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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Lawrence, Jinsha Budagam, Dinesh Kumar Mukilan, P. Veerappan, Kavitha Chandrasekhar, E. Barathi, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Visa Department of Ms in Software Engineering CA United States Dhanalakshmi Srinivasan College of Engineering Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India Department of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508456

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance. © 2025 IEEE.

关键词： Network security

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Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1617-1626页

作者： Mohanty, Bipin Ch. Subudhi, P.K. Dash, Ratnakar Mohanty, Bidyadhar Department of Electronics and Communication Engineering BPUT Odisha Rourkela 769015 India Department of Electronics and Communication Engineering Nalanda Institute of Technology Odisha Bhubaneshwar 754005 India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela 769008 India

Brain tumor classification using Magnetic Resonance Imaging (MRI) is a pivotal area in medical diagnostics, with the potential to influence early detection and subsequent treatment strategies. Over the years, various machine learning and deep learning models have been proposed to enhance the accuracy of MRI-based tumor detection. In this evolving landscape, this paper introduces a distinctive deep-learning model that harnesses the power of a soft attention mechanism. We employ a meticulously designed Convolutional Neural Network (CNN) comprising four convolution layers. One of the significant innovations in our approach is the method of feature extraction. Instead of extracting features solely from the final layer, as is common in many models, our approach aggregates and combines features from all layers. This ensures that the vital characteristics intrinsic to each layer are not lost but rather amalgamated into a robust and comprehensive feature vector. The incorporation of a soft attention mechanism at the terminal stages ensures that the most salient and clinically relevant features are emphasized, enhancing classification accuracy. To validate the efficacy of our proposed model, we employed standard datasets for training and testing. A comparative analysis with existing state-of-the-art models affirms the superiority and potential of our approach in the realm of MRI-based brain tumor classification. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Brain tumor CNN Deep learning MRI Soft-attention

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Hybrid renewable energy storage system using modified power management strategies in standalone microgrid

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International Journal of Power and Energy Conversion 2025年第2期16卷 205-220页

作者： Shankar, Gowri Varatharaju, V.M. Subramanian, Sendil Kumar Sathyabhama, B. Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur517583 India Oman Engineering Department University of Technology and Applied Sciences Ibra Oman Department of Electrical and Electronics Engineering S.A. Engineering College Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Bangalore Trunk Road Varadharajapuram Poonamallee Tamil Nadu Chennai600123 India

The utilisation of renewable power sources has been more common as a dependable and clean energy source. Two possible alternative sources of energy include renewable energy sources, such as solar energy and wind energy. The audit para monitoring system (APMS) and the versatile energy spectral algorithm (VESA) control based hybrid system that has been discussed have been built with the support of the MATLAB simulation 2017b software. These characteristics include switching losses, electromagnetic interference, efficiency, power factor, and total harmonic distortion. In the proposed system, the average voltage may be generated with a total harmonic distortion (THD) of 4.73% and a switching loss of 3.8%. The proposed VESA was able to produce outcomes that were better. A total harmonic distortion (THD) of 4.09% and a switching loss of around 3.5% are both characteristics of the voltage that it generates. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Hybrid power

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Impact of Process-Induced Inclined Side-Walls on Gate Leakage Current of Nanowire GAA MOSFETs

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IEEE Transactions on Electron Devices 2024年第3期71卷 2196-2202页

作者： Maniyar, Ashraf Raj, Pushp Srinivas, P.S.T.N. Kumar, Arun Chang-Liao, Kuei-Shu Tiwari, Pramod Kumar Indian Institute of Technology Patna Department of Electrical Engineering Bihar Patna801106 India Vellore Institute of Technology School of Electronics Engineering Vellore801106 India National Institute of Technology Silchar Department of Electronics and Communication Engineering Silchar788010 India National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan

In this work, the influence of process-induced sidewall inclination on direct tunneling gate leakage current (IG) in the trapezoidal (Tz) channel NW GAA MOSFETs has been comprehensively investigated using experimental data and the TCAD simulations results. Variability in device parameters as channel length, height, and width have been taken into account when we examined gate leakage current. The side-wall inclination angle has a significant influence on the gate leakage current, where a rise in IG of up to two times is observed when the inclination angle is increased from 0 to 20. Moreover, it has also been observed that the direct tunneling gate leakage current is significantly influenced by the gate overlap and underlap areas. © 1963-2012 IEEE.

关键词： III-V semiconductors

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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Interference Mitigation for Reverse Spectrum Sharing in B5G/6G Satellite-Terrestrial Networks

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IEEE Transactions on Vehicular Technology 2023年第3期73卷 4247-4263页

作者： Lee, Hao-Wei Chen, Chun-Chia Liao, Chen-I Stephanie Medles, Abdelkader Lin, Debby Fu, I-Kang Wei, Hung-Yu The Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan The MediaTek Inc. Hsinchu City300 Taiwan The Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan

Satellite and terrestrial network (TN) convergence is an important trend to offer truly ubiquitous worldwide coverage in the B5G/6G era. Based on the recently published 3GPP Rel-17 Non-Terrestrial Network (NTN) specifications, the industry is actively driving commercialization and enabling satellite connection over the cellular phone. However, the spectrum shortage problem will become more severe after satellite services broadly penetrate consumer markets. Spectrum sharing between satellite and cellular networks is a promising solution to mitigate the spectrum shortage problem. However, it will inevitably result in severe interference across two systems if not properly managed. We propose a novel interference mitigation technique that jointly considers the satellite network with earth-fixed beamforming capability and the cellular network. The proposed Interference-aware radio resource group sharing mechanism combined with sub-optimal TN and NTN grouping algorithm optimizes the overall system capacity. It also ensures the signal quality over each link operates at an acceptable level. The mechanism demonstrates the feasibility of NTN-TN spectrum sharing and outperforms the baselines with 94% to 138% capacity utility, which is contributed by enhanced 72% TN available bandwidth and 45% to 133% NTN throughput. © 2023 IEEE.

关键词： Satellites

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems Science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics Research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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