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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Padhiary, Mrutyunjay Powar, Swati Asokan, Anju Sundar Rajan, M. Al-Said, Khaleel Al Said, Nidal Assam University Triguna Sen School of Technology Department of Agricultural Engineering Assam Silchar788011 India Mit Adt University Computer Science Engineering Maharashtra Pune India Cse Nehru College of Engineering and Research Centre Kerala Thrissur India Arbaminch University Ethiopia Faculty of Electrical and Computer Engineering Arbaminch Institute of Technology India Middle East University Faculty of Arts and Educational Sciences Department of Educational Technology Amman Jordan Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

The use of artificial intelligence (AI) in robotics, particularly in the advancement of gaming robotics, has drawn a lot of interest as technological and scientific developments continue to advance. This innovation opens up new possibilities for creating smart autonomous robots for gaming. Nevertheless, there is more work to be done to achieve great accuracy and consistency in motion management. The challenges of the robotics' level of smart and the motion-capturing structure's capacity to prevent interruption still require resolution, although numerous methods have been put out to improve monitoring accuracy by merging various data. This study proposes an adaptable reinforcement learning (RL)-oriented multifaceted data combination (AdRL-MDC) system for training a robotic hand to play games alongside humans. It incorporates an adaptable training process for updating the ensemble classification algorithm, an RL paradigm that provides the robots with smart knowledge, and a multifaceted data combination framework that is resistant to interruption. The following studies demonstrate the AdRL-MDC system's previously indicated functionality. The ensemble framework, which combines a convolutional neural network (CNN), performs well in terms of accuracy and computing duration. Furthermore, the depth vision-oriented CNN classification algorithm achieves one hundred percent recognition accuracy, implying that the forecasted motions are the real value. © 2025 IEEE.

关键词： Autonomous robots Convolutional neural network Gaming Motion management Reinforcement Learning

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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Coordinated Multi-Robot Exploration and Slime Mould Algorithm for Multi-Robot Space Exploration

Coordinated Multi-Robot Exploration and Slime Mould Algorith...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Gudivaka, Rajya Lakshmi Kumar Gudivaka, Raj Prashanth, V. Almusawi, Muntather Saranya, N. Naga Wipro Andhra Pradesh India Platinum Infosys Irving TX United States Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350360660

In multi-robot space, conducting research is the critical task to perform flexible without presence of humans the multi-robots are used like landers and rovers for exploring the area which is unknown environment. The multi-robots face problems in exploring the area due improper planning and communication between the robots in specified paths. To solve the problem, Coordinated Multi-robot space Exploration Slime Mould Algorithm (CME-SMA) is proposed here. This integration includes a parallel communication protocol to enhance exploration efficiency and reduce computational complexity. The algorithm focuses on generating optimal collision-free paths in barrier-filled environments by first evaluating neighboring cells using deterministic CME for cost and utility values. SMA is then applied to refine the solutions further. The CME-SMA algorithm is validated through numerous simulations under various environmental conditions, demonstrating consistency in achieving expected outcomes such as exploration rate and time efficiency. The CME-SMA method explored the area is 98.79% with 0 failed run and execution time is 37.15 sec in map 1when compared with existing methods. © 2024 IEEE.

关键词： Multipurpose robots

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Vision-Based Navigation for a Small-Sized Drone: A Simple AI Strategy at IMAV’22

Vision-Based Navigation for a Small-Sized Drone: A Simple AI...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Adil Farooq Syeda Zillay Nain Zukhraf Sadman Shafi Mohsin Kamal Narumol Chumuang Department of Electrical and Computer Engineering University of Cyprus Cyprus Department of Electrical Engineering Penn State University United States School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Department of Digital Media Technology Muban Chombueng Rajabhat University Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M.

关键词： Technological innovation Video on demand Navigation Heuristic algorithms Web sites Collision avoidance Artificial intelligence

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Development of Green Computing through Power Management in Wireless Networks using Machine Learning 7

Development of Green Computing through Power Management in W...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raja, S.R. Arya, Hemlata Ramya, D. Sivakumar, S. Arigela, Arun Kumar Venu, Nookala Saveetha Deemed University SIMATS Saveetha College of Liberal Arts And Science Department of Computer Science Chennai India Department of Computer Science & Engineering Madhya Pradesh Gwalior India Sathyabama Institute of Science And Technology Electrical And Electronics Engineering Rajiv Gandhi Salai Chennai India Vel Tech Rangarajan Dr. Sagunthala R And D Institute of Science And Technology Department of Electrical And Electronics Engineering Chennai Tamilnadu Avadi600062 India Department of Csit Telangana Hyderabad India Centre For IoT Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350359299

This study investigates the use of Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to improve power utilization in green data centers. The study examines each model's individual capabilities in addressing key concerns about energy efficiency and sustainability. SVM is extremely accurate in workload prediction and resource allocation, whereas CNN excels in real-time monitoring and cooling system efficiency. RNN contributes by anticipating fluctuations in energy use and adapting to dynamic workload changes over time. The research uses ten hypothetical trials to demonstrate the models' consistent performance across multiple measures. These results suggest that SVM, CNN, and RNN have transformative potential in the development of flexible, efficient, and sustainable green data centers. The study adds to the larger discussion about green computing by emphasizing the role of powerful machine learning models in shaping the future of ecologically responsible data center operations. © 2024 IEEE.

关键词： Support vector machines

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Artemis Removable-Canister Waste Accumulation System & Handler (ARC-WASH): A Portable Modular Space Lavatory

Artemis Removable-Canister Waste Accumulation System & Handl...

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2022 IEEE Aerospace Conference, AERO 2022

作者： Payra, Syamantak Paradiso, Joseph A. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States Responsive Environments Group Massachusetts Institute of Technology Mit Media Lab United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

The Artemis Removable-Canister Waste Accumulation System and Handler (ARC-WASH) is a portable, modular space lavatory designed for use onboard spacecraft in both microgravity and terrestrial/lunar gravity. In designing this translunar waste collection and storage system, a systems engineering approach is combined with product design principles and inspiration from everyday objects to create a toilet that prioritizes crew comfort, inclusivity, and ease of use by all astronauts, while maintaining cleanliness, efficiency, and functionality in the most demanding of environments. The flexible, modular design of the ARC-WASH accommodates astronauts of all genders, solves problems faced with previous generations of space toilets, and introduces an unprecedented degree of versatility for use in landers, rovers, and even future space habitats. © 2022 IEEE.

关键词： Manned space flight

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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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New 5G Kaiser-Based Windowing to Reduce Out of Band Emission

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computers, Materials and Continua 2022年第2期71卷 2721-2738页

作者： Hammoodi, Ahmed Audah, Lukman Al-Jobouri, Laith Mohammed, Mazin Abed Aljumaily, Mustafa S. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia School of Engineering Arts Science and Technology University of Suffolk IP4 1QJ United Kingdom College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Electrical Engineering and Computer Science Department University of Tennessee KnoxvilleTN37901 United States

OFDM based waveforms are considered as the main part of the latest cellular communications standard (namely 5G). Many inherited problems from the OFDM-Based LTE are still under investigation. Getting rid of the out of band emissions is one of these problems. Ensuring low out of band emission (OOBE) is deemed as one of the most critical challenges to support development of future technologies such as 6G and beyond. Universal Filtered Multi Carrier (UFMC) has been considered as one of the candidate waveforms for the 5G communications due to its robustness against Inter Carrier Interference (ICI) and the Inter Symbol Interference (ISI). It is also a preferred option because it is the most appropriate for low latency scenarios. In this paper, a novel approach is proposed that makes use of modified Kaiser-Bessel filter-based pulse windowing instead of standard Dolph-Chebyshev filter for UFMC based waveform. The aim of proposing the new approach is to enable the reduction of spectral leakage into nearby sub-bands. A comprehensive study for the modified Kaiser-Bessel filters is performed and the results are presented in terms of several Key Performance Indicators (KPIs). Based on the results of the simulation, the UFMC Kaiser-Hankel window demonstrated lower sidebands and better power spectral density, when compared with the traditional Orthogonal frequency-division multiplexing (OFDM) and UFMC as well as the normal UFMC Kaiser window. In addition, the real test for the kaiser window with 5G waveform is lower OOBE than conventional 5G waveform (CP-OFDM and UFMC). The OOBE reduction of 31% of the Kaiser vs. the Dolph-Chebyshev filter, 68% of Kaiser Hankel over the Dolph-Chebyshev, and 20% of Kaiser Hankel over Kaiser filter have been reported in this paper. The Power Spectral Density has been improved accordingly. © 2022 Tech Science Press. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Exploring Electromagnetic Time-Reversal as a Method to Monitor Cable Temperature in Medium-Voltage Networks 10

Exploring Electromagnetic Time-Reversal as a Method to Monit...

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10th International Conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Ragusa, Antonella Wouters, Peter A. A. F. Sasse, Hugh Duffy, Alistair Rachidi, Farhad Rubinstein, Marcos De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands School of Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland Institute for Information and Communication Technologies University of Applied Sciences and Arts Western Switzerland Yverdon-les-Bains Switzerland

ISBN: (纸本)9788986510225

The paper addresses the research question of whether the electromagnetic time reversal (EMTR) technique can be used to estimate the propagation speed of a signal on power cables. This value changes in the long term due to ageing of the cable insulation and in the short term due to the cable temperature variation. A first numerical investigation of the EMTR effectiveness in estimating the propagation speed in a simple cable system is described showing that EMTR is a promising technique to be used also for the monitoring of power cable temperature. Future research will be directed to implementation of the methodology in more complex networks. © 2024 The Korean Institute of electrical Engineers (KIEE).

关键词： Power cables

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