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Abnormal Behavior Detection Based on D–S Evidence Theory for Air–Ground-Integrated Vehicular Networks

IEEE Internet of Things Journal 2025年第9期12卷 11347-11355页

作者： Jun Zeng, Jian Chao, Han-Chieh Wei, Jianguo Tianjin University College of Intelligence and Computing Tianjin300000 China Fo Guang University Department of Artificial Intelligence Yilan262307 Taiwan Tamkang University Department of Artificial Intelligence Taipei251301 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan Institute of Computer Science and Innovation UCSI University Kuala Lumpur56000 Malaysia

The advancement of intelligent connected vehicles and aerial computing has garnered extensive attention from scholars worldwide. In particular, high-altitude platforms (HAPs) and autonomous aerial vehicles (AAVs) have emerged as effective tools to extend vehicular network coverage and enhance real-time monitoring capabilities. This study, grounded in the data from the Yizhuang Intelligent Connected Autonomous Driving Demonstration Zone, introduces an air-ground integrated vehicular network model for abnormal driving behavior detection using D-S evidence theory. The model integrates aerial computing and vehicular networks in a cohesive manner, scrutinizes the data associated with routine driving behaviors, and integrates the outcomes of various analyses through evidence theory at the decision-making level, culminating in the estimation of the probability of abnormal driving behaviors. Simulation experiments results demonstrate that this algorithm not only enhances the precision of abnormal behavior identification to a remarkable 97.1% but also significantly accelerates the detection process and improves the robustness. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Assessing the stiffness of intact bone marrow with non-contact optical coherence elastography 12

Assessing the stiffness of intact bone marrow with non-conta...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Singh, Amandeep Singh, Manmohan Aglyamov, Salavat R. Mayerich, David Larin, Kirill V. Department of Biomedical Engineering University of Houston TX77204 United States Department of Mechanical and Aerospace Engineering University of Houston TX77204 United States Department of Electrical and Computer Engineering University of Houston TX77204 United States

ISBN: (纸本)9781510683907

Bone marrow plays an important role in regulating immunity for homeostasis and controlling stromal cell trafficking. Bone marrow cells are sustained by the framework of connective tissues that preserve the mechanical and functional integrity of the tissue. Optical coherence elastography (OCE) is a potential method to measure the biomechanical properties of bone marrow with high spatio-temporal resolution. In this work, we demonstrate the quantification of wave speed at different locations along the diaphysis of mice femur samples using non-contact air-coupled ultrasound OCE (ACUS-OCE). The preliminary results from the current approach show that the ACUS-OCE can characterize the mechanical stiffness of intact bone marrow and can be used for minimally invasive and ex vivo clinical applications. © 2025 SPIE.

关键词： Bone

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： Software testing

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Steerable rolling of a 1-DoF robot using an internal pendulum

arXiv

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arXiv 2025年

作者： Xu, Christopher Y. Yan, Jack Lum, Kathleen Yim, Justin K. Department of Electrical and Computer Engineering Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign United States

We present ROCK (Rolling One-motor Controlled rocK), a 1 degree-of-freedom robot consisting of a round shell and an internal pendulum. An uneven shell surface enables steering by using only the movement of the pendulum, allowing for mechanically simple designs that may be feasible to scale to large quantities or small sizes. We train a control policy using reinforcement learning in simulation and deploy it onto the robot to complete a rectangular trajectory. © 2025, CC BY.

关键词： Pendulums

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Eve Said Yes: AirBone Authentication for Head-Wearable Smart Voice Assistant

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IEEE Transactions on Mobile Computing 2025年

作者： Huang, Chenpei Zhong, Hui Prakash, Pavana Shi, Dian Yuan, Xu Pan, Miao University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Delaware Department of Computer and Information Science NewarkDE19716 United States

Recent advances in speech and language processing have led to the rise of smart voice services like Alexa, Google Home, and Siri. However, these advancements also increase security risks due to sophisticated voice domain attacks. Instead of relying on acoustic clues to detect replayed or synthesized speech, we utilize microphones and motion sensors in head-wearable devices to authorize legitimate users through bone-conducted vibrations, enabling multi-factor authentication (MFA) for spoken voice. Our proposed two-stage authentication system, AirBone, captures air and bone conduction (AirBone) signals and exploits two authentication factors sequentially. The first stage, called temporal consistency scoring (TCS), employs signal processing to verify the recorded AC and BC signals are concurrent and originate from the same vocalization process. Statistical tools are employed to distinguish legitimate attempts against false-triggering or acoustic attacks. The second stage leverages deep learning to verify the user's unique bone conduction patterns in the vibration domain. Specifically, we enhance the robustness through data augmentation with constant-Q transform and adversarial training, improving the model's ability to detect impersonation and machine-induced vibrations. Thanks to these designs, AirBone authentication offers enhanced security via MFA with no extra cost of user effort. In addition, our experimental results demonstrate a 96.3% overall accuracy, robustness against AirBone noise and room impulse responses, and 0.3% Equal Error Rate (EER) against acoustic and cross-domain attacks. © 2002-2012 IEEE.

关键词： Authentication

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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LiDAR-Aided NLoS Path Identification for Enhanced Indoor mmWave Communication 22

LiDAR-Aided NLoS Path Identification for Enhanced Indoor mmW...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Davis, Dalton Lomboy, Rafaela Ho, Long Eltayeb, Mohammed E. California State University Sacramento Department of Computer Science Sacramento United States California State University Sacramento Department of Electrical & Electronic Engineering Sacramento United States

ISBN: (纸本)9798331508050

This paper presents a Light Detection and Ranging (LiDAR) based technique for identifying non-line-of-sight (NLoS) communication paths in indoor millimeter-wave (mmWave) environments. This is achieved by using LiDAR distance and backscattered intensity measurements to i) create a map of the communication environment, and ii) identify a set of potential reflective surfaces for NLoS communication. Experimental results demonstrate that the proposed technique, which combines environmental geometry and LiDAR scatter data for ray-tracing, achieves performance comparable to traditional exhaustive search beam training methods, particularly for first-ranked (top1) alternative NLoS paths. This offers a promising solution for enhancing indoor mmWave communications in dynamic environments prone to line-of-sight link blockages without the need for a fully dedicated beam training stage. © 2025 IEEE.

关键词： beam alignment beam prediction indoor LiDAR LiDAR point cloud mmWave non-line-of-sight (NLoS) ray-tracing

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Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI

arXiv

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arXiv 2025年

作者： Ehsanul Haque, Md. Jahidul Islam, S.M. Maliha, Jeba Shakhauat Hossan Sumon, Md. Sharmin, Rumana Rokoni, Sakib Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Mathematics Bangladesh Agricultural University Mymensingh Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Food and Nutrition University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of CKD and better patient outcomes requires early detection. Nevertheless, limitations lie in traditional diagnostic methods, especially in resource constrained settings. This study proposes an advanced machine learning approach to enhance CKD detection by evaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically, among these, the fine-tuned CatBoost model demonstrated the best overall performance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of 97.35% of the studies. The proposed CatBoost model has used a nature inspired algorithm such as Simulated Annealing to select the most important features, Cuckoo Search to adjust outliers and grid search to fine tune its settings in such a way to achieve improved prediction accuracy. Features significance is explained by SHAP-a well-known XAI technique-for gaining transparency in the decision-making process of proposed model and bring up trust in diagnostic systems. Using SHAP, the significant clinical features were identified as specific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus. The potential of advanced machine learning techniques in CKD detection is shown in this research, particularly for low income and middle-income healthcare settings where prompt and correct diagnoses are vital. This study seeks to provide a highly accurate, interpretable, and efficient diagnostic tool to add to efforts for early intervention and improved healthcare outcomes for all CKD patients. © 2025, CC BY.

关键词： Patient treatment

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LBPNET: Detection of Fake Face Currency through LBP-Based Convolutional Neural Networks

LBPNET: Detection of Fake Face Currency through LBP-Based Co...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Y.V. Bhaskar Reddy C Satya Kumar Rayalla Ashok Sai Nithin Mandlem P. Ashok Reddy M.Babu Reddy Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India Department of Computer Science and Engineering Vardhaman College of Engineering Department of Computer Science Krishna University Machilipatnam

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The escalating sophistication of counterfeit face currency demands innovative solutions for robust detection. In this study, we propose LBPNET, a novel approach employing Local Binary Pattern (LBP) feature extraction and Convolutional Neural Networks (CNNs) to address the challenges posed by realistic fake currencies. LBP descriptors are extracted from currency images, capturing subtle patterns invisible to the human eye. These descriptors serve as inputs for a CNN, named LBPNET, trained to discern genuine from counterfeit face currencies. The model undergoes a comprehensive training phase, incorporating diverse currency images, to establish a generalized and adaptive detection system. In testing, new currency images are evaluated using the trained LBPNET to identify the presence of fake or non-fake currency, showcasing the model's adaptability to emerging counterfeit variations. This approach integrates machine learning and image processing to enhance detection capabilities, offering a promising solution for the evolving landscape of counterfeit face currency across forensic and social media domains.

关键词： Training Adaptation models Adaptive systems Forensics Machine learning Feature extraction Convolutional neural networks Currencies Faces Testing

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Machine Learning Based Phishing Website Detection System - Natural Language Processing Approach 2

Machine Learning Based Phishing Website Detection System - N...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Naik, S. Vinay Harika, K. Bahadure, Nilesh Bhaskarrao Verma, Kamal Kant Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

Natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies. © 2025 IEEE.

关键词： Phishing

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