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A Novel Multitouch Recognition Algorithm for Infrared Touch Screens Based on Geometric Construction and clustering

IEEE SENSORS JOURNAL 2025年第10期25卷 17671-17682页

作者： Zuo, Jiancun Ji, Zhangyuan Sun, Jingguo Li, Hewei Wang, Haowei Peng, Yue Shanghai Polytech Univ Sch Comp & Informat Engn Shanghai 201209 Peoples R China

Both sensor layout and sensor number determine the scan line density and limit the performance of modern infrared touchscreens in terms of recognition accuracy, positioning error, minimum touch object size, response time, and maximum number of touch points. This article explores methods to optimize the multitouch recognition algorithm, improving touchscreen performance without increasing hardware costs. To efficiently and accurately calculate touch point locations, a multitouch recognition algorithm based on geometric construction and clustering is proposed. The algorithm divides the set of intersection points into core, noncore, and noise regions by optimizing the median of blocked scan lines. A K-d tree is constructed for the intersection set using fast library for approximate nearest neighbor (FLANN), followed by the application of the KNN-BLOCK-DBSCAN clustering algorithm. Touch point coordinates are determined by merging and splitting clusters based on their density reachability. Experimental results show that our algorithm outperforms the polygon-cutting method in recognition accuracy, minimum touch object size, and response time. The response time is reduced by over 50%, with a minimum touch object diameter of 4 mm in the center and 5 mm in the corners, while eliminating touch-insensitive areas across the entire touch area.

关键词： clustering algorithms Touch sensitive screens Accuracy Hardware Costs Classification algorithms Tactile sensors Time factors Intelligent sensors Optical transmitters clustering geometric construction infrared touch screen multitouch recognition

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Enhancing clustering Performance With Tensorized High-Order Bipartite Graphs: A Structured Graph Learning Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2616-2631页

作者： Zhao, Zihua Cao, Zhe Xin, Haonan Wang, Rong Wu, Danyang Wang, Zhen Nie, Feiping Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China Northwest A&F Univ Shaanxi Engn Res Ctr Intelligent Percept & Anal Ag Yangling 712100 Peoples R China

clustering based on structured graph learning involves acquiring a proximity matrix with an explicit clustering structure from the original one. However, the original proximity matrix often lacks some must-links compared to the groundtruth, constraining the upper bound of clustering performance. High-order proximity information can mitigate this limitation, yet traditional high-order proximity matrix-based methods are time-intensive. To tackle this, we propose the Tensorized High-order Bipartite Graphs-based structured proximity matrix learning method (THBG). Firstly, we introduce a high-order bipartite graph proximity matrix with a swift computation method, incorporating high-order information and significantly reducing computational overhead. Secondly, we apply tensor nuclear norm minimization to the tensor composed of high-order bipartite graphs, learning a low-rank tensor representation that effectively harnesses the consistency of high-order information. Concurrently, a structured bipartite graph proximity matrix with an explicit clustering structure is adaptively learned based on the low-rank tensor representation and Laplace rank constraint. Experimental results demonstrate the superiority and great potential of this method. Code available: https://***/r/THBG-D10D.

关键词： Tensors Bipartite graph Noise clustering algorithms Turning Sparse matrices Minimization Matrix decomposition Learning systems Laplace equations clustering structured proximity matrix high-order bipartite graph tensor nuclear norm

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Hierarchical Federated Learning With Distributed clustering and Multichannel ALOHA

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IEEE SENSORS JOURNAL 2025年第7期25卷 12128-12142页

作者： Da Silva, Rodolfo Viturino Nogueira Dos Reis, Ana Flavia Brante, Glauber Souza, Richard Demo Univ Tecnol Fed Parana UTFPR Grad Program Elect & Comp Engn BR-80230901 Curitiba Brazil Inst Nacl Telecomunicacoes INATEL BR-37536001 Santa Rita Do Sapucai Brazil Fed Univ Santa Catarina UFSC Dept Elect & Elect Engn BR-88040900 Florianopolis Brazil

In an age characterized by seamless interconnectivity, the quantity of Internet of Things (IoT) devices has experienced substantial growth in recent years, with projections indicating further expansion. In this context, federated learning (FL) plays an important role in the future of wireless communications, offering numerous advantages over traditional centralized learning approaches, including data privacy preservation, reduced bandwidth usage, improved accuracy, and customization. However, selecting an appropriate wireless protocol and data transmission method for FL is crucial. In this work, we adopt the multichannel ALOHA protocol due to its asynchronous nature and simple implementation compared to other protocols. This article focuses on optimizing multichannel ALOHA communication within a hierarchical FL (HFL) system by creating a device-to-device (D2D) clustering scheme, which enables a single base station (BS) to serve more devices and drastically reduces the achievable error.

关键词： Device-to-device communication Internet of Things Protocols Wireless communication Synchronization Sensors clustering algorithms Wireless sensor networks Federated learning Sensor phenomena and characterization clustering device-to-device (D2D) federated learning (FL) multichannel ALOHA

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Local High-Order Graph Learning for Multi-View clustering

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 761-773页

作者： Wang, Zhi Lin, Qiang Ma, Yaxiong Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Northwest Minzu Univ Sch Math & Informat Lanzhou 730030 Peoples R China

As the accumulation of multi-view data continues to grow, multi-view clustering has become increasingly important in research fields like data mining. However, current methods have been criticized for their unsatisfactory performance, such as insufficient exploration of intra-view high-order relationships and poor characterization of inter-view diverse features. To overcome these challenges, we propose a novel approach called Local High-order Graph Learning for Multi-View clustering (LHGL_MVC). Our method aims to explore high-order relationships within a view while also considering diverse information between views. In LHGL_MVC, we learn the initial graphs of each view through self-representation, which are decomposed into consistent and diverse parts to better capture the diversity of different views. Based on consistent parts, we propose a novel local high-order graph learning approach to more effectively explore high-order relationships between samples within each view. At the same time, we leverage high-order relationships between views using the rotated tensor nuclear norm. Finally, we obtain a unified graph for clustering by fusing all consistent affinity graphs and their high-order graphs with adaptive weights. All procedures are integrated into an overall objective function, which mutually promotes during the optimization process. The comprehensive experiments conducted on eleven real-world datasets demonstrate that LHGL_MVC significantly outperforms existing algorithms in various measurements, highlighting the superiority of the proposed method.

关键词： Matrix decomposition clustering algorithms Tensors Feature extraction Correlation Vectors Data structures Multi-view clustering local high-order graph diversity tensor nuclear norm

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Efficient Solvers for Wyner Common Information With Application to Multi-Modal clustering

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第3期71卷 2054-2074页

作者： Huang, Teng-Hui El Gamal, Hesham Univ Sydney Sch Elect & Comp Engn Camperdown NSW 2050 Australia Univ Sydney Fac Engn Camperdown NSW 2050 Australia

In this work, we propose computationally efficient solvers for novel extensions of Wyner common information. By separating information sources into bipartite, the proposed Bipartite common information framework has difference-of-convex structure for efficient non-convex optimization. In known joint distribution cases, our difference-of-convex algorithm(DCA)-based solver has a provable convergence guarantee to local stationary points. As for unknown distribution settings, the insights from DCA combined with the exponential family of distributions for parameterization allows for closed-form expressions for efficient estimation. Furthermore, we show that the Bipartite common information applies to multi-modal clustering without employing ad-hoc clustering algorithms. Empirically, our solvers outperform state-of-the-art methods in clustering accuracy and running time over a range of non-trivial multi-modal clustering datasets with different number of data modalities.

关键词： Optimization Vectors Indexes Convergence Mutual information Feature extraction clustering algorithms Unsupervised learning Inference algorithms Entropy Wyner common information algorithms optimization variational inference conditional independence multi-modal learning clustering unsupervised

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CECEHO-GCS: A New Green Energy-Efficient clustering Protocol Based on Intelligent Optimization Theory in Industrial IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10907-10919页

作者： Zhou, Peng Cao, Qike Cao, Bingyu Chen, Wei Jin, Bo Zhao, Fengda Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Henan Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal Portuguese Natl Acad Sci P-1600477 Lisbon Portugal

To address the issues of battery dependence, energy consumption, and energy management imbalance in industrial wireless sensor networks (IWSNs), this study proposes a green and energy-saving multiobjective clustering scheme to improve network efficiency and reduce environmental pollution. Given that conventional methods struggle to effectively optimize IWSN clustering, this article specifically designs a novel multiobjective clustering model. During the optimization process, this model comprehensively considers four key performance indicators: 1) total remaining energy, 2) average network delay;3) average network packet loss rate;and 4) average distance from cluster heads (CHs) to the base station (BS), achieving holistic optimization of network performance. To further enhance clustering efficiency and network stability, this article also introduces a green energy-saving scheme based on the chaotic elite clone elephant herding optimization algorithm (i.e., CECEHO-GCS). This scheme ingeniously incorporates chaos operators in the initialization stage to enrich solution diversity and introduces clone and elite operators in the evolution stage, aiming to retain superior solutions and significantly enhance the algorithm's search capabilities. Through comparative experiments with four existing advanced clustering schemes: 1) LEACH-C;2) LEACH-R;3) ESCVAD;and 4) ARSH-FATI-CHS, the model and the algorithm proposed in this article, CECEHO-GCS, demonstrate significant advantages in improving network energy efficiency and service quality. Specifically, CECEHO-GCS has achieved an improvement of at least 19.27% in network lifespan and at least 16.89% in data throughput, opening up new avenues for green energy conservation and sustainable development in IWSNs.

关键词： clustering algorithms Optimization Wireless sensor networks Energy efficiency Energy consumption Green products Protocols Production Cloning Delays clustering protocol disposable battery elephant herding optimization green energy-saving industrial wireless sensor network (IWSN)

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DSCR: A Dynamic Secure clustering Routing Scheme for UANETs Based on Reputation Mechanism

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 14109-14123页

作者： Gong, Yinyan Chen, Yuxiang Li, Kuanching Liang, Wei Li, Xiong Wang, Jin Xiang, Yang Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Sanya Res Inst Sanya 572024 Peoples R China Sch Comp Sci & Engn Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Univ Elect Sci & Technol China Inst Cyber Secur Sch Comp Sci & Engn Chengdu 611731 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic 3122 Australia

In UAV ad hoc networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism dynamically secure cluster routing scheme (DSCR) for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra- and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs.

关键词： Routing clustering algorithms Heuristic algorithms Autonomous aerial vehicles Voting Network topology Ad hoc networks Reinforcement learning Vehicle dynamics Packet loss UAV Ad hoc networks clustering reinforcement learning reputation mechanism routing

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Distributed multi-object tracking under limited field of view heterogeneous sensors with density clustering

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SIGNAL PROCESSING 2025年 228卷

作者： Chen, Fei Van Nguyen, Hoa Leong, Alex S. Panicker, Sabita Baker, Robin Ranasinghe, Damith C. Univ Adelaide Sch Comp & Math Sci Adelaide SA 5005 Australia Curtin Univ Sch Elect Engn Comp & Math Sci Bentley WA 6102 Australia Def Sci & Technol Grp Platforms Div Canberra Australia Def Sci & Technol Grp Land & Integrated Force Div Edinburgh SA Australia

We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with synthetic and real-world trajectory datasets demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.

关键词： Label consistency clustering algorithms Multi-sensor multi-object tracking Distributed multi-object tracking

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Toward Autonomous Distributed clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 2065-2072页

作者： Yang, Jie Lin, Chin-Teng Univ Technol Sydney Australian AI Inst GrapheneX UTS HAI Ctr Computat Intelligence & Brain Comp Interface LabS Sydney Australia

Compared to traditional centralized clustering, distributed clustering offers the advantage of parallel processing of data from different sites, enhancing the efficiency of clustering while preserving the privacy of the data at each site. However, most existing distributed clustering techniques require manual tuning of several parameters or hyperparameters, which can pose challenges for practical applications. This paper introduces a novel parameter-free distributed clustering framework known as distributed torque clustering (DTC). When dealing with data or subdata distributed across various sites, DTC predominantly executes two steps. The first step is a data reduction at each site using torque clustering, and the second step involves performing global clustering with weighted torque clustering. We compare DTC against six state-of-the-art distributed clustering algorithms and automatic centralized clustering techniques on ten large-scale or medium-scale datasets. The results show that the average rank of DTC is at least three times better than those of the other algorithms across all the datasets. Additionally, DTC can accurately predict the ground-truth number of clusters in nine out of ten datasets, further demonstrating its competitive performance and practical potential.

关键词： clustering algorithms Torque clustering methods Heuristic algorithms Complexity theory Partitioning algorithms Distributed databases clustering distributed clustering parameter-free autonomous unsupervised learning

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EM-Based Algorithm for Unsupervised clustering of Measurements from a Radar Sensor Network

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 787-801页

作者： Yan, Linjie Addabbo, Pia Fiscante, Nicomino Clemente, Carmine Hao, Chengpeng Giunta, Gaetano Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Giustino Fortunato I-8210 Benevento Italy Univ Roma Tre Ind Elect & Mech Engn Dept I-00146 Rome Italy Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

This article deals with the problem of clustering data returned by a radar sensor network that monitors a region where multiple moving targets are present. The network is formed by nodes with limited functionalities that transmit the estimates of target positions (after a detection) to a fusion center without any association between measurements and targets. To solve the problem at hand, we resort to model-based learning algorithms and instead of applying the plain maximum likelihood approach, due to the related computational requirements, we exploit the latent variable model coupled with the expectation-maximization algorithm. The devised estimation procedure returns posterior probabilities that are used to cluster the huge amount of data collected by the fusion center. Remarkably, we also consider challenging scenarios with an unknown number of targets and estimate it by means of the model-order selection rules. The clustering performance of the proposed strategy is compared to that of conventional data-driven methods over synthetic data. The numerical examples point out that the herein proposed solutions can provide reliable clustering performance overcoming the considered competitors.

关键词： Radar Radar tracking clustering algorithms Time measurement Random variables Computational modeling Target tracking Batch algorithms expectation-maximization (EM) measurement clustering multiple moving targets radar sensor network unsupervised learning

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