i-manager Publications

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 11 Issue 2

Neural Network-Based Monitoring of Critical Parameters in Drinking Water for Enhanced Quality Assurance
Monitoring and Improvement of PV Panel Efficiency Due to Thermal Effect
Model Predictive Control of a Serial Link Robot Manipulator
Direct Torque Control (DTC) and Three Phase Induction Motor Simulation in MATLAB/Simulink
IoT-Enabled Smart Water Tank Monitoring System with Android Application Control

Most Cited

Volume 9 Issue 2 July - December 2021

Research Paper

Harmony Search Algorithm and Teaching-Learning-based Optimization Approaches for Optimal Allocation and Sizing of PV Distributed Generations under Several Loading Conditions

Mahmoud Abdallah Attia* , Mohamed Ezzat, Ibrahim M. Diaa*

-Department of Electric Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Attia, M. A., Ezzat, M., and Diaa, I. B. (2021). Harmony Search Algorithm and Teaching-Learning-based Optimization Approaches for Optimal Allocation and Sizing of PV Distributed Generations under Several Loading Conditions. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 1-14. https://doi.org/10.26634/jic.9.2.14954

Abstract

This article aims to reduce power losses and improve system reliability by determining the optimal number, location and size of Photovoltaic Distributed Generators (PV-DG). The PV-DG is modeled as a negative load, draws reactive power from grid and injects real power to the grid. Optimum allocation of PV-DGs and their sizing is obtained by harmony search algorithm (HS) and teaching-learning-based optimization (TLBO) approaches. IEEE 33-bus distribution system was successfully demonstrated by the proposed approach. HS has proven its superiority against TLBO algorithm as it uses less DGs rating devices to enhance distribution system performance. The new contribution in this research is to optimize the number of DGs and does not operate with a given number of DGs (1 DG, 2 DG ,…..) as most of previous works. Another contribution is to study the optimization under several loading conditions.

References

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[2]. Algarni, A. A., & Bhattacharya, K. (2009). Disco operation considering DG units and their goodness factors. IEEE Transactions on Power Systems, 24(4), 1831-1840.

[3]. Ali, E. S., Abd Elazim, S. M., & Abdelaziz, A. Y. (2016). Ant lion optimization algorithm for renewable distributed generations. Energy, 116, 445-458.

[4]. Augugliaro, A., Dusonchet, L., Favuzza, S., Ippolito, M. G., & Sanseverino, E. R. (2010). A backward sweep method for power flow solution in distribution networks. International Journal of Electrical Power & Energy Systems, 32(4), 271-280.

[5]. Banerjee, B., & Islam, S. M. (2011). Reliability based optimum location of distributed generation. International Journal of Electrical Power & Energy Systems, 33(8), 1470-1478.

[6]. Bompard, E., Carpaneto, E., Chicco, G., & Napoli, R. (2000). Convergence of the backward/forward sweep method for the load-flow analysis of radial distribution systems. International journal of Electrical Power & Energy Systems, 22(7), 521-530.

[7]. Carpinelli, G., Celli, G., Mocci, S., Pilo, F., & Russo, A. (2005). Optimisation of embedded generation sizing and siting by using a double trade-off method. IEE Proceedings-Generation, Transmission and Distribution, 152(4), 503-513.

[8]. El-Khattam, W., Hegazy, Y. G., & Salama, M. M. A. (2005). An integrated distributed generation optimization model for distribution system planning. IEEE Transactions on Power Systems, 20(2), 1158-1165.

[9]. Elmitwally, A. (2013). A new algorithm for allocating multiple distributed generation units based on load centroid concept. Alexandria Engineering Journal, 52(4), 655-663.

[10]. Esmaili, M. (2013). Placement of minimum distributed generation units observing power losses and voltage stability with network constraints. IET Generation, Transmission & Distribution, 7(8), 813-821.

[11]. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60-68.

[12]. Hien, N. C., Mithulananthan, N., & Bansal, R. C. (2013). Location and sizing of distributed generation units for loadabilty enhancement in primary feeder. IEEE Systems Journal, 7(4), 797-806.

[13]. Kaundal, V., Mondal, A. K., Sharma, P., & Bansal, K. (2015). Tracing of shading effect on underachieving SPV cell of an SPV grid using wireless sensor network. Engineering Science and Technology, an International Journal, 18(3), 475-484.

[14]. Keane, A., & O'Malley, M. (2005). Optimal allocation of embedded generation on distribution networks. IEEE Transactions on Power Systems, 20(3), 1640-1646.

[15]. Kim, K. H., Lee, Y. J., Rhee, S. B., Lee, S. K., & You, S. K. (2002, July). Dispersed generator placement using fuzzyGA in distribution systems. In IEEE Power Engineering Society Summer Meeting, (Vol. 3, pp. 1148-1153). IEEE.

[16]. Kumar, N., Chelliah, T. R., & Srivastava, S. P. (2016). Analysis of doubly-fed induction machine operating at motoring mode subjected to voltage sag. Engineering Science and Technology, an International Journal, 19(3), 1117-1131.

[17]. Liu, B., Zhuo, F., Zhu, Y., & Yi, H. (2014). System operation and energy management of a renewable energy-based DC micro-grid for high penetration depth application. IEEE Transactions on Smart Grid, 6(3), 1147-1155.

[18]. Mithulananthan, N., Oo, T., & Phu, L. V. (2015). Distributed generator placement in power distribution system using genetic algorithm to reduce losses. Science & Technology Asia, 55-62.

[19]. Muneer, W., Bhattacharya, K., & Canizares, C. A. (2011). Large-scale solar PV investment models, tools, and analysis: The Ontario case. IEEE Transactions on Power Systems, 26(4), 2547-2555.

[20]. Nayanar, V., Kumaresan, N., & Gounden, N. G. A. (2016). Wind-driven SEIG supplying DC microgrid through a single-stage power converter. Engineering Science and Technology, an International Journal, 19(3), 1600-1607.

[21]. Nekooei, K., Farsangi, M. M., Nezamabadi-Pour, H., & Lee, K. Y. (2013). An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Transactions on Smart Grid, 4(1), 557-567.

[22]. Niknam, T., Ranjbar, A. M., Shirani, A. R., Mozafari, B., & Ostadi, A. (2005, October). Optimal operation of distribution system with regard to distributed generation: a comparison of evolutionary methods. In Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, (Vol. 4, pp. 2690-2697). IEEE.

[23]. Rao, R. S., Narasimham, S. V. L., & Ramalingaraju, M. (2008). Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm. International Journal of Electrical Power and Energy Systems Engineering, 1(2), 116-122.

[24]. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.

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[27]. Shaaban, M. F., Atwa, Y. M., & El-Saadany, E. F. (2012). DG allocation for benefit maximization in distribution networks. IEEE Transactions on Power Systems, 28(2), 639-649.

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[29]. Sunisith, S., & Meena, K. (2014). Backward/forward sweep based distribution load flow method. International Electrical Engineering Journal (IEEJ), 5(9), 1539-1544.

[30]. Sutthibun, T., & Bhasaputra, P. (2010, May). Multiobjective optimal distributed generation placement using simulated annealing. In ECTI-CON2010: The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (pp. 810-813). IEEE.

[31]. Wang, L., & Singh, C. (2008). Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(6), 757-764.

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Research Paper

Expert Methodology for Loss Minimization in Electrical Distribution System through Reconfiguration

P. Ravi Babu*

Department of Electrical and Electronics Engineering, CVR College of Engineering, Hyderabad, India.

Babu, P. R. (2021). Expert Methodology for Loss Minimization in Electrical Distribution System through Reconfiguration. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 15-23. https://doi.org/10.26634/jic.9.2.18046

Abstract

Using the heuristic search approach, this article has attempted a methodology to reduce power (I2R) loss through optimal reconfiguration in the radial distribution system, resulting in a better and optimal design. The suggested approach is verified by testing it on IEEE 33-bus and IEEE 69-bus systems. MATLAB simulation is used to test the proposed solution and the results are presented in this paper.

References

[1]. Abdelaziz, A. Y., Mohammed, F. M., Mekhamer, S. F., & Badr, M. A. L. (2009). Distribution systems reconfiguration using a modified particle swarm optimization algorithm. Electric Power Systems Research, 79(11), 1521-1530.

[2]. Babu, P. R., Pasunuru, S., & Vaishnavi, G. (2017, August). Genetic algorithm and reconfiguration for loss minimization in radial distribution system. In 2017, International Conference on Smart grids, Power and Advanced Control Engineering (ICSPACE) (pp. 294-297). IEEE.

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[8]. Duan, D. L., Ling, X. D., Wu, X. Y., & Zhong, B. (2015). Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm. International Journal of Electrical Power & Energy Systems, 64, 88-95.

[9]. Ghosh, S., & Sherpa, K. S. (2008). An efficient method for load-flow solution of radial distribution networks. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2(9), 2094 -2101.

[10]. Goswami, S. K., & Basu, S. K. (1992). A new algorithm for the reconfiguration of distribution feeders for loss minimization. IEEE Transactions on Power Delivery, 7(3), 1484-1491.

[11]. Kashem, M. A., Ganapathy, V., Jasmon, G. B., & Buhari, M. I. (2000, April). A novel method for loss minimization in distribution networks. In Proceedings of International Conference on Electric Utility Deregulation and Restructuring and Power Technologies). (pp. 251-256). IEEE.

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[14]. Ramesh, L., Chowdhury, S. P., Chowdhury, S., Natarajan, A. A., & Gaunt, C. T. (2009). Minimization of power loss in distribution networks by different techniques. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 3(4), 661-667.

[15]. Rao, R. S., Ravindra, K., Satish, K., & Narasimham, S. V. L. (2012). Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Transactions on Power Systems, 28(1), 317-325.

[16]. Rathore, P. K. S., Chauhan, D. S., & Singh, R. P. (2019). Decentralized solar rooftop photovoltaic in India: On the path of sustainable energy security. Renewable Energy, 131, 297-307.

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Research Paper

A Comparative Study on Different Structures of PWM based MEMS Pressure Sensor

K. Sivasundari*

Department of Electronics and Instrumentation, Annamalai University, Chidambaram, Tamil Nadu, India.

Sivasundari, K. (2021). A Comparative Study on Different Structures of PWM Based MEMS Pressure Sensor. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 24-27. https://doi.org/10.26634/jic.9.2.16815

Abstract

Pressure measurement is critical in many process industries. For commercial purposes, industry uses a variety of pressure sensors for measurement, including sensors based on Micro Electro-Mechanical Systems (MEMS) technology. In this paper, a comparative study with two different Pulse-Width Modulation (PWM) based MEMS pressure sensor has been made. A similar study was made on a sensor with parallel plate mechanical switch and a cantilever based mechanical switch. The result shows that a better mechanical stability can be achieved by the parallel plate structure and better stability is achieved with cantilever structure.

References

[1]. Belavič, D., Santo Zarnik, M., Hrovat, M., Makarovič, K., Kocjan, S., Hodnik, M., ... & Holc, J. (2011, September). LTCC-based capacitive pressure sensor in a harsh environment. In 18th European Microelectronics & Packaging Conference (pp. 1-4). IEEE.

[2]. Brox, D., Mohammadi, A. R., & Takahata, K. (2011). Non-lithographically microfabricated capacitive pressure sensor for biomedical applications. Electronics Letters, 47(18), 1015-1017.

[3]. Chiang, C. F., Graham, A. B., Messana, M. W., Provine, J., Buchman, D. T., O'Brien, G. J., & Kenny, T. W. (2011, June). Capacitive absolute pressure sensor with independent electrode and membrane sizes for improved fractional capacitance change. In 2011, 16th International Solid-State Sensors, Actuators and Microsystems Conference (pp. 894-897). IEEE.

[4]. Eswaran, P., & Malarvizhi, S. (2012). Design analysis of MEMS capacitive differential pressure sensor for aircraft altimeter. International Journal of Applied Physics and Mathematics, 2(1), 14-20.

[5]. Ho, S. S., Rajgopal, S., & Mehregany, M. (2010, January). Stainless steel capacitive pressure sensor for high pressure and corrosive media applications. In 2010, IEEE 23rd International Conference on Micro Electro Mechanical Systems (MEMS) (pp. 647-650). IEEE.

[6]. Marghescu, C., Ionescu, C., Svasta, P., Santo Zarnik, M., & Belavič, D. (2010, May). FE modeling of capacitive pressure sensors realized in LTCC technology. In 33rd International Spring Seminar on Electronics Technology, ISSE 2010 (pp. 350-353). IEEE.

[7]. Sivasundari, K., Daniel, R. J., & Sumangala, K. (2017). Evolution, modelling and simulation of MEMS PWM pressure sensor employing cantilever switch and SOI diaphragm. Microsystem Technologies, 23(8), 3559-3574.

[8]. Sivasundari, K., Sugantharathnam, D. M., & Daniel, R. J. (2019). Fabrication of surface micromachined molybdenum cantilever beams for PWM MEMS pressure sensor and issues that affect yield. Microsystem Technologies, 25(7), 2535-2545.

[9]. Wheeler, J. W., Dabling, J. G., Chinn, D., Turner, T., Filatov, A., Anderson, L., & Rohrer, B. (2011, August). MEMS-based bubble pressure sensor for prosthetic socket interface pressure measurement. In 2011, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2925-2928). IEEE.

[10]. Ya, M. L., Khan, S., Nordin, A. N., Al-Khateeb, K. A., Islam, M. R., & Naji, A. W. (2011, May). Novel MEMS fully differential capacitive transducer design and analysis. In 2011, 4th International Conference on Mechatronics (ICOM) (pp. 1-4). IEEE.

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Research Paper

Detecting Obstacles using Image Processing and Ultrasonic Sensor

P. Thirusenthil Kumaran* , B. Shivani, V. Roshini*, M. Akash****

*-****Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, India.

Kumaran, P. T., Shivani, B., Roshini, V., and Akash, M. (2021). Detecting Obstacles using Image Processing and Ultrasonic Sensor. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 28-37. https://doi.org/10.26634/jic.9.2.18229

Abstract

This paper presents obstacle detection using image processing and ultrasonic sensor devices. This innovation allows visually handicapped people to navigate confidently and quickly using ultrasonic instrumentation and image processing using Raspberry Pi to detect nearby obstacles and guide them with real-time voice information. This solution makes use of Google Assistant and the Python programming language. The main goal of this project is to assist visually challenged people, and the device can be attached to a hat. This is a simple wearable gadget for the blind.

References

[1]. Agrawal, M. P., & Gupta, A. R. (2018, April). Smart stick for the blind and visually impaired people. In 2018, Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 542-545). IEEE.

[2]. Chaccour, K., & Badr, G. (2016, September). Computer vision guidance system for indoor navigation of visually impaired people. In 2016, IEEE 8th International Conference on Intelligent Systems (IS) (pp. 449-454). IEEE.

[3]. Chen, J., Li, Z., Dong, M., & Wang, X. (2010, June). Blind path identification system design base on rfid. In 2010, International Conference on Electrical and Control Engineering (pp. 548-551). IEEE.

[4]. Dey, N., Paul, A., Ghosh, P., Mukherjee, C., De, R., & Dey, S. (2018, March). Ultrasonic sensor based smart blind stick. In 2018, International Conference on Current Trends Towards Converging Technologies (ICCTCT) (pp. 1-4). IEEE.

[5]. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2009). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627-1645.

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[8]. Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., ... & Murphy, K. (2017). Speed/accuracy tradeoffs for modern convolutional object detectors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7310-7311).

[9]. Innet, S., & Ritnoom, N. (2009, February). An application of infrared sensors for electronic white stick. In 2008, International Symposium on Intelligent Signal Processing and Communications Systems (pp. 1-4). IEEE.

[10]. Kallara, S. B., Raj, M., Raju, R., Mathew, N. J., Padmaprabha, V. R., & Divya, D. S. (2017, November). Indriya—a smart guidance system for the visually impaired. In 2017, International Conference on Inventive Computing and Informatics (ICICI) (pp. 26-29). IEEE.

[11]. Nada, A. A., Fakhr, M. A., & Seddik, A. F. (2015, July). Assistive infrared sensor based smart stick for blind people. In 2015, Science and Information Conference (SAI) (pp. 1149-1154). IEEE.

[12]. Nagarajan, R., Yaacob, S., & Sainarayanan, G. (2002, June). Fuzzy clustering in vision recognition applied in NAVI. In 2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622) (pp. 261-266). IEEE.

[13]. Papageorgiou, C. P., Oren, M., & Poggio, T. (1998, January). A general framework for object detection. In Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) (pp. 555-562). IEEE.

[14]. Paul, I. J. L., Sasirekha, S., Mohanavalli, S., Jayashree, C., Priya, P. M., & Monika, K. (2019, February). Smart Eye for Visually Impaired-An aid to help the blind people. In 2019, International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-5). IEEE.

[15]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779-788).

[16]. Ren, P., Fang, W., & Djahel, S. (2017, September). A novel YOLO-Based real-time people counting approach. In 2017, International Smart Cities Conference (ISC2) (pp. 1-2). IEEE.

[17]. Sainarayanan, G., Nagarajan, R., & Yaacob, S. (2007). Fuzzy image processing scheme for autonomous navigation of human blind. Applied Soft Computing, 7(1), 257-264.

[18]. Karimi, G. (2021, April 15). Introduction to YOLO Algorithm for Object Detection.

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Research Paper

Automatic Speed Control System for Vehicles using Lab View

Viswanathan K.* , Ashok R.**

*-**Department of Electrical and Electronics Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.

Viswanathan, K., and Ashok, R. (2021). Automatic Speed Control System for Vehicles using Lab View. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 38-43. https://doi.org/10.26634/jic.9.2.15945

Abstract

Vehicle accident rates are proportional with the increase in vehicular traffic. Road accidents results in injuries, loss of life and wealth, and in addition, causes lots of personal inconveniences due to the legal proceedings and insurance claim procedures. Most accidents occur due to the human error and non-compliance with traffic rules. The objective of this paper is to propose a technological solution for the implementation of road signs in cases where the driver inadvertently misses these signs. The solution uses RFID technology, thereby alerts the driver and reduces the speed of the vehicle. This project will be useful in sensitive areas such as hospitals, school areas, accident prone areas, etc. where speed limits are enforced. When a vehicle enters a speed limit zone, the vehicle receives an RF signal from a specially programmed RFID tag placed on the lanes, and the vehicle is controlled based on the programmed information. In any worst case scenario the vehicle would be stopped with the help of RFID EM-18 module installed in the vehicle.

References

[1]. Allsop, R. E. (1976). SIGCAP: A computer program for assessing the traffic capacity of signal-controlled road junctions. Traffic Engineering & Control, 17(819), 338-341.

[2]. Ferreira, M., Fernandes, R., Conceição, H., Viriyasitavat, W., & Tonguz, O. K. (2010, September). Selforganized traffic control. In Proceedings of the seventh ACM International workshop on VehiculAr InterNETworking (pp. 85-90).

[3]. Ilie-Zudor, E., Kemeny, Z., Egri, P., & Monostori, L. (2006, November). The RFID technology and its current applications. In Conference Proceedings of the Modern Information Technology in the Innovation Processes of the Industrial Enterprises (MITIP) (pp. 29-36).

[4]. Li, C. (2010, July). Automatic vehicle identification (AVI) system based on RFID. In 2010 International Conference on Anti-Counterfeiting, Security and Identification (pp. 281-284). IEEE.

[5]. List, G. F., & Cetin, M. (2004). Modeling traffic signal control using Petri nets. IEEE Transactions on Intelligent Transportation Systems, 5(3), 177-187.

[6]. Sato, Y., & Makanae, K. (2006). Development and evaluation of in-vehicle signing system utilizing RFID tags as digital traffic signs. International Journal of ITS Research, 4(1), 53-58.

[7]. Telaprolu, M. K., Varun, K., Rao, S. N., Sarma, V. V., Ratnakanth, E. K., & Banda, V. (2009, November). Microcontroller based automation of variable electronic speed governor. In 2009, IEEE International Conference on Vehicular Electronics and Safety (ICVES) (pp. 151-155). IEEE.

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 11 Issue 2

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Volume 9 Issue 2 July - December 2021

Harmony Search Algorithm and Teaching-Learning-based Optimization Approaches for Optimal Allocation and Sizing of PV Distributed Generations under Several Loading Conditions

Mahmoud Abdallah Attia* , Mohamed Ezzat**, Ibrahim M. Diaa***

*-**Department of Electric Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt. ***Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Abstract

References

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Expert Methodology for Loss Minimization in Electrical Distribution System through Reconfiguration

P. Ravi Babu*

Department of Electrical and Electronics Engineering, CVR College of Engineering, Hyderabad, India.

Babu, P. R. (2021). Expert Methodology for Loss Minimization in Electrical Distribution System through Reconfiguration. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 15-23. https://doi.org/10.26634/jic.9.2.18046

Abstract

References

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A Comparative Study on Different Structures of PWM based MEMS Pressure Sensor

K. Sivasundari*

Department of Electronics and Instrumentation, Annamalai University, Chidambaram, Tamil Nadu, India.

Sivasundari, K. (2021). A Comparative Study on Different Structures of PWM Based MEMS Pressure Sensor. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 24-27. https://doi.org/10.26634/jic.9.2.16815

Abstract

References

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Detecting Obstacles using Image Processing and Ultrasonic Sensor

P. Thirusenthil Kumaran* , B. Shivani**, V. Roshini***, M. Akash****

*-****Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, India.

Kumaran, P. T., Shivani, B., Roshini, V., and Akash, M. (2021). Detecting Obstacles using Image Processing and Ultrasonic Sensor. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 28-37. https://doi.org/10.26634/jic.9.2.18229

Abstract

References

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Automatic Speed Control System for Vehicles using Lab View

Viswanathan K.* , Ashok R.**

*-**Department of Electrical and Electronics Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.

Viswanathan, K., and Ashok, R. (2021). Automatic Speed Control System for Vehicles using Lab View. i-manager's Journal on Instrumentation and Control Engineering, 9(2), 38-43. https://doi.org/10.26634/jic.9.2.15945

Abstract

References

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Mahmoud Abdallah Attia* , Mohamed Ezzat, Ibrahim M. Diaa*

-Department of Electric Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

P. Thirusenthil Kumaran* , B. Shivani, V. Roshini*, M. Akash****