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A robust blockchain-based watermarking using edge detection and wavelet transform

Multimedia Tools and Applications 2025年第14期84卷 12739-12763页

作者： Mannepalli, Praveen Kumar Richhariya, Vineet Gupta, Susheel Kumar Shukla, Piyush Kumar Dutta, Pushan Kumar Chowdhury, Subrata Hu, Yu-Chen G H Raisoni Institute of Engineering and Technology Nagpur India LNCT University Bhopal India University Institute of Technology RGPV Bhopal India School of Engineering and Technology Amity University Kolkata Kolkata India AP Chittoor India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

Nowadays, image security is a crucial component of the scientific community. The transparent marking is often used to keep medical imaging information from being stolen without permission. In this work, a novel combination of blockchain-based encryption and invisible picture watermarking was presented. In order to achieve watermarking, edge detection of discrete wavelet transform coefficients is being used. Using the discrete wavelet transform to break up the medical image gives you multi-resolution coefficients. For the HH wavelet band, the edge detecting method is employed to make the edge coefficients. For watermark embedding, the difference between dilation and edge coefficient is used in order to enhance robustness. The image with the watermark is encrypted by employing a hashing process that is based on blockchain technology and was developed particularly for medical pictures. After the decryption procedure is finished at the conclusion of the decoding process, the picture is then regenerated. The results of both stages are presented in a sequential order. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Ensuring Digital Sovereignty in Cross-chain EHR Sharing: A Relay-as-a-Service Approach for Secure Healthcare Interoperability

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Procedia computer science 2025年 254卷 48-57页

作者： Dharavath Ramesh Thakur Santosh Munesh Chandra Trivedi Chi Hieu Le Department of Computer Science and Engineering Indian Institute of Technology (Indian School of Mines) Dhanbad Jharkhand India Department Centre for Life Sciences Ecole Centrale School of Engineering Mahindra University Hyderabad India Department of Computer Science and Engineering National Institute of TechnologyAgartala Tripura India Faculty of Engineering and Science University of Greenwich Kent ME4 4TB UK

Electronic Health Records (EHRs) stored in cloud environments often face privacy challenges in healthcare data management due to the divide between patient ownership and institutional control. Blockchain technology offers a promising solution with its features of immutability and traceability. However, existing blockchain-based approaches for EHR privacy preservation are limited to single institutions and fail to address the critical need for cross-chain compatibility and digital sovereignty. To bridge this gap, we propose two novel strategies: Polkadot-based Cross-chain for EHR-preserving Blockchain (PCEB) and Relay-as-a-Service-based Cross-chain for EHR-preserving Blockchain (RaSCEB). PCEB utilizes Polkadot's relay communication to securely share EHR data across multiple healthcare networks while preserving patient privacy and ensuring digital sovereignty. RaSCEB introduces Relay-as-a-Service (RaaS) to enable seamless EHR sharing across blockchain ecosystems, empowering patients with control over their data while maintaining regulatory compliance and sovereignty over their digital health records. Both approaches are validated through comprehensive security analysis and performance evaluations. We also present an interoperability framework tailored for permissioned blockchain networks, emphasizing trust derived from consensus mechanisms. Our work addresses the urgent need for cross-chain compatibility in EHR privacy preservation and advances interoperability solutions while safeguarding digital sovereignty in healthcare and beyond.

关键词： Electronics Health Records (EHRs) Blockchain interoperability Cross blockchain Digital sovereignty

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Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM

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IEEE Transactions on Industry Applications 2025年第3期61卷 3623-3636页

作者： Hosseini, Narges Khadem Toshani, Hamid Abdi, Salman Sharifzadeh, Sarah University of Cambridge Engineering Department CambridgeCB2 1TN United Kingdom University of Cambridge Electrical Engineering Department CambridgeCB2 1TN United Kingdom University of East Anglia School of Engineering NorwichNR4 7TJ United Kingdom Swansea University School of Mathematics and Computer Science SwanseaSA1 8EN United Kingdom

Fine-tuning the hyperparameters of the Support Vector Machine (SVM) is crucial to making it an accurate classifier in fault detection. The convergence rate of the optimiser and its simplicity are also vital for achieving the optimal boundary. Therefore, a highly accurate fault detection algorithm that combines SVM with a simple, easy-to-implement, and stable optimiser called Projection Recurrent Neural Network (PRNN) is proposed in this paper. The algorithm focuses on detecting bearing faults in induction motors using experimentally measured stator currents. The initial dataset is pre-processed using Discrete Wavelet Transform (DWT), Power Spectral Density (PSD), and cepstrum analysis. A feature set is then derived from several statistical metrics, and Kernel Principal Component Analysis (KPCA) is used to select the most salient features. The SVM is trained using these discriminative features, and its optimisation problem is reformulated as Constrained Nonlinear Programming (CNP). The PRNN is introduced to solve the CNP to effectively determine the optimal decision boundary of the SVM. The study evaluates the accuracy of the proposed algorithm and highlights the advantages of using PRNN in SVM, its dependence on the type of kernel function and the selected features. A 4 kW induction motor is used as a prototype machine for experimental data collection under healthy and faulty conditions. The results show that the proposed algorithm leads to more accurate fault detection compared to the conventional SVM and recent fault detection methods. © 1972-2012 IEEE.

关键词： Discrete wavelet transforms

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LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation

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computers in Biology and Medicine 2025年 184卷 109353-109353页

作者： Alwadee, Ebtihal J. Sun, Xianfang Qin, Yipeng Langbein, Frank C. School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors’ complex heterogeneity. Moreover, energy sustainability targets and resource limitations, especially in developing countries, require efficient and accessible medical imaging solutions. The proposed architecture, a Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these issues. It is specifically designed to reduce computational requirements significantly while maintaining high segmentation performance. By incorporating parallel convolutions, it enhances feature representation by capturing multi-scale information. It further integrates an attention mechanism to refine segmentation through selective feature recalibration. LATUP-Net achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS 2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively. Hausdorff distance metrics further indicate its improved ability to delineate tumor boundaries. With its significantly reduced computational demand using only 3.07 M parameters, about 59 times fewer than other state-of-the-art models, and running on a single NVIDIA GeForce RTX3060 12 GB GPU, LATUP-Net requires just 15.79 GFLOPs. This makes it a promising solution for real-world clinical applications, particularly in settings with limited resources. Investigations into the model's interpretability, utilizing gradient-weighted class activation mapping and confusion matrices, reveal that while attention mechanisms enhance the segmentation of small regions, their impact is nuanced. Achieving the most accurate tumor delineation requires carefully balancing local and global features. The code is available at https://***/ca/code-bca. © 2024 T

关键词： Magnetic resonance imaging

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ARUC-GAN: A CycleGAN-Based Attention Residual U-Net for Low-Dose CT Denoising in Smart Healthcare

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IEEE Access 2025年 13卷 72544-72557页

作者： Mansour, Mohammad Djenouri, Djamel Said, Lobna A. Latif, Shahid Maalel, Ahmed Abdelhadi, Ameer M. S. McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4L8 Canada University of the West of England School of Computing and Creative Technologies BristolBS16 1QY United Kingdom Giza12588 Egypt University of Sousse ISSAT Sousse RIADI Laboratory National School of Computer Science ENSI Sousse4003 Tunisia

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover, conventional denoising methods struggle with the non-uniform noise distributions in LDCT images and often require complex projection data, which limits their effectiveness and generalizability. Herein, we propose an AI-based denoising approach using an Attention Residual U-Net (ARU-Net) architecture integrated into the CycleGAN framework named Attention Residual U-Net CycleGAN (ARUC-GAN). The proposed framework outperforms state-of-the-art denoising models, such as RED-CNN, EDCNN, and CTformer, according to experimental evaluation on the Mayo Clinic abdominal CT dataset. The model exhibits a peak signal-to-noise ratio (PSNR) of 34.82 dB, a structural similarity index (SSIM) of 0.85, and an RMSE of 0.018. Furthermore, the model successfully maintains edge structures with an Edge Keeping Index (EKI) of 0.835. The visual examination validates ARUC-GAN’s superior texture and detail preservation. The results indicate that the suggested method has great promise as a smart health tool for improving diagnosis accuracy in LDCT scans. © 2013 IEEE.

关键词： Electronic health record

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Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT based Self-Sustainable IoT Network

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Hassan, Ahmad Kamal Abbas, Ziaul Haq Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Pakistan University of Brighton School of Architecture Technology and Engineering Brighton BN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： beamforming energy efficiency Intelligent reflecting surface (IRS) Internet of Things (IoT) phase cooperative network power splitting SWIPT

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ML-Track: Passive Human Tracking Using WiFi Multi-Link Round-Trip CSI and Particle Filter

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5155-5172页

作者： Shi, Fangzhan Li, Wenda Tang, Chong Fang, Yuan Brennan, Paul V. Chetty, Kevin University College London Department of Security and Crime Science LondonWC1E 6BT United Kingdom Heriot-Watt University School of Engineering and Physical Sciences EdinburghEH14 4AS United Kingdom University of Southampton Department of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom

In this study, we present ML-Track, an innovative uncooperative passive tracking system leveraging WiFi communication signals between multiple devices. Our approach is realized with three pivotal techniques. First, we introduce a novel protocol termed multi-link round-trip CSI, which enables multi-link bistatic Doppler detection within a WiFi network. Second, a phase error cancellation method is developed, and we demonstrate a 0.92 rad reduction in error (0.96 to 0.04 rad) experimentally. Lastly, we propose a particle-filter-based back-end to track a moving human in the room passively without the need for the participant to carry any type of cooperative or active device. A prototype system is constructed using four Raspberry Pi CM4 units and subjected to real-world evaluations. Experimental results indicate a median error of approximately 0.23 m for tracking, which corresponds to a relative error of 5.8% based on the 4 m side length of the experimental field. Compared to existing studies, a distinct advantage of our system is it can run with non-MIMO (single-antenna) WiFi devices, making it particularly suitable for budget or low-profile WiFi hardware. This compatibility makes it an ideal fit for real-world Internet-of-Things (IoT) devices. Moreover, in terms of computational demands, our solution excels, delivering real-time performance on the Raspberry Pi CM4 while utilizing just 20% of its CPU capability and drawing a modest 2.5 watts of power. © 2002-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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AIGC-assisted Federated Learning for Vehicular Edge Intelligence: Vehicle Selection, Resource Allocation and Model Augmentation

arXiv

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arXiv 2025年

作者： Qiang, Xianke Chang, Zheng Min, Geyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehicular applications. Although FL has great potential to improve the architecture of intelligent vehicular networks, challenges arise due to vehicle mobility, wireless channel instability, and data heterogeneity. To mitigate the issue of heterogeneous data across vehicles, artificial intelligence-generated content (AIGC) can be employed as an innovative data synthesis technique to enhance FL model performance. In this paper, we propose AIGC-assisted Federated Learning for Vehicular Edge Intelligence (GenFV). We further propose a weighted policy using the Earth Mover’s Distance (EMD) to quantify data distribution heterogeneity and introduce a convergence analysis for GenFV. Subsequently, we analyze system delay and formulate a mixed-integer nonlinear programming (MINLP) problem to minimize system delay. To solve this MINLP NP-hard problem, we propose a two-scale algorithm. At large communication scale, we implement label sharing and vehicle selection based on velocity and data heterogeneity. At the small computation scale, we optimally allocate bandwidth, transmission power and amount of generated data. Extensive experiments show that GenFV significantly improves the performance and robustness of FL in dynamic, resource-constrained environments, outperforming other schemes and confirming the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning 28th

Enhancing LLM Code Generation Using Natural Language Process...

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031834714

The rapid rise in popularity of Generative AI and Large Language Models (LLMs) has brought both innovation and controversy, particularly regarding plagiarism and IP law infringements. However, one underexplored concern is the generation of code by these models, which, despite their potential, often includes errors and promotes poor programming practices. This paper explores new methods to address these issues by integrating LLMs with Automated Machine Learning (AutoML). By leveraging AutoML’s capabilities in hyperparameter tuning and model selection, we propose a novel approach for generating robust machine learning algorithms. This integration aims to enhance the accuracy and reliability of code generation while mitigating legal risks. Our findings include the application of Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to interpret chatbot prompts, thereby improving the generation and customization of machine learning models. The proposed methodology demonstrates practical implementation and high prediction accuracy, offering a promising solution to the current challenges faced by LLM-based code generation. In summary the findings of the paper are as follows: A new implementation of natural language processing for natural language understanding in the context of chatbot prompts aims to serve as an initial step for feature extraction, which will be utilised by an AutoML system to generate machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Automated Parts Segmentation of Sperm via a Contrastive Learning-Based Part Matching Network

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IEEE Transactions on Automation science and engineering 2025年 22卷 15314-15325页

作者： Chen, Wenyuan Song, Haocong Shan, Guanqiao Dai, Changsheng Liu, Hang Jiang, Aojun Sun, Chen Ru, Changhai Librach, Clifford Zhang, Zhuoran Sun, Yu University of Toronto Department of Computer Science TorontoONM5S 2E4 Canada University of Toronto Department of Mechanical and Industrial Engineering TorontoONM5S 3G8 Canada Dalian University of Technology Institute of Robotics and Intelligent Systems Liaoning Dalian China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China CReATe Fertility Centre TorontoONM5G 1N8 Canada

Sperm morphology measurement is vital for diagnosing male infertility, which involves quantification of multiple subcellular parts for each sperm. Instance-aware part segmentation networks have been introduced to address this task by automatically identifying individual sperm and segmenting their subcellular parts. However, major limitations of state-of-the-art instance-aware part segmentation networks include: 1) they are time-consuming and computational expensive due to sequential processing and multi-stage frameworks;2) they perform poorly for densely packed sperm that overlap or cross over one another. To overcome these challenges, this paper proposes 1) integrating instance identification and subcellular part segmentation within a single-stage framework to save inference time and memory usage;2) dividing a sperm target into simpler components (head and tail) to improve prediction accuracy, followed by a contrastive learning-based matching method to pair the head and tail. Experimental results on our clinically collected human sperm dataset demonstrated that the proposed network not only outperformed state-of-the-art CP-Net (by 3.5% APpvol) but also achieved realtime inference (48.0 frames per second), effectively meeting the clinical requirements for automated parts segmentation of sperm. © 2004-2012 IEEE.

关键词： Image segmentation

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