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International Conference on Communication and Electronics Systems (ICCES)

作者： G. Uganya D. Rajalakshmi Ramarao Vangapandu M. Dinesh Jayant Giri Mahmoud Odeh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Vignan's Institute of Engineering for Women Vishakhapatnam Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The “Internet of things” is the developing technology to transfer the world into smart innovation. “IoT” security is the most problematic issue in recent applications due to its properties including ‘centralized architecture’, ‘limited capacity devices’, and ‘ambiguity’. So, blockchain with “IoT” applications provides secure data communication compared to traditional “IoT” applications. Blockchain is a recent growing technology that has features including ‘decentralized structure’, ‘transparency’, ‘immutable records’, ‘increased capacity’, and ‘quick backtracks’. The ‘cryptography hash algorithm’ is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm.

关键词： Hash functions Technological innovation Accuracy Public key cryptography Blockchains Encryption Internet of Things data communication

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Determination of Subscribers Coordinates using Flying Network for Emergencies 24

Determination of Subscribers Coordinates using Flying Networ...

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24th International Conference on Advanced Communication Technology, ICACT 2022

作者： DInh, Truong Duy Vishnevsky, Vladimir Pham, Van Dai Le, Duc Tran Kirichek, Ruslan Koucheryavy, Andrey Bonch-Bruevich Saint-Petersburg State University of Telecommunications Prospekt Bolshevikov Department of Software Engineering and Computer Technology St. Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow Russia The University of Danang-University of Science and Technology Information Technology Faculty 54 Nguyen Luong Bang Danang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia

ISBN: (纸本)9791188428090

In the last decade, unmanned aerial systems have emerged in different fields, such as entertainment, military, Internet of Things, emergencies. In these systems, unmanned aerial vehicles (UAVs) communicate with each other based on a flying network to provide services to customers such as live streaming, high-speed access point, etc. The mobility of UAVs can bring numerous advantages in emergency cases, for example, rescuing and searching victims in areas where the network infrastructure is not available. However, the crucial task needed to be done first is to locate the victims' coordinates. In this paper, we propose a method for detecting the coordinates of subscribers with the Wi-Fi signals generated from victims' phones in the absence of network infrastructure of communication operators using a flying network for emergencies based on UAV-swarms. We develop a new protocol for communication between UAVs and UAV-swarms. A structure of UAV-swarms is considered to optimize the searching time. The results of the proposed method are achieved by simulation. © 2022 Global IT Research institute-GiRI.

关键词： Wi-Fi

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Simplified Framework for Diagnosis Brain Disease Using Functional Connectivity

Simplified Framework for Diagnosis Brain Disease Using Funct...

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Communication & Computing (WCONF), World Conference on

作者： T. Swarnalatha B. Supraja Akhila Akula Raaid Alubady K Saikumar Patakottu Prasadareddy Department of CSE PBR Visvodaya Institute Of Technology And Science Kavali Department of CSE GNITC Hyderabad Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Technical Engineering College Al-Ayen University Thi-Qar Iraq College of Information Networks University of Babylon Babylon Iraq Computer Science Malla Reddy University Malla Reddy University School of Engineering Maisammaguda Dulapally Hyderabad Telangana Dept. of ECE Student MVGR College of Engineering Vijayaram Nagar campus Chintalavalasa Vizianagaram Andhra Pradesh

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Diagnosing brain infections is another region where artificial intelligence and neuroscience research meet. Functional connectivity (FC) examination has turned into the standard method for checking out fMRI information. The quantitative investigation of functional magnetic resonance imaging (fMRI) information might uncover significant variables that assistance in clinical assessment. Then again, the greater part of the review done in the past has zeroed in on placing mind sicknesses into bunches in light of low-request FC characteristics and has disregarded the conceivable significance of high-request utilitarian connections between cerebrum regions. To protect this issue, physicists formulated an unusual MFC a multi-level fusion classification paradigm for recognizing mental disorders. To begin, they built a model using a deep neural network (DNN) to collect and formulate vital fundamental portrayals for the depressed-request and extreme-request FC designs we fashioned. During the preparation of the DNN model, there were both uncontrolled and directed learning steps. During the managed calibrating, model learning was added to make the element portrayal more reduced inside each class and simpler to distinguish from different classes. Then, at that point, to characterize mind illnesses, we consolidated the theoretical staggered FC highlights we had learned and utilized various leveled stacking learning techniques to prepare a troupe classifier. Deliberate tests were finished with two genuine, enormous-scope fMRI datasets. The recommended MFC model worked effectively in ordering information for various preprocessing processes, different mind parcellations, and different cross-approval techniques, which shows that it works and can be utilized in a wide range of circumstances. By and large, this work gives an effective method for consolidating significant low-request and high-request FC examples to make it more straightforward to order mind sicknesses.

关键词： Neuroscience Reviews Mental disorders Stacking Cerebrum Artificial neural networks Functional magnetic resonance imaging Standards Medical diagnostic imaging Diseases

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IoT based Patients Monitoring System in Healthcare Service

IoT based Patients Monitoring System in Healthcare Service

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Sudheer Hanumanthakari SVVSR Kumar Pullela Shankar Nayak Bhukya K. Vijayalakshmi S Rehan Ahmad Narendra Kumar Department of Electronics and Communication Engineering Faculty of Science and Technology IFHE Hyderabad Telangana India Department of Computer Science and Engineering Aditya College of Engineering Andhra Pradesh India Department of Computer Science Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Zoology Hiralal Mazumdar Memorial College for Women Kolkata West Bengal India Department of Electronics and Communication Engineering RNS Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665460859

The medical field requires constant updating and the usage of advanced technology. This is due to the fact that different kinds of diseases as increased as the population of world has increased. Scientists and doctors try to come up with different types of preliminary tests and other diagnostic technologies to detect the presence of a disease in its early stages. Though the preliminary test for different health issues is different it all consists of the measurement of basic vital parameters like temperature and the d pressure etc. To ease this process this study aims the development a circuit which is capable of constantly monitoring the vitals of a patient and displaying the result to an attendant. The principal flow of the circuit is initiated by the sensors that are instantly fastened to the patient. The sensors continuously gather and transmit the patient's vital indicators to the node MCU model. The AWS cloud then receives data from the model. The platform compares the collected data to the specified value. If no anomalies are found, the cloud simply records the data before stopping. However, if there are any anomalies, the cloud orders the node MCU model to activate the emergency output gadgets, such as the buzzer and the LED, to immediately alert the attendant or the nurses. A website is also created, and it is utilised to display the patient's information as well as vital records. Along with the patient's vital signs, it also shows an emergency condition when one of the patients has certain irregularities in one or more of the parameters. The physicians and other staff can keep an eye on all the patients at once without having to frequently visit a specific room thanks to this display of emergency information. This system is anticipated to be enhanced in the future with additional capabilities including automated parameter control without human intervention. In other words, the temperature is regulated when the ambient humidity or the body's temperature is a

关键词： Temperature sensors Temperature measurement Clouds Water heating Medical services Humidity Sensors

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Design of a Mobile data Collection Robot for Learning-based Localization and Autonomous Driving

Design of a Mobile Data Collection Robot for Learning-based ...

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Innovations in Intelligent Systems and Applications Conference

作者： Ali Ömer Baykar Jens Lambrecht Ayhan Kural Selçuk Eray Uygur Ahmet Yıldız Department of Mechanical Engineering Istanbul Technical University Istanbul Türkiye Department of Mechanical Engineering Turkish-German University Istanbul Türkiye Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany Turkish-German University Istanbul Türkiye Macromedia University of Applied Sciences Hamburg Germany

This study introduces a mobile robot capable of collecting position and corresponding visual data seamlessly from both indoor and outdoor settings within the same sequence. The mobile robot has been specifically designed to navigate obstacles such as stairs and steps during transitions between indoor and outdoor environments. To accomplish this, the robot incorporates differential driving dynamics and is equipped with essential sensors including two stereo cameras, LIDAR, IMU, and GNSS. The entire system operates on the Robot Operating System (ROS). Consequently, it becomes possible to create a comprehensive dataset that encompasses not only the routes traversed by mobile vehicles but also includes all vehicle and pedestrian roads, as well as indoor spaces, found within a campus environment.

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Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach

Optimizing Electric Vehicle Fleet Operations with Predictive...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Ramakrishna S S Nuvvula Polamarasetty P Kumar Alighazi Siddiqui S. Thamizharasan Chai Ching Tan Raaid Alubady Baseem Khan Department of Electrical and Electronics Engineering NMAM Institute of Technology NITTE Deemed to be University Karnataka India Department of Electrical and Electronics Engineering GMR Institute of Technology Rajam India College of Computer Science and Information Technology Jazan University Rajiv Gandhi College of Engineering and Technology Nakhon Sawan Rajabhat University Technical Engineering College Al-Ayen University Thi-Qar Iraq College of Information Networks University of Babylon Babylon Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia Department of Electrical and Electronic Engineering Technology University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15% reduction in greenhouse gas emissions, substantial annual cost savings exceeding $550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.

关键词： Renewable energy sources Schedules Costs Electric vehicles Smart grids Predictive analytics Sustainable development

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Advancing Telestroke Services Technology with Cloud and AI Solutions for Enhanced Care

Advancing Telestroke Services Technology with Cloud and AI S...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mothiram Rajasekaran R. Meenakshi P. K. Dhal R. Aiyshwariya Devi S. Sujatha R. Kamalakannan Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tami Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics engineering S.A. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Telestroke services have become an essential component of stroke therapy, especially in areas with few specialists' medical facilities. To further improve telestroke services, this paper investigates the possibility of integrating cloud computing with artificial intelligence (AI) technologies. With the scalability, accessibility, and cost-effectiveness of cloud infrastructure, healthcare professionals worldwide may communicate and share data easily. Machine learning algorithms, image analysis tools, and other forms of AI are enhancing telestroke services by giving doctors access to sophisticated decision-making capabilities. Better patient outcomes result from these systems' capacity to facilitate quicker and more precise diagnosis and treatment planning. Predictive analytics powered by AI can identify patients at risk early on and provide individualized treatment plans that can better meet each patient's requirements. By combining cloud and AI technology, telestroke services can improve patient care by analyzing large datasets, allocating resources more efficiently, and fostering better teamwork among specialists. It may perform beyond geographical restrictions and ensure that stroke patients all around the globe have equal access to high-quality treatment. The potential is to transform stroke care delivery using data-driven insights and improved decision-making skills. It simplifies processes and improves efficiency.

关键词： Cloud computing Technological innovation Decision making Medical treatment Transforms Stroke (medical condition) Prediction algorithms Teamwork Artificial intelligence Predictive analytics

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Visible Light Positioning Based on a Single Luminaire: A Novel Visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Rajasekar Alok Kumar Prema Latha V Jayapal N Boddu Soundarya Santhoshi K. Karthik Mathiyalagan Sivaramkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electrical and Electronics Engineering Guru Gobind Singh Educational Society Technical Campus Bokaro Jharkhand India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Mechanical Engineering S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of BIOSCIENCES Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems.

关键词： Location awareness Deep learning Fault detection Feature extraction data augmentation Electrical fault detection data models Convolutional neural networks Integrated circuit modeling Long short term memory

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