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Simulation of the Electrical Control Unit (ECU) in Automated Electric Vehicles for Reliability and Safety Using On-Board Sensors and Internet of Things 5

Simulation of the Electrical Control Unit (ECU) in Automated...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Sunil Kumar, M. Anuradha, T. Srinivas, Yerram Santhi Sri, T. Catherine, T.J. Aurelia, Sagaya Department of Computer Science and Engineering AP Tirupathi India KCG College of Technology Department of Electrical and Electronics Engineering Chennai India Vignana Bharathi Institute of Technology Department of ECE Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India R.M.K. College of Engineering and Technology Department of EEE RSM Nagar Puduvoyal India CHRIST University Department of Computer Science Bangalore India

ISBN: (纸本)9781665493604

The adaptation of the energy storage system (ESS) with high power and energy density remains a difficulty for electric vehicles (EVs), despite the increasing demand they are experiencing around the world. A lightweight, compact ESS is necessary to deliver the responsive performance and driving range that modern vehicles need. When planning for widespread use of EVs, it's important to give careful attention to the factors of ESS selection, sizing, and administration. One of the most promising future mobility alternatives is the hybrid electric vehicle (HEV), which offers improved fuel economy and lower pollution levels. As a result, one of the most pressing needs is for automakers to develop new technologies for vehicle design that might help lessen emissions and boost economy. The environmental impact of emissions from light-duty cars is growing in tandem with the annual increase in the number of such vehicles on the road. The usage of other modes of transportation, such as ships and planes, is on the rise, but road transportation will always be the most common. Electronic Control Units, or ECUs, have been increasingly commonplace in cars during the past few decades. Vehicle network multicore CPU scheduling is notoriously difficult. This study's findings consist of a straightforward power-sharing control approach for the HESS based on battery and UC, with the goal of extending the battery's useful life in a city environment. © 2023 IEEE.

关键词： Energy storage

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Analysis of the Performance of Software Defined Networks (SDN) Versus Networks with TCP/IP Architecture 2

Analysis of the Performance of Software Defined Networks (SD...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tanguturi, Rama Chaithanya Amutharaj, J. Prakash, N. Subramani, K. Taqui, Syed Noeman Sree Southry, S. Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Raja Rajeswari College of Engineering Department of Information Science and Engineering Bengaluru560074 India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Department of Physics Tamil Nadu Dindigul India Saveetha School of Engineering Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India

ISBN: (纸本)9798350327533

It is expected that the transfer speeds on average would rise from 39Mbps to 75.4Mbps. For this reason, the idea of implementing centralized networks, with easy administration and traffic management through policies, has become essential in recent years. Software-defined networks (SDN) and traditional TCPIIP architecture networks are two distinct approaches to network design and management. While TCPIIP architecture networks have been the norm for decades, SDN is a relatively new concept that promises to revolutionize network performance, flexibility, and scalability. In this work, the performance of SDN versus networks with TCPIIP architecture is analyzed and compared. In the analysis of the performance of software-defined networks and networks with TCPIIP architecture, it is predicted to have a favourable response in the evaluation of different metrics such as latency, jitter and bandwidth in both networks;but with better performance in the case of SDN networks. © 2024 IEEE.

关键词： Flexibility and Adaptability Integration with Cloud Services Management and Configuration Security Software-defined networks (SDN)

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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The Impact of Hot Carrier Injection-Induced Device Degradation for Lower-Power FinFETs

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Journal of Electronic Materials 2023年第2期52卷 1391-1399页

作者： Chen, Yu-Lin Yeh, Wen-Kuan Hsu, Heng-Tung Chen, Ke-Horng Lien, Der-Hsien Lin, Wen-Chin Yu, Tien-Han Chiu, Yu-Sheng Godwinraj, D. Godfrey, D. Wu, Chien-Hung International College of Semiconductor Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Microelectronics National Cheng Kung University Tainan Taiwan Department of ECE Amal Jyothi College of Engineering Kerala Kottayam India Department of Optoelectronics and Materials Engineering Chung Hua University Hsinchu Taiwan Department of Electronics and Communication Engineering Dayananda Sagar University Bengaluru India National Applied Research Laboratories Hsinchu Taiwan

The impact of hot carrier injection (HCI) on the performance of standard and low-VT FinFETs are investigated and benchmarked with each other. For this investigation, these FinFETs were fabricated with various gate lengths (L) from 16 to 36 nm. HCI-induced transconductance degradation in standard devices for gate length variations of 36 nm down to 16 nm was 75%, while it was 35% for low-VT devices. Similarly, the degradation of threshold voltage in standard devices for gate length variations of 36 nm down to 16 nm was 39%, while it was 36% for low-VT device. In this work, as the device is subject to HCI, we found that: (1) short-channel devices cause severe degradation on the threshold voltage and transconductance (gm) compared to long-channel devices, owing to the higher electric field for short-channel devices at the gate edge, (2) standard devices exhibit a more stable threshold voltage than that of low-VT power devices, since the TiN barrier layer prevents Al atom diffusion into the HfO2 layer, and (3) the transconductance efficiency of standard devices is better than that of low-VT power devices. Further, the lower-VT devices show lower transconductance degradation than standard devices. However, the transconductance degradation of the long-channel standard and low-VT devices are the same. Compared to short-channel low-VT devices, extended-channel standard devices are more immune to the HCI effect. © 2022, The Minerals, Metals & Materials Society.

关键词： Threshold voltage

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Challenges in Cyber Physical Social Systems and Internet of Things 5

Challenges in Cyber Physical Social Systems and Internet of ...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Revathi, S. Raja, J. Mohanraj, M. Malathi, K. Mallala, Balasubbareddy Vidhya, R.G. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sri Eshwar college of engineering Department of Computer Science and Business Systems Coimbatore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798331504403

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems. © 2024 IEEE.

关键词： Cyber-Physical Systems Cyber-Physical-Social Systems (CPSS) Cybersecurity Internet of Things (IoT) Social Media

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A Comprehensive Examination for P2P Communication in Multi-hop Overlay Network 15

A Comprehensive Examination for P2P Communication in Multi-h...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srinivasan, M. Gnanasekar, A.K. Juliet, N. Chandrakala, K. Abid, Y. Bannari Amman Institute of Technology Department of EEE Sathyamangalam India P.T. Lee Chengalvaraya Naicker College of Engineering and Technology Department of CSE Kancheepuram India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of ECE Chennai India Saveetha University Saveetha School of Engineering Department of VLSI Microelectronics Chennai India

ISBN: (纸本)9798350370249

P2P communique in multi-hop overlay networks has emerged as increasingly important because of the growing call for green and comfy information transmission in numerous programs, file sharing, actual-time streaming, and online gaming. The prevailing solutions for P2P communique in multi-hop overlay networks are regularly afflicted by boundaries consisting of low performance, network congestion, and vulnerability to attacks. To cope with these challenges, a comprehensive exam is proposed to research and compare the overall performance and security of P2P communique in multi-hop overlay networks. This examination will focus on various elements, including routing protocols, facts dissemination techniques, congestion manipulation, and safety mechanisms. By conducting experiments on actual international networks and simulations, this examination aims to pick out and benchmark the strengths and weaknesses of different approaches in P2P conversation. The findings of this exam may be utilized to broaden advanced techniques for P2P communique in multi-hop overlay networks. © 2024 IEEE.

关键词： Information dissemination

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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Automated Machine Learning-Based Gestational Monitoring Framework in Wearable Internet of Things Environment 2nd

Automated Machine Learning-Based Gestational Monitoring Fram...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Nandini, K. Seetha, J. Jegajothi, B. Rajmohan, R. Nageswari, D. Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Computer Science SRM Institute of Science and Technology SRMIST Bharathi Salai Ramapuram Campus Chennai600089 India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Department of Electronics and Communication Science Agurchand Manmull Jain College Meenambakkam Chennai India Department of Electrical and Electronics Engineering RMK College of Engineering and Technology Chennai India

ISBN: (纸本)9789811901072

Recently, Internet of Things (IoT) and wearable technologies have become popular in diverse areas, and smart health is an important application area. Specifically, IoT and wearable technologies find useful in linking different medicinal gadgets, sensors, and physicians in offering better healthcare services in remote areas. It results in enhanced patient safety, minimum healthcare cost, improved healthcare service accessibility, and high operational efficiency in the healthcare sector. At the same time, high-quality care during pregnancy is essential to determine the probable difficulties earlier and guarantee the healthiness of mother as well as fetus. Several research works have been existed in the literature to monitor the maternal lifetime. But they are developed for a particular health issue and based on short-term data collection approaches. Since maternal monitoring necessitates long-term services, this paper designs an automated machine learning-based gestational monitoring framework in wearable IoT environment. The goal of this paper is to derive a new improved salp swarm optimization (ISSO) with extreme learning machine (ELM), called ISSO-ELM model for continual maternal health monitoring. The ISSO-ELM model encompasses different sub-processes namely data acquisition, pre-processing, classification, and parameter optimization. The presented model enables IoT devices and wearables to gather healthcare data about pregnant women. Then, the gathered data are pre-processed to remove the unwanted noise. Besides, the ELM model is employed as a classifier to determine the occurrence of difficulties. Moreover, ISSO algorithm is applied as a parameter tuning technique to optimally adjust the parameters that exist in the ELM model. The extensive experimental analysis highlighted the enhanced performance of the ISSO-ELM model over the recent state-of-art methods with the precision, recall, and accuracy of 94.00, 88.00, and 90.07%. © 2023, The Author(s), under exclusiv

关键词： Internet of things

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Deep Learning-Based Image Recognition for Electronic Components Identification 5

Deep Learning-Based Image Recognition for Electronic Compone...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Royappa, Angelina Monga, Sandeep Geetha, H. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India S.A. Engineering College Department of ECE Tamil Nadu Chennai India Oriental Institute of Science and Technology Department of Computer Science and Engineering India St.Joseph's College of Engineering OMR Semmenchery Department of Mathematics Tamil Nadu Chennai India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques in the field of picture recognition for the purpose of identifying electronic components. Because of the growing complexity and variety of electronic devices, it is essential for manufacturing, maintenance, and quality control to be able to identify components in a way that is both efficient and accurate. This is accomplished through the utilization of deep learning algorithms, the utilization of convolutional neural networks (CNNs), and advanced image processing techniques in order to improve recognition skills. The suggested model displays higher accuracy in differentiating diverse electronic components, overcoming problems such as differences in size, orientation, and lighting conditions. Moreover, the model succeeds in overcoming these challenges effectively. Based on the findings, it appears that deep learning-based image recognition provides a strong solution for automating the identification process in electronic component analysis. This, in turn, contributes to better efficiency and reliability in the electronics industry. This research makes a contribution to the expanding field of computer vision and highlights the potential of deep learning in the advancement of electrical component identification systems. © 2024 IEEE.

关键词： Image enhancement

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