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第35届中国控制与决策会议

作者： YiZhi Sun Di Wu Liang Gao YongFeng Gao Yan Pan Nan Ding School of Control Science and Control Engineering Dalian University of Technology School of Naval Arch and Ocean Engineering Dalian University of Technology School of Computer science and technology Dalian University of Technology

In this paper,we propose a safety-critical formation control method based on distributed nonlinear model predictive control strategy,which controls the path following and formation maintenance of the multiple mobile robots,while ensuring the collision ***,we adopt the distributed framework with high real time ***,based on the distributed optimization framework,discrete-time control barrier function constraints are transformed into smooth differentiable constraints to complete the polytopic obstacle avoidance with a small horizon by using the strong duality of convex ***,the simulation results of three robots are given to prove the effectiveness of the proposed algorithm,and it can realize the local path generation based on real-time optimization in the narrow environment.

关键词： formation control collision avoidance nonlinear model predictive control discrete-time control barrier function

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Mobile Crowdsourcing Based on 5g and 6g: A Survey

SSRN

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SSRN 2024年

作者： Wang, Yingjie Yingxin, Li Weilong, Wang Peiyong, Duan Maradapu Vera Venkata Sai, Akshita Cai, Zhipeng School of Computer and Control Engineering Yantai University Yantai264005 China Department of Computer Science Georgia State University AtlantaGA30303 United States

With the rapid development of Internet of Things (IoT), 5G and 6G networks, and advancements in hardware and software devices has enabled mobile devices to be equipped with rich sensors (smart phones, smart bracelets, etc.). This led to devices with powerful sensing capabilities that can easily connect and communicate with other devices. As a result, ordinary users could efficiently complete various sensing data tasks and get paid accordingly, promoting the rapid development of Mobile Crowdsourcing (MCS) technology and effectively facilitating people's daily lives. However, the current MCS is still a long way from being complete and still faces several challenges. Therefore, many researchers have conducted extensive research on MCS in recent years. In this survey, we aim to provide a comprehensive summary of the recent research progress on MCS. With respect to the main research directions and real-world applications of MCS, we review the research in three main areas (task allocation, privacy protection and incentive mechanism) and real-world application scenarios of MCS. Although there is a lot of existing research on MCS, there are still some crucial problems to be solved. Therefore, we conclude with some lessons learned and suggest future research directions by analyzing the studies covered in this paper. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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SIFT-based Approach for Dorsal Hand Veins Images Recognition 26

SIFT-based Approach for Dorsal Hand Veins Images Recognition

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Egorov, Dobroslav P. Mizinov, Pavel V. Kravchenko, Oleg V. Konnova, Natalia S. Osipenko, Gavriil V. Kotel'nikov Institute of Radio Engineering and Electronics of Ras Moscow Russia Bauman Moscow State Technical University Department of Information Security Moscow Russia Computer Science and Control of Ras Federal Research Center Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350361728

In this paper we present a system for person identification using dorsal hand veins pattern images. A simple approach is developed based on scale-invariant feature transform (SIFT) method for image features extraction, fast approximate nearest neighbors algorithm (FLANN) for keypoints matching in a preselected region-of-interest (ROI), and subsequent utilization of dense layer neural network for accurate friend-or-foe predictions. The efficiency of the considered approach is evaluated using an open dataset, and confusion matrices are calculated. The obtained results confirm again that dorsal hand veins identification systems are particularly promising for high-security settings. The proposed approach is implemented in software for Orange pi single-board computers. © 2024 IEEE.

关键词： Nearest neighbor search

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On Finite Word Length Computing Error of Fixed-Point SAR Imaging Processing*

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Chinese Journal of Electronics 2025年第3期23卷 645-648页

作者： Yizhuang Xie Bocheng Zhu Wen Yan Weijie Zhang School of Information and Electronics Beijing Institute of Technology Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Science and Technology on Electronic Information Control Laboratory Chengdu China

Synthetic aperture radar (SAR) imaging system is generally realized by fixed-point with the purpose of reducing system implementation scale and enhancing real-time performance. Finite word length computing error of fixed-point SAR imaging system is studied. The characteristic of computing error in SAR imaging system is analyzed. A finite word length computing error model of SAR imaging system is built, by means of which the empirical formula of system's output noise-to-signal ratio is derived. Based on the empirical formula, SAR imaging system processing word length is presented according to different processing granularities. The validity of this proposed finite word length computing error model of SAR imaging system is verified by system level fixed-point simulation.

关键词： Computational modeling Imaging Transforms Radar polarimetry Real-time systems Synthetic aperture radar System implementation

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Attitude optimization of unmanned helicopters based on spike neural membrane computing 3

Attitude optimization of unmanned helicopters based on spike...

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3rd International Conference on Electronics, Electrical and Information engineering, ICEEIE 2023

作者： Xu, Jiachang Yu, Dongliang Hong, Ding Guo, Jia Jiang, Lin Zhou, TaoJing School of Computer Science and Engineering Anhui University of Science and Technology Anhui Province Huainan City232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Province Huainan City232001 China National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China

ISBN: (纸本)9781510671423

The effectiveness of inspection tasks performed by unmanned helicopters during underground inspections is directly influenced by the performance of attitude control. Therefore, it is crucial to optimize the attitude control of unmanned helicopters. Spike neural membrane computing offers Turing computing versatility and high efficiency in various time collaborations. This paper presents a study on attitude optimization control of unmanned helicopters in underground coal mines using spike neural network computing. The approach involves constructing an extended spike neural membrane system based on the attitude dynamics model of an unmanned helicopter, with the aim of further optimizing the system and algorithm. The experimental results demonstrate that the optimized spike neural membrane system is capable of effectively achieving attitude control of the unmanned helicopter under normal conditions. Furthermore, it can successfully maintain the attitude angle within the desired range, even in the presence of wind interference. Additionally, a comparison with traditional sliding mode control and the Designed Optimization Model (OSNMS) reveals that the OSNMS proposed in this paper significantly enhances the attitude control performance of unmanned helicopters. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Game-Theoretic-Based Resource Allocation Algorithm for SCMA Max-Min Problem to Maximize Fairness 7

Game-Theoretic-Based Resource Allocation Algorithm for SCMA ...

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7th IEEE International Conference on Electronics and Communication engineering, ICECE 2024

作者： Cheraghy, Maryam Soltanpour, Meysam Abuhaija, Belal Abdalla, Hemn Barzan Ehimwenma, Kennedy Wenzhou-Kean University dept. Computer Science and Technology Wenzhou China Shandong University dept. Control Science and Engineering Jinan China

ISBN: (纸本)9798331517755

This paper tackles a joint optimization problem in uplink sparse code multiple access (SCMA) networks to enhance network fairness, i.e., Jain's fairness index (JFI). We propose a novel game-theoretic approach, the worst-case player first-strategy selection (WCPFSS) algorithm, to maximize the minimum achievable rate for all subcarriers while ensuring fair power allocation and optimized subcarrier assignment. This approach effectively manages user interference because each subcarrier acts as a player to exchange their strategies, which are clusters of users associated with each subcarrier. The simulation results confirm the superiority of the proposed WCPFSS method w.r.t. JFI for differential setup parameters. © 2024 IEEE.

关键词： Code division multiple access

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DEGAN: A Deep Learning Model for SAR Image Segmentation in Aquacultural Raft Monitoring

DEGAN: A Deep Learning Model for SAR Image Segmentation in A...

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Neuromorphic Computing (ICNC), International Conference on

作者： Shuo Lian Jun Wang Min Han Jianchao Fan School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Department of Computer Science and Department of Data Science City University of Hong Kong Kowloon Hong Kong China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

Offshore aquacultural raft information extraction from synthetic aperture radar (SAR) images is crucial for large-scale marine resource exploitation and environmental protection. In this paper, a deep learning model named Detail-Enhancing Generative Adversarial Network (DEGAN) is proposed for SAR image segmentation to monitor aquacultural rafts. DEGAN incorporates several key modules to enhance segmentation performance, including $f$ -divergence to improve the discrim-inator's ability to handle noisy data, and the Convolutional Block Attention Module (CBAM) for more effective spatial and channel attention. Within CBAM, multi-scale feature fusion is employed to capture both fine and coarse details across different resolutions. Additionally, the Simple, Parameter-Free Attention Module (SimAM) is introduced to enhance feature refinement by deriving neuron importance through an energy function. These modules, when integrated into the GAN framework, work together to improve noise suppression and detail preservation, critical for accurate segmentation in challenging SAR images. Experimental results demonstrate that DEGAN outperforms five baseline models in four performance metrics, making it highly effective for aquacultural raft monitoring in complex marine environments.

关键词： Deep learning Image segmentation Generative adversarial networks Feature extraction Radar polarimetry Noise measurement Spatial resolution Monitoring Synthetic aperture radar Aquaculture

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3dmnet: A 3d Multi-Attention Network for Pulmonary Nodule Detection in 3d Ct Scans

SSRN

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SSRN 2025年

作者： Jin, Haodong Zhang, Linsong Jian, Muwei Yu, Hui School of Control Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan China School of Computer Science & Engineering Linyi University Linyi China cSCAN University of Glasgow Glasgow United Kingdom

Artificial intelligence (AI)-assisted diagnosis has become an urgent and active research topic in modern healthcare. The automatic detection of pulmonary nodules through three-dimensional computed tomography (CT) not only significantly reduces the workload of primary care physicians but also aids in the early screening of lung cancer, providing patients with critical additional treatment time. However, existing methods primarily focus on single-slice nodule detection and fail to fully leverage the 3D information embedded within the CT volume, ultimately leading to suboptimal detection accuracy. To overcome this limitation, we propose 3DMNet, a 3D multi-attention convolutional neural network that effectively utilizes the 3D information embedded in multi-dimensional CT data. Specifically, to simulate the clinical diagnostic process of radiologists identifying nodule locations across consecutive CT slices, we develop an innovative 3D spatial attention mechanism. This mechanism enhances feature representations by applying projection transformations across multiple dimensions of the feature map, followed by rigorous attention calculations, thereby capturing intra- and inter-slice spatial information. Furthermore, by integrating data-driven global attention and channel attention modules, we achieve a more refined capture of global spatial dependencies and inter-channel relationships. Extensive comparative analysis on widely used datasets demonstrates that the proposed framework outperforms mainstream methods. © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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Enriching Physical-Virtual Interaction in AR Gaming by Tracking Identical Real Objects

arXiv

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arXiv 2025年

作者： Yu, Liuchuan Huang, Ching-I Wang, Hsueh-Cheng Yu, Lap-Fai Computer Science Department George Mason University FairfaxVA United States Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Electrical and Computer Engineering Department and Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taiwan Faculty of Computer Science Department George Mason University FairfaxVA United States

Augmented reality (AR) games, particularly those designed for headsets, have become increasingly prevalent with advancements in both hardware and software. However, the majority of AR games still rely on pre-scanned or static scenes, and interaction mechanisms are often limited to controllers or hand-tracking. Additionally, the presence of identical objects in AR games poses challenges for conventional object tracking techniques, which often struggle to differentiate between identical objects or necessitate the installation of fixed cameras for global object movement tracking. In response to these limitations, we present a novel approach to address the tracking of identical objects in an AR scene to enrich physical-virtual interaction. Our method leverages partial scene observations captured by an AR headset, utilizing the perspective and spatial data provided by this technology. Object identities within the scene are determined through the solution of a label assignment problem using integer programming. To enhance computational efficiency, we incorporate a Voronoi diagram-based pruning method into our approach. Our implementation of this approach in a farm-to-table AR game demonstrates its satisfactory performance and robustness. Furthermore, we showcase the versatility and practicality of our method through applications in AR storytelling and a simulated gaming robot. Our video demo is available at: https://***/ rPGkLYuKvCQ. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational efficiency

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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