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Annual Conference of Industrial electronics Society

作者： Jaydeep Saha He Bin Sai Srinivas Manohar Rahul S. Bhujade Tang Gongyue Sanjib Kumar Panda Department of Electrical & Computer Engineering (ECE) National University of Singapore (NUS) Institute of Microelectronics (IME) Agency for Science Technology & Research (A*STAR) Singapore

Though e-motorbikes comprise a considerable proportion of electric vehicles (EVs), it's drive-train design is rarely presented in the literature. This paper focuses on reviewing the important design considerations for drive-train inverters of next-generation e-motorbikes. A compilation of state-of-the-art e-motorbikes' specifications reveals that the inverter requirements are diverse in terms of power with a thrust towards enhanced efficiency and power density. A review of the power-electronic design of this inverter is presented to elicit the topology choices, battery-pack design based on DC-link voltage choice, as well as the benefits of using new wide-bandgap devices and latest ceramic capacitors. An overview of the inverter's cooling system design shows the merits of liquid-cooling for compact inverter design.

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Integrated Wearable System for Enhanced Soldier Health Monitoring and Battlefield Awareness

Integrated Wearable System for Enhanced Soldier Health Monit...

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Advancements in Electrical, electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： S. Umamaheswari V. Jagannath B. Shuriya Dept of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India Department of Computer Science Engineering PPG Institute of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The proposed system uses advanced wearable technologies to make military personnel safe, healthy, and situation-aware. This array of sensors-heart rate and temperature monitors, proximity sensors, and an ESP32 camera for video streaming in real-time-would provide all the support a military need in battle. Vital signs monitoring is real-time; threshold-based alerts will result in an instant response to the abnormal condition. RF and Wi-Fi modules will be used in order to get reliable communication with data transfer among soldiers and the command center within minimal latency, robust performance for a variety of distances, and the talk time with resource usage focus for ensuring longer operation from the same battery. Scalability and flexibility of the system can be used to accommodate diverse operational needs in military or emergency responses.

关键词： Temperature sensors Radio frequency Scalability Streaming media Cameras Real-time systems Sensors Reliability Biomedical monitoring Sensor arrays

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Retraction Note: Adaptive rate and modulation scheme for M2M communication using quantum optical and 5G communication

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Chillakuru, Prameela Devi Vignesh, G. D. Maheswari, S. Sabeenian, R. S. Mouleswararao, B. Markkandan, S. Artificial Intelligence and Data Science Velammal Institute of Technology Panchetti Ponneri Chennai India Department of ECE St Joseph’s College of Engineering Chennai India Department of ECE Panimalar Engineering College Chennai India Department of ECE Sona College of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur dt India School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India

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Design and Implementation of Accident Detection and Ant Optimization in VANETs in Smart Cities

Design and Implementation of Accident Detection and Ant Opti...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： L.Lakshmi Tejaswi C. Balaji ATA Kishore Kumar S Selvalakshmi G.B. Santhi D. Arulanantham Department of MCA QIS College of Engineering and Technology AP IN Department of Computer Science Dr MGR Educational and Research Institute Chennai Department of ECE School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Electronics and Instrumentation Engineering R.M.K. Engineering College Chennai Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Department of ECE Builders Engineering College Kangeyam

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

A comprehensive framework for accident detection and ant optimization in Vehicular Ad-hoc Networks (VANETs) within smart cities. It emphasizes the need for efficient communication networks for accident response and traffic improvements. The proposed model integrates various components, including onboard units, traffic cameras, and dashboard systems, to swiftly detect accidents and coordinate emergency responses. Additionally, the use of ant optimization algorithms enhances route optimization and reliability in VANETs, leveraging bio-inspired principles for efficient data transmission. The abstract highlights the relevance of VANETs in improving road safety, traffic management, and emergency response mechanisms, offering a promising solution for smart city infrastructure.

关键词： Smart cities Vehicular ad hoc networks Transportation Throughput Routing Routing protocols Delays Security Optimization Accidents

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AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems

AI-Driven Energy Optimization: Household Power Consumption P...

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2024 International Conference on microelectronics, ICM 2024

作者： Kolluru, Vinoth Kumar Challagundla, Yagnesh Chintakunta, Advaitha Naidu Roy, Bappadittya Bermak, Amine Renuka Devi, S.M. Stevens Institute of Technology Department of Data Science HobokenNJ United States University of Florida Engineering Education Dept. United States University of North Carolina Department of Computer Science and Engineering Charlotte United States School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India College of Science and Engineering Hamad Bin Khalifa Universityy Ar-Rayyan Qatar Dept. of Ece Hyderabad500104 India

ISBN: (纸本)9798350379396

Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) network models for estimating home power consumption using time-series data. The first model employs a standard LSTM technique with multivariate characteristics including global active power, voltage, and sub-metering variables. This technique strikes a balance between prediction accuracy and computing economy, making it ideal for resource-constrained applications. The second model provides an optimized version of the LSTM, which uses PyTorch and Ray Tune for hyperparameter optimization. The optimization focuses on tweaking learning rates, batch sizes, and LSTM layers to improve model accuracy and convergence speed. Using hyperparameter tuning, the Mean Squared Error (MSE) for global active power forecasts is decreased to 0.0018, proving its appropriateness for low-resource IoT systems. Both models are tested on a resampled real-world household electric power consumption dataset for effective training. The study emphasizes the advantages of multivariate time-series analysis and hyperparameter optimization, demonstrating that the optimized LSTM model can accurately predict energy consumption, enhance energy management in smart homes, and lower computing costs. Future work will look into combining more IoT data streams and real-world deployment for further improvement. The findings add to the expanding body of knowledge about energy optimization in IoT environments, addressing the crucial demand for effective, real-time energy management systems. © 2024 IEEE.

关键词： Mean square error

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Color Retention Assessment of Duhat Dye to Cotton Fabric Using Color Fastness Test and Digital Image Processing

Color Retention Assessment of Duhat Dye to Cotton Fabric Usi...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Novelero, Jilbert Mallari, Rennel Cruz, Jennifer Dela Gomez, Donna Banal, Ricardo School of Electrical Electronics and Computer Engineering Mapua University Metro Manila Philippines College of Engineering and Architecture Angeles University Foundation Angeles City Philippines School of Eece School of Graduate Studies Mapua University Manila Philippines College of Engineering Systems Plus College Foundation Ece Department Angeles City Philippines

ISBN: (纸本)9798350302684

Synthetic dyes have changed perceptions of natural dyes, but their chemical origins pose environmental risks. Seeking an eco-friendly alternative, the indigenous fruit duhat from the Philippines shows promise as a potential dye for cotton fabrics. This study aimed to explore duhat's dyeing capability using a cost-effective and non-invasive testing technique. The evaluation began by creating a duhat filtrate and dyeing white cotton fabrics following standard procedures from the AATCC 61 1A handbook for color fastness testing through washing. The results and statistical analysis showed that when 150g of duhat skin was mixed with 50 mL of ethyl alcohol, it produced a filtrate with commendable color retention. An image enhancement technique converted RGB images to grayscale, aiding visual grading of color changes in cotton fabrics. The duhat skin dye scored a grade of 4 for noticeable color change and a grade of 3 for moderate staining on adjacent undyed fabric, according to the results from the gray scale for color change and staining. Furthermore, a paired t-test on the numerical data indicated no significant color change after the color fastness test compared to the fabric's original color. In conclusion, the experimentation suggests that duhat could serve as a potential natural dye source for cotton fabrics. Embracing duhat's coloring properties may reduce our reliance on synthetic dyes and help protect the environment. © 2023 IEEE.

关键词： Cost effectiveness

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Enhanced Optimization-based Node Localization Scheme for WSN 8

Enhanced Optimization-based Node Localization Scheme for WSN

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8th International Conference on Communication and electronics Systems, ICCES 2023

作者： Senthil Murugan, M. Ravindranath, P.V. Jayareka, K.S. Sundareswaran, T. Singh, Charanjeet Subbaiyan, Arunmurugan St.Joseph's Institute of Technology Omr Electrical and Electronics Chennai119 India School of Computer Studies Coimbatore Sulur641402 India Sona College of Technology Department of Computer Science and Engineering Salem India Rmk College of Engineering and Technology Physics Chennai India Deenbandhu Chhotu Ram University of Science and Technology Electronics and Communication Department Murthal India Periyar Maniammai Institute of Science and Technology Ece Tamilnadu Thanjavur India

ISBN: (纸本)9798350396638

The location of a sensor node is crucial for several uses of WSN, including environmental sensing, search and rescue, geographical routing and tracking, and so on. The accuracy with which individual sensor terminals in a wireless sensor system can be located has a substantial bearing on the network's overall effectiveness. Using information about the locations of anchor nodes gathered from a variety of measures to pinpoint the unknown target nodes' placements is known as localization. This is a problem classed as NP-hard, which means it cannot be solved using classical deterministic methods. To overcome this difficulty in wireless sensor networks, presented an enhanced version of a swarm intelligence technique called the whale optimization method. This implementation, using a quasi-reflected-based learning method, fixes the problems with the original whale optimization approach. In order to ensure that the proposed metaheuristic performs as well as existing state-of-the-art metaheuristics, it is evaluated using the same network architecture and experimental settings. Proposed method use a Gaussian-modified RSSI to achieve a more accurate reading of the range and a new whale optimization algorithm to optimize the positioning of the nodes to boost the positioning accuracy, both of which are designed to compensate for the shortcomings of the positioning algorithm of both Received signal strength indicator (RSSI) ranging model. Based on the results of 20 separate benchmark function tests, the upgraded whale algorithm outperforms the whale optimization method and other swarm intelligence systems. The suggested location algorithm provides more precise placement than the original RSSI method. It is mentioned that the cluster intelligence algorithm has significant benefits over the currently implemented RSSI in positioning WSN nodes, and the improved algorithm that is described in this work has even more benefits compared to various cluster intelligence methods in tends to

关键词： Swarm intelligence

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Adaptive Vehicle Control System for Improved Safety With Image Processing

Adaptive Vehicle Control System for Improved Safety With Ima...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： N. Vijayalakshmi L. Priya S. Ramaraj Menakadevi Nanjundan Satheesh Kumar Palanisamy Ayodeji Olalekan Salau Sepiribo Lucky Braide Department of Computer Science and Engineering Alliance School of Advanced Compuitng Alliance University Bangalore Department of ECE Sri Eshwar College of Engineering (Autonomous) Kinathukadavu Coimbatore Tamilnadu India Department of Computer Science and Engineering Karpagam College of Engineering India Department of ECE Karpagam Institute of Technology Coimbatore Department of ECE BMS Institute of Technology and Management Bengaluru Karnataka India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies.

关键词： Adaptive systems Roads Pipelines Motors Cameras Real-time systems Safety Smart transportation Cruise control Vehicles

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A Review on Wideband High-Gain Low-THz Antennas for Wireless Applications

A Review on Wideband High-Gain Low-THz Antennas for Wireless...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Dhamodharan Srinivasan M Premkumar S Deepa Nivethika P Dhilipkumar S Parameswari M Kalpana Chowdary Department of Computer and Communication Engineering Centre for Blockchain and Cybersecurity Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering SSM Institute of Engineering and Technology Dindigul India School of Computer Science and Engineering VIT University Chennai India Department of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of ECE Sri Sairam Insitute of Technology Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas.

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Robust Modelling of an Artificial Intelligence Assisted Brainwaves based Robotic Motion Control System using Internet of Things and EEG Signals

Robust Modelling of an Artificial Intelligence Assisted Brai...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： S. Vijayalakshmi M Bharaneedharan K. Priya Kalaivani G. Saranya S. Ravi Department of ECE R.M.K. Engineering College Chennai Department of ECE S.A. Engineering College Chennai Department of CSE Jeppiaar Engineering College Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Large-scale customization is becoming more apparent in consumer markets. More complicated products with frequent modifications to its variants necessitate new assembly procedures. Close cooperation between human workers and robots is an idea to tackle issues within this trend. At now, there are limitations and hurdles that prohibit close cooperation in an effort to safeguard human operators. This occurs because safety systems aren’t very good at predicting future actions or intentions; they mostly respond to potential dangers. Predicting human behavior is still quite difficult, although there are probabilistic models that try to get around these problems. It would be ideal, then, to be able to physically gauge future movement intentions. The article introduces a new method for measuring the intention to move the upper limbs using a portable electroencephalogram (EEG). Up to 0.7 seconds before to executing a motor activity, the human brain continually assesses and evaluates it. This suggests that a safety system might benefit from early movement warning capabilities. A new data processing approach, Brainwave based Robotic Motion Control (BRMC), is presented to quickly categorize EEG-signals with little fine-tuning work. To assess the efficacy of the suggested scheme, it is cross-validated with the traditional model known as Mobile based Robotic Motion Control (MRMC) and is based on the logic of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. Furthermore, we discovered that a suggested model named BRMC can distinguish between signals that include blinks and ones that do not. Additionally, we discovered that BRMC can classify blink counts, and that various blink counts are utilized for forward, left, and right movement. The engine is slowed down by a model that has been taught to distinguish between blink signals and those that do not. According to the results of the experiments, the prototype can be controlled to go to a location that has alrea

关键词： Robot motion Computational modeling Predictive models Brain modeling Control systems Electroencephalography Safety

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