this context. In this paper an irrigation management model is given to estimate agricultural parameters using mathematical calculations with specific example of potato crop. Using WSN agricultural parameters like depth of water, soil water tension and system capacity etc. are estimated for irrigation management system to maintain optimum SWT for better crop yield and increase the application efficiency of irrigation system by 10%. Keywords: Wireless Sensor Network, Agriculture, Potato field. 1. INTRODUCTION Modern agriculture needs tools and technologies that can improve production efficiency, product quality, postharvest operations, and reduce their environmental impact. Automation in agriculture brings about a fundamental contribution to what is now known as precision agriculture (or precision farming). A definition of precision agriculture may be the following: the technique of applying the right amount of input (water, fertilizer, pesticide, etc.) at the right location and at the right time to enhance production and improve quality, while protecting the environment [8]. Application of precision agriculture is fulfilled with a field-wide sensor network able to monitor relevant parameters, for example, soil moisture and air temperature, and to transmit data wirelessly to the farmer location, so that appropriate measures can be adopted [1, 9].Another issue of particular relevance in large fields is irrigation. Electronic devices are needed to monitor watering systems. Wireless sensor networks can help monitoring fields, vineyards and orchards, thus helping farmers to prevent damages to their crops and increasing crop production [24,25,26]. Here a typical example of potato field is considered. Potato is a water-stress-sensitive crop. Potatoes have a relatively shallow root system that provides very little margin for irrigation errors. Potato plants are more productive and produce higher quality tubers when watered precisely using soil water tension (SWT) than if they are under or over irrigated. Soil humidity provides useful guidelines to avoid water stress by projecting when to irrigate. Intelligent humidity sensor used in WSN node provides good estimates of humidity for many soils [2,3 ]. An irrigation management model based on mathematical calculation is proposed for the better crop yield. The system employs a group of WSN nodes deployed in the potato field for sensing the necessary parameters and the RF communication of WSN node is used to transmit the measured data to base station [27,28]. The proposal is made on the fact that the optimum irrigation is one of the key factors to improve the crop yield. The

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Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963 WSN node plays an important role in measuring and transmitting the valuable data from agricultural field to base station and irrigation management system. Figure 1 is an Outline of a WSN deployment in a potato field.

Figure 1 Outline of a WSN deployment in a potato field

The remainder of the paper is organized as follows. In the next section, a brief introduction of WSNs architecture and its major component is given. The irrigation management model is described comprehensively in Section 3. Section 4 presents general information about the related work done related to the agricultural application of WSN. Section 5 describes the methodology adopted to implement the proposed irrigation model. Whereas conclusion is given in the last section. 2. Wireless Sensor Network Architecture The general architecture of a wireless sensor node is presented in Fig. 2. As seen from the figure, commonly, a wireless sensor node is composed of four major components which are namely, the sensing unit, the processing unit, the power unit and finally the wireless transceiver unit [2,4].

Figure 2 General architecture of a wireless sensing node.

The sensing unit converts such measured physical quantities as humidity, pressure, temperature, fuel tank level, flow rate, position, velocity, acceleration, chemical concentration, etc. into a voltage signal and thereafter digitizes it to produce digital output for processing [29]. The processing unit with a microcontroller controls all of the functions of the sensor node and manages the communication protocols to carry out specific tasks.

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Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963 Communication between the WSN node and the network is provided by the transceiver unit. And finally the power unit, which is the most crucial component of a sensor node, supplies mandatory power to all of these units. In addition to these major components, a sensor node may also include application depended components such as power generator, location finding system and mobilizer. Power generators, like solar cells, may be utilized to support the power unit for prolonging the sensor node lifetime. 3. The Irrigation management model To implement the irrigation management scheme in WSN node for potato field monitoring it is necessary to calculate and understand the seasonal water demand, peak water demand, effect of irrigation, irrigation system used, and depth of water to be applied and finally the system capacity is to be calculated [14,18,20]. Potatoes have little tolerance for water stress. Tuber market grade, tuber specific gravity, and tuber processing quality are all critically influenced by water stress during tuber bulking. The incentives for a grower to maintain a precise irrigation management scheme or irrigation scheduling method, to keep the soil water potential within a narrow range of values are significant[21,22]. • Under-irrigation leads to losses in tuber quality, market grade, total yield, and contract price. • Over-irrigation leads to erosion, disease susceptibility, water loss, extra energy costs for pumping, nitrogen leaching, and increased crop needs. 3.1. The Effect of Irrigation Agricultural plants need sunlight, nutrients, and water to grow. All plants have a minimum annual water requirement to survive and an optimum annual water requirement for maximum production. Therefore adequate amount of irrigation is necessary for improving the crop yield. The key factor is not to add a drop of water more than required and not a drop less than needed for adequate plant growth. 3.2. Irrigation system Irrigation method is an important consideration in irrigation scheduling. For potatoes, the leading irrigation method is sprinkler irrigation and Drip irrigation. In drip irrigation system, water is applied by allowing it to flow over the surface by gravity or through drip valves. The common gravity systems are flood and furrow. 3.3. Irrigation scheduling methods In order for an irrigation schedule to be effective, it has to tell us when to water and how much to apply.Seasonal water demand and peak daily use vary considerably from crop to crop and from one field to the next. Deciding when to irrigate and how much water to apply are the two most difficult decisions to make in managing irrigation systems. 3.3.1. Depth of water to apply For any given plant, the daily rate of use will increase until the plant reaches maturity, and then it will decrease, Figure3. The peak water use rate occurs at the height of the growing season. Table1 shows the peak use for potato crop with both short and long growing seasons, and the root zone depth from which potato crop extracts most of its moisture [10,12]. Two characteristics of soil must be considered in determining how much water should be applied: (1) the rate at which soil can absorb (store) water, or the infiltration rate; and (2) the total amount of water that can be stored. The infiltration rate is determined by the soil texture and the total amount of water that can be stored is determined by the soil depth. Loams and clays can hold more water than sands. Also the type and the depth of subsoil make a difference in the amount of water that can be stored per irrigation[11,13,15].

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Figure 3 Typical daily water demand of plants

These tables for potato crop for the particular growing season length are used to obtain values for (1) the root zone depth of principal moisture extraction and (2) the peak daily water use. It is important to know beforehand the soil type or profile on which the irrigation is to take place and the root zone depth of principal moisture extraction of the crop. Then Table 2 can be used to determine the number of inches of water to store per irrigation[5,6,7]. Table 1. Root zone depth and peak water use rate[11].

Length of growing season Crop

Corn Potatoes Tomatoes

Root zone depth of principle moisture extraction (in) 36 24 48

180–210 days Peak use (in/day) 0.30 0.38 0.20

210–250 days Peak use (in/day) 0.35 0.20 0.22

Table 2. Net amount of water to store per irrigation.

Net amount of water to store for sample problem. Net amount of water to store (in) for various root zone depths Soil profile 24 in 30 in 36 in Slit loam over compacted subsoil 2.50 3.00 3.25 Heavy clay or clay loam soil 2.00 2.40 2.85 Fine sandy loam over compacted subsoil 2.00 2.40 2.80

48 in 4.25 3.85 3.25

The root zone depth, peak daily use, and net amount of water to store per irrigation for a crop of potato grown on a location where the season is greater than 210 days on a heavy clay or clay loam soil is a calculated using tables1 and 2. These figures and referring table 2 the net amount of water to store per irrigation is 2.85 inches. The irrigations systems do not operate at 100% efficiency because some of the water will evaporate, some runs off, and some percolates below the crop root zone. The term application efficiency is used to describe these losses. It is defined as the ratio of the depth of water stored to the depth of water applied, expressed as a percent. The application efficiency of a well designed irrigation system will be between 60 and 80%. To increase the application efficiency up to 70% for the potato crop the amount of water applied should be:

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Depth of water to apply =

Depth of water stored Application efficiency

DWS AE DWS 2.85 in DWA = = 4.07in = AE 0.70 ≈ 50 to 60 cb for sprinklers on silt loam DWA =

≈ 60 cb and 30 cb for furrow and drip irrigation, respectively, on silt loam 2.85 in DWS = IRI = = 7.5 day in PDU 0.38 day Where, DWA = Depth of water to apply (in); DWS = Depth of water to store (in); CB = Centibars; AE = Application efficiency (as a decimal) IRI=Irrigation interval (days); DWS=Depth of water to store (in); PDU= Peak water use (in/day). If there is rainfall during the irrigation period, the irrigation interval should be adjusted accordingly. For example, if there were 1.25 inches of rain on the potato crop in the mathematical model described in eq , then we would divide the amount of rain (in) by the water needs of the crop (in/day) and extend the interval the corresponding number of days. The peak demand of irrigation for the potato crop (1.25 ÷ 0.38 = 3.2 days).Instead of irrigating again in 7.5 days, the irrigation interval could be extended to 10 or 11 days. 3.3.2. Soil Water Tension (SWT) SWT is a measure of how strongly water is held by the soil. Potato plant performance is closely related to the amount of tension the plant has to exert to move water from the soil into the plant roots. The SWT irrigation criteria that optimize potato yield and grade vary by production area. Based on above calculations for potato yield and grade responses to irrigation, ideal potato SWT irrigation criteria is 50 to 60 cb for sprinklers on silt loam. Table 3. Recommended maximum soil moisture tension (centibars) for various crops.[11].

Crop Onion Tomato

SWT 45-65 60-150

Crop Corn Potato

SWT 50-80 30-60

3.3.3. System capacity System capacity is the maximum amount of water that an irrigation system can deliver on a continuous basis. The required pumping capacity of an irrigation system depends on the area to be irrigated (ac), the depth of water to apply (in), and the length of time that the irrigation system is operated (hr). A self-propelled unit as shown in figure 4 may be able to run several days without stopping. The required capacity of a system, in gallons per minute is given by:

RSC =

453 x A x DWA IRP x HPD

Where, RSC = required system capacity (gal/min); 450 = Units conversion constant; A = Area irrigated (ac); DWA = Depth of water to apply per irrigation (in); IRP = Irrigation period (day); HPD = Time operating (hr/day). The required system capacity (gal/min) for the potato crop is calculated where the field area is 200 acres, and the system can operate for 18.0 hr per day for 7.7 days

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RSC =

453 x 200

Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963 ac x 4.07in

18.0hr day gal 368742 = = 2660.49 min 138.6 7.7 DAY x

For 200 acres of long-season potato grown on clay loam soil over compacted subsoil, irrigated with a system that is 70% efficient and limited to operating 18 hours per day for 7.7 days, the system must be able to deliver 2660 gallons of water per minute. 4. Related Works The potato crop yield exhibits a strong dependency on the adequate irrigation. The crop yield can be improved significantly by using WSN nodes and calculating exact amount of irrigation requirements. In this paper, the approach is to optimize the irrigation by using WSN node. The WSN node is used with various built in algorithms; WSN has been used in agricultural application such as but is not used for irrigation optimization in potato field through WSN nodes yet. This approach introduces new challenges (e.g. the interfacing problem, data processing issues, wake up problem, Noise etc.) and new opportunities to resolve such issues through efficient transceiver design of WSN node. More closely related to this work are the WSN nodes being developed and used by Specknet computing [16]. Many precision agriculture systems and equipments are available for the agricultural field [20]. These are having varied application and are not targeted for low power WSN node. The WSN node used for potato field monitoring presented in this paper, serves a different purpose. The aim of this WSN node is to utilize the processor of WSN node to reduce the irrigation overhead, thereby increasing the overall potato crop yield [23, 33] and making the system efficient for potato field. 5. Methodology An Agriculture sensor node developed to be deployed in a potato field and to sense the environment of the potato field. The WSN node is consisting of Intelligent Humidity sensors, microcontroller and low power radio transceivers to collect data in the field and transmit it to a remote receiver outside the field. To prevent node from the humidity in the potato field a corrosion proof casing is used. After setting up the network topology, node runs its application software. The application software begins its active period by turning on its sensors and sensing the environment of the potato field. The application software reads humidity of the potato field from sensors and reports the result to the base via its other WSN nodes. If it receives any packets from its supporting nodes during this active time, it relays the packets to its other nodes. After the transmission of its sensing data, node waits for its working schedule such as sensing period. As it receives its sensing period, the application software turns off its attached sensors and puts the transceiver to power down mode. Finally, it sets up the internal sleep timer, goes to its sleep period and waits for the expiration of the timer. After the expiration of the timer, the application restarts its next active periods by turning on the transceiver and the sensors and continues to sense the environment of the potato field. The WSN nodes with the intelligent humidity sensor and the low power wireless transceiver will be deployed to collect data and record SWT for facilitating irrigation management. The system consists of a number of WSN nodes for data acquisition and control systems on Agricultural farms. The data collection by WSN nodes will be transmitted to a base receiver outside the field stored processed and analyzed. The data can be viewed with the push of a button and can be downloaded to a laptop computer or PDA. The processed SWT data make it possible to determine soil moisture trends and to predict or modify irrigation schedules for better crop yield and increase the application efficiency of irrigation system by 10%. The figure 4 shows an irrigation management system for the potato field. After calculating and estimating, the above parameter (the seasonal water demand, peak water demand, effect of irrigation, irrigation system used, and depth of water to be applied and the system capacity) can be embedded in to base station pump controller as shown in figure 4 for the effective irrigation management in the potato field.

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Figure 4 Irrigation management system for potato field

6. Conclusion The proposed irrigation management scheme using WSN can be applied from planting to harvest as a tool for appropriate irrigation tactic to improve the crop yield. The WSN nodes can also be effectively employed to collect data of soil water availability, soil compaction, soil fertility, biomass yield, plant water status, local climate data, insect-disease-weed infestation, crop yield, etc. The potential of irrigation scheduling using WSN nodes to improve yield and to save water has been demonstrated in this paper. Further it was demonstrated proper irrigation management and data collection for particular crop can be done using WSN node effectively. Irrigation management using WSN can ensure a better crop yield of good quality in spite of the stressful environmental conditions and also increases the application efficiency of irrigation system by 10 %. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

IEEE 1451.2 Standard, (1998), “A Smart Transducer Interface for Sensors and Actuators. Transducer to Microprocessor Communication Protocols and Transducer Electronic Data Sheet (TEDS) Formats”. Piscataway, NJ: IEEE Standards Department, Sensor Device Data/Handbook, DL200/D Rev. 4, (1998), Motorola Semiconductor Products Sector, Austin, TX.Najafi, K., .Smart Sensors,. J. Micromechanics and Microengineering, Vol. 1, pp. 86.102. Ina, O.,( 1989), “Recent Intelligent Sensor Technology in Japan”,. Soc. Automotive Engineers, SAE891709,. Bharat, K.; Broder, A. (1998): A technique for measuring the relative size and overlap of public Web search engines. Computer Networks, 30(1–7), pp. 107–117. Maitan, J., (1994) “Overview of the Emerging Control and Communication Algorithms Suitable for Embedding Into Smart Sensors”, Proc. Sensors Expo, Cleveland, Sept. 20.22, pp. 485-500. E. J. Connoly , H. T. M. Pham , J. Groeneweg , P. M. Sarro and P. J. French, (2004) “Relative humidity sensors using porous SiC membrane and Al electrodes”, Sens. Actuators B, Chem., vol. 100, pp. 216. Installation Instructions for the HCH-1000 Series Capacitive Humidity Sensors ISSUE 150018326. S. B. Crary, W. G. Baer, J. C. Cowles, and K. D. Wise, (1990), “Digital compensation of high-performance silicon pressure transducers”, Sensors and Actuators A, 21–23:70–72. N. Yazdi, A. Mason, K. Najafi, and K. D.Wise, (2000), “A generic interface chip for capacitive sensors in low-power multi-parameter microsystems”, Sensors and Actuators A, 84:351–361. N. Yazdi, H. Kulah, and K. Najafi, (2004), “Precision readout circuits for capacitive microaccelerometers”, In Proceedings of IEEE Sensors Conference, pages 28–31. Aline Baggio, (2004), “Wireless sensor networks in precision agriculture”, The Netherlands IEEE Pervasive Computing, 3(1):38–45, Jan-Mar 2004. P. M. Faia, C. S. Futado and A. J. Ferreira, (2004), “Humidity sensing properties of a thick-film titania prepared by a slow spinning process,” Sens. Actuators B, Chem., vol. 101, pp. 190, Jun. 2004. E. J. Connoly , G. M. O" Halloran , H. T. M. Pham , P. M. Sarro and P. J. French, (2002), “Comparison of porous silicon, porous polysilicon and porous silicon carbide as materials for humidity sensing applications,” Sens. Actuators A, Phys., vol. 99, pp. 25, Apr. 2002.

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Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963 [14] A.Salehi , A. Nikfarjam and D.-J. Kalantari, (2006), “Pd/porous-GaAs Schottky contact for hydrogen sensing applications,” Sens. Actuators B, Chem., vol. 113, pp. 419, Jan. 2006. [15] B. S. Kang , S. Kim , F. Ren , B. P. Gila , C. R. Abernathy and S. J. Pearton, (2005), “Comparison of MOS and Schottky W/Pt–GaN diodes for hydrogen detection,” Sens. Actuators B, Chem., vol. 104, pp. 232, Jan. 2005. [16] Pertijs, Michiel A.P., Huijsing, Johan H., (2007), “Precision Temperature Sensors in CMOS Technology”, Springer, 2007. [17] John L. Schmalzel,Fernando Figueroa,Jon Morris,Mark Turowski, (2008), "Making Smart Sensors Intelligent: Building on the IEEE 1451.x Standards", 54th International Instrumentation Symposium, Pensacola, FL USA, pp. 1-33,May 2008. [18] Y. M. Niu, Y. S. Wong and G. S. Hong, (1998), "An intelligent sensor system approach for reliable tool flank wear recognition", International Journal of Advanced Manufacturing Technology, Springer London,Vol.14, No.2, pp. 77-84, February, 1998. [19] Randy Frank, (2000), “Understanding smart sensors”, Artech House sensors library, 2000. [20] Apala Ray, (2009), “Planning and Analysis Tool for Large Scale Deployment of Wireless Sensor Network”, International Journal of Next-Generation Networks (IJNGN), Vol.1, No.1, pp. 29 – 36, December 2009. [21] Jon S. Wilson, (2005), “Sensor Technology Handbook”, Elsevier Inc. 2005. [22] Dr. Shuchita Upadhayaya and Charu Gandhi, (2009), “Quality of Service Routing in Mobile Ad Hoc Networks Using Location and Energy Parameters”, International Journal of Wireless & Mobile Networks (IJWMN), Vol 1, No 2, pp. 139-147, November 2009. [23] NI Multisim user manualTM, “Analog Devices Edition: Getting Started with NI Multisim Analog Devices Edition”, available online. http://www.ni.com/pdf/manuals/372330a.pdf [24] Ramon PallaÁs-Areny, John G. Webster, (2000), “Sensors and signal conditioning”, A Wiley-Interscience publication, 2000. “HCH-1000 Series Capacitive Humidity Sensor”, available online, [25] Honeywell Datasheet®, http://sensing.honeywell.com/index.cfm/ci_id/146573/la_id/1/document/1/re_id/0. [26] Christos Efstratiou, Nigel Davies, Gerd Kortuem, Joe Finney, Rob Hooper, Mark Lowton, (2007), "Experiences of Designing and Deploying Intelligent Sensor Nodes to Monitor Hand-Arm Vibrations in the Field", Proc. of International Conference On Mobile Systems, Applications And Services, pp. 127-138,June 2007. [27] Shinya Ito and Kenji Yoshigoe, (2009), “Performance Evaluation of Consumed energy- Type-Aware Routing (Cetar) for Wireless Sensor Networks”, International Journal of Wireless & Mobile Networks (IJWMN), Vol 1, No 2,pp. 90-101, November 2009. [28] Peter Ashenden, Gregory Peterson, Darrell Teegarden, (2005 ), “The System Designer's Guide to VHDL-AMS”, Elsevier Inc, 2005. [29] Harry L. Field and John B. Solie, (2007), “Introduction to Agricultural Engineering Technology : A Problem Solving Approach”, Springer Science & Business Media, 2007. [30] Creed Huddleston, (2007), “Intelligent Sensor Design Using the Microchip dsPIC®” , Elsevier Inc., 2007. [31] Hemanta Kumar Kalita and Avijit Kar, (2009), “Wireless Sensor Network Security Analysis”, International Journal of NextGeneration Networks (IJNGN), Vol.1, No.1, pp. 1 – 10, December 2009. [32] Gerard C.M. Meijer, (2008 ), “Smart Sensor Systems”, John Wiley & Sons, 2008. [33] Kazem Sohraby, Daniel Minoli, Taieb Znati, (2007 ), “Wireless sensor networks: technology, protocols, and applications”, John Wiley & Sons, Inc., 2007.

ABOUT THE AUTHORS Kshitij Shinghal has 11 Years of experience in the field of Academic and is actively involved in research & development activities. He obtained his Masters degree (Digital Communication) in 2006 from UPTU, Lucknow. He started his career from MIT, Moradabad. Presently he is working as an Associate Professor & Head, Deptt of E&C Engg., at MIT Moradabad. He has published number of papers in national journals, conferences and seminars. He has guided two Masters, more than sixty students of B. Tech, and guiding three M. Tech. theses. He is an active Member of Various Professional Societies such as ISTE, IACSIT, IAENG etc. Arti Noor has 17 Years of R&D experience in the field of VLSI design & technology characterization, VHDL & computer programming, and speech synthesis. She obtained her Ph.D. (Electronics and communication Engineering) in 1990 from Banaras Hindu University, Banaras. She started her career from CEERI, Pilani and then joined the CDAC, Noida as Scientist EI. Presently she is working as an Associate Professor (Scientist-E) & HoD, M. Tech VLSI Division at CDAC Noida. She was also involved in various research development activities in CEERI, Pilani and in CDAC, Noida. She worked on many consultancy projects for ISRO Bangalore, IIT Delhi, ISAC Bangalore, and VSSC Trivandram. She has guided Two Ph.D in the area of Microelectronics and VLSI Design, 50 students of B.Tech/M.Sc./ M. Tech/ ME, more than 15 M.Tech. theses. Neelam Srivastava has 22 Years of experience in the field of Wireless Sensor Network design. She obtained her Ph.D. (Electronics and communication Engineering) in 2004 from Lucknow University, Lucknow. She started her career from IET, Lucknow. Presently she is working as an Associate Professor, Deptt of E&C Engg., at IET Lucknow. She is involved in various research & development activities. She is guiding two Ph.D. in the area of Wireless Sensor Networks, 50 students of B. Tech, more than 15 M. Tech. theses.

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Kshitij Shinghal et. al. / International Journal of Engineering Science and Technology Vol. 2(8), 2010, 3955-3963 Raghuvir Singh has experience in Research, Development, Teaching and Administration for more than 40 years. He obtained his B.Sc., B.E. (Telecommunication), M.E. (Electronics) and Ph.D. (Electronics and communication Engineering) Degrees in 1958, 1962 & 1970 respectively. He worked in CEERI, Pilani and retired as Head of Electronics & Communication Engineering Department of University of Roorkee (presently IIT Roorkee). He was awarded the IETE award in 1965, Khosla Research Award in 1970 and Anna University National Award for his outstanding career and contribution to Engineering and Technology in 1994.His name was recommended by IETE Award Committee for the FICCI Award in 1999. He has supervised 5 Ph.D. theses, 45 M.E. dissertations and has more than 50 publications in National and International Journals and conferences to his credit.

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