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MUDRA: A Multi-dimensional Privacy Attainment Framework for Healthcare Blockchain Transactions

IEEE Transactions on Consumer Electronics 2025年

作者： Chhabra, Archana Saha, Rahul Kumar, Gulshan Kim, Tai Hoon Lovely Professional University School of Computer Science and Engineering Punjab India Chonnam National University School of Electrical and Computer Engineering Korea Republic of

The integration of blockchain technology into consumer electronics is transforming healthcare by enhancing data security and streamlining operations in devices like wearable health monitors, smart medical devices, and electronic health records. However, this integration raises significant privacy concerns, especially regarding potential data leaks through automated processes like smart contracts. Traditional Private Information Retrieval (PIR) methods struggle with balancing data accessibility and patient confidentiality. This creates computational inefficiencies and interoperability challenges that hinder the implementation of standardized privacy measures in blockchain. We introduce the MUlti-Dimensional pRivacy Attainment (MUDRA) framework, a novel solution addressing critical privacy challenges in the storage and retrieval of patient and healthcare service provider (HSP) data. MUDRA leverages the innovative Dual Server Ring Private Information Retrieval (DS-RPIR) protocol to securely retrieve Personal Health Information (PHI) without publishing sensitive data to the blockchain, ensuring enhanced privacy and preventing unauthorized access. By integrating the Interplanetary File System (IPFS), MUDRA preserves data confidentiality and supports time-controlled revocation, allowing patients to set time-limited access to their medical records, automatically revoking access after the specified period. The framework introduces a new metric, Privacy Attainment (PA), to quantify transaction privacy and evaluate the private retrieval of sensitive data. When implemented on the Hyperledger Fabric network, MUDRA achieves 3.5% higher throughput (59 TPS), reduced **time complexity O(nlogn), and 8 ms latency-2.25 ms less than existing solutions. These performance improvements, coupled with robust privacy features, position MUDRA as a pioneering solution for addressing privacy concerns in the rapidly evolving field of healthcare consumer electronics. © 2025 IEEE.

关键词： Electronic health record

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover

Information Constrained Optimal Transport: From Talagrand, t...

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作者： Bai, Yikun Wu, Xiugang Ozgur, Ayfer Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has wide range of applications from economics to machine learning. In this paper, we introduce and study an information constrained variation of this problem. Our study yields a strengthening and generalization of Talagrand's celebrated transportation cost inequality. Following Marton's approach, we show that the new transportation cost inequality can be used to recover old and new concentration of measure results. Finally, we provide an application of this new inequality to network information theory. We show that it can be used to recover almost immediately a recent solution to a long-standing open problem posed by Cover regarding the capacity of the relay channel. © 1963-2012 IEEE.

关键词： Machine learning

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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End-of-Sentence Token Modeling for Streaming Conformer-Based Korean Children's Speech Recognition Applied to a Social Robot

End-of-Sentence Token Modeling for Streaming Conformer-Based...

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2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Jeong, Seunghun Park, Yejin Kim, Hong Kook Ai Graduate School Korea Republic of Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of

ISBN: (纸本)9798350344318

This paper proposes an end-of-sentence (EOS) token modeling approach for streaming conformer-based Korean children's speech recognition applied to a social robot. The baseline system for speech recognition is a streaming conformer-based speech recognition model with the joint connectionist temporal classification attention-based encoder-decoder architecture, and it is trained with the AIHub Child•Infant Free-Talk dataset. However, it is difficult to determine sentence boundaries in children's speech because the speaking style of children is somewhat different from that of adults. Thus, the proposed approach is to insert EOS tokens into each sentence of a large children's dataset to detect sentence boundaries. Finally, we evaluated the performance of the proposed approach on a dataset collected under scenarios of a voice interaction between children and a social robot. Consequently, the proposed EOS token modeling decreased character error rate by 1.7%, compared to the baseline. © 2023 IEEE.

关键词： Speech recognition

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 1-11页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Efficient Multi-purpose Video Annotation for Fast Labeling

Efficient Multi-purpose Video Annotation for Fast Labeling

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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Mobaraki, Mobina Ahani, Soodeh Yi, Kwang Moo Asadi, Mahyar Van Heusden, Klaske Dumont, Guy A. University of British Columbia Electrical and Computer Engineering Vancouver Canada University of British Columbia Computer Science Vancouver Canada Novarc Technologies Co. Vancouver Canada School of Mechanical Engineering Kelowna Canada

ISBN: (纸本)9798350359855

We propose an efficient method to label the frames of a video consistently and quickly. Our method is motivated by welding videos, where molten metal moves continuously, and some frames may be noisy due to the high intensity of arc and spatters that can obscure the desired point to be annotated. The traditional annotation methods which pause the videos and annotate each frame individually are time-consuming and may result in inconsistent labeling between different annotators for noisy frames of a video. Our new proposed method benefits from the fact that video sequences are contiguous. We track the location of the cursor while the video is being played, with the user controlling the playback speed, including the playback direction. We process the recorded cursor locations to convert them to the final annotation. Compared to frame-to-frame annotation methods, we show that our proposed interface speeds up the annotating process by 21 times while maintaining consistency of the labels. © 2023 IEEE.

关键词： Liquid metals

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Smart Processing of Exceptional Values in electrical Networks Using Neuro-Fuzzy Algorithms

Smart Processing of Exceptional Values in Electrical Network...

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2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Anwar, Mahammad Kothiyal, Arnav Nandankar, Praful V. Barve, Amit Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Ballari Visvesvaraya Technological University Department of Electrical and Electronics Engineering Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Technology Faculty of Engineering and Technology Parul University Department of Computer Science and Engineering P.O. Limda Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350348460

This research introduces a novel smart meter topology within the context of an integrated PV-Wind hybrid system for DC electric micro-networks. The proposed topology incorporates advanced techniques such as Linear Interpolation, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for identifying and correcting outliers in the collected data. The integration of the smart meter with outlier detection and correction modules enhances the accuracy and reliability of the measurements, contributing to the overall efficiency of the micro-network. Linear Interpolation is employed to estimate and fill in missing data points, ensuring a continuous and consistent dataset. The ANN module is designed to identify outliers by learning patterns from historical data, allowing for real-time anomaly detection. Furthermore, the ANFIS module enhances the system's adaptability by providing a fuzzy logicbased approach to correct outliers, taking into account the dynamic nature of renewable energy sources in the hybrid system. The proposed smart meter topology offers a comprehensive solution for ensuring the integrity of data collected in DC electric micronetworks. By leveraging the synergies of Linear Interpolation, ANN, and ANFIS, the system demonstrates improved robustness against data anomalies, thereby enabling more accurate monitoring, control, and optimization of the PV-Wind hybrid system. This research contributes to the advancement of smart grid technologies, particularly in the realm of renewable energy micro-networks, by addressing the challenges associated with data quality and reliability. © 2024 IEEE.

关键词： Integrated Control Mean Squared Error Micro-Networks Modeling outliers smart meter

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on tinyML Platforms

IEEE Design and Test

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IEEE Design and Test 2024年

作者： Shaharear, Md Ragib Mazumder, Arnab Neelim Mohsenin, Tinoosh Johns Hopkins University Department of Electrical & Computer Engineering United States University of Maryland Department of Computer Science & Electrical Engineering Baltimore County United States

Vision Transformers (ViTs) have demonstrated significant improvements in image classification tasks. However, deploying them on resource-constrained tinyML platforms presents considerable challenges due to their high computational demands and dynamic power consumption. Current methods rely heavily on computationally intensive architecture search techniques to identify optimal configurations, which are not well-suited for tinyML devices. This paper introduces ViT-Reg, a regression-based hardware-aware fine-tuning approach that identifies suitable ViT architectures for tinyML platforms. The proposed method enables efficient exploration of the configuration space, drastically reducing the computational overhead typically associated with architecture searches. ViT-Reg is hardware-aware, utilizing polynomial regression to narrow the search space while treating accuracy as a constraint. In experiments conducted on the CIFAR-10 and Tiny-ImageNet datasets, ViT-Reg deployed on Nvidia Jetson Nano achieved a 55.6% and 37.4% reduction in dynamic power consumption, along with a 65% and 60% improvement in energy efficiency compared to baseline ViT models. Finally, ViT-Reg provides an 8× improvement in energy efficiency relative to recent hardware implementations of the VGG model. © 2024 IEEE.

关键词： real-time and energy efficient deployment Regression tinyML Hardware Vision Transformer

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