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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rajagopal, R. Arunarani, A.R. Arivarasi, A. Ingle, Anup Ravichandran, T. Prakash, R. Vijaya Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India School of Computing College of Engineering and Technology Srm Instutite of Science and Technology Department of Compuational Intelligence Tamilnadu Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350300888

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources. © 2023 IEEE.

关键词： Resource allocation

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Performance and Security Evaluation of Chaotic Maps for Image Scrambling in Real-Time Applications 3

Performance and Security Evaluation of Chaotic Maps for Imag...

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3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Subashini, B. Panjatcharam, V.G. Saradha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept of Data Science and Business Systems Chennai India Vet Institute of Arts & Science(Co-Ed) College Dept of Mca Thindal Erode India Nandha Engineering College (Autonomous) Dept of Mca Erode India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Image scrambling is an essential technique in secure image transmission, designed to obscure visual data from unauthorized access while preserving the ability to recover the original content. This study investigates the application of five chaotic maps such as Logistic Map, Arnold Cat Map, Lorenz System, Henon Map and Tent Map for image scrambling. Performance is evaluated using key metrics: Normalized Cross-Correlation (NCC), entropy, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and time complexity. Results indicate that the Arnold Cat Map provides the highest entropy (5.8680) and strong decorrelation (NCC = 0.8715), making it ideal for high-security applications. The Logistic Map, with the fastest execution time (0.0039 seconds), is suitable for real-time applications, offering moderate security and good image quality (PSNR = 11.5170, SSIM = 0.0229). The Lorenz System exhibited the best decorrelation (NCC = 0.8657), but its high computational cost limits its use in time-sensitive environments. By comparing the PSNR and SSIM results, this study highlights the balance between visual quality and security, suggesting that chaotic maps can be tailored for specific s ecurity n eeds. The novelty lies in the systematic comparison of these chaotic maps, providing a comprehensive evaluation for optimizing security and performance. © 2025 IEEE.

关键词： Arnold Cat Map Henon Map Image Scrambling Logistic Map Lorenz System Tent Map

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

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IEEE Communications Surveys and Tutorials 2025年

作者： Li, Yichen Wang, Haozhao Xu, Wenchao Xiao, Tianzhe Liu, Hong Tu, Minzhu Wang, Yuying Yang, Xin Zhang, Rui Yu, Shui Guo, Song Li, Ruixuan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Beijing China Soochow University School of Computer Science and Technology Suzhou China Southwestern University of Finance and Economics School of Computing and Artificial Intelligence Chengdu China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to overcome challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. This survey focuses on a comprehensive examination of the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices. We begin with an introduction to the background and fundamentals of non-centralized learning and continual learning. Then, we review existing solutions from three levels to represent how existing techniques alleviate the catastrophic forgetting and distribution shift. Additionally, we delve into the various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. Furthermore, we establish a large-scale benchmark to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches. Finally, we discuss the important challenges and future research directions in NCCL. © 2021 IEEE.

关键词： Learning algorithms

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Optimizing Traffic Signal Control Using Reinforcement Learning for Enhanced Traffic Flow Efficiency

Optimizing Traffic Signal Control Using Reinforcement Learni...

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Progressive Innovations in Intelligent Systems and Data Science (ICPIDS), International Conference on

作者： M. Rajkumar Namit Dasappanavar Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Tamil Nadu India B. Tech-Information Technology School of Information Technology & Engineering Vellore Institute of Technology VIT University Tamil Nadu India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

In this paper, the techniques used to optimize traffic light timing in urban environments are discussed. It remains a significant challenge, especially with the increasing complexity of current traffic systems. Tabular Q-learning was the mainstay of early methods from the 1990s, but it was constrained by narrow state spaces and simple traffic measures, which frequently led to less-than-ideal management techniques. [Recent advancements in deep reinforcement learning (DRL) offer new possibilities by combining deep learning with RL to estimate Q-values more effectively. However, most existing studies still focus on simple intersection models, lack comprehensive cycle timing, and do not utilize the latest advancements in DRL, such as dueling deep Q networks. By creating a model that can handle intricate intersection scenarios with several phases and high-dimensional action spaces, this research seeks to overcome current shortcomings. Smooth transitions between traffic signal phases are integrated with advanced value based deep reinforcement learning techniques in the proposed approach. Comprehensive simulations are used to assess how well the model performs in terms of timing optimization for traffic control systems, showing enhanced performance under certain conditions. This research leverages advanced Dueling DQN techniques to address the complexities of multi-phase intersection management, distinguishing it from existing models that primarily focus on single-phase intersections [16].

关键词： Technological innovation Q-learning Urban areas Aerospace electronics Traffic control Deep reinforcement learning Timing Complexity theory Intelligent systems Optimization

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Locomotion Control of Cyborg Insects by Charge-Balanced Biphasic Electrical Stimulation

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Cyborg and Bionic Systems 2024年第1期5卷 149-161页

作者： Zhong Liu Yongxia Gu Li Yu Xiang Yang Zhiyun Ma Jieliang Zhao Yufei Gu School of Computing and Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China New York University Abu Dhabi Abu Dhabi 129188United Arab Emirates

The integration of electronic stimulation devices with insects in the context of cyborg insect systems has great application potential,particularly in the fields of environmental monitoring,urban surveillance,and rescue *** considerable advantages compared to the current robot technology,including flexibility,durability,and low energy consumption,this integration faces certain challenges related to the potential risk of charge accumulation caused by prolonged and repetitive electrical *** address these challenges,this study proposes a universal system for remote signal output control using infrared *** proposed system integrates high-precision digital-to-analog converters capable of generating customized waveform electrical stimulation signals within defined *** enhances the accuracy of locomotion control in cyborg insects while maintaining real-time control and dynamic parameter *** proposed system is verified by *** experimental results show that the signals generated by the proposed system have a success rate of over 76.25%in controlling the turning locomotion of cyborg insects,which is higher than previously reported *** addition,the charge-balanced characteristics of these signals can minimize muscle tissue damage,thus substantially enhancing control *** study provides a comprehensive solution for the remote control and monitoring of cyborg insects,whose flexibility and adaptability can meet various application and experimental *** results presented in this study lay a robust foundation for further advancement of various technologies,particularly those related to cyborg insect locomotion control systems and wireless control mechanisms for cyborg insects.

关键词： remote control electrical stimulation charge accumulation cyborg insects charge balanced biphasic signals environmental monitoringurban cyborg insect systems robot technologyincluding

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Control Hamiltonian selection for quantum state stabilization using deep reinforcement learning

Control Hamiltonian selection for quantum state stabilizatio...

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第43届中国控制会议

作者： Chunxiang Song Yanan Liu Daoyi Dong Huadong Mo the School of Engineering and Technology University of New South Wales the School of Engineering University of Newcastle Centre for Quantum Dynamics Griffith University the School of Engineering Australian National University the School of Systems and Computing University of New South Wales

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Quantum state stabilization is a pivotal element in the realm of quantum control, forming the bedrock for various quantum tasks. To achieve the stabilization of a quantum state, it is imperative to formulate effective control channels(represented by control Hamiltonians) and devise the appropriate control signals. In this study, we introduce a novel approach, the selection of control Hamiltonians through Deep reinforcement learning(SCH-DRL), to address the challenge of control Hamiltonian selection in quantum control. Deep reinforcement learning(DRL) is employed to generate control signals corresponding to control Hamiltonians, and SCH-DRL utilizes these control signals to recognize a set of simple and efficient control Hamiltonians, depending on different target states. This approach not only provides a method for control Hamiltonian selection in quantum state stabilization but also unveils the untapped potential of DRL for a broad spectrum of applications in the field of quantum information. Through applications in two-qubit and three-qubit scenarios, we demonstrate how the SCH-DRL method adeptly selects the quantity of control Hamiltonians for achieving the desired stability of quantum states.

关键词： quantum state stabilization control Hamiltonian selection deep reinforcement learning(DRL)

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Exploring the Effects of Glasses on mmWave: Attenuation, Thermal Impact, and Implications for 5G and Beyond Mobile Technologies

Exploring the Effects of Glasses on mmWave: Attenuation, The...

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International Conference on Information Networking

作者： Md Jannatul Rakib Joy Negin Foroughimehr Ali Yavari 6G Research and Innovation Laboratory School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The growing demand for higher bandwidth has pushed mobile carrier frequencies into the millimeter-wave (mmWave) spectrum such as high-band 26 GHz, to meet the requirements of 5G networks and advanced communication technologies. While mmWave frequencies provide the necessary bandwidth for high-speed data transmission, they pose significant challenges, especially in urban environments where signals interact with common building materials like glass. This study focuses on the effects of 26 GHz mm Wave propagation through various glass types commonly found in modern urban architecture. Our experiment measured electric field (E-field) strength loss, attenuation, and temperature variations as the waves passed through different glass samples. Results show that highly reflective and coated glass types, such as Low-E and silver reflective glass, caused significant E-field strength reduction reaching up to ${9 3 \%}$. The experiment also revealed a noticeable temperature increase in some glass samples, further influencing signal degradation. These findings underline the importance of consideration of the glass materials as they can cause signal loss, impacting reliability of the 5G and beyond connectivity in urban environments.

关键词： Temperature measurement Degradation Silver 5G mobile communication Millimeter wave technology Glass Materials reliability Attenuation Loss measurement Millimeter wave communication

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Detection of Yoga Poses Using CNN and LSTM Models 3rd

Detection of Yoga Poses Using CNN and LSTM Models

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3rd International Conference on computing, Communication and Learning, CoCoLe 2024

作者： Rukmini, Bheemanapalli Sushmini, Chaganti Sai Biswal, Saubhagya Ranjan Computer Science Engineering School of Computing and Informatics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India

ISBN: (纸本)9783031790409

Yoga is a holistic practice that encompasses physical, mental, and spiritual well-being. Central to yoga are its postures, or "asanas," which are designed to promote flexibility, strength, and balance in the body. Each yoga posture holds its own significance, targeting specific muscle groups and energy centers. Proper alignment in these postures is crucial not only for reaping physical benefits but also for cultivating mindfulness and concentration. In this work, we presented an approach to detect the yoga poses using CNN and LSTM methods. For each model, the data loading process involves reading images, resizing them, and organizing them into appropriate formats for training and testing. The CNN model comprises several convolutional and pooling layers, while the LSTM model includes LSTM layers for temporal sequence processing. The combined CNN-LSTM model emerged as a robust solution, leveraging the strengths of both architectures to achieve a comprehensive understanding of yoga poses. Its performance was evaluated on a test dataset, revealing insights into its accuracy and loss metrics. Notably, the CNN model exhibited an accuracy of 85.53%, surpassing other models studied in the research. This highlights the efficacy of the CNN approach in recognizing spatial features inherent in yoga postures, contributing significantly to the overall success of the model in identifying and categorizing yoga stances from input images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolution

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