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Vector Field-Guided Circumnavigation for an Unmanned Surface Vehicle With Boundary and Obstacle Constraints

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Wu, Nailong Wang, Jigang Wang, Yueying He, Shuping Donghua University College of Information Science and Technology Shanghai201620 China Donghua University Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai201620 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

This article aims to tackle the challenge of rapid target circumnavigation by unmanned surface vehicles (USVs) under boundary and obstacle constraints through leveraging an emerging computing architecture specifically tailored for unmanned systems. Owing to the underactuation of USV dynamic systems and the presence of various time-varying surface disturbances, attaining rapid and accurate position control along with maneuverability poses a significant challenge to USVs. Therefore, when an USV maneuvers around a target in a confined search area with obstacles, it is essential for the guidance law to consider collision risks and environmental disturbances. This paper proposes a modified Lyapunov vector field with heading deviation compensation to circle the target in complex search regions. The vector field is designed to increase the radial velocity of the USV and accelerate convergence towards the target circle during the pursuit phase. By combining the proposed Lyapunov vector field with a boundary vector field, the USV can swiftly avoid boundaries and dynamic obstacles and then continue circling the target. The proposed computing architecture extends the application scope of the target circumnavigation to practical water surface environments, which is validated by extensive simulations and rigorous experiments. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection

Hierarchical Supervision and Shuffle Data Augmentation for 3...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chuandong Liu Chenqiang Gao Fangcen Liu Pengcheng Li Deyu Meng Xinbo Gao School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Signal and Information Processing Chongqing China Xi'an Jiaotong University Xi'an China Macau University of Science and Technology Taipa Macau

State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudo-labeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSDA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://***/azhuantou/HSSDA.

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signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Signal structure information-based data association for mane...

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IEEE International Conference on radar

作者： Chang Gao Hongwei Liu Pramod K. Varshney Junkun Yan Penghui Wang National Laboratory of Radar Signal Processing Xidian University Xi'an China Department of Electrical Engineering and Computer Science Syracuse University NY USA

ISBN: (纸本)9781665468893

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.

关键词： Correlation Convolution Simulation radar Euclidean distance Transforms radar tracking

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Detecting Deepfake Video by Learning Two-Level Features with Two-Stream Convolutional Neural Network 20

Detecting Deepfake Video by Learning Two-Level Features with...

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6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

作者： Zhao, Zheng Wang, Penghui Lu, Wei National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9781450377089

Deepfake techniques has made face swapping in video easy to use. Nowadays, the spread of Deepfake videos over networks is concerned worldwide. This work proposes an approach to more accurate and robust detection of them. Since artifacts left by Deepfake tools can be largely categorized into two classes of different levels, i.e. semantic and noise level, we adopt a two-stream convolutional neural network (CNN) to capture the 2-level features concurrently. Xception network is trained only as the first stream to detect semantic anomalies such as the editing artifacts around face contour, detail missing, and geometric inconsistence in eyes. Meanwhile, the 2nd stream, which contain the constrained convolution filter and median filter, is designed to capture the tampering traces in local noises. By concatenating the 2-level features learned from the both streams, our method obtains very comprehensive knowledge about the existence of face swapping. The experimental results have shown its advantage over the existing methods on both the accuracy and robustness. © 2020 ACM.

关键词： Convolutional neural networks

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Experimental Demonstration of a Hybrid Ofdma/Noma Scheme for Multi-User Underwater Wireless Optical Communication Systems

SSRN

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SSRN 2023年

作者： Chen, Danyang Zhang, Xue Fan, Kai Wang, Jianping Lu, Huimin Wang, Qingxuan Wu, Shuai Hao, Rui Li, Zhao Jin, Jianli School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of High-end Printing Equipment Signal and Information Processing Beijing Institute of Graphic Communication Beijing102600 China Shunde Innovation School of University of Science and Technology Beijing Foshan528399 China

In this work,a hybrid multiple access scheme for multi-user underwater wireless optical communication(UWOC) systems is proposed and presented. The scheme combines the respective advantages of orthogonal frequency-division multiple access(OFDMA) and non-orthogonal multiple access(NOMA) schemes to reduce the unfairness among users caused by the unique high-frequency fading characteristics of the underwater optical channel. Multi-verse optimizer(MVO) algorithm is further introduced for solving the nonlinear problem of user power allocation. Experimental results in different water quality environments demonstrate that the hybrid OFDMA/NOMA scheme with MVO effectively improves the communication performance of the established 4-user UWOC system compared to the pure NOMA scheme and the pure OFDMA scheme, and a system sum rate of 3.94 Gbit/s is achieved. © 2023, The Authors. All rights reserved.

关键词： Frequency division multiple access

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Yang Zhen Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computer Science and Technology Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost.

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Inter- and Intra-sentence Interaction Network for Multi-choice Reading Comprehension

Inter- and Intra-sentence Interaction Network for Multi-choi...

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Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), International Conference on

作者： Xiaobo Xu Turdi Tohti Askar Hamdulla Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang University Urumqi China

Multiple choice reading comprehension is a challenging task in natural language processing, which aims to select the correct answer from a set of candidate options when given a passage and a question. When modeling the relationship between passages and questions, previous approaches usually focus only on the features between passages and questions and ignore the changes in the information within the passages after the interaction between passages and questions. For example, grammatical features and phrase structure can change. In this work, we propose a Inter- and Intra-sentence Interaction Network, which first performs passage-question and passage-option inter-sentence interactions, focusing on the features among passage, question and option sentences. Then the passages obtained from the inter-sentence interactions are subjected to intra-sentence interactions through an attention mechanism, focusing on the information inside the passages. Finally, the vector representation of question-aware passage and the vector representation of option-aware passage are fused by the gating mechanism to dynamically select the vector of passages containing the answers to the questions. The experimental results show that excellent results are achieved on both RACE and MCTest datasets, confirming that intra-sentence interaction can effectively improve the model's performance.

关键词： Human computer interaction Solid modeling Focusing Virtual reality Natural language processing Task analysis Artificial intelligence

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DOA Estimation Method Based on Improved Variable-Step-Size LMS Algorithm

DOA Estimation Method Based on Improved Variable-Step-Size L...

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2021 International Symposium on Computer technology and Information science, ISCTIS 2021

作者： Qin, Yongfeng Li, Simin Tang, Zhiling School of Electrical Engineering and Automation Guilin University of Electronic Technology Guilin China Guangxi University of Science and Technology School of Mechanical and Transportation Engineering Liuzhou China Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin China

ISBN: (纸本)9781665414418

As an important branch of array signal processing, the beamforming algorithm can form gain in the direction of the desired signal and null in the direction of the interference signal, which can effectively suppress the interference of multipath signals. To address the problem that the classical spatial spectrum estimation algorithm does not have high resolution for the estimation of coherent sources, a DOA estimation method based on the Witch function and normalization of variable-step-size LMS algorithm is proposed in this paper. By defining the reciprocal of the array pattern as the spatial spectrum, the proposed method regards the DOA estimation as a peak search. The simulation results show that the proposed method can effectively suppress the interference of multipath signals. And it has similar performance with the classical spatial spectrum estimation method. Compared with other beamforming methods, the proposed method has convergence speed and better stead-state performance. © 2021 IEEE.

关键词： Direction of arrival

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A Time-domain strip-map processing scheme for FMCW imaging

A Time-domain strip-map processing scheme for FMCW imaging

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IEEE International Conference on radar

作者： Tao Zhang Guisheng Liao Yachao Li Tong Gu Tinghao Zhang Chilian Chen National Laboratory of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Technology on Datalink The 20th Research Institute of CETC Xi'an China

ISBN: (纸本)9781665468893

For strip-map frequency-modulated continuous wave synthetic aperture radar (FMCW-SAR) imaging, a time-domain processing scheme is proposed in this paper. For no loss of resolution, the angle-overlapped unified polar coordinate is established. Time-domain FMCW back-projection integration is carried out in sub-aperture(SA) process. And the SA wavenumber fusion and full-strip-map wavenumber fusion are successively performed to obtain whole strip-map image. Simulation and real SAR data mapping are used to demonstrate the effectiveness of proposed scheme.

关键词： Time-frequency analysis Image resolution Azimuth Imaging radar imaging Apertures Data processing

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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Efficient Robust Principal Component Analysis via Block Kryl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shun Fang Zhengqin Xu Shiqian Wu Shoulie Xie School of Information Science and Engineering Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Signal Processing RF & Optical Dept. Institute for Infocomm Research A*STAR Singapore

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.

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