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2nd International Conference on electronics and Renewable Systems, ICEARS 2023

作者： Suman, Turpati Kaliappan, S. Natrayan, L. Dobhal, Dinesh Chandra Department of Electronics and Communication Engineering Kurnool518 452 India Velammal Institute of Technology Department of Mechanical Engineering Chennai601204 India Saveetha School of Engineering Department of Mechanical Engineering Simats 602107 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

Internet of Things (IoT) is an emerging technology that is taking the current technology to the next phase of connectivity by connecting any device to the internet. It may be a mobile phone device, a speaker or even a connected vehicle. The number of IoT-based devices people use is increasing day by day. The quantity of smart gadgets people purchase on both offline and online platforms has increased to a significant number in the past few years. This research article proposes social networking between IoT devices, by the interconnection of IoT devices based on a Cloud computing method. Through cloud computing, number of Edge platform-based, IoT devices can be connected to a common cloud called Fog computing, which in turn connects to the Cloud network. The social networking of IoT devices enables users to switch and share data between their IoT devices. Various computational programs and block chain techniques develop applications that allow the host network to communicate with IoT devices with secure access to the data shared by all IoT devices connected to the Cloud without any data loss or security breach. © 2023 IEEE.

关键词： Internet of things

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Comparison of Handover Latency Using Mobility Protocols for Wi-MAX Networks 15

Comparison of Handover Latency Using Mobility Protocols for ...

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

作者： Deshpande, Vivek Ravivarman, G. Reddy, B. Karthika, K. Tusha Vinay Kumar, S.B. Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Quantum University Research Center Quantum University India Karnataka Bangalore India

ISBN: (纸本)9798350370249

Due to the growing call for for wireless facts offerings, handover latency should be minimized a good way to ensure consumer revel in. so as to reduce handover latency, one-of-a-kind mobility protocols are to be had for We-MAX networks, including fast Base Station Switching (FBSS), Dynamic pleasant of carrier (Duos), and greater dispensed Channel get admission to (EDCA). This paper offers a comparative study of handover latencies for these three mobility protocols. The research was carried out the usage of a Web-MAX network simulator to simulate numerous scenarios. results show that EDCA was discovered to be the first-class in phrases of handover latency, with an average latency of 1.18 ms, at the same time as fast Base Station Switching and Duos had a latency of 6.02 ms and 13.43 ms respectively. Additionally, the effects showed that EDCA decreased the handover latency by using 14.4%, as compared to FBSS and 39.2% in comparison to Duos. The look at additionally suggests that EDCA is extra suitable for handover in We-MAX networks, due to its low latency and high performance. ©2024 IEEE.

关键词： Wimax

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Challenges of Deep Learning in Medical Image Analysis - Improving Explainability and Trust

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2023年第1期4卷 68-75页

作者： Dhar, Tribikram Dey, Nilanjan Borra, Surekha Simon Sherratt, R. Jadavpur University Department of Electrical Engineering Kolkata712235 India Techno International New Town Department of Computer Science Engineering Kolkata700156 India K. S. Institute of Technology Department of Electronics and Communication Engineering Bengaluru560109 India University of Reading Department of Biomedical Engineering ReadingRG6 6AH United Kingdom

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage;preoperative planning;intra-operative guidance, etc. However, deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems. © 2020 IEEE.

关键词： Deep neural networks

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： K. Palanivel Rajan J. Relin Francis Raj R. Santhana Krishnan G. Balamuralikrishnan G. Vidyasree J. Nirmal Jothi Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College Coimbatore India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts

关键词： Measurement Accuracy Heuristic algorithms Ecosystems Forestry Real-time systems Classification algorithms Biodiversity Random forests Monitoring

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LM-SVM-DT Based Working State Recognition for Washing Machine's Audio Signal

LM-SVM-DT Based Working State Recognition for Washing Machin...

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2022 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Hu, Yuping Gao, Cuiyun He, Xiaohong Lu, Zhonghai Anhui Jianzhu University School of Electronics and Information Engineering Hefei China School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

In order to make a reasonable and effective judgment on the quality inspection and fault diagnosis of intelligent electrical appliances, this paper proposes a method of working state recognition for washing machine based on audio signal, named as LM-SVM-DT. The whole working process of the washing machine is divided into four basic states: water intake, soaking, washing and dehydration. The Log-mel features of the audio signal after bandpass filtering are extracted and modeled by the decision tree classification method based on SVM. That is, soaking and non-soaking states are separated at first, then washing and non-washing states are separated in non-soaking states, and finally water intake and dehydration states are separated in non-washing states. Taking the standard-washing-mode of a certain type of washing machine as an example to verify the algorithm, the experimental results show that the state recognition rate is as high as 0.9920. The results show that the model proposed in this paper is effective and feasible. © 2022 IEEE.

关键词： Decision trees

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Hysteresis Current Control of Buck-Boost Non-Isolated Onboard Charger for Electric Vehicles 49

Hysteresis Current Control of Buck-Boost Non-Isolated Onboar...

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49th Annual Conference of the IEEE Industrial electronics Society, IECON 2023

作者： Komurcugil, Hasan Guler, Naki Bayhan, Sertac Gulbudak, Ozan Eastern Mediterranean University Department of Computer Engineering Famagusta Turkey Gazi University Technology Faculty Department of Electrical-Electronic Engineering Ankara Turkey Qatar Environment and Energy Research Institute Doha Qatar Karabuk University Department of Electrical & Electronics Engineering Turkey

ISBN: (纸本)9798350331820

This paper proposes a hysteresis current control (HCC) method for a single-inductor buck-boost non-isolated onboard charger for electric vehicles. The charger is capable of working both in the boost and buck modes. The proposed HCC relies on the buck-boost inductor current and its reference which is generated using a proportional-resonant (PR) controller using grid current error. The reference current generated by PR controller is modified to suppress the oscillations in the inductor currents. An active damping by using a virtual resistor connected in series to filter inductor is used which does not require an additional sensor. A proportional-integral (PI) controller is used to generate the amplitude of grid current reference, which is utilized in constant current (CC) and constant voltage (CV) modes. The effectiveness of the proposed control strategy as well as the control method, is investigated by simulation studies by considering two different battery voltage levels (48V and 350V). The results show that the proposed method is able to charge the battery in CV and CC modes. Moreover, the grid current is maintained in unity power factor at a reasonably low total harmonic distortion (THD) which is smaller than the limits recognized by international standards. © 2023 IEEE.

关键词： Charging (batteries)

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique

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Procedia computer Science 2025年 258卷 4105-4116页

作者： Nilesh Bhaskarrao Bahadure Sudhanshu Gonge Jagdish Chandra Patni Bhoomi Shah Nagrajan Raju Prasenjeet D. Patil Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Thanjavur Tamil Nadu India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality.

关键词： Berkeley wavelet transformation enhancement magnetic resonance imaging (MRI) segmentation morphology

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SAUN: Energy-Efficient Hardware Architecture for Systolic Array Implementation of the U-Net Model

SAUN: Energy-Efficient Hardware Architecture for Systolic Ar...

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IEEE India Conference (INDICON)

作者： Chaitanya Modiboyina Bhargavi Pinni Indrajit Chakrabarti Soumya Kanti Ghosh Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Convolution operations in Convolution Neural Networks (CNNs) inherently offer significant data reuse potential, yet traditional Weight Stationary (WS) and Output Stationary (OS) dataflows fail to fully exploit this advantage. Furthermore, data reuse in Systolic Arrays (SAs) following the WS dataflow is restricted by the array's dimensions. To address these limitations, we propose a novel mixed dataflow approach Input Feature Map First (FF) and Channel First (CF) for Systolic Array-based U-Net (SAUN) accelerators. This architecture enhances data reuse by utilizing Weight Register Files (WRFs) in Processing Element (PE) arrays, allowing for flexible weight mapping. Furthermore, our accelerator supports both Standard Convolution (SCONV) and Transpose convolution (TCONV) operations, facilitating the reuse of input feature maps and partial sums during channel wise accumulation, thereby reducing access to on-chip SRAM.

关键词： Power demand Convolution Neural networks Random access memory computer architecture Systolic arrays System-on-chip Registers Arrays Standards

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Novel and Robust Energy Efficient Protocol for Authentication Purpose in IIoT

Novel and Robust Energy Efficient Protocol for Authenticatio...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Kirubakaran, D. Velmurugan, S. Rekha, R. Sasirekha Saravanan, S. Panurangan, Raji Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering T J S Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai600127 India Department of Artificial Intelligence and Data Science Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Saveetha Nagar Thandalam Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

An developing technology called the Internet of Things (IoT) is anticipated to offer answers for several industrial industries. Wireless sensor networks (WSNs), a foundational IoT technology, may be utilized to gather the necessary environmental characteristics for certain applications. Security in WSN has grown to be a huge concern as a result of the sensor node's resource limitations and the wireless channel's open nature. To ensure the legitimacy of data access in WSN, authentication may be utilized as a fundamental security function. For various security needs, Chang and Le have presented two authentication techniques for WSN. However, their protocol has significant security and functionality flaws and is unable to provide appropriate mutual authentication. To address the shortcomings of earlier methods, we introduce a three-factor user authentication scheme for WSN. The proposed protocol's security is examined, and its performance, functionality, and security are contrasted with those of other relevant protocols. The comparison and simulation findings by NS-3 demonstrate how reliable and resource-efficient the suggested protocol is for IoT applications. © 2023 IEEE.

关键词： energy efficient Internet of Things Packet Delivery Ratio Public Key Infrastructure Wireless Sensor Networks

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RF MEMS Resonance Sensor for Measuring Microplastics Concentration

RF MEMS Resonance Sensor for Measuring Microplastics Concent...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Kim, J.H. Lee, K.H. Cha, C.U. Hong, Y.T. Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Korea Electronics Technology Institute Department of Industrial Engineering Seongnam Korea Republic of

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Recently, microplastics have become a big problem, but conventional particle analyzers that can detect microplastics require a lot of time and cost, so in this paper, in order to solve this issue, a low-cost RF sensor was designed to detect microplastics. The proposed MEMS sensor consists of a microfluidic structure and an LC resonator. First, it is a principle of collecting microplastics through a microfluidic structure and measuring the concentration of microplastics using an LC resonance structure. Through the structure, it was confirmed that the concentration of 0.001% unit could be detected, and a circuit that could identify not only the concentration but also the type of substance was proposed. © 2022 IEEE.

关键词： Microfluidics

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