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Potentiality of Avalanche Transit Time Devices for Biomedical Applications: A Comprehensive Review

Biomedical Materials and Devices 2025年 1-17页

作者： Shobitha, G. Sherlin Ghivela, Girish Chandra Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur 441108 India

The impact ionization avalanche transit time (IMPATT) diode is a widely used solid-state device that can deliver high power at microwave, millimeter-wave, and terahertz frequencies. Though widely utilized in radar and communication systems, its use in biomedical applications has not yet been fully explored. IMPATT diodes can have a significant role to play in medical imaging, therapeutic treatment, and diagnostic imaging, especially in microwave imaging, which can be used to take high-resolution, non-invasive scans for detecting early disease. This research compares the performance of different IMPATT diode models in biomedical use by assessing the performance parameters such as operating frequency, power output, and efficiency. A comparative analysis was conducted in order to establish the aptness of using IMPATT diodes in medical treatments and imaging. Issues of noise generation, heat management, and safety issues were also explored in order to establish the viability of using them in healthcare technologies. The findings confirm that IMPATT diodes hold much promise for use in the biomedical field due to their potential for producing high-frequency signals, which are indispensable in non-invasive imaging and correct diagnostics. However, limitations such as high levels of noise and thermal and frequency instability issues need to be addressed for full potential realization within medical facilities. The comparison highlights specific IMPATT models that stand out compared to others when it comes to stability and efficiency, thus making them more suitable for use in the biomedical field. IMPATT diodes possess significant potential for enabling medical imaging and diagnosis with non-invasive, high-resolution scanning. Their utilitarian application to biomedical systems remains dependent on reducing noise, minimizing thermal issues, and obtaining regulatory approval. A solution to these problems can facilitate the practical adoption of IMPATT diodes as part of emergi

关键词： Avalanche transit time Biological monitoring Biomedical application Breast cancer detection Microwave imaging Tumor detection

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Alzheimer’s Disease Detection and Classification using Modified Hybrid CNN-LSTM Network 6

Alzheimer’s Disease Detection and Classification using Modi...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nisha, A.V. Rajeev, S.K. Soorya, M. Shaheer, S. Dept. of Electronics and Communication Engineering Younus College of Engineering and Technology Kerala Kollam India

ISBN: (纸本)9798331522667

Alzheimer’s Disease (AD) is an escalating neurodegenerative disorder and the leading cause of dementia, with early detection being essential for prompt action and slowing disease progression. However, due to subtle and overlapping brain deterioration patterns, early AD diagnosis is difficult. This study proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically detect and classify AD from axial brain magnetic resonance imaging (MRI) images. The model uses VGG16, pre-trained on ImageNet, for feature extraction, with an LSTM layer to capture temporal dependencies in MRI slices. The dataset includes three categories: AD, Cognitively Impaired (CI), and Normal Control (NC) subjects. To improve generalization, data augmentation techniques such as median blurring and horizontal flipping were used. The model, which was trained with the Adam optimizer, achieved a test accuracy of 0.9942 and a loss of 0.030. High sensitivity and specificity were shown by the performance evaluation which included a classification report and evaluation matrix, particularly in distinguishing AD from CI cases. This hybrid CNN-LSTM model effectively leverages spatial and temporal information for early AD detection, highlighting its potential to improve automated diagnostic tools and enable timely intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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Design and Implementation of Hand Exoskeleton for Rheumatoid Arthritis Rehabilitation

Design and Implementation of Hand Exoskeleton for Rheumatoid...

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2025 IEEE International Students' Conference on Electrical, electronics and Computer Science, SCEECS 2025

作者： Balaji, Sudharshan Jaba Deva Krupa, A. Bharanidivya, Madhavan Dhanalakshmi, Samiappan Srm Institute of Science and Technology College of Engineering and Technology Department of Electronics and Communication Engineering Chennai603203 India

ISBN: (纸本)9798331529833

Inflammation of the joints is a hallmark of rheumatoid arthritis (RA), a long-term inflammatory disease that can cause significant discomfort, limited range of motion, and impaired hand function. Developing a tailored hand exoskeleton to help RA patients carry out routine activities and regain some dexterity in their hands is the primary goal of this study. This exoskeleton is designed specifically for RA sufferers. It uses precise actuation technology and soft, adaptable materials to help in gripping and finger extension without making the patient uncomfortable or requiring them to move against their will. With its built-in sensors, the exoskeleton keeps tabs on the user's every motion, analyzing their joint locations and patterns to provide real-time feedback and tailor adjustments to their specific mobility and pain. RA sufferers will be able to modify the support levels of the exoskeleton based on the type of activity they are performing and the amount of pain they are experiencing. Control will be achieved through an interface that is easy to use. This project aims to produce an assistive device that improves the quality of life for individuals with rheumatoid arthritis (RA) by boosting freedom in daily activities while minimizing joint stress. This will be accomplished by merging several biomedical principles with modern robotics. © 2025 IEEE.

关键词： Exoskeleton (Robotics)

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Impact of Gate Oxide and Dielectric Pocket on Performance Enhancement of Hetero Gate Oxide TFETs 3rd

Impact of Gate Oxide and Dielectric Pocket on Performance En...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Mahato, Prasenjit Kavi, Kavindra Kumar Swain, Sukanta Kumar Raj, Abhishek Sharma, Shashi Kant Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Department of Electronics and Communication Engineering Motilal Nehru National Institute of Technology Allahabad India Department of Electronics and Communication Engineering Vidya Memorial Institute of Technology Tupudana Ranchi India

ISBN: (纸本)9789819784639

This work reports the impact of hetero gate oxide and dielectric pocket on the performance of conventional TFET. The conventional TFET has been optimized by introducing hetero gate oxide of HfO2-SiO2 and dielectric air-pocket. Both these approaches improve the lateral electric field at the source-channel interface and weakens the lateral electric field at the drain-channel interface. Hence, the ON-current is enhanced and OFF-current is reduced. At last, the optimized value of work-function of the gate metal has been obtained at 4.3 eV. The TCAD simulation results show a significant increase in ON-current (ION = 10.24 × 10−4) and reduction in leakage current (IOFF = 9.659 × 10−17). The value of ION/IOFF ratio and subthreshold swing was found to be 1.06 × 1013 and 11.776 mV/decade respectively that confirms a considerable improvement in the performance of the proposed TFET structure. The transconductance for the proposed device structure was found to be 1.935mS. These improvements confirm that the proposed device DP-HGO-DG-TFET is suitable for high frequency and low power applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Leakage currents

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Cloud technology-Enabled IoT Milk Quality Assessment and Billing System for Dairy Cooperatives

Cloud Technology-Enabled IoT Milk Quality Assessment and Bil...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Mahendrakumar, S. Karthikeyan, G. Muthuvinayagam, M. Vijayakumari, G. Jamuna, P. Vanitha, K. Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode638012 India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Mahendra Engineering College Department of Electrical and Electronics Engineering Mallasamudram Namakkal India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam638108 India Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Velalar College of Engineering and Technology Department of Medical Electronics Erode638012 India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Milk mostly comprises water, with 13% of its composition consisting of proteins, lipids, carbs, vitamins, and minerals. Regrettably, milk and dairy products are recognized as the most polluted commodities globally. Adulteration may transpire through the incorporation of basic adulterants, such as water, into natural milk, leading to a substantial decline in its nutritional quality. The suggested system establishes a milk quality monitoring system based on the IoT. The system's foundation is an ESP32 Wroom module that acquires real-time data from many sensors. The sensors comprise a pH sensor for acidity measurement, a waterproof temperature sensor for monitoring storage temperature, a Light-Dependent Resistor (LDR) for possible contamination detection, and a TDS sensor for evaluating total mineral content. The gathered data is subsequently analyzed to assess the milk quality. The system determines an appropriate milk price based on the examined quality data. The price information and sensor readings are presented on two platforms: a Blynk cloud application for remote monitoring and a 16×2 LCD display for on-site viewing. This advanced solution enables dairy producers to guarantee milk quality, enhance pricing tactics, and reduce possible losses. © 2025 IEEE.

关键词： pH sensors

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Exploring MIMO-NOMA: Enhancing Multi-user communication with Power Allocation 6th

Exploring MIMO-NOMA: Enhancing Multi-user Communication with...

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6th International Conference on Intelligent communication, Control and Devices, ICICCD 2024

作者： Naveen, B. Ramaswamy, T. Subba Rao, S.P.V. Pothalaiah, S. Saikiran, S. Siri Chandana, A. Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of Electronics and Communication Engineering Vignana Bharathi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9789819783281

This paper presents a novel approach to integrating non-orthogonal multiple access (NOMA) within a multiple-input multiple-output (MIMO) framework, emphasizing new power allocation strategies designed to optimize multi-user communication efficiencies. The study introduces a unique MIMO-NOMA setup in which two users—categorized based on their signal strength as weak and strong—are served simultaneously by a single transmitter. This research offers a detailed comparison with traditional orthogonal multiple access (OMA) techniques, assessing key performance metrics such as achievable sum rates and outage probabilities. Utilizing advanced MATLAB simulations, the study evaluates system behaviour across various scenarios, highlighting the innovative aspects of MIMO-NOMA. The findings underscore that MIMO-NOMA not only surpasses MIMO-OMA in terms of sum rates by exploiting simultaneous transmissions but also significantly lowers outage probabilities for both user types. Concluding with an exploration of future enhancements, the paper specifically suggests implementing sophisticated beamforming techniques to further improve the performance and efficiency of MIMO-NOMA systems, marking a substantial advancement in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Beamforming

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Fall Detection System for Elderly People using Piezoresistive Textile Pressure Sensor

Fall Detection System for Elderly People using Piezoresistiv...

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2025 IEEE International Students' Conference on Electrical, electronics and Computer Science, SCEECS 2025

作者： Vyas, Rishabh Shirley, J. Abanah Jansi, R. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Kattankulathur603203 India

ISBN: (纸本)9798331529833

Fall detection systems are critical for ensuring the safety and well-being of the elderly and others with mobility challenges. This paper presents the design and development of a fall detection system employing a textile pressure sensor constructed using piezoresistive Velostat material, sandwiched between two layers of conductive fabric. The developed pressure sensor is embedded into wearable socks, offering a flexible and unobtrusive solution for continuous monitoring. The piezoresistive nature of the sensor allows it to detect pressure changes when a fall occurs, capturing real-time data. The system processes this data to distinguish between normal activities and fall events based on predefined thresholds. The fall detection system also features wireless communication for real-time alerts, ensuring immediate assistance can be provided when a fall is detected. This approach provides an efficient and non-intrusive method for enhancing safety, with potential applications in home care and healthcare monitoring for at-risk individuals. © 2025 IEEE.

关键词： Wearable sensors

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Efficiency Enhancement of Perovskite Photovoltaic Cells by Incorporation of NiO2 HTL 4

Efficiency Enhancement of Perovskite Photovoltaic Cells by I...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Kumar, Raushan Priyadarshi, Alok Kumar, Shivam Kumar, Abhishek Department of Electronics and Communication Engineering School of Engineering SR University Telangana Warangal506371 India Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electronics and Communication Engineering National Institute of Technology Jamshedpur India

ISBN: (纸本)9798331530402

In order to achieve high hole capturing rates and minimal minority carrier loss in PV cells, the hole transport layer (HTL) materials in Copper Zinc Tin Sulfide (CZTS) photovoltaic cells (PCs) are essential. Up to this point, CZTS cells based on HTL materials with high minority carrier loss minimization have only been developed. In order to create an effective CZTS PV cell, numerous researchers are looking into wide energy bandgap HTL materials. This paper proposes a novel wide bandgap NiO2 as a HTL nanomaterial for a high-efficiency photovoltaic cell. Additional analysis, energy band alignment matching, layer thickness and doping density optimization, and base-based proposed PV cell construction. The CZTS/NiO2 based PV cell performs better, with Voc=1127 mV, Jsc=28.17, FF=84.26%, efficiency=26.74%, and EQE=82%. © 2025 IEEE.

关键词： Semiconductor doping

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Empowering Anomaly Detection in IoT Traffic through Multi-view Subspace Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15911-15925页

作者： Nie, Fengyuan Liu, Weiwei Liu, Guangjie Gao, Bo Huang, Jianan Tian, Wen Yuen, Chau Nanjing University of Science and Technology School of Automation Nanjing210094 China Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Nanyang Technological University School of Electrical and Electronics Engineering 63978 Singapore

With the frequent occurrence of information security incidents within the Internet of Things (IoT) landscape, there has been an increasing emphasis on anomaly detection in IoT traffic. Recently, supervised machine learning techniques have shown significant potential on this topic. However, the intricate nature of IoT network environments has posed a challenge in acquiring sufficient labeled samples of abnormal traffic. In comparison to supervised learning, unsupervised learning has more lenient sample requirements. Researchers have proposed various unsupervised detection methods, yet limitations persist. Firstly, unsupervised learning, lacking guidance from labeled information, necessitates a more diverse range of traffic perspectives for comprehensive information coverage. Secondly, despite efforts to extract multi-view traffic features from various perspectives, existing methods struggle to integrate these features effectively, limiting interpretability and introducing redundancy and noise. Lastly, conventional unsupervised methods often rely heavily on manually crafted features, potentially leading to biased and limited representations. In this paper, we propose an unsupervised IoT traffic anomaly detection method based on multi-view subspace learning. Specifically, we first construct a multi-view traffic representation, including a protocol field view and a payload semantic view. Subsequently, a multi-view subspace learning algorithm is designed to project the different views of traffic onto a unified and low-rank subspace, optimized using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) strategy. Finally, spectral clustering is employed to accomplish IoT traffic anomaly detection. We benchmark the proposed method on multiple IoT traffic datasets and diverse computational platforms. The experimental results demonstrate that the method outperforms other state-of-the-art approaches in terms of accuracy and computational efficie

关键词： Self-supervised learning

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Performance Enhancement of MIMO-OFDM System with Perfect Channel Estimation Using Delayed Feedback 1

Performance Enhancement of MIMO-OFDM System with Perfect Cha...

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1st International Conference on Advances in Computer Science, Electrical, electronics, and communication Technologies, CE2CT 2025

作者： Suban, A. Jagaprathaban, J. Nithin Narayanan, S. Thanaseelan, A. UG-Final year in Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518578

Using multiple broadcast and capturing antennas to create MIMO channels can enhance capacity by a factor equal to the smaller of the number of broadcasting or capturing elements. To reduce inter-symbol interference and increase system capacity, Subcarrier-Based Transmission Technique (OFDM) is employed in MIMO channels (MIMO-OFDM) to enable efficient wideband transmission in this study. Two separate space-time codes are used by the MIMO-OFDM system for two sets of two broadcasting antennas. Pre-whitening and Closest-point interpretation based on Iterative interference suppression are used at the receiver to decode the separate space-time codes. The primary goal of this work is to estimate the ideal channel utilising delayed feedback, in which the transmitter and receiver estimate the feedback. Guard bits are used here for the OFDM signals. We discovered that LS is a perfect channel by examining the estimators' results in MATLAB. Thus, the optimal method for channel estimation is delayed feedback. Additionally, the experimental results guarantee improved performance when a MIMO-OFDM system employs Delayed Feedback and Channel Estimation. © 2025 IEEE.

关键词： Space time codes

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