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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ece 2024

作者： Salehin, Sultanus Islam, Akib Jayed Pranta, Swapnil Das, Nobel Paul, Shreya Ul Alam, Sayem Pavel, Tanvir Ahamed Uddin, Arafat The University of Alabama Department of Ece Tuscaloosa United States Norwegian University of Science and Technology Computational Colour & Spectral Imaging Gjφvik Norway Port City International University Electrical and Electronic Engineering Chattogram Bangladesh National Institute of Technology Electrical and Electronics Engineering Tamil Nadu Trichy India The University of Alabama Electrical and Computer Engineering Tuscaloosa United States

ISBN: (纸本)9798331535476

Implantable medical devices (IMDs), such as pacemakers, neurostimulators, and drug delivery systems, are being revolutionized by advancements in radio frequency (RF)-based wireless power transfer (WPT). This study focuses on the design and optimization of implantable rectennas, which convert electromagnetic radiation into DC power for IMDs. Key considerations include operating frequency, antenna dimensions, biocompatibility, and power conversion efficiency. A comprehensive human tissue model, incorporating safety parameters such as Specific Absorption Rate (SAR), is proposed to evaluate antenna performance in realistic conditions. The results highlight an impressive 88.65% RF-to-DC conversion efficiency, achieved through innovations in material science and advanced 3D printing techniques. These advancements not only improve the compactness and efficiency of rectennas but also open new possibilities for enhancing the reliability and safety of biomedical power systems. © 2024 IEEE.

关键词： Rectennas

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Temperature Variation Modelling in Mushroom Growing Hall with IAN-Bidirectional GRU Model 4

Temperature Variation Modelling in Mushroom Growing Hall wit...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kalpanadevi, D. Devi, S. Nandhini Silas Stephen, D. Jadhav, Sunita Ali Khan, P.M.D. Muralidharan, J. School of Science Gitam University Department of Computer Science Karnataka India V. S. B. Engineering College Department of Cse Karudayanpalayam Tamilnadu Karur India Panimalar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Chennai India Krishna Mahavidyalaya Rethare Bk Department of Botany Maharashtra Satara India School of Computing Mohan Babu University Andhra Pradesh Tirupati India Kpr Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India

ISBN: (纸本)9798331540364

Modern developments in computing and electronics have made intelligent systems suitable for use in the mechanization of agricultural processes. This proposed used multilayered perceptron and radial basis function networks to forecast the temperature shifts in the mushroom cultivation chamber based on independent variables including water temperature, ambient temperature, water tap, fresh air and circulation air dampers, and water tap. Preprocessing, feature extraction, and training the model are the three phases that comprise this proposed method. Reflectance calibration, normalization, and spectral smoothing are the steps involved in data preprocessing. Feature extraction made quick work of extracting information from mushroom photos by employing the SIFT algorithm. The proposed model was trained using an IAN-Bidirectional GRU. When compared to more conventional approaches, the proposed methodology is superior. The method's application resulted in a 93.75% improvement in accuracy. © 2024 IEEE.

关键词： Radial basis function networks

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Classifying Atrial Fibrillation Using Deep Residual Networks (ResNet) on ECG Signals 2

Classifying Atrial Fibrillation Using Deep Residual Networks...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Shukla, Aasheesh Ankam, Sreejyothsna Hussein, Layth Durgadevi, G. Ashwini, S. Arunachalam, P. GLA University Department of Electronics & Communication Engineering Mathura India GRIET Bachupally Department of CSBS Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Biomedical Engineering TamilNadu Chennai600062 India

ISBN: (纸本)9798350368109

Atrial Fibrillation (AF) is a complex most prevalent arrhythmia with many complications that affect the affected individual's overall health as his/her propensity to stroke and heart failure. This study seeks to investigate the effectiveness of Deep Residual Networks (ResNet) in the categorisation of AF from ECG signals given that this will help improve the AF diagnostic accuracy and speed. The ResNet model is based on deep architecture and uses residual connections to learn various patterns from ECG data. The process includes data preprocessing: filtering, normalization and seg-mentation;model training and testing. The results show a high accuracy of the method, the sensitivity, and specificity, which is higher than in many traditional and some modern techniques. The use of ResNet-based approach suggested for automated AF detections seems to be promising with its clinical application and the consequent effects on patients technology. Improvements for future work may be made to fine-tune the model and the integration of the proposed solution with other diagnostic aids for easier diagnosis of arrhythmias. © 2024 IEEE.

关键词： Electrocardiography

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A Novel Hardware Approach to Image Encryption Using Chaotic Systems

A Novel Hardware Approach to Image Encryption Using Chaotic ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Ali Abdelli Wajih El Hadj Youssef Lazhar Khriji Mohsen Machhout Faculty of Sciences of Monastir Laboratory of Electronics and Microelectronics (LEME) Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Sultanate of Oman

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents a novel chaotic pseudo-random number generator (PRNG) architecture that integrates the XoShiRo128++ algorithm with a chaotic logistic map, combined with an XOR operation using a Linear Feedback Shift Register (LFSR). The hybrid PRNG harnesses the high statistical quality of XoShiRo128++ and the sensitivity of chaotic systems to initial conditions, producing robust and unpredictable sequences. This generator is applied to image encryption using the XOR operation, and its performance and security are thoroughly evaluated. The hardware implementation is carried out on a Zybo Z7 board from the Zynq-7000 family, a resource-constrained FPGA platform. The performance analysis encompasses FPGA resource utilization, timing efficiency, and image-based metrics such as entropy and pixel correlation. The results demonstrate that the proposed chaotic PRNG provides a secure and efficient encryption system, making it highly suitable for resource-limited environments and promising for lightweight cryptographic applications.

关键词： Sensitivity Correlation Generators Hardware Entropy Encryption Timing Internet of Things Field programmable gate arrays Logistics

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Machine Learning Approaches on Pedestrian Detection in an autonomous vehicle 5

Machine Learning Approaches on Pedestrian Detection in an au...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Ranganayaki, V. Jency Rubia, J. Ramesh, P.S. Rammohan, K. Lincy, R.Babitha Deepak, A. Dr.N.G.P. Institute of Technology Dr NGP Nagar Electrical & Electronics Engineering Coimbatore641048 India K. Ramakrishnan College of Engineering Samayapuram Department of ECE Tiruchirappalli621112 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Computer SE Chennai India St. Martin's Engineering College CSE Department Secunderabad India Sri Eshwar College of Engineering Kinathukadavu Computer and Communication Engineering Department Coimbatore641005 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ECE India

ISBN: (纸本)9781665493604

In autonomous driving, detecting pedestrians is a safety-critical activity, and the decision to avoid a person must be made as quickly as possible with as little delay as possible. In this work, INRIA and PETA datasets are taken. The progression of the work that is being proposed is broken up into three phases. The first step is to detect edges, the second step is to group colours, and the third step is extracting the feature, which includes screening body parts of pedestrians and detecting shoulder lines. The machine learning classifiers such as SVM, Naïve Bayes and KNN are taken for predicting the pedestrian in the road. The accuracy for SVM, Naïve Bayes and KNN are calculated as 93.58, 94.42 and 98.44 respectively. With the KNN model, it achieves the highest accuracy for predicting the exact images. © 2023 IEEE.

关键词： Nearest neighbor search

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AI-Enhanced Cloud Security Framework for IoT Networks Using a Predictive Analytics Approach 3

AI-Enhanced Cloud Security Framework for IoT Networks Using ...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Dankan Gowda, V. Gujar, Shantanu Sudhir Shaikh, Salman Firoz Shandilya, Saurabh Sudhakar Reddy, N. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of Computer Science RochesterNY14623 United States Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India Mohan Babu University School of Engineering Department of ECE Tirupati India

ISBN: (纸本)9798350354171

The use of IoT with cloud computing has seen a massive improvement when it comes to data processing as well as connectivity. However, this has its own set of security problems like violation of access, leakage of information and issues with respect to scaling up. These challenges are fundamental and complex, and so this paper seeks to introduce an AI-cloud security framework that incorporates the use of predictive analytical functions. It is used to provide a real-time measure of security threats in IoT networks with a view of safeguarding both the data and the devices used. The values of precision and recall were 95 percent and 90 percent, respectively, with an overall anomaly detection rate higher than the previous threat detection methods. The framework also unveiled overall improvements in response time and false positive rates over the conventional methods. Such findings suggest that implementing AI and predictive analytics into cloud security frameworks for IoT networks are highly conceivable. Concerning future work, the research underlines the need for security solutions that can adapt to IoT's increasing connectivity requirements in numerous applications and would open the path for the next developments in IoT protection frameworks. © 2024 IEEE.

关键词： Cloud security

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Confidential Computing as an Attempt to Secure Service Provider's Confidential Client Data in a Multi-Tenant Cloud Environment 44

Confidential Computing as an Attempt to Secure Service Provi...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Novkovic, Bojan Bozic, Anita Golub, Marin Gros, Stjepan Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Department of Electronics Croatia

ISBN: (纸本)9789532331011

Cloud-oriented infrastructure posed itself as a predominant deployment paradigm in the recent decade due to its ease of provisioning and relatively low cost. However, entrusting a third party with sensitive data in a multi-tenant environment brings about increased data breach risks. The aim of this paper is to give an insight into challenges and threats encountered in mitigating data breach and repudiation risks for service providers utilizing cloud-based environments. Through constructing and studying a possible cloud-based service, we form a corresponding threat model with a special focus on risks originating from internal actors. We explore confidential computing as a possible solution for data confidentiality and mitigation of internal data breach risks for Software as a Service (SaaS) providers. © 2021 Croatian Society MIPRO.

关键词： Costs

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Machine learning applied to functionalized graphene sensors for noninvasive detection of renal diseases

Machine learning applied to functionalized graphene sensors ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lisa Sarkar Arindam Bhattacharyya Avik Sett Gairik Karmakar Tarun Kanti Bhattacharyya Department. of Electronics and Electrical Communication Engineering IIT Kharagpur Kharagpur India Department of Computer and Electrical Engineering UC Davis Department. of Microelectronics TU Delft Delft Netherlands

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Breath biomarker detection has been a significant non-invasive approach for disease diagnosis. This method has significant potential for early diagnosis and accurate analysis of diseases. Emission from breath contains several volatile organic compounds. Among them, ammonia is a very commonly found VOC and mainly responsible for chronic kidney diseases. There exist several strategies to detect ammonia, however they demonstrate severe limitations such as cross-sensitivity and poor selectivity. This work demonstrates the synergistic effect of sensor functionalization and application of machine learning for selective detection of ammonia in the environment. The sensor exhibits high degree of selectivity towards ammonia owing to enormous hydroxyl groups contributed through curcumin. At 500 ppm ammonia, the sensor demonstrates 274% response and very high selectivity among seven volatile organic compounds. The machine learning models were trained with the help of sensor transients. Random Forest and CNN models were applied to predict the presence of ammonia in a mixture. Random Forest achieved 96.25% accuracy compared to 89% accuracy of CNN. Hence, Random Forest algorithms applied to curcumin functionalized reduced graphene oxide sensors can detect ammonia vapors with very high efficiency among a mixture of gases.

关键词： Volatile organic compounds Ammonia Gases Accuracy Graphene Chronic kidney disease Sensors Transient analysis Random forests Diseases

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Retraction Note: A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Elamaran, E. Murugaveni, S. Jyothi, S. Prabhu, M. Ramkumar Chitra, M. P. Talasila, Vamsidhar Department of Electronics and Communication Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Global Academy of Technology Bangalore India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Panimalar Engineering College Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

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An In-Depth Prediction on IoT-Driven Precision Location Using Particle Swarm Optimization 15

An In-Depth Prediction on IoT-Driven Precision Location Usin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimegalai, L. Neelima, P. Pandian, A. Sevuga Bandili, Sunil Kumar Jai Ganesh, B. Ramachandran, Lalitha R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Spmvv School of Engineering and Technology Department of Cse Andhra Pradesh Tirupati India Karnataka Bengaluru India Saint Louis University School for Professional Studies Department of Computer and Information Sciences St. LouisMO63103 United States R.M.D. Engineering College Department of Ece Tamilnadu Kavaraipettai India R.M.K.Engineering College Department of Science and Humanities Tamilnadu Kavaraipettai India

ISBN: (纸本)9798350370249

A whole new era of connectedness has begun with the introduction of the Internet of Things (IoT), which makes it possible for inanimate objects to speak with one another in a straightforward manner. Within Internet of Things frameworks, accurate position has become a key component as a result of applications such as asset tracking, navigation, and environmental monitoring. When it comes to accuracy, scalability, and the ability to react to changing environments, the solutions that are currently available for IoT-driven precise positioning present some limits. When the precise location of something is of the utmost significance, it is necessary to have a solution that is both effective and flexible. The purpose of this research is to investigate the IoT precise location and to provide a novel approach that makes use of Particle Swarm Optimization (PSO) to enhance both efficiency and accuracy. For the purpose of achieving pinpoint accuracy in locating, the proposed method integrates PSO algorithms into the existing framework of the IoT. Through a series of simulations and tests, we will evaluate the performance of the PSO-enhanced system in comparison to that of traditional methods of localization. © 2024 IEEE.

关键词： Dynamic Environments IoT Localization Particle Swarm Optimization Precision Location

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