i-manager Publications

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 15 Issue 1

Most Cited

Volume 13 Issue 1 September - November 2022

Research Paper

7 Level Symmetrical Inverter for Photovoltaic System

Shamiksha Vande* , Aaditya Khare**

* Department of Power Electronics Engineering, Raipur Institute of Technology, Raipur, Chattisgarh, India.

** Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

Vande, S., and Khare, A. (2022). 7 Level Symmetrical Inverter for Photovoltaic System. i-manager's Journal on Electronics Engineering, 13(1), 1-12. https://doi.org/10.26634/jele.13.1.19299

Abstract

This research presents the design, implementation, and evaluation of a seven-level symmetrical inverter for photovoltaic systems. The inverter is based on a cascaded H-bridges topology with distinct DC assets and is capable of efficiently converting DC power from a photovoltaic system to AC power with low Total Harmonic Distortion (THD). The system consists of a solar panel, charge controller, battery bank, and a multilevel inverter circuit. The inverter employs Pulse Width Modulation (PWM) techniques to control the output voltage and frequency, ensuring that it is suitable for the AC load. The performance of the inverter is analyzed using Fast Fourier Transform (FFT) to determine the THD, and the results are compared with those of other multilevel inverter topologies such as diode clamped (neutral clamped) and flying capacitors. The efficiency of the seven-level symmetrical inverter is evaluated under different load conditions and compared with other multilevel inverters to determine potential energy savings and performance improvements. The analysis provides insights into the advantages and limitations of the seven-level symmetrical inverter for photovoltaic systems, highlighting the importance of distinct DC assets in achieving high efficiency and low THD. The results of this research can be useful for designing and implementing high-performance and cost-effective photovoltaic systems.

References

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[2]. Ali, A. H., Hamad, H. S., & Abdulrazzaq, A. A. (2019). An adaptable different-levels cascaded h-bridge inverter analysis for PV grid-connected systems. International Journal of Power Electronics and Drive System, 10(2), 831-841.

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[5]. Kaliamoorthy, M., Rajasekaran, V., & PraveenRaj, G. (2015, January). A novel single phase cascaded multilevel inverter for hybrid renewable energy sources. In 2015 International Conference on Advanced Computing and Communication Systems (pp. 1-10). IEEE.

[6]. Mekhilef, S., & Kadir, M. N. A. (2010). Novel vector control method for three-stage hybrid cascaded multilevel inverter. IEEE Transactions on Industrial Electronics, 58(4), 1339-1349.

[7]. Mustapha, M. F. (2013). Elimination THD using 9-level DC to AC Cascade H-bridge Multilevel Inverter (Doctoral dissertation, Universiti Tun Hussein Malaysia).

[8]. Prasad, K. D., & Venkateswarao, G. (2015). Multi-Level inverter with facts capability for reliable power distributions using wind energy as source. International Journal of Science Engineering and Advance Technology, 3(10), 777-784.

[9]. Sandeep, N., & Yaragatti, U. R. (2017). Operation and control of an improved hybrid nine-level inverter. IEEE Transactions on Industry Applications, 53(6), 5676-5686.

[10]. Sarkar, I., & Fernandes, B. G. (2014). 9-level hybrid symmetric cascaded multilevel converter for induction motor drive. Department of Electrical Engineering, Indian Institute of Technology Bombay.

[11]. Susheela, N., & Kumar, P. S. (2017). Performance evaluation of multicarrier based techniques for single phase hybrid multilevel inverter using reduced switches. Indonesian Journal of Electrical Engineering and Computer Science, 7(3), 676-686.

[12]. Wu, J. C., & Chou, C. W. (2013). A solar power generation system with a seven-level inverter. IEEE Transactions on Power Electronics, 29(7), 3454-3462.

[13]. Ye, M., Kang, L., Xiao, Y., Song, P., & Li, S. (2018). Modified hybrid modulation strategy with power balance control for H bridge hybrid cascaded seven level inverter. IET Power Electronics, 11(6), 1046-1054.

[14]. Zhang, Y., Wang, Q., Hu, C., Shen, W., Holmes, D. G., & Yu, X. (2019). A nine-level inverter for low-voltage applications. IEEE Transactions on Power Electronics, 35(2), 1659-1671.

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Research Paper

Optimizing Battery Charging Efficiency and Longevity through Smart Charging Techniques in Microcontroller-Based Systems

D. Suja Darling*

Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

Darling, D. S. (2022). Optimizing Battery Charging Efficiency and Longevity through Smart Charging Techniques in Microcontroller-Based Systems. i-manager's Journal on Electronics Engineering, 13(1), 13-25. https://doi.org/10.26634/jele.13.1.19347

Abstract

This paper explores the topic of optimizing battery charging efficiency and longevity through smart charging techniques in microcontroller-based systems. With the increasing demand for portable and wireless devices, efficient battery charging and longer battery life have become essential. The use of microcontroller-based systems allows for more precise and effective control of battery charging, resulting in improved efficiency and longevity. The current research is on intelligent battery charging systems and battery management systems and discusses various smart charging techniques that can be employed in microcontroller-based systems, including fuzzy logic control, model predictive control, and hybrid algorithms. The paper also highlights the importance of battery management and the need for a comprehensive battery management system to optimize charging efficiency and battery longevity. Finally, the paper presents several case studies and experimental results to demonstrate the effectiveness of these smart charging techniques in improving battery charging efficiency and longevity in microcontroller-based systems.

References

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[2]. Awasthi, A., Venkitusamy, K., Padmanaban, S., Selvamuthukumaran, R., Blaabjerg, F., & Singh, A. K. (2017). Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy, 133, 70-78.

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[6]. Chen, J., Xu, C., Wu, C., & Xu, W. (2016). Adaptive fuzzy logic control of fuel-cell-battery hybrid systems for electric vehicles. IEEE Transactions on Industrial Informatics, 14(1), 292-300.

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[8]. Garcia-Trivino, P., Torreglosa, J. P., Fernandez- Ramirez, L. M., & Jurado, F. (2018). Decentralized fuzzy logic control of microgrid for electric vehicle charging station. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6(2), 726-737.

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[11]. Lazaroiu, G. C., & Roscia, M. (2022). Fuzzy logic strategy for priority control of electric vehicle charging. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19236-19245.

[12]. Lee, J. H., Moon, J. S., Lee, Y. S., Kim, Y. R., & Won, C. Y. (2011, November). Fast charging technique for EV battery charger using three-phase AC-DC boost converter. In IECON 2011-37th Annual Conference of the IEEE Industrial Electronics Society (pp. 4577-4582). IEEE.

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[15]. Reindl, A., Schneider, V., Meier, H., & Niemetz, M. (2020). Software update of a decentralized, intelligent battery management system based on multimicrocomputers. In Tagungsband 2. Symposium Elektronik und Systemintegration ESI 2020:" Intelligente Systeme und ihre Komponenten: Forschung und industrielle Anwendung" (pp. 8-19).

[16]. Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014). Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of power sources, 256, 110-124.

[17]. Rutto, D. M. (2015). Battery charger efficiency and voltage behaviour in vented lead acid batteries in Kenya. International Journal of Sciences: Basic and Applied Research (IJSBAR), 20(1), 184-191.

[18]. Savari, G. F., Krishnasamy, V., Sathik, J., Ali, Z. M., & Aleem, S. H. A. (2020). Internet of Things based real-time electric vehicle load forecasting and charging station recommendation. ISA Transactions, 97, 431-447.

[19]. Singh, A. K., & Pathak, M. K. (2016, July). An improved two-stage non-isolated converter for on-board plug-in hybrid EV battery charger. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-6). IEEE.

[20]. Tsai, C. C. (2013, July). A reduced Li-Ion battery charger for portable applications. In 2013 Ninth International Conference on Natural Computation (ICNC) (pp. 1718-1722). IEEE.

[21]. Wang, Y., Zhou, C., & Chen, Z. (2022). Optimization of battery charging strategy based on nonlinear model predictive control. Energy, 241, 122877.

[22]. Yong, J. Y., Ramachandaramurthy, V. K., Tan, K. M., & Mithulananthan, N. (2015). A review on the state-of-theart technologies of electric vehicle, its impacts and prospects. Renewable and Sustainable Energy Reviews, 49, 365-385.

[23]. Zhang, Y., Liu, X., Wei, W., Peng, T., Hong, G., & Meng, C. (2020). Mobile charging: A novel charging system for electric vehicles in urban areas. Applied Energy, 278, 115648.

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Review Paper

Improving the Performance and Functionality of AI and Machine Learning in Electronic Devices and Systems

Sheryl Radley* , K. P. Ajitha Gladis**

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India.

** Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Radley, S., and Gladis, K. P. A. (2022). Improving the Performance and Functionality of AI and Machine Learning in Electronic Devices and Systems. i-manager's Journal on Electronics Engineering, 13(1), 26-38. https://doi.org/10.26634/jele.13.1.19345

Abstract

The goal of this research is to enhance the performance and functionality of Artificial Intelligence (AI) and Machine Learning (ML) in electronic devices and systems. Electronic devices and systems have become an integral part of our lives, with an increasing amount of data being generated and processed. The integration of AI and ML technologies in these devices and systems can significantly improve their performance, efficiency, and user experience. The research will focus on developing new algorithms, improving existing ones, or finding new ways to integrate AI and ML into electronic devices and systems. Areas of focus may include image and speech recognition, natural language processing, and decision making. In conclusion, this research aims to improve the performance and functionality of AI and ML in electronic devices and systems by developing new algorithms, improving existing ones, and finding new ways to integrate AI and ML into these devices and systems. The ultimate goal is to make electronic devices and systems more intelligent, efficient, and useful for users.

References

[1]. Ahmed, S., Hossain, M. F., Kaiser, M. S., Noor, M. B. T., Mahmud, M., & Chakraborty, C. (2021). Artificial intelligence and machine learning for ensuring security in smart cities. In Data-Driven Mining, Learning and Analytics for Secured Smart Cities: Trends and Advances (pp. 23-47). Cham: Springer International Publishing.

[2]. Anastasi, S., Madonna, M., & Monica, L. (2021). Implications of embedded artificial intelligencemachine learning on safety of machinery. Procedia Computer Science, 180, 338-343.

[3]. Fischer, M., Heim, D., Hofmann, A., Janiesch, C., Klima, C., & Winkelmann, A. (2020). A taxonomy and archetypes of smart services for smart living. Electronic Markets, 30(1), 131–149.

[4]. Fuchs, D. J. (2018). The dangers of human-like Bias in machine-learning algorithms. Missouri S & T's Peer to Peer, 2(1), 15.

[5]. Howard, A., Zhang, C., & Horvitz, E. (2017). Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems. IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), 1–7.

[6]. Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.

[7]. Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159–190.

[8]. Kühl, N., Mühlthaler, M., & Goutier, M. (2020). Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media. Electronic Markets, 30(2), 351–367.

[9]. Martens, B. (2018). The importance of data access regimes for artificial intelligence and machine learning. JRC Digital Economy Working Paper (pp. 23).

[10]. Ongsulee, P. (2017, November). Artificial intelligence, machine learning and deep learning. In 2017 15th international conference on ICT and knowledge engineering (ICT&KE) (pp. 1-6). IEEE.

[11]. Peters, M., Ketter, W., Saar-Tsechansky, M., & Collins, J. (2013). A reinforcement learning approach to autonomous decision-making in smart electricity markets. Machine Learning, 92(1), 5-39.

[12]. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S.S. (2019). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 1–36.

[13]. Rawat, D. B. (2021, April). Secure and trustworthy machine learning/artificial intelligence for multi-domain operations. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III (Vol.11746, pp. 44-54). SPIE.

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Review Paper

A Rigorous Approach to Microprocessor Verification using UVM

A. Bindu*

Department of Electrical and Electronics Engineering, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Bindu, A. (2022). A Rigorous Approach to Microprocessor Verification using UVM. i-manager's Journal on Electronics Engineering, 13(1), 39-50. https://doi.org/10.26634/jele.13.1.19344

Abstract

In today's fast-paced technology industry, microprocessors play an increasingly important role in a wide range of applications. However, verifying the correctness of complex microprocessor designs remains a significant challenge. To address this issue, a rigorous approach to microprocessor verification using the Universal Verification Methodology (UVM) is proposed. UVM provides a standardised and scalable approach to verifying digital designs, including microprocessors, and has been widely adopted in the industry. This research proposes a UVM-based verification framework for microprocessors that can identify and eliminate design errors early in the development cycle. The proposed approach covers functional verification, performance verification, and hardware-software co-verification. The effectiveness of the proposed approach is evaluated through a case study of a commercial microprocessor design, where the UVM-based verification framework successfully detected and resolved several design bugs. The results demonstrate the potential of the proposed rigorous approach to microprocessor verification using UVM to enhance the quality and reliability of microprocessor designs.

References

[1]. Barbirotta, M., Mastrandrea, A., Menichelli, F., Vigli, F., Blasi, L., Cheikh, A., ... & Olivieri, M. (2020, October). Fault resilience analysis of a RISC-V microprocessor design through a dedicated UVM environment. In 2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) (pp. 1-6). IEEE.

[2]. Chen, J., Wang, Z., Ding, Y., Feng, H., & Chen, D. (2021, September). Design and verification of RISC-V CPU based on HLS and UVM. In 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) (pp. 659-664). IEEE.

[3]. Fathy, K., & Salah, K. (2016, December). An efficient scenario based testing methodology using uvm. In 2016 17th International Workshop on Microprocessor and SOC Test and Verification (MTV) (pp. 57-60). IEEE.

[4]. Hasan, O., & Tahar, S. (2015). Formal verification methods. In Encyclopedia of Information Science and Technology (pp. 7162-7170). IGI Global.

[5]. Herdt, V., Große, D., Jentzsch, E., & Drechsler, R. (2020, September). Efficient cross-level testing for processor verification: A RISC-V case-study. In 2020 Forum for Specification and Design Languages (FDL) (pp. 1-7). IEEE.

[6]. Jones, R. B., O'Leary, J. W., Seger, C. J., Aagaard, M. D., & Melham, T. F. (2001). Practical formal verification in microprocessor design. IEEE Design & Test of Computers, 18(4), 16-25.

[7]. Liew, Y. H. (2022). Verification of RISC-V design with Universal Verification Methodology (UVM) (Doctoral dissertation, UTAR).

[8]. Loh, S. H., Liew, Y. H., & Sim, J. J. (2022, October). VLSI design course with verification of RISC-V design using universal verification methodology (UVM). In 2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 7-12). IEEE.

[9]. Mahmoud, K., Ahmed, R., Ayman, K., Aymau, M., Taie, W., Ibrahim, Y., ... & Salah, K. (2022, September). Towards a generic UVM. In 2022 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-6). IEEE.

[10]. Mehta, A. B. (2018). ASIC/SoC Functional Design Verification: A Comprehensive Guide to Technologies and Methodologies. Springer, Cham.

[11]. Qamar, S., Butt, W. H., Anwar, M. W., Azam, F., & Khan, M. Q. (2020, February). A comprehensive investigation of universal verification methodology (UVM) standard for design verification. In Proceedings of the 2020 9th International Conference on Software and Computer Applications (pp. 339-343).

[12]. Salah, K. (2017, December). A unified UVM architecture for flash-based memory. In 2017 18th International Workshop on Microprocessor and SOC Test and Verification (MTV) (pp. 1-4). IEEE.

[13]. Sharma, G., Bhargava, L., & Kumar, V. (2022). A composite System C-UVM abstract optimal path selection verification architecture for complex designs. Microelectronics Reliability, 131, 114508.

[14]. Stotland, I., Shpagilev, D., & Starikovskaya, N. (2016, October). UVM based approaches to functional verification of communication controllers of microprocessor systems. In 2016 IEEE East-West Design & Test Symposium (EWDTS) (pp. 1-4). IEEE.

[15]. Tieth, S. S., & Menghal, P. M. (2022). Verification of microprocessor using universal verification methodology. In Smart Technologies for Energy, Environment and Sustainable Development, Vol 1: Select Proceedings of ICSTEESD 2020 (pp. 495-502). Springer Nature Singapore, Singapore.

[16]. Warke, T. P. (2018). Verification of SD/MMC Controller IP Using UVM (Doctoral dissertation, Rochester Institute of Technology).

[17]. Yun, Y. N., Kim, J. B., Kim, N. D., & Min, B. (2011, November). Beyond UVM for practical SoC verification. In 2011 International SoC Design Conference (pp. 158-162). IEEE.

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Review Paper

RedTacton's Human Area Network-Based Healthcare Monitoring System

S. Stewart Kirubakaran *

Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.

Kirubakaran, S. S. (2022). RedTacton's Human Area Network-Based Healthcare Monitoring System. i-manager's Journal on Electronics Engineering, 13(1), 51-56. https://doi.org/10.26634/jele.13.1.19346

Abstract

RedTacton is a Human Area Networking (HAN) device that uses the surface of the human body to transmit data faster. It utilizes the tiny electric field that is emitted on the surface of the human body setting it apart completely from wireless and infrared technologies. When a human body comes into contact with a RedTacton transceiver, a transmission path is generated. It is possible to communicate using any part of the body, including the face, legs, arms, feet, hands, and fingers. The RedTacton chip has a transmitter and a receiver for accepting and sending digital data. This paper discusses RedTacton's working principles, and features. This paper describes a healthcare monitoring system that monitors patients' body temperatures, blood pressure, heart rates, and glucose levels without the use of wires, Bluetooth, Local Area Network, Radio Frequency (RF), or other external equipment. Consequently, the capital and investment cost is reduced.

References

[1]. Akshara, B., Dhivya, M., Anubama, S., & Kanimozhi, B. (2019). Intra-body communications in biomedical applications. Engineering Reports, 2(1), 103-107.

[2]. Jha, S. (2016). Red tacton human area networking. International Journal of Latest Trends in Engineering and Technology (IJLTET), 7(2), 615-622.

[3]. Nagatsuma, T., & Shinagawa, M. (2002). Photonic measurement technologies for high-frequency electronics. NTT Review, 14(6), 12-24.

[4]. Perwej, Y. (2016). A literature review of the human body as a communication medium using RedTacton. Communications on Applied Electronics (CAE), 4(9), 7-17.

[5]. Shinagawa, M. (2004). Development of electro-optic sensors for intra-body communications. NTT Technical Review, 2(2), 6-11.

[6]. SketchBubble. (n.d.). Human Area Network.

[7]. Upadhyaya, P. & Joshi, A. (2013). Use of redtacton device for transferring data in under water laboratory. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 798-799.

[8]. Upadhye, V., & Kallatti, P. C. (2016). Redtacton. International Journal of New Innovations in Engineering and Technology, 4(3), 17-23.

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 15 Issue 1

Most Read

Most Cited

Volume 13 Issue 1 September - November 2022

7 Level Symmetrical Inverter for Photovoltaic System

Shamiksha Vande* , Aaditya Khare**

* Department of Power Electronics Engineering, Raipur Institute of Technology, Raipur, Chattisgarh, India. ** Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

Vande, S., and Khare, A. (2022). 7 Level Symmetrical Inverter for Photovoltaic System. i-manager's Journal on Electronics Engineering, 13(1), 1-12. https://doi.org/10.26634/jele.13.1.19299

Abstract

References

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Optimizing Battery Charging Efficiency and Longevity through Smart Charging Techniques in Microcontroller-Based Systems

D. Suja Darling*

Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

Darling, D. S. (2022). Optimizing Battery Charging Efficiency and Longevity through Smart Charging Techniques in Microcontroller-Based Systems. i-manager's Journal on Electronics Engineering, 13(1), 13-25. https://doi.org/10.26634/jele.13.1.19347

Abstract

References

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Improving the Performance and Functionality of AI and Machine Learning in Electronic Devices and Systems

Sheryl Radley* , K. P. Ajitha Gladis**

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India. ** Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Radley, S., and Gladis, K. P. A. (2022). Improving the Performance and Functionality of AI and Machine Learning in Electronic Devices and Systems. i-manager's Journal on Electronics Engineering, 13(1), 26-38. https://doi.org/10.26634/jele.13.1.19345

Abstract

References

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A Rigorous Approach to Microprocessor Verification using UVM

A. Bindu*

Department of Electrical and Electronics Engineering, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Bindu, A. (2022). A Rigorous Approach to Microprocessor Verification using UVM. i-manager's Journal on Electronics Engineering, 13(1), 39-50. https://doi.org/10.26634/jele.13.1.19344

Abstract

References

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RedTacton's Human Area Network-Based Healthcare Monitoring System

S. Stewart Kirubakaran *

Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.

Kirubakaran, S. S. (2022). RedTacton's Human Area Network-Based Healthcare Monitoring System. i-manager's Journal on Electronics Engineering, 13(1), 51-56. https://doi.org/10.26634/jele.13.1.19346

Abstract

References

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* Department of Power Electronics Engineering, Raipur Institute of Technology, Raipur, Chattisgarh, India.

** Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

* Meenakshi College of Engineering, Chennai, Tamil Nadu, India.

** Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.