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arXiv 2025年

作者： Liu, Shuo Xiao, Wei Belta, Calin A. The Department of Mechanical Engineering Boston University BrooklineMA United States The Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology CambridgeMA United States The Department of Electrical and Computer Engineering The Department of Computer Science University of Maryland College ParkMD United States

In safety-critical control systems, ensuring both system safety and smooth control input is essential for theoretical guarantees and practical deployment. Existing Control Barrier Function (CBF) frameworks, especially High-Order CBFs (HOCBFs), effectively enforce safety constraints but often lead to nonsmooth or discontinuous control inputs that can degrade system performance or violate actuator limitations. This paper introduces Filtered Control Barrier Functions (FCBFs), which extend HOCBFs by incorporating an auxiliary dynamic system - referred to as input regularization filter - to produce Lipschitz continuous control inputs. The proposed framework ensures safety, control bounds, and smoothness simultaneously by integrating FCBFs and HOCBFs within a unified quadratic program (QP). Theoretical guarantees are provided and simulations on a unicycle model demonstrate the effectiveness of the proposed method compared to standard and smoothness-penalized HOCBF approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Quadratic programming

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Permissioned Blockchain Security Pqfabric from Quantum and Classical Attacks

Permissioned Blockchain Security Pqfabric from Quantum and C...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： C. Tamilarasi K Bhavadrarani Thiagarajan Kittappa N. Subashini V. Anitha Vimalraj E Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur. Department of Mathematics Rajalakshmi Institute of Technology Chennai Department of Mathematics Sathyabama Institute of Science and Technology Chennai Department of CSE Panimalar Engineering College Poonamallee Chennai School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding Byzantine Fault Tolerance (BFT) with quantum-safe encryption. The system gives protection against calculation based attacks by Shor and Grover, tending to huge imperfections in existing blockchain structures. Besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks.

关键词： Production systems Technological innovation Quantum computing Supply chains Standards organizations Oral communication Throughput Fabrics Blockchains Encryption

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LoBee: Bidirectional Communication between LoRa and ZigBee based on Physical-Layer CTC

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IEEE Transactions on Wireless Communications 2025年

作者： Gao, Demin Wang, Haoyu Chen, YongRui Ye, Qiaolin Wang, Weizheng Guo, Xiuzhen Wang, Shuai Liu, Yunhuai He, Tian Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China University of Minnesota The Department of Computer Science and Engineering 55414 United States University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Southeast University The School of Computer Science and Engineering Nanjing211189 China City University of Hong Kong Department of Computer Science 999077 Hong Kong College of Control Science and Engineering Zhejiang 310027 China Peking University The Department of Computer Science and Engineering Beijing100871 China University of Minnesota The Department of Computer Science and Engineering Minneapolis55414 United States

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band. © 2002-2012 IEEE.

关键词： Zigbee

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Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning 8th

Revolutionize Infectious Prevention Using Artificial Intelli...

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8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024

作者： Verma, Dinesh Kumar Singh, Shweta Dubey, Shivendra Raghuwanshi, Kapil Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal India Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9783031709050

The study and treatment of human infection continues to be examined in relation to generative AI, deep learning, machine learning, and AI (artificial intelligence). We provide an overview of AI’s current and prospective uses, as well as how it relates to clinical infection control. Research studies, Clinical trial, and meta-analytic received precedence when screening 1617 the PubMed database findings. The review’s narrative format centres on investigations that employ clinically validated prospectively acquired data from the real world, as well as studies with transformative possibility, including unique pharmaceutical discovery and microbiome-based interventions. Clinical imaging analysis (e.g., tuberculosis of the lungs diagnosis), tools for clinical decision-support (e.g., antimicrobial recommending, sepsis prediction), digital culture plate reading, antimicrobial resistance profiling, and malaria diagnosis are a few areas where there is proof to support the medical value of artificial intelligence (AI) used for diagnostics in laboratories. To date, most studies have not included medical metrics or real-world validation. Comparability is hampered by substantial variation in research methodology and reports. There are a lot of practical and ethical concerns, such as bias risk and algorithm transparency. Though the practical medical value of artificial intelligence (AI) tools for infections investigation and control seems to be much more modest, enthusiasm for the research and creation of these tools is certainly gaining momentum. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： RVS Praveen Meenakshi Maindola Anandhi R J Nagarjuna Thandra Saloni Bansal Vishal Sharma Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Information Science Engineering New Horizon College of Engineering Bangalore Dept. Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged.

关键词： Deep learning Autonomous underwater vehicles Accuracy Transfer learning Object detection Data augmentation Real-time systems Data models Environmental monitoring Convolutional neural networks

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Influence Ranking of Hate Users in Content Propagation on Twitter

Influence Ranking of Hate Users in Content Propagation on Tw...

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Advanced Research in Computing (ICARC), International Conference on

作者： Suresha Perera Indika Perera Supunmali Ahangama Department of Computer Science and Engineering University of Moratuwa Katubedda Sri Lanka Department of Information Technology University of Moratuwa Katubedda Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Social media platforms are now essential for communication, but they also lead to the quick spread of hate speech, which leaves people and society at serious risk. To lessen the influence of hate content, it is crucial to identify the top influential users who spread it. This research ranks the influence of hate users who propagate Twitter content. A novel method of Influence Score for measuring user influence was developed by integrating activity and engagement metrics, such as the number of hate tweets, retweets, likes, and replies. Users were divided into LOW, MEDIUM, and HIGH influence groups using k-means clustering, which allowed for a more focused examination of user behaviour. The findings showed that a smaller percentage of HIGH-influence users contribute to spreading hate content than users with LOW and MEDIUM influence. The validation process made clear how much better the suggested influence score was than followers, highlighting the value of using many metrics to find real influencers. The validation of the Influence Score was confirmed by comparison with crowdsourced rankings. With its scalable approach for detecting and classifying the influence of hate users, this study gives policymakers and social media administrators useful information. Platforms can significantly reduce the spread of hate content by targeting user clusters based on their influence levels and implementing actions based on the severity of each cluster. Future researchers can adopt this as a validation method for evaluating and improving their hate user ranking approaches.

关键词： Measurement Video on demand Social networking (online) Blogs Hate speech Cultural differences Web sites Reliability Standards Monitoring

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Generalizable Physics-Guided Convolutional Neural Network for Irregular Terrain Propagation

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IEEE Transactions on Antennas and Propagation 2025年

作者： Huang, Siyi Qin, Hao Hou, Weibin Zhang, Xinyue Zhang, Xingqi University of Alberta Department of Electrical and Computer Engineering Canada University College Dublin School of Electrical and Electronic Engineering Ireland China Academy of Information and Communications Technology Beijing China

The application of split-step parabolic equation methods for radio wave propagation across irregular terrains has gained widespread attention. However, the computational intensity of these methods limits their practical use, leading to the exploration of machine learning (ML) techniques as an alternative. A significant hurdle for ML models in the field of electromagnetics is their ability to precisely forecast relevant quantities in situations not covered by their training data, which have not been considered in the current ML-assisted propagation models over irregular terrain. To that end, we propose a generalizable physics-guided propagation modeling framework of high fidelity. This framework is adept at generalizing across various terrain types and antenna configurations, showcasing extrapolation capabilities beyond its training dataset. Our approach innovates by embedding prior knowledge from deterministic models into the network architecture. Furthermore, we demonstrate that adapting the network structure to align with the electromagnetic properties of terrain propagation markedly improves the model's predictive accuracy and generalizability. © 1963-2012 IEEE.

关键词： Landforms

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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HARQ-based Quantized Average Consensus over Unreliable Directed Network Topologies

arXiv

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arXiv 2025年

作者： Charalampous, Neofytos Makridis, Evagoras Rikos, Apostolos I. Charalambous, Themistoklis Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

—In this paper, we propose a distributed algorithm (herein called HARQ-QAC) that enables nodes to calculate the average of their initial states by exchanging quantized messages over a directed communication network. In our setting, we assume that our communication network consists of unreliable communication links (i.e., links suffering from packet drops). For countering link unreliability our algorithm leverages narrowband error-free feedback channels for acknowledging whether a packet transmission between nodes was successful. Additionally, we show that the feedback channels play a crucial role in enabling our algorithm to exhibit finite-time convergence. We analyze our algorithm and demonstrate its operation via an example, where we illustrate its operational advantages. Finally, simulations corroborate that our proposed algorithm converges to the average of the initial quantized values in a finite number of steps, despite the packet losses. This is the first quantized consensus algorithm in the literature that can handle packet losses and converge to the average. Additionally, the use of the retransmission mechanism allows for accelerating the convergence. © 2025, CC BY.

关键词： Packet loss

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Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review

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Journal of Electrical and computer engineering 2025年第1期2025卷

作者： Sathish, J. Ramash Kumar, K. Saraswathi, D. Department of Electrical and Electronics Engineering Dr.N.G.P. Institute of Technology Coimbatore48 India School of Computer Science and Engineering VIT University Chennai India

Electric vehicles (EVs) are a promising zero-emission technology in the automobile industry, but they face several challenges in terms of performance, reliability, and safety. Batteries are the heart of the EV system which helps to run the vehicle with reliability. Batteries during the process of running undergo various changes that need to be addressed. On the other hand, real-time data analysis and online access to information are necessary conditions in the modern world. Machine learning and deep learning algorithms mimic humans by focusing on statistical data and algorithms on a real-time basis. Therefore, in today’s research, machine learning and deep learning algorithms are used in EV technologies to obtain a more efficient and capable system. The battery management system (BMS) is the main part that is often in need of data processing of battery parameters and diagnosis of the problem. This paper explores the comprehensive literature review on machine learning and deep learning approaches for BMS in EVs. The state of charge (SOC) estimation, charge equalization and cell balancing, fault detection and diagnosis, and thermal management systems using various combined machine learning and deep learning techniques are discussed. By synthesizing insights from various studies, this article presents improved parameters and valuable inferences. This article aims to highlight the pivotal role of artificial intelligence (AI) and deep learning in improving the functionality of the BMS, ultimately contributing to the performance and longevity of EVs. Copyright © 2025 Sathish J. et al. Journal of Electrical and computer engineering published by John Wiley & Sons Ltd.

关键词： battery management systems charge equalization deep learning EVs fault detection and diagnosis hybrid EVs machine learning SOC thermal management systems

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