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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Enhancing Security in OpenID Connect Single Sign-On (SSO) systems: A Comprehensive Vulnerability Analysis and Mitigation Approach 4

Enhancing Security in OpenID Connect Single Sign-On (SSO) Sy...

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Al-Shabi, Mohamed Bennour, Akram Al-Sarem, Mahamed Khalaf, Osama Ibrahim Sikder, Rajesh Algburi, Sameer Department of management Information system Taibah University Medina Saudi Arabia "LAMIS" Laboratory Echahid Chikh Larbi Tebessi University Tebessa Algeria College of Computer Science and Engineering Taibah University Medina Saudi Arabia Al-Nahrain University Jadriya Baghdad Iraq Information Technology University of the Cumberlands United States Al-Kitab University College of Engineering Techniques Kirkuk Iraq

ISBN: (纸本)9781839539954

Single Sign-On (SSO) systems have become popular for simplifying the login process by allowing users to authenticate with a single identity provider (IDP). However, the widespread use of these systems raises concerns about user privacy, as IDPs like Google or Facebook can gather extensive information about user behavior on the web. This poses a significant challenge for privacy-conscious users who wish to limit the disclosure of their web access behavior to third-party organizations. In this paper, we present a comprehensive and large-scale practical study focused on the OpenID Connect protocol, a widely used SSO protocol. Our analysis delves into the various stages of the protocol's operation, highlighting its security flaws and vulnerabilities. Additionally, we conduct a systematic examination of the privacy implications associated with user access to SSO systems. We provide a detailed account of how easily user information can be accessed, shedding light on the potential risks. © The Institution of engineering & Technology 2023.

关键词： Differential privacy

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Candidate Evaluation with Multimodal Data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Design of Highly Reliable Fault Backtracking Calculation for Space Applications 15

Design of Highly Reliable Fault Backtracking Calculation for...

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15th International Conference on computer Research and Development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Beijing Chongqing China Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

The design architecture of backtracking calculation control processing for space large manipulator control is proposed. SRAM-Type FPGA is used to build the hardware full three-mode computing core, and the processor architecture is designed based on FPGA. The register value and cache access value of the single instruction calculation process of the three computing processors are used to compare the three modes in the anti-fuse FPGA. At the same time, the results of three computing processors were compared by SPARC CPU. Fault backtracking calculation control, when a single computing module occurs an error, first detect the instruction execution position of the faulty module, and then carry out register-level data recovery for the faulty module, and restore to the instruction before the fault to accelerate the operation to complete the 'backtracking' process, until the other two modules complete the calculation synchronization. According to the theoretical derivation and analysis, it is clear that under the linear increasing detection window, the maximum time of fault recovery is only related to the three-mode ratio transmission rate. According to this conclusion, the hardware digital logic design of the key modules of fault detection and recovery is carried out. The overall structure is simple and easy to implement, digital logic only occupies less than 100,000 logic gates. Compared with traditional methods, the fault recovery efficiency is improved by at least 90%. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Machine Learning Algorithm For Developing A Smart Health system

A Machine Learning Algorithm For Developing A Smart Health S...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Shemily P John P Anju S. Barath Kumar P. Vijayaraghavan Nishesh Nigam G. Amirthayogam General Studies Unit University College of Bahrain Kingdom of Bahrain Department of Computer Science and Business System (csbs) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Biotechnology Karpagam Academy of Higher Education Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science & Engineering (cse) IES Institute of Technology and Management Bhopal M.P India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The merging of revolutionary technologies like deep learning has greatly affected the healthcare sector, changing diagnosis and decision-making processes. This study tries to explore the role of neural networks in Digital Health research by examining 192 records drawn from the database used by Scopus. Employing a carefully made search term, the study tries to identify nations that excel in book output, primary research subjects, top funding backers, and common research terms in this domain. Notably, the debut paper on AI in smart health appeared in 2011, signalling the origins of a sizeable surge of academic output. India has emerged as the leader in performing study in this area, showing a remarkable growth trend. Furthermore, the research shows that Access from the IEEE is the top magazine, having the largest number of papers within this area. This extensive review serves as an important tool for scholars, policy makers, and healthcare workers, giving deep insights into the development of artificial intelligence in Smart Health study. The results show a positive trend, demonstrating that artificial intelligence in Connected Health is set for substantial increase in the future. The creativity and sincerity of this study highlight its importance in developing knowledge and defining the future environment of healthcare innovation.

关键词： Technological innovation Ethics Machine learning algorithms Databases Reviews Smart healthcare Neural networks

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A Novel Relocating Recognition Algorithm in Overlapping Region for Multi-UAVs Cluster

A Novel Relocating Recognition Algorithm in Overlapping Regi...

引用

2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Li, Chen Qi, Xuelei Zhao, Juntao Liao, Xuanhong Lin, Zitian Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Shenyang China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong High School Attached to Northeast Normal University International Curriculum Center Changchun China School of Automation Science and Engineering South China University of Technology Guangzhou China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel relocation recognition algorithm for the multi-UAV cluster. First, the autonomous localization algorithm based on visual-inertial fusion is proposed. Then, the algorithm for vocabulary updating and relocating is designed. Finally, practical testing is implemented to verify and improve the efficiency of exploration ability and the accuracy of the autonomous navigation and positioning in the complex overlapping regions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Recommendation systems in Education : A Bibliometric Analysis

Recommendation Systems in Education : A Bibliometric Analysi...

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International Conference on Business and Industrial Research (ICBIR)

作者： Hendra Mayatopani Dina Fitria Murad Nor Adnan Yahaya School of Computing University Malaysia of Computer Science & Engineering Selangor Malaysia Information System Pradita University Tangerang Indonesia Information Systems Department Binus Online Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration.

关键词： Deep learning Filtering Collaborative filtering Education Bibliometrics Market research Information age International collaboration Recommender systems Optimization

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Subgraph matching-based reference placement for printed circuit board designs

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The Journal of Supercomputing 2024年第16期80卷 24324-24357页

作者： Zhu, Ziran Li, Yilin Su, Miaodi Zhang, Shu Su, Haiyuan Xiao, Yifeng He, Huan Chen, Jianli Chang, Yao-Wen National ASIC System Engineering Center Southeast University Nanjing China College of Mathematics and Computer Science Fuzhou University Fuzhou China Department of Electrical Engineering University of Southern California Los Angeles USA Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC and System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Reference placement is promising to handle the increasing complexity in printed circuit board (PCB) designs, which aims to find the isomorphism of the placed template in component combination to reuse the placement. In this paper, we convert the netlist information into a graph and then model the reference placement as a subgraph matching problem. Since the state-of-the-art subgraph matching methods usually recursively search the solutions and suffer from high time and memory consumption in large-scale designs, we develop a novel subgraph matching algorithm D2BS with diversity tolerance and improved backtracking to guarantee matching quality and efficiency. The D2BS algorithm is founded on a data structure called the candidate space (CS) structure. We build and filter the candidate set for each query node according to our designed features to construct the CS structure. During the CS optimization process, a graph diversity tolerance strategy is adopted to achieve efficient inexact matching. Then, hierarchical matching is developed to search the template embeddings in the CS structure guided by branch backtracking and matched-node snatching strategies. Based on the industrial PCB designs, experimental results show that D2BS outperforms the state-of-the-art subgraph matching method in matching accuracy and running time.

关键词：

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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