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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Sasikala, G. Gomathi, P. Kirubakaran, D. Rajalingam, A. Pagunuran, Jubert R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India V.S.B. Engineering College Department of Ece India R.M.K Engineering College Department of Computer Science and Engineering India University of Technology and Applied Sciences College of Engineering and Technology Engineering Department Shinas Oman

ISBN: (纸本)9798350388916

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include mTCmOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems. © 2024 IEEE.

关键词： VLSI circuits

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Enhanced brain Tumor Detection and Classification Using Deep Neural Networks 2

Enhanced Brain Tumor Detection and Classification Using Deep...

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2nd IEEE International Conference on Networks, multimedia and Information technology, NmITCON 2024

作者： mamatha, m. Nataraj, K.R. Raghav, S. Nandini, S. Puttegowda, Kiran Sunil Kumar, D.S. Vivekananda Institute of Technology Department of Ece Karnataka Bengaluru India Don Bosco Institute of Technology Research and Development Department of Ece Karnataka Bengaluru India Sir M Visvesvaraya Institute of Technology Department of Ise Karnataka Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Ece Karnataka Mandya India Vidyavardhaka College of Engineering Department of Ece Karnataka Mysuru India Anatta innovations Bgs B-School Bengaluru India

ISBN: (纸本)9798350372892

brain tumors, characterized by abnormal cell growth within the brain, encompass a diverse range of types, including meningioma, pituitary, and glioma tumors. Early detection and accurate classification of these tumors are crucial for timely intervention and treatment planning. Recent advancements in deep learning and machine learning techniques have significantly enhanced the capabilities of automated tumor detection and segmentation. This study focuses on leveraging pre-trained deep convolutional neural networks (CNNs) for feature extraction, classification, and segmentation of brain tumors. Specifically, models such as VGG16 and EfficientNetb7, well-known for their effectiveness in image classification tasks, are employed. These pre-trained networks are adapted and fine-tuned using brain tumor datasets to extract discriminative features and improve classification and segmentation accuracy. The utilization of pre-trained CNNs, particularly VGG16 and EfficientNetb7, proves effective in automatically classifying and segmenting meningioma, pituitary, and glioma tumors. These models achieve high accuracy rates, demonstrating their capability to handle complex image data and effectively distinguish between different tumor types. The proposed study underscores the significance of deep learning approaches, specifically pre-trained CNNs, in advancing the field of brain tumor detection and classification. by harnessing the power of models like VGG16 and EfficientNetb7, researchers can achieve robust performance in automated tumor analysis tasks, paving the way for improved clinical outcomes and patient care. © 2024 IEEE.

关键词： Convolutional neural networks

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Fatigue and deformation mechanisms of ultrasonic spot-welded dissimilar joints of a magnesium alloy to a clad aluminum alloy

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Journal of magnesium and Alloys 2025年第5期13卷 1939-1952页

作者： Dash, Soumya Sobhan Fernandes, Rylan Christopher Shang, Xiao Zou, Yu Peng, He Jiang, Xianquan Fang, Xiangfan ma, Ninshu Li, Dongyang Chen, Daolun Department of Mechanical Industrial and Mechatronics Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada Department of Materials Science and Engineering University of Toronto TorontoONM5S 3E4 Canada Department of Materials Science and Engineering Cornell University IthacaNY14850 United States Department of Metallurgical and Materials Engineering Indian Institute of Technology Roorkee Uttarakhand 247667 India College of Engineering and Technology Southwest University Chongqing400715 China School of Materials and Energy Southwest University Chongqing400715 China Institute of Automotive Lightweight Design University of Siegen Siegen57076 Germany Department of Joining Mechanics and Analyses Joining and Welding Research Institute Osaka University Osaka Ibaraki567-0047 Japan Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2H5 Canada

A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solid-state ultrasonic spot welding (USW) to evaluate the microstructure, tensile lap shear strength, and fatigue properties. The tensile strength increased with increasing welding energy, peaked at a welding energy of 1000 J, and then decreased due to the formation of an increasingly thick diffusion layer mainly containing Al12mg17 intermetallic compound at higher energy levels. The peak tensile lap shear strength attained at 1000 J was attributed to the optimal inter-diffusion between the magnesium alloy and softer AA1230-clad Al layer along with the presence of ‘fishhook’-like mechanical interlocks at the weld interface and the formation of an indistinguishable intermetallic layer. The dissimilar joints welded at 1000 J also exhibited a longer fatigue life than other mg-Al dissimilar joints, suggesting the beneficial role of the softer clad layer with a better intermingling capacity during USW. While the transverse-through-thickness (TTT) failure mode prevailed at lower cyclic loading levels, interfacial failure was the predominant mode of fatigue failure at higher cyclic loads, where distinctive fatigue striations were also observed on the fracture surface of the softer clad Al layer. This was associated with the presence of opening stress and bending moment near the nugget edge despite the tension-tension lap shear cyclic loading applied. © 2025

关键词： Cyclic loads

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HealthGuard: A blockchain-based Framework for Secure EHR management

HealthGuard: A Blockchain-Based Framework for Secure EHR Man...

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Computing for Sustainability and Intelligent Future (COmP-SIF), International Conference on

作者： Vandit Jaiswal Roshan Singh Srinivas b V Shreyash Kumar Dept. of Information Science and Engineering B.M.S. Institute of Technology & Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Traditional Electronic Health Record (EHR) systems experience centralized vulnerabilities which explain how healthcare data breaches reach about 45% of cases. Operability requirements reveal patient data to multiple entities which creates breakable data structures that compromise security. blockchain technology gives patients an alternative system that operates with decentralized security while maintaining transparency. HealthGuard exists to provide medical patients complete control over their healthcare records with built-in privacy features and educational decision solutions. The authentication security comes from Ethereum combined with metamask services while decentralized storage solutions create revolutionary methods to protect sensitive information. The system operates more efficiently because smart contracts simplify connections between patients and their healthcare providers including lab technicians. Healthcare facilities can achieve advanced data protection through blockchain storage which improves patient care interconnectivity with individualized care plans. The combination of smart contracts together with decentralized applications helps reduce healthcare administration costs which leads to efficiency growth. Through blockchain technology the healthcare system develops better security and changes toward patient-centered care which enables people to manage their health information actively.

关键词： Privacy Smart contracts medical services Decentralized applications blockchains Peer-to-peer computing Security Electronic medical records Sustainable development Interoperability

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HealthGuard: A blockchain-based Framework for Secure EHR management

HealthGuard: A Blockchain-Based Framework for Secure EHR Man...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COmP-SIF 2025

作者： Jaiswal, Vandit Singh, Roshan Srinivas, b.V. Kumar, Shreyash B.M.S. Institute of Technology & Management Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331538538

关键词： Electronic health record

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mPPT Control using modified Sepic Converter in PV modules Connected DC micro Grid Systems 7

MPPT Control using Modified Sepic Converter in PV Modules Co...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Varatharaju, V.m. Senthilkumar, b. manivannan, R. mahalakshmi, S. Geetha, C. Gomathi, S. University of Technology and Applied Science Ibra Oman V.S.B Engineering College Karur India St.Joseph's Institute of Technology Chennai India R.M.K. Engineering College Chennai India Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350397284

This research study applies the predictive control method to implement a distributed maximum power point tracking algorithm, which enhances the energy-extracting capabilities of a cascaded power optimizer-based system in variable environmental conditions. Two control loops are used in the developed method, one for each converter to implement a model predictive control loop based on tracking the maximum power point and the other for power optimization across all cascaded PV modules. Closed loop response of FOPID and ANN controllers simulation results are compared and verified with the prototype. The experimental findings support high energy output, rapid dynamic response, and almost distinct oscillations near the mPP. © 2023 IEEE.

关键词： maximum power point trackers

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An Efficient Learning Assisted malaria Parasites Identification methodology based on blood Smear Images

An Efficient Learning Assisted Malaria Parasites Identificat...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Lalitha, S.D. Remya, P. Priyanka, R. Rajendran, N. Sivakamasundari, N. R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal Chennai India Mailam Engineering College Department of Chemistry Mailam India S.A. Engineering College Department of EEE Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Vandalur Chennai India Hindustan Institute of Technology and Sciences Department of Mechatronics Padur Chennai India

ISBN: (纸本)9798350319200

malaria remains a global health challenge, necessitating accurate and efficient methods for the identification of malaria parasites from blood smear images. In this study, we propose an innovative approach for malaria parasites identification that leverages the power of deep learning and gradient boosting. Our methodology combines the feature extraction capabilities of the mobileNet convolutional neural network with the robust classification capabilities of XGboost. The mobileNet model is employed to extract discriminative features from blood smear images, providing a rich representation of the parasites' morphological characteristics. Subsequently, XGboost is utilized as the classification model to distinguish between infected and uninfected blood samples based on these extracted features. This hybrid approach showcases remarkable accuracy and efficiency, offering a reliable solution for malaria diagnosis. Experimental results demonstrate the superiority of our methodology, achieving precise identification of malaria parasites while minimizing false positives. The proposed model takes the lead with an impressive accuracy score of 0.98, signifying its exceptional capability in distinguishing between different classes within the dataset. This model, tailored for the specific task at hand, utilizes a combination of innovative techniques to achieve superior *** innovative learning-assisted approach holds great promise for enhancing malaria diagnosis, particularly in resource-constrained settings, and represents a significant step towards combatting this life-threatening disease more effectively. © 2023 IEEE.

关键词： malaria Parasites mobileNet XGboost

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Parkinson's Disease Detection using Hand Drawn Spirals and Waves

Parkinson's Disease Detection using Hand Drawn Spirals and W...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K mathias, Tanya Srinivasa, K.G. Niranjanamurthy, m. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

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modified POX Controller for Enhancing Quality of Experience of multimedia Streaming in a Realistic Software Defined Vehicular Ad Hoc Networks 6

Modified POX Controller for Enhancing Quality of Experience ...

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6th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2022

作者： Sarvade, Varun P. Kulkarni, Shrirang A. B. G. M. Institute of Technology Department of Computer Science and Engineering Karnataka Mudhol India The National Institute of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665456982

In today's digital era, multimedia streaming is one of the major sources of internet traffic and it is also expected to grow significantly in upcoming years. Even though traditional Vehicular Ad Hoc Networks (VANET) provide solutions for multimedia content delivery through best effort, the Quality of Experience (QoE) of these techniques is crucially damaged by dynamic network changes, fluctuations in bandwidth and congestion in the network due to lack of global view. However, all these limitations can be substantially reduced by the introduction of Software Defined Network (SDN) architecture. Hence this hybrid network called Software Defined Vehicular Network (SDVN) introduces a global view, flexibility, scalability and smartness in multimedia streaming. The proposed work uses a POX controller with an OpenFlow Spanning Tree topology discovery unit-based SDN framework for the real-world vehicular network. This modified POX was compared with the traditional vehicular network and a normal POX for QoE parameters like multi Scale Structural Similarity, Three Component Structural Similarity, blurring, Delta, mean sum of absolute difference and Noise estimation. For testing, a Standard Definition (SD) video was streamed between vehicular nodes under dense and real-world traffic scenarios. The QoE metrics of modified POX improved between 1 % to 42%. Hence the proposed modified POX presents an enhanced end-to-end QoE for multimedia streaming service. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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ELECTROАNАLYSIS OF mАSPIN IN WHOLE bLOOD

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UPb Scientific bulletin, Series b: Chemistry and materials Science 2024年第1期86卷 157-164页

作者： brаtei, Аlexаndru Аdriаn Stefаn-Vаn Stаden, Rаlucа-Ioаnа Ilie-mihаi, Ruxаndrа-mаriа Nаtionаl University of Science аnd Technology POLITEHNICА Buchаrest Fаculty of Chemicаl Engineering аnd Biotechnologies Buchаrest Romania Lаborаtory of Electrochemistry аnd PАTLАB Buchаrest Nаtionаl Institute of Reseаrch for Electrochemistry аnd Condensed Mаtter Buchаrest Romania Lаborаtory of Electrochemistry аnd PАTLАB Buchаrest Nаtionаl Institute of Reseаrch for Electrochemistry аnd Condensed Mаtter Splаiul Independentei No. 202 Buchаrest Romania Nаtionаl University of Science аnd Technology POLITEHNICА Buchаrest Fаculty of Chemicаl Engineering аnd Biotechnologies Romania

mаspin is а recently introduced biomаrker thаt is utilized in the diаgnosis of gаstric аnd colon cаncers. Electroаnаlysis of mаspin wаs performed using аn electrode constructed by modifying а grаphite pаste decorаted with spheroidаl Cu with ß-cyclodextrin. А high sensitivity (185.69 А mol-1mL) wаs obtаined, coupled with extremely low determinаtion limits (5.12 pg mL-1) аnd detection (2.05 pg mL-1) were obtаined. The electrode cаn be reliаbly employed in order to do the аnаlysis of mаspin in sаmples of whole blood. © 2024, Politechnica University of bucharest. All rights reserved.

关键词： Electrodes

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