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arXiv 2025年

作者： Yang, Shuhang An, Puguang Yang, Peng Cao, Xianbin Wu, Dapeng Oliver Quek, Tony Q.S. School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

To tackle the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper introduces an innovative SnS near-field CE framework grounded in adaptive subarray partitioning. Conventional methods relying on equal subarray partitioning often lead to suboptimal divisions, undermining CE precision. To overcome this, we propose an adaptive subarray segmentation approach. First, we develop a spherical-wave channel model customized for line-of-sight (LoS) XL-MIMO systems to capture SnS traits. Next, we define and evaluate the adverse effects of over-segmentation and under-segmentation on CE efficacy. To counter these issues, we introduce a novel dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which merges cost-effective power measurements with a DHBF structure, enabling joint subarray partitioning and decoupling. A robust partitioning algorithm, termed power-adaptive subarray segmentation (PASS), exploits statistical features of power profiles, while the DHBF utilizes subarray segmentation-based group time block code (SS-GTBC) to enable efficient subarray decoupling with limited radio frequency (RF) chain resources. Additionally, by utilizing angular-domain block sparsity and inter-subcarrier structured sparsity, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV), employing discrete Fourier transform (DFT) codebooks to lower complexity. Extensive simulation results validate the exceptional performance of the proposed framework over its counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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Hybrid JADE-SPBO-Based Optimization for Improved Multilevel Image Segmentation

Hybrid JADE-SPBO-Based Optimization for Improved Multilevel ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hussam N. Fakhouri Amjad Hudaib Sandi N. Fakhouri Mohannad Alkhalaileh Ahmad K. Al Hwaitat Data Science and Artificial Intelligence Department University of Petra Amman Jordan King Abdullah II School of Information Technology University of Jordan Amman Jordan Computer Science Department University of Jordan Amman Jordan College of Education Humanities and Social Sciences Al Ain University Al Ain United Arab Emirates

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper introduces the Hybrid JADE-SPBO optimization algorithm for robust and efficient multilevel image segmentation, integrating JADE's adaptive mutation and crossover with SPBO's psychology-inspired learning for balanced exploration and exploitation. Tested on standard benchmark images, the proposed method achieves notable improvements over state-of-the-art metaheuristics, evidenced by higher PSNR, SSIM, and FSIM scores. At challenging segmentation thresholds, Hybrid JADE-SPBO attains PSNR values up to 22.50 compared to competing methods' 21.08, while similarly improving SSIM from around 0.82 to 0.89 and FSIM from 0.83 to 0.91. These results highlight the algorithm's ability to consistently detect precise thresholds and handle complex search spaces, offering a versatile tool for applications in image processing.

关键词： Image segmentation Three-dimensional displays Navigation Metaheuristics Psychology Robustness Noise measurement Tuning Standards Convergence

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Exploring blockchain solutions: Integrating cryptocurrency with IoT for enhanced security

Exploring blockchain solutions: Integrating cryptocurrency w...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： M Shravan Shital Kakad Umar M Mulani Geeti Sharma Mohit Tiwari Mary Shiba C School of Business SR University Warangal Telangana India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune Maharashtra KJ College of Engineering and Management Research Pune Faculty of Management Studies (CMS Business School) Jain Deemed to be University Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Delhi Department of Computer Science and Business Systems R.M.K Engineering College Thiruvallur

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem.

关键词： Privacy Ecosystems Focusing Bitcoin Reliability engineering Hardware Blockchains Safety Internet of Things Security

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Federated learning model with dynamic scoring-based client selection for diabetes diagnosis

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Knowledge-Based Systems 2025年 320卷

作者： Ahmed, Shamim Kaiser, M. Shamim Chaki, Sudipto Aloteibi, Saad Moni, Mohammad Ali Institute of Information Technology Jahangirnagar University Dhaka1342 Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia AI and Digital Health Technology Artificial Intelligence and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia AI and Digital Health Technology Rural Health Research Institute Charles Sturt University Orange2800 Australia School of Health and Rehabilitation Sciences The University of Queensland St LuciaBrisbane Australia

Federated learning (FL) is a privacy-preserving paradigm in distributed machine learning that enables clients to collaboratively train models without sharing their raw data. However, the variety of client data, device setups, and network conditions provides serious difficulties for FL systems. Random client sampling in such environments often leads to suboptimal outcomes, including lower model accuracy, slower convergence rates, and reduced fairness. To address these issues, this study proposes a dynamic client selection mechanism based on a scoring system that evaluates clients based on three key parameters: accuracy, loss, and execution time. We propose a scoring-based framework for adaptive client selection in federated learning (FL) and implement it in an ML-driven diabetes detection system. Evaluations with 200 communication rounds demonstrate improved global and local model performance, faster convergence, and optimized resource utilization. The framework dynamically selects clients, improving execution efficiency and addressing key FL challenges, including data heterogeneity, fairness, communication overhead, and privacy. Our findings highlight its potential for scalable and efficient FL in healthcare applications while paving the way for future advancements in adaptive client selection. © 2025 The Authors

关键词： Federated learning

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RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation

arXiv

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arXiv 2025年

作者： Yuan, Chengbo Joshi, Suraj Zhu, Shaoting Su, Hang Zhao, Hang Gao, Yang Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Technology Tsinghua University China Shanghai Qi Zhi Institute China Shanghai Artificial Intelligence Laboratory China

Visual augmentation has become a crucial technique for enhancing the visual robustness of imitation learning. However, existing methods are often limited by prerequisites such as camera calibration or the need for controlled environments (e.g., green screen setups). In this work, we introduce RoboEngine, the first plug-and-play visual robot data augmentation toolkit. For the first time, users can effortlessly generate physics- and task-aware robot scenes with just a few lines of code. To achieve this, we present a novel robot scene segmentation dataset, a generalizable high-quality robot segmentation model, and a fine-tuned background generation model, which together form the core components of the out-of-the-box toolkit. Using RoboEngine, we demonstrate the ability to generalize robot manipulation tasks across six entirely new scenes, based solely on demonstrations collected from a single scene, achieving a more than 200% performance improvement compared to the no-augmentation baseline. All datasets, model weights, and the toolkit will be publicly released. © 2025, CC BY.

关键词： Semantic Segmentation

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Mobile Edge Computing Networks: Online Low-Latency and Fresh Service Provisioning

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IEEE Transactions on Communications 2025年

作者： Yi, Yuhan Zhang, Guanglin Jiang, Hai Donghua University College of Information Science and Technology Shanghai China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

Edge service caching can significantly mitigate latency and reduce communication and computing overhead by downloading and caching service (application) data from clouds. The freshness of cached service data is critical when providing satisfactory services to users, but has been overlooked in existing research efforts. In this paper, we study the online low-latency and fresh service provisioning in mobile edge computing (MEC) networks. Specifically, we jointly optimize the service caching, task offloading, and resource allocation. To solve the formulated joint online long-term optimization problem, we design a Lyapunov-based online framework that decouples the problem at temporal level into a series of per-time-slot subproblems. For each subproblem, we propose an online integrated optimization-deep reinforcement learning (OIODRL) method, which consists of an optimization stage and a learning stage. In the optimization stage of OIODRL, a quadratically constrained quadratic program (QCQP) transformation and a semidefinite relaxation (SDR) method are utilized;in the learning stage of OIODRL, a deep reinforcement learning (DRL) algorithm is applied. Extensive simulations show that the proposed OIODRL method achieves a near-optimal solution and outperforms benchmark methods. © 1972-2012 IEEE.

关键词： Mobile edge computing

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THE-TAFL: Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning and Learning Rate Optimization

Internet of Things (The Netherlands)

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Internet of Things (The Netherlands) 2025年 32卷

作者： Ullah, Farhan Mohammad, Nazeeruddin Mostarda, Leonardo Cacciagrano, Diletta Ullah, Shamsher Zhao, Yue Cybersecurity Center Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Mathematics and Computer Science University of Perugia Perugia 06123 Italy Division of Computer Science University of Camerino Camerino 62032 Italy National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou 325060 China

The healthcare industry is becoming more vulnerable to privacy violations and cybercrime due to the pervasive dissemination and sensitivity of medical data. Advanced data security systems are needed to protect privacy, data integrity, and dependability as confidentiality breaches increase across industries. Decentralized healthcare networks face challenges in feature extraction during local training, hindering effective federated averaging and learning rate optimization, which affects data processing and model training efficiency. This paper introduces a novel approach of Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning (THE-TAFL) and Learning Rate Optimization. In this paper, we combine Transformer-based Adaptive Federated Learning (TAFL) with learning rate optimization to improve the privacy and security of healthcare information on edge devices. We used data augmentation approaches that generate robust and generalized input datasets for deep learning models. Next, we use the Vision Transformer (ViT) model for local training, generating Local Model Weights (LMUs) that enhance feature extraction and learning. We designed a training optimization method that improves model performance and stability by combining a loss function with weight decay for regularization, learning rate scheduling, and gradient clipping. This ensures effective training across decentralized clients in a Federated Learning (FL) framework. The FL server receives LMUs from many clients and aggregates them. The aggregation procedure utilizes adaptive federated averaging to aggregate the LMUs based on the performance of each client. This adaptive method ensures that high-performing clients contribute more to the Global Model Update (GMU). Following aggregation, clients receive the GMU to continue training with the updated parameters, ensuring collaborative and dynamic learning. The proposed method provides better performance on two standard datasets using various numb

关键词： Adaptive Federated Learning Edge computing Healthcare Privacy & Security Medical imaging Transforming Healthcare Edge Vision transformer

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Deep Learning Advancing Image Classification - Neural Network Design and Performance Analysis

Deep Learning Advancing Image Classification - Neural Networ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Palraj Karuppasamy Vigilson Prem Monickaraj Revathi Subramaniam Darshankumar J Mathankumar M Manjunathan Alagarsamy Information Technology Ramco Institute of Technology Rajapalayam India Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai India Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Trichy India Aeronautical Engineering Kumaraguru College of Technology Coimbatore India Electrical and Electronics Engineering SRM Institute of Science and Technology Tiruchirappalli India Electronics and Communication Engineering K. Ramakrishnan College of Technology Tiruchirappalli India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper develops, tests, and evaluates neural network-based plant disease classification methods using healthy, powdery, and rust datasets. Agriculture requires an early plant disease diagnosis to avoid crop losses. Given their picture categorization abilities, Convolutional Neural Networks (CNNs) can diagnose plant diseases. In this study, healthy and diseased plant leaves were photographed. Labelling images by class facilitates supervised neural network learning. Resizing, normalization, and supplementation improve dataset quality and diversity. Using CNN architecture for classification. After optimizing model parameters with labeled training data, the learning algorithm tests them on unseen test data during training, validation, and testing. Trial results contain accuracy curves showing training epoch performance and model accuracy on the test set as a percentage. The model displays its class classification performance in a confusion matrix. Critical data analysis proves the tactics work and suggests future effort. Key findings emphasize the importance of neural network-based plant disease detection systems and their potential impact on farming.

关键词： Deep learning Training Plant diseases Accuracy Computational modeling Neural networks Crops Production Data models Convolutional neural networks

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Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

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2nd International Conference on Next-Generation Computing and information Systems, ICNGCIS 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. Shabaz, Mohammad Kollu, Archana Sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based Support Vector Machine (KSVM) optimized by Whales algorithm. The combination kernel SVM shows better accuracy and less computational time compared to single kernel of SVM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： Support vector machines

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