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Modelling Performance Analysis in VLSI Testing Methodologies

Modelling Performance Analysis in VLSI Testing Methodologies

International Conference on VLSI, Signal Processing, Power electronics, IoT, Communication and Embedded Systems, VSPICE-2022

作者： Karthikeyan, K. Maragathasundari, S. Kameswari, M. Vanalakshmi, R. Swedheetha, C. Department of Mathematics Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Vaigai College of Engineering Madurai India

ISBN: (纸本)9789819944439

In order to improve the quality of devices before they are delivered to customers, VLSI testing processes have been created to detect damaged devices using automated test equipment. To characterize a chip and identify manufacturing issues that must be resolved before full production, a variety of devices must be tested. This testing methodology for services includes a time-consuming and error-prone planning process. Through the use of a queuing strategy, the performance level of the process used in these testing approaches is investigated and examined. In order to utilize the concept of queuing, the process of VLSI testing procedures is transformed into a mathematical problem. The supplemental variable approach is then used to resolve this mathematical queuing problem. A simulation process is used to create performance measurements for this type of queuing system of VLSI testing techniques using the MATLAB and R tools. Results are as anticipated. A specific discussion of how to prevent or minimize disruptions so that the system can run effectively is included in the analysis section. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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An Adaptive Reversible Data Hiding Scheme using Image Processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

Image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. This study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). The goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security Image Processing Pixel Sorting Reversible Data Hiding Steganography

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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FPGA-Based System for Real-time Epileptic Seizure Detection using KNN Classifier 2

FPGA-Based System for Real-time Epileptic Seizure Detection ...

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2nd International Conference on Electrical, electronics, Information and Communication Technologies, ICEEICT 2023

作者： Jaffino, G. Jose, J Prabin Raman, R. Venkatesan, R.S. Aarthi, V.P.M.B. School of Electronics Engineering Vellore Institute of Technology Vellore India Aditya Engineering College Electronics and Communication Engineering Surampalem India Dr Mahalingam College of Engineering and Technology Electronics and Communication Engineering Pollachi India Kalasalingam Academy of Research and Education Electronics and Communication Engineering Krishnankoil India

ISBN: (纸本)9798350397635

Epileptic seizure detection is a crucial task in the field of biomedical signal processing. Seizures are abnormal electrical discharges in the brain that can lead to various symptoms, such as convulsions, loss of consciousness, and cognitive impairment. People with epilepsy experience repeated seizures that happen suddenly and without warning. More than 70 million people worldwide suffer from epileptic seizure illness. Health professionals examine and identify the epilepsy in electroencephalography (EEG) signal using visual perception. The real time visual analysis of EEG signal is tough and challenging process. Therefore, it is essential to identify seizure event in EEG signal using a real time system in order to prevent patient from unexpected death. In this work Parks-McClellan algorithm was used to design the finite impulse response (FIR) filters, which can be used to remove the noise present in the EEG signals. The preprocessed signal is modelled using proposed weighted moving average model. From the modelled signal the features such as mobility feature, Fluctuation index and weighted spectral entropy features are extracted. The K nearest neighbor classifier is used to classify the seizure and normal EEG signal based on the extracted features. The system that has been developed employs the utilization of the Xilinx System Generator methodology on the Zynq-7000 fully programmable SOC (System-on-a-Chip) platform. This approach enables the implementation of a fully customizable and flexible system, utilizing the inherent capabilities of the SOC and the System Generator tool. Furthermore, the use of the Zynq-7000 platform enables integration of both hardware and software components, providing a more efficient and powerful solution. © 2023 IEEE.

关键词： Electroencephalography

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Internet of Healthcare Things-Enabled Open-Source Non-invasive Wearable Sensor Architecture for Incessant Real-Time Pneumonia Patient Monitoring 2nd

Internet of Healthcare Things-Enabled Open-Source Non-invasi...

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2nd International Conference on Women Researchers in electronics and Computing, WREC 2023

作者： Abubeker, K.M. Baskar, S. Roberts, Michaelraj Kingston Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Amal jyothi College of Engineering Kerala Kanjirapally India Department of ECE Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9789819970766

The elderly, the chronically sick, and those who require constant monitoring benefit greatly from integrating IoT features into medical equipment, since it improves service quality and efficiency. Patients can have better outcomes in acquiring and transmitting vital healthcare data such as heart rate, SpO2, and body temperature using modern sensors and graphical processing unit (GPU). This paper presented the Medical and Healthcare IoT (MH-IoT), an IoT-based novel architecture built on open-source software and hardware for healthcare and ambient-assisted living. The MH-IoT framework incorporates non-invasive MLX90614D and MAX30102 sensors to enable uninterrupted health monitoring. Compared with conventional IoT-based healthcare facilities, the MH-IoT framework ensures and preserves patient safety, keeps connections alive when it matters most, and cuts down on human error. As a future-proof IoT solution, the MH-IoT cloud platform enables global device connectivity related to pregnancies and critical care at home for isolated patients in the İntensive Care Unit (ICU). Different test cases are executed, and comparisons are made with state-of-the-art devices to evaluate the efficacy of the developed wearable sensor platform. Since it uses open-source software and hardware, the proposed GPU-based MH-IoT technology is an excellent option for healthcare connectivity. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wearable sensors

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E-Commerce engineering Navigating Web Development Based Voice for Local 5

E-Commerce Engineering Navigating Web Development Based Voic...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Nule, Monish Sidram Tondewad, Priyanka Dhanvijay, Mrinai Modern Education Society's College of Engineering Dept. of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350338287

Our Research in Rural areas of India shows that Different Communities running Different Small Scalable Enterprises and businesses need Social and Online Reach for which Technology and Financial Assistance are needed. It is observed that small businesses have low customer reach due to low financial assistance as a result they lack reach and individual business websites are also not possible in some Rural areas of India. The paper outlines the systematically designed platform for the change and development of a small Local market that aims to bring authentic products. The paper will discuss how it reduces the monetary burden on the shopkeeper's side. In the recent survey conducted on and outside campus with different age groups, we found that most users would like to choose online platforms like V-vocal. This paper concludes the V- vocal platform and algorithm contribute to small business and their growth. Social Media Influencers' impact has been discussed briefly. © 2023 IEEE.

关键词： Rural areas

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Development of an Expert System for Advanced Podiatric Treatments and Ergonomics 3

Development of an Expert System for Advanced Podiatric Treat...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vigneshwaran, P. Pandian, V Buvanesh Radha, M. Mohan, B. Krishnan, R Santhana Raj, J Relin Francis P.S.R. Engineering College Department of Electronics and Communication Engineering Sivakasi India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Krishnankoil India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331523893

An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image processing-based foot imprint analyser, and a machine learning-driven data analyser. The foot pressure analyser employs an array of FSR sensors with piezo resistive material Velostat, accommodating various foot types. The collected data is processed using thermal image algorithms to create foot pressure images. The image processing unit of the system utilizes edge detection techniques to extract relevant features from the foot pressure images, forming a dataset for machine learning. A neural network is trained using this dataset to identify lower limb abnormalities accurately. The expert system encompasses an extensive knowledge base, storing foot anatomy, pressure point norms, common foot conditions, footwear information, and also analyses lower limb extremities like arthritis and synovial accumulations etc., especially helps in geriatric care. The system generates comprehensive reports summarizing the analysis results, facilitating treatment planning for specific applications, age groups, genders, and foot shapes and giving real-time feedback and recommendations based on the Expert's knowledge to aid in treatment planning, ensuring accessibility and data security. Future developments include 3D printing integration, research support, and machine learning enhancements. The system promises to advance foot health diagnostics and treatments significantly, benefiting healthcare professionals and patients. © 2025 IEEE.

关键词： Geriatrics

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Design and Fabrication of Aluminium Nitride Based Micro Cantilever Sensor for the Detection of Carbon Dioxide

Design and Fabrication of Aluminium Nitride Based Micro Cant...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Rao, Arjun Sunil Guru Prasad, B. Sannakashappanavar, Basavaraj S. Rashmi, K.R. Manipal Academy of Higher Education Department of Electronics and Communication Engineering Karnataka Manipal India Alva's Institue of Engineering and Technology Department of Electronics and Communication Engineering Moodbidri India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bangalore India Excel Pre-University College Department of Physics Guruvayankere India

ISBN: (纸本)9798331540821

In this work, we describe the design and fabrication of the microcantilever for CO2 detection employing a new aluminum nitrate. The microcantilever was designed using COMSOL Multiphysics, and it was then fabricated using a combination of bulk and surface micromachining techniques. A Laser Doppler Vibrometer (LDV) was used to test the devices and determine their resonant frequency, while a Field Emission Scanning Electron Microscope (FESEM) was used to analyze the device's dimensions. The fabricated device displays first harmonics at 729 Hz, second harmonics at 845 Hz, and third harmonics at 2191 Hz. Subsequently, the developed device was utilized with a gas sensor setup to detect CO2, and it responded well to the gas. In conclusion, there is a strong correlation between the synthesized device properties and the simulated outcomes obtained from the resonant frequency calculation. © 2024 IEEE.

关键词： Aluminum nitride

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