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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Hala Ibrahim Haytham Azmi M. Watheq El-Kharashi Mona Safar Computer and Systems Engineering Ain Shams Univeristy Cairo Egypt Siemens DISW Cairo Egypt Microelectronics Department Electronics Research Institute Cairo Egypt

Due to the scale of modern systems, pre-silicon security has become a major concern for design and verification engineers. In this paper, we propose a formal verification framework for the verification of different arbiter circuits with different protocols and sizes using SystemVerilog Assertions (SVA). We also propose a formal way of the modeling and insertion of hardware Trojans of different trigger and payload types without applying any modifications to the Design Under Test (DUT). The obtained results show the formal analysis statistics in terms of time and memory for Trojan-free designs for a set of all-proven properties. It also shows how Trojan insertion affects the pass-fail criteria of the formal properties, where at least a single property fails due to the inserted Trojan. The proposed work can be generalized to verify the correct functionality and security of an arbiter circuit placed within any complex system.

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Innovative Approaches to Image Processing: Real-Time Applications and Future Prospects

Innovative Approaches to Image Processing: Real-Time Applica...

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Advancement in Technology (ICONAT), International Conference for

作者： Kiran Chand Ravi K.S. Kannan R. Porselvi S. Banumathi R. Manikandan A Sultan Saleem Department of ECE MLR Institute of Technology Hyderabad Department of CSE Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu Department of Electronics and Communication Engineering Tagore Engineering College Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics & Communication Engineering Panimalar Engineering College Chennai Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The significance of the saying “a picture is worth a thousand words” is evident in our daily experiences, showcasing the profound impact of images. This paper delves into the contemporary and emerging applications of imaging in our everyday lives, offering a broad spectrum for further exploration. With the advent of digital image processing, there has been a transformative shift in various technical domains, opening avenues for extensive research. There exists considerable potential for exploration and advancement within the realm of image processing, as highlighted in this paper. It encapsulates the foundational stages of digital image processing while emphasizing the diverse areas where research endeavors can enhance the quality of human life.

关键词： Digital images Imaging Feature extraction Real-time systems

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Sparse Autoencoder with SGReLU Activation Based Network Intrusion Detection System

Sparse Autoencoder with SGReLU Activation Based Network Intr...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Chandra Sekar P Komuravelly Sudheer Kumar Garikapati Bindu R. Dinesh Kumar Pradeep Kumar S Department of ECE Siddartha Institute of Science and Tech Puttur India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India Department of ECE Saveetha School of Engineering Thandalam India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

A Network Intrusion Detection System (NIDS) is the most important function, which is utilized to monitor malevolent activities frequently and detect these security attacks in Internet of Things (IoT) network. An improvement of effective performance and security is the most needed in the IDS system. This Sparse Autoencoder with Self-Gated Rectified Linear Unit (SGReLU) activation model for intrusion detection systems. The proposed Sparse Autoencoder with SGReLU activation function is evaluated using CIC-IDS-2017 and NSL-KDD dataset. The CIC-IDS-2017 and NSL-KDD dataset is standardized using Label encoder and one hot encoder method which improves the model performance. The pre-processed features are selected by using second percentile method and recursive feature elimination algorithms and the selected features are classified by using Sparse Autoencoder with SGReLU activation function which provides significant results. The proposed Sparse Autoencoder with SGReLU activation function model provides better results by utilizing evaluation metrics like accuracy, precision, f1-score values and PAR about 99.99%, 99.99%, 99.99% and 0.06 by using CIC-IDS-2017 dataset respectively when compared to the LSTM-CNN, NSOM, statistical method and LSTM-AE.

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Exploring Convolutional Neural Networks for Fingerprint Damage Detection and Classification

Exploring Convolutional Neural Networks for Fingerprint Dama...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： R. Swathi Vipashi Kansal Shyamala Prajoona Valsalan Vipul Vekariya Syed Noeman Taqui Department of Electrical and Electronics Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics and Communication Engineering Government Polytechnic for Women Ramannagara Karnataka India Department of Electrical and Computer Engineering Dhofar University Salalah Dhofar Oman Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering SIMTS Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This study proposes a novel approach to fingerprint classification using Deep Convolutional Neural Networks (CNNs), leveraging datasets from prominent sources like the National Institute of Standards and Technology (NIST), Fingerprint Verification Competition (FVC), and the SHUFFLE database. The research involves a systematic pipeline, beginning with data collection and preprocessing to create a comprehensive dataset that represents real-world variations in fingerprint patterns. A tailored CNN architecture is designed to capture hierarchical features essential for accurate classification, trained on the amalgamated dataset using state-of-the-art optimization algorithms and loss functions. The model's performance is rigorously evaluated across diverse fingerprint patterns using separate datasets from NIST, FVC, and SHUFFLE, showcasing its superiority and adaptability compared to existing benchmark methods. Additionally, the study addresses privacy and ethical considerations associated with biometric data usage, ensuring the model's reliability and applicability in various fingerprint recognition applications.

关键词： Adaptation models Systematics Biological system modeling Pipelines Fingerprint recognition NIST Convolutional neural networks

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MNNP: Design and Development of Traffic Sign Identification and Recognition System to Support Smart Vehicles using Modified Neural Network Principles

MNNP: Design and Development of Traffic Sign Identification ...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Anitha G R. Hemalatha Sakthisaravanan. N K. R. Kavitha M. Tamilselvi Ata Kishore Kumar Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Department of EEE S. A. Engineering College Chennai Department of ECE Sona College of Technology Salem Department of ECE SRM Institute of Science and Technology Chennai Department of ECE School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh

In recent times, the acceleration of automotive and computer vision technologies has sparked significant interest in autonomous vehicles. These vehicles' capacity to operate securely and effectively hinges on their adeptness at accurately identifying traffic signs. Consequently, traffic sign recognition has emerged as a pivotal element within autonomous driving systems. Scientists have been hard at work looking at various approaches to accomplishing this recognition, mostly drawing on machine learning and deep learning techniques. To address this issue, we offer a Modified Neural Network approach that combines the Haar cascade algorithm with a MobileNet model classifier. Our developed model is rigorously trained on the GTSRB dataset and then put to the test on a wide variety of traffic sign types. The culmination of our efforts yields a remarkable testing accuracy rate of 97.25%. This finding highlights the possible effectiveness of our technique in improving autonomously vehicle traffic sign identification, which in turn helps to improve the security and effectiveness of autonomous driving systems.

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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electronics and Renewable Systems (ICEARS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif R. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vishnoi, Rahul Verma, Sanjeev Kumar Sharma, Vishal Yuvaraj, K. Gopinath, S. Dongre, Deepika Tmu Department of Ece India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India School of Media Film & Television Studies Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641105 India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined. © 2023 IEEE.

关键词： Aging paradigm Diagnostic strategy Electrical insulation Electrostatic shielding Hyperparameters Life test plan optimization Lifetime Linear regression technique Mainstream test result Minimization Optimization problems Root-average-squared error Statistical properties Test subjects Thermal degradation

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Organic Farming in Drainage Systems with Advanced Automation through Robotics and IoT 1

Organic Farming in Drainage Systems with Advanced Automation...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Shaik, Althaf Badruzaman, Nafisa Gajendran, Devadharshini Geethalakshmi, M. Anand, Jose Kcg College of Technology Electrical and Electronics Engineering Chennai India Kcg College of Technology Aerospace Engineering Chennai India Kcg College of Technology Computer Science Engineering Chennai India Kcg College of Technology Mathematics Chennai India Kcg College of Technology Department of Ece Chennai India

ISBN: (纸本)9798350397840

Wastage of water is huge in India. A report from the Central Water Commission shows that India gets as much as 4,000 billion cubic meters of precipitation, yet just 8% of that is conserved efficiently. As per Composite Water Management Index (CWMI) report almost 600 million individuals, which is practically half of the nation's population, are confronting water scarcity issues. At the same time, the aggregate sum of the farming area started to decline on the grounds because of its utilization for metropolitan, rural, and business advancement. If this condition continues, there will be no agricultural land in the future for growing crops. There is currently no Internet of Things (IoT) application being introduced in drainage basins for crop production for smart agriculture. The efficient cultivation of crops in the drainage basin with the aid of IoT and robotics will have a significant impact on sustainability measures. The usage of the principle of buoyancy for growing crops on the drainage basin is an innovation that makes an impact in saving water. For the healthy growth of crops in the drainage basin, the water quality parameter is monitored continuously using IoT devices. This proposed method includes a Fe6 capsule which is eco- friendly to absorb the dirt present in the water. This paperwork gives the possibility of 'Organic farming in drainage system with advanced automation through robotics and IoT' in which robotics is included in automation. © 2022 IEEE.

关键词： Internet of things

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Recognition of home activities for incomplete spinal cord injury areas utilizing models of hidden Markov simulation

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Signal, Image and Video Processing 2023年第6期17卷 3009-3017页

作者： Kalyani, P. Manasa, Y. Altahan, Baraa Riyadh Ahammad, Sk. Hasane Suman, M. Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of ECE Vardhaman College of Engineering Hyderabad Hyderabad India Department of CSE Siddhartha Institute of Technology Vijayawada India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Hilla Babil Iraq Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Faculty of Electronic Engineering Electronics and Electrical Communications Engineering Department Menoufia University Menouf Egypt Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

Alerting investigators with practitioners for patient variability in the mobilization of activity undergone in the identification, recognition, and motor impairments subject to patient treatment enhancement. By adapting recognition models to populations, conventional practices associated with machine learning (ML) can enhance patient activity recognition (AR). Here, the supervised ML classifier is stationary, with the observations fed to the hidden Markov model (HMM). Also, empirically test the efficiency of our model with activities generated by the incomplete vision of ambulatory sessions in spinal cord injury (SCI). Walking, wheeling, lying down, sitting up, standing up, and stair climbing were among the exercises. The maximum classification accuracy using static classifiers alone using within-subject cross-validation was 86.3% (85.5–87.0, 95% confidence). We increased the accuracy to 88.9% by adding an HMM to the classification model (88.2–89.6). Compared to other classifiers, the proposed method outcome of an extra 2.8% showed considerable enhancement for precision classification when employing the classifier under hybridization and stationary characteristics. As a result of this improved activity identification, doctors may be able to choose or fine-tune the best physical or pharmacological therapy to increase patient mobility. Classifiers evaluated and trained in the laboratory utilizing within-subject cross-validation had a 91.6% accuracy rate. Conclusions Despite only a slight increase in activity levels, ambulatory people suffering from SCI enable to grow their ranges of Corticotropin-releasing factor (CRF) throughout the initial stage of inpatient recovery.

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Artificial intelligence based malignant lymphoma type prediction using enhanced super resolution image and hybrid feature extraction algorithm

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computers in Biology and Medicine 2025年 193卷 110335页

作者： UmaMaheswaran, S.K. Brahmaiah, V. Priyanka Suresh, Chalumuru Balaji, S.R. Ahmad, Faiyaz Department of Mathematics Sri Sai Ram Engineering College Tamil Nadu Chennai600078 India Department of ECE VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad500090 India Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad500090 India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Engineering Jamia Millia Islamia New Delh110025 India

In the medical field, the most common and frequent type of blood cancer is lymphoma. Accurately predicting and early response to lymphoma treatment will be useful for initiating treatment plans to achieve a greater rate of cure or reduced risk of treatment-related morbidity and death. Numerous research studies have revealed several pre-treatment predictive variables, including age, lactate dehydrogenase (LDH), Ann Arbor stage, and ECOG performance. The 18F-FDG PET imaging method makes it possible to assess human tissues' glycolytic activity at the cellular level. It has been extensively utilized for lymphoma staging and diagnosis. Its usefulness for evaluating the response to therapy in lymphoma patients has been demonstrated in a number of promising investigations. However, target training data are typically insufficient for deep neural network training in outcome prediction tasks, making it challenging to provide accurate prediction. Thus, the ensemble learning based target training model has been used with the hybrid feature extraction models, to train the model based on texture, colour and shape. The various types of malignant lymphoma images are gathered as a dataset and pre-processed using spatial image filtering procedures to remove specific unwanted spatial frequencies from an image. Contrast stretching is a technique used to enhance contrast in images and the NNU-Net has been employed to attain the super resolution images for learning in-depth information from the images. After pre-processing the images, the GLRLM, CLCM and HU-moment features extraction techniques are used for extracting the texture, shape and colour features from the images. In order to forecast the type of malignant lymphoma, the retrieved images are finally classified utilizing the Bi-LSTM, DBN, and RBFN by stacking and providing the gathered learning features from each algorithm to the Meta classifier. The proposed model's performance is compared with the most cutting-edge transfer lear

关键词： Deep neural networks

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