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Mining Stable Communities in Temporal Networks by Density-Based Clustering

IEEE TRANSACTIONS ON BIG DATA 2022年第3期8卷 671-684页

作者： Qin, Hongchao Li, Rong-Hua Wang, Guoren Huang, Xin Yuan, Ye Yu, Jeffrey Xu Northeastern Univ Dept Comp Sci Shenyang 110004 Liaoning Peoples R China Beijing Inst Technol Dept Comp Sci Beijing 100081 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Clustering algorithms Big Data Collaboration Image edge detection detection algorithms Computer science Data mining Community detection temporal networks stable community structural graph clustering density-based clustering

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To Develop Multi-Object detection and Recognition Using Improved GP-FRCNN Method 8

To Develop Multi-Object Detection and Recognition Using Impr...

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Loganathan, Ganesh Babu Fatah, Tooraj Hassan Yasin, Elham Tahsin Hamadamen, Nawroz Ibrahim Tishk International University Faculty of Engineering Department of Mechatronics Kurdistan Region Erbil Iraq Noble Private Institute Information Technology Kurdistan Region Erbil Iraq University of Salahaddin Institute of Higher Education & Scientific Research Software Engineering Department Kurdistan Region Erbil Iraq

ISBN: (纸本)9781665497619

The Region-based Convolutional Neural Network (R-CNN), which is more rapid, has been recognized as the best detection algorithm. With our design basis for Faster RCNN (FRCNN), the designers reclassify the proposed object recognition changes with just a rough spatial estimate of the incident. The researchers solve problems such as delicate to complex environments and its principles are derived reductions each time it is -actually confronted with noisier initiatives. Through which designers categorize proposed changes to object recognition using a rough geometric estimate of the scene. This simple expansion requires complex changes of scale, which the authors describe in their entirely throughout this study. Moreover, Geometric Proposals with FRCNN (GP-FRCNN) performed relatively well on small and large things, which has always been a problem for computer vision algorithms. Any use of GP-FRCNN for monitoring film analysis is simple and requires no specific geometric description. We tested the algorithm on the UA-DETRAC database, finding that GP-FRCNN beat the FRCNN by 21%. © 2022 IEEE.

关键词： Training Image recognition Databases Object recognition Proposals Intelligent structures detection algorithms

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Comprehensive Review on Facemask detection Techniques in the Context of Covid-19

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IEEE ACCESS 2021年 9卷 106839-106864页

作者： Nowrin, Afsana Afroz, Sharmin Rahman, Md. Sazzadur Mahmud, Imtiaz Cho, You-Ze Bangladesh Univ Profess Dept Informat & Commun Technol ICT Dhaka 1216 Bangladesh Jahangirnagar Univ Inst Informat Technol Dhaka 1342 Bangladesh Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization (WHO), wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the "no mask, no service" method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future sco

关键词： COVID-19 Feature extraction Coronaviruses detection algorithms Object detection Machine learning algorithms Faces Covid-19 convolutional neural network deep neural network facemask detection Covid-19 health object detection machine learning

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A 2.66 μW Clinician-Like Cardiac Arrhythmia Watchdog Based on P-QRS-T for Wearable Applications

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022年第5期16卷 793-806页

作者： Xu, Xinzi Cai, Qiao Zhao, Yang Wang, Guoxing Zhao, Liebin Lian, Yong Shanghai Jiao Tong Univ Dept Micro Nano Elect Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada Shanghai Jiao Tong Univ Sch Med Xin Ilua Hosp Shanghai Engn Res Ctr Telligence Pediat SERCIP Shanghai 200092 Peoples R China

A wearable electrocardiogram (ECG) device is an effective tool for managing cardiovascular diseases. This paper presents a low power clinician-like cardiac arrhythmia watchdog (CAW) for wearable ECG devices. The CAW is based on a novel P-QRS-T detection algorithm that makes use of clinical features to identify abnormalities. Implemented in 0.18 mu m CMOS process, the CAW consumes 2.66 mu W for 80 bpm heart rate at 1.2 V supply with an area of 0.578 mm(2). Verified on QT database, the average sensitivity/positive predictivity for P-wave, QRS complex and T-wave are over 93.39%/88.55%, 99.69%/99.48%, and 97.13%/93.18% respectively, across over 190000 beats. It shows over 99.8% arrhythmia detection accuracy for 43 subjects evaluated on MIT-BIH database.

关键词： Electrocardiography Feature extraction Heart beat Biological neural networks Wearable sensors Energy efficiency detection algorithms Clinician-like cardiac arrhythmia classification ECG P and T waves detection QRS detection wearable ECG sensors

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A Deep Learning Parking detection Algorithm Using W-Shape Magnetic Wireless Sensor Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Zhang, Ying Wu, Chang Wu, Peng You, Yuan Yuan, Yifei Univ Elect Sci & Technol China UESTC Inst Informat & Commun Engn Chengdu 611731 Peoples R China

With the number of motor vehicles in cities rapidly increasing, scheduling and management in large parking lots need to be timelier and more accurate, and vehicle detection technology plays a crucial role in this process. Magnetic sensors have attracted enormous interest in vehicle detection owing to their low cost and easy installation. However, most of the studies extract features manually, and the results are given by the potential difference in magnetic signal which is susceptible to environmental interference. Moreover, each parking space corresponds to a sensor, which is costly to deploy and maintain in a large parking lot. The main challenge lies in the difficulty of eliminating the interferences from vehicles in adjacent parking spaces that decrease the accuracy. In this article, a deep learning parking detection algorithm based on $W$ -shape magnetic wireless sensor network is proposed for large parking lots with vertical parking spaces. The number of required sensors is greatly reduced by analyzing the sensor deployment which can greatly reduce the costs accordingly. To improve the adaptability of the algorithm in different environments, the deep learning model is trained with the collaborative information from multiple sensors. Test in the actual scene shows that the algorithm can significantly reduce costs and improve adaptability while ensuring high detection accuracy. Thus, it is easier to implement in large parking lots.

关键词： Costs detection algorithms Magnetic sensors Interference Perpendicular magnetic anisotropy Maintenance engineering Feature extraction Magnetic sensor parking detection self-attention transformers wireless sensor networks (WSNs)

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Deep Atrous Spatial Features-Based Supervised Foreground detection Algorithm for Industrial Surveillance Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第7期17卷 4818-4826页

作者： Shahbaz, Ajmal Jo, Kang-Hyun Univ Ulsan Sch Elect & Comp Engn Ulsan 44610 South Korea Univ Ulsan Dept Elect & Comp Engn Ulsan 44610 South Korea

Camera-based surveillance systems largely perform an intrusion detection task for sensitive areas. The task may seem trivial but is quite challenging due to environmental changes and object behaviors such as those due to night-time, sunlight, IR camera, camouflage, and static foreground objects, etc. Convolutional neural network based algorithms have shown promise in dealing with these challenges. However, they are exclusively focused on accuracy. This article proposes an efficient supervised foreground detection (SFDNet) algorithm based on atrous deep spatial features. The features are extracted using atrous convolution kernels to enlarge the field-of-view of a kernel mask, thereby encoding rich context features without increasing the number of parameters. The network further benefits from a residual dense block strategy that mixes the mid and high-level features to retain the foreground information lost in low-resolution high-level features. The extracted features are expanded using a novel pyramid upsampling network. The feature maps are upsampled using bilinear interpolation and pass through a 3x3 convolutional kernel. The expanded feature maps are concatenated with the corresponding mid and low-level feature maps from an atrous feature extractor to further refine the expanded feature maps. The SFDNet showed better performance than high-ranked foreground detection algorithms on the three standard databases. The testing demo can be found at https://***/file/d/1z_zEj9Yp7GZeM2gSIwYKvSzQlxMAiarw/view?usp=sharing.

关键词： Feature extraction Convolution Kernel Surveillance Standards detection algorithms Change detection algorithms Convolutional neural networks (CNN) dual-camera sensors infrared (IR) camera intelligent surveillance systems

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QMIX Aided Routing in Social-Based Delay-Tolerant Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第2期71卷 1952-1963页

作者： Han, Chenchen Yao, Haipeng Mai, Tianle Zhang, Ni Guizani, Mohsen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Sixth Res Inst China Elect Corp Beijing 100846 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

Delay-tolerant network (DTN) is a network that's designed to operate effectively in heterogeneous networks that may lack continuous network connectivity. It is characterized by their lack of instantaneous end-to-end paths, resulting in difficulties in designing effective DTN routing protocols. Traditional routing algorithms largely rely on greedy schemes. Such schemes can not guarantee the packets will be eventually transmitted to their destinations, thereby presenting a poor transmission efficiency. Recently, the social-based method has attracted a large amount of attention in wireless network routing. It can use the community and the centrality information to increase the delivery rate of the whole network. Therefore, in this article, we introduce the social-based mechanism to our DTN routing design. Besides, how the distributed nodes can learn the collaboration strategies is another challenge. Inspired by the recent success of multi-agent learning in online control, we adopt a centralized training and distributed execution learning paradigm and design a hierarchical social-based DTN architecture. Based on this, we propose a collaborative multi-agent reinforcement learning (termed as QMIX) aided routing algorithm.

关键词： Routing Computer architecture Routing protocols detection algorithms Training Heuristic algorithms Relays Dec-POMDP delay-tolerant networks QMIX routing protocol social attributes

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Finding Core-Periphery Structures With Node Influences

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第2期9卷 875-887页

作者： Shen, Xin Aliko, Sarah Han, Yue Skipper, Jeremy, I Peng, Chengbin Ningbo Univ Coll Informat Sci & Engn Ningbo 315000 Peoples R China London Interdisciplinary Biosci Consortium London WC1E 6BT England UCL Expt Psychol London WC1E 6BT England

Detecting core-periphery structures is one of the outstanding issues in complex network analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances found that many networks from real-world data can be better modeled with multiple pairs of core-periphery nodes. In this study, we propose to use an influence propagation process to detect multiple pairs of core-periphery nodes. In this framework, we assume each node can emit a certain amount of influence and propagate it through the network. Then we identify nodes with large influences as core nodes, and we utilize a maximum influence chain to construct a node-pairing network to determine core-periphery pairs. This approach can take node interactions into consideration and can reduce noises in finding pairs. Experiments on randomly generated networks and real-world networks confirm the efficiency and accuracy of our algorithm.

关键词： detection algorithms Complex networks Image edge detection Periodic structures Stochastic processes Psychology Proteins Core-periphery structure complex networks node influences computational modeling unsupervised learning

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Fusing of a Continuous Output PDR Algorithm With an Ultrasonic Positioning System

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IEEE SENSORS JOURNAL 2022年第3期22卷 2464-2474页

作者： Thio, Vincent Aparicio, Joaquin Anonsen, Kjetil Bergh Bekkeng, Jan Kenneth Booij, Wilfred Univ Oslo Dept Phys N-0371 Oslo Norway Univ Oslo Dept Informat N-0373 Oslo Norway Norwegian Def Res Estab FFI N-2027 Kjeller Norway Univ Oslo Dept Technol Syst N-0371 Oslo Norway Forkbeard Technol AS N-0283 Oslo Norway

The availability of inertial navigation sensors in smartphones has facilitated the development of pedestrian dead reckoning (PDR) models on a large scale. These models often consist of a step detection algorithm combined with a heading estimation routine. Common approaches to step detection include searching for peaks/valleys in the acceleration signal, principle frequency estimation, and machine learning techniques. Since the sensors embedded in smart devices are prone to noise, the position error grows unbounded if unchecked and requires periodical corrections based on external measurements. In this work, we propose a novel step detection algorithm based on sine-wave approximation of the acceleration signal. This method detects step fractions as well as full steps, which allows for continuous and real-time updates. The step detection algorithm is combined with a heading estimation routine described in our previous work to obtain a stand-alone PDR model. To mitigate error accumulation, we fuse the proposed model with position and heading measurements provided by a commercial indoor positioning system based on ultrasound. We evaluate the performance of the PDR and fused model in an open office environment, by walking along a trajectory while carrying a smartphone in hand or in the pocket. The results demonstrate the feasibility of the sine-wave approximation approach to step detection, as well as the expected benefits of fusing PDR with the ultrasonic system.

关键词： Acoustics Accelerometers Sensors detection algorithms Position measurement Legged locomotion Estimation Indoor positioning pedestrian dead reckoning step detection ultrasonic positioning system

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Distributed Secure State Estimation for Cyber-Physical Systems Against Replay Attacks via Multisensor Method

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IEEE SYSTEMS JOURNAL 2022年第4期16卷 5720-5728页

作者： Su, Lei Ye, Dan Zhao, Xin-Gang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China

This article investigates the distributed secure state estimation problem for cyber-physical systems under replay attacks. The main purpose is to design the distributed observers such that the estimation error system is stable. First, a detection algorithm is proposed to not only expose the behavior of the attacker but also identify which sensor is tampered. Then, the distributed observers are designed and the adverse effects are eliminated. Moreover, in view of the linear matrix inequality technique, a sufficient condition is presented, which ensures that the estimation error system is stable and achieves an $H_{\infty }$ property. Finally, a practical inverted pendulum example is employed to elaborate the effectiveness and superiority of the presented design and detection method.

关键词： Sensors Observers detection algorithms Watermarking Cyberattack Estimation error Time measurement detection mechanism distributed state estimation multisensor measurements replay attack

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