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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sandhiya, R. Prasath, S. Viswanath, R.N. Padmapriya, R. Kumar, Arun M. Ramaraj, Kottaimalai Department of Biomedical Engineering Aarupadai Veedu Institute of Technology Chennai India Humanities and Science Aarupadai Veedu Institute of Technology Chennai India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798350340235

Various PA (photo acoustic) contrast agents which is range from plasmodia metal nanoparticles to FDA-approved dyes can be targeted to specific receptors which is in intact with the living tissue. Compared with endogenous contrast of tissue, Photo Acoustic Process contrast agent can be channelized to a specific threshold to give betterment of results with higher contrast resolution for accuracy calculation. The quantum dots and Gold coating Nanomaterial conjugates are to be designed for exogenous contrast agent for stomach carcinoma detection in ultrasound imaging. In this Paper we are About to design Core shell nanoparticle is made up of gold coated Nano particle with Zinc Oxide Quantum dots using Green S ynthesis Analysis Method Photo acoustic imaging can be used to detect growing tumors with great sensitivity or to study emerging vasculature in high resolution. The extraction of gold nanoparticles using FTIR and AFM UV analysis methods is used to complete this process. © 2023 IEEE.

关键词： Atomic Force Microscopy (AFM) Fourier Transform Infrared Spectroscopy (FTIR) Ultraviolet visible Spectroscopy (UV-VIS) Zinc Oxide (ZNO)

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High-Performance Code Compression Using Adaptive Encoding for RISC Processor 4th

High-Performance Code Compression Using Adaptive Encoding fo...

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4th international conference on Energy Systems, Drives and Automations, ESDA 2021

作者： Lakshminarayana, D. Ganesan, V. Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India Department of ECE KG Reddy College of Engineering &Technology Hyderabad India Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9789819936908

In this paper, high-performance code compression using adaptive encoding for RISC processor is implemented. The main intent of reduced instruction set computer (RISC) processor is to utilize the small and optimized instructions. Code compression in RISC processor will encode the optimized data to a certain extent. Initially, the data is moved from one place to another using data bus buffer. Adaptive encoding will compress the lossless data and characterize the lossless data. Basically, encoding in RISC processor will improve the usage of look-up tables and slices. It causes the more utilization of area, so to overcome this problem, adaptive encoding in RISC processor is implemented. From results, it is observed that the total delay of conventional encoding in RISC processor is 21.015 ns and adaptive encoding in RISC processor is 4.128 ns. Logic delay of conventional encoding in RISC processor is 12.843 ns, and adaptive encoding in RISC processor is 3.493 ns. Route delay of conventional encoding in RISC processor is 8.172 ns, and adaptive encoding in RISC processor is 0.725 ns. From the simulation results, it is observed that the total delay, logic delay, route delay, memory usage, number of slices, and number of LUTs are reduced effectively compared with the conventional encoding in RISC processor. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Table lookup

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Data Security Storage Mechanism Based on Blockchain Network

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Computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks with imperfect CSI 2

Secrecy Performance Analysis of Hybrid RF-FSO Cooperative Sa...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Ma, Yuanyuan Chen, Han Xu, Zhan Zhi, Ruxin Ma, Chunshuai Zheng, Yiyang Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China No.723 Research Institute China State Shipbuilding Corporation Yangzhou China Beijing Information Science and Technology University Beijing China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

This study examines the secrecy outage performance of uplink transmission within a hybrid cooperative satellite-aerial-terrestrial network (SATN) that integrates radio-frequency (RF) and free-space optical (FSO) technologies. Specifically, in the proposed cooperative SATN, a terrestrial source (S) conveys its information to a satellite receiver (D) with the assistance of an aerial relay (R), while a group of eavesdropping aerial terminals (Eves) attempts to intercept the confidential data. RF and FSO transmission methods are employed over the S-R and R-D links, respectively, both subject to imperfect channel state information (CSI). By considering the randomness of R, D, and Eves, and utilizing a stochastic geometry framework, we investigate the secrecy outage performance of the considered SATN. A closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, simulations are conducted to validate the accuracy of the analysis. © 2024 IEEE.

关键词： Stochastic systems

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Chevrel phase: A review of its crystal structure and electrochemical properties

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Progress in Natural science:Materials International 2023年第1期33卷 8-15页

作者： Meng Liu Guocheng Lv Tianming Liu Hao Liu Lingchang Kong Lintao Tian Wenxiu Rao Yuxin Li Libing Liao Juchen Guo Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes National Laboratory of Mineral Materials School of Materials Science and Technology China University of Geosciences(Beijing) School of Science China University of Geosciences Materials Science and Engineering Program University of California-Riverside Department of Chemical and Environmental Engineering University of California – Riverside

Chevrel phase compounds have attracted increasing attention as electrochemical energy storage materials and electro-catalysts. Benefiting from their unique crystal structure, Chevrel phase compounds can not only function as the host structures for reversible intercalation of a broad range of cations, but also exhibit high catalytic activity in electrochemical reduction reactions. Here we provide an overview of recent progress in the development of Chevrel phase materials including new understanding of structural features, synthetic methods, and electrochemical properties. A brief conclusion and perspectives on the future development of Chevrel phase materials are also provided.

关键词： Chevrel phase Electrochemical properties Energy storage Electro-catalysis

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Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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Edge-Driven Biometrics and Facial Recognition for Virtual Assistant

Edge-Driven Biometrics and Facial Recognition for Virtual As...

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2023 International Conference on Energy, Materials and communication Engineering, ICEMCE 2023

作者： Ramalingam, H.M. Fazil, Mohamed Pallikonda Rajasekaran, M. Kottaimalai, R. Vishnuvarthanan, G. Arunprasath, T. Mangalore Institute of Technology & Engineering Department of Electronics and Communication Engineering Mangalore Karnataka India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Vit Bhopal University Data Science Division Sehore Madhya Pradesh Kothrikalan India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350393378

In modern society, time is essential. Recent advances in technology have accelerated the emergence of assistance systems for individuals in their everyday lives. Quick and correct information at the right time needs computerization. In the existing voice assistants, the proper authentication of the user for security purposes is not precise. Inculcating the face and voice biometrics will add up security for the system. To ensure each user's data and personal information is properly maintained, we provide Multifactor authentication. Also, a lot of time is spent on tedious repetitive tasks which can be reduced by using a virtual assistant. This paper presents a combination of different technologies like Edge driven biometrics, Computer vision, and Machine Learning. The model was designed and developed for both personal mode and general mode. Usability testing was carried out for several use scenarios in order to assess performance. © 2023 IEEE.

关键词： Face recognition

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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Aging State Evaluation of Transformer Oil-paper Insulation based on PDC Asymmetric Current 4

Aging State Evaluation of Transformer Oil-paper Insulation b...

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4th IEEE International Conference on Electrical Materials and Power Equipment, ICEMPE 2023

作者： Zhang, Mingze Lei, Shengjie Liang, Yilin Liu, Heqian Shi, Yunbo Liu, Ji Hrbust Harbin University of Science and Technology Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Hrbust School of Measurement and Communication Engineering Harbin China Harbin University of Science and Technology Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin China Electric Power Research Institute State Grid Heilongjiang Electric Power Company Limited Harbin Harbin China Harbin University of Science and Technology School of Measurement and Communication Engineering Harbin China

ISBN: (纸本)9798350334678

The PDC test is an important means to evaluate the main insulation status of the transformer. When the traditional PDC test current is used to analyze the aging state, it is found that there is an obvious asymmetry between polarization and depolarization current, which will affect the accuracy of the evaluation results. Therefore, this paper has carried out research on the asymmetric PDC variation law of oil-paper insulation. Several groups of oil-paper insulation models with different degrees of aging were prepared in the laboratory environment. Under the polarization voltage, the change law of the transient electric field distribution in the aging model was calculated. According to the traditional interface polarization theory, the influence of insulation aging on the traditional interface polarization behavior is analyzed. Finally, this paper uses the collapsible charge model to explain the causes of PDC current asymmetry. Finally, the quantitative characterization relationship between the amount of decomposing charge and the aging of oil-paper insulation was established. The research results of this paper provide theoretical support for improving the accuracy of insulation state assessment of oil-immersed power equipment based on the PDC testing method. © 2023 IEEE.

关键词： Oil filled transformers

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