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IEEE Networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

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A Planar Quad-Band Bandpass Filter Employing Transmission Lines Loaded with Tri-Stepped Impedance Open-and Dual-Stepped Impedance Short-Ended Resonators

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Progress In Electromagnetics Research C 2024年 147卷 65-72页

作者： Arif, Kamran Phani Kumar, Kanaparthi V. Barik, Rusan Kumar Chakaravarthi, Geetha Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli620015 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India School of Microelectronics Southern University of Science and Technology Guangdong Shenzhen518055 China

A highly miniaturized bandpass filter with quad-band response is demonstrated in this article. The proposed quad-band bandpass filter has a novel topology comprising series quarter wavelength transmission lines loaded with tri-stepped impedance open-ended resonators and a dual-stepped impedance short-ended resonator. The proposed quad-band bandpass filter configuration is validated by theoretically verifying the transmission zeros and pole frequencies using even-odd mode analysis. A prototype operating at 0.475 GHz, 1.695 GHz, 3.48 GHz, and 4.53 GHz is designed, implemented, and experimented. The tested insertion losses at these center frequencies are 0.38 dB, 0.71 dB, 1.03 dB, and 1.22 dB, and the return loss is better than 10 dB in each passband. Each passband is isolated by a transmission zero with a rejection better than 40 dB. The proposed quad-band filter occupies a compact size of 0.146 × 0.087λ2g and is distinguished by its high compactness, wide bandwidth, multiple transmission zeros and poles, and high performance compared to benchmark designs making it more suitable for multi-band wireless applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jing Xu, Yuanrong Dong, Suyu Wang, Wei Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Weihai China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350386226

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system's effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time. © 2024 IEEE.

关键词： Image enhancement

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Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020

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Research 2024年第2期2023卷 647-658页

作者： Chao Yang Qingquan Li Xuqing Wang Aihong Cui Junyi Chen Huizeng Liu Wei Ma Xuanyan Dong Tiezhu Shi Fanyi Meng Xiaohu Yan Kai Ding Guofeng Wu MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area&Guangdong Key Laboratory of Urban Informatics&Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Geography Hong Kong Baptist UniversityHong Kong Special Administrative Region 999077China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China institute for Advanced Study and Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China School of Civil Engineering Chongqing Jjiaotong UniversityChongging 400074China Department of Cil and Environmental Engineering Tohoku UniversitySendai 980-8579Japan School of Artificial Intelligence Shenzhen PolytechnicShenzhen 518055China School of Computer Science and Technology Dongguan University of TechnologyDongguan 523419China

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas(PAs)and threatened *** the 21st century,both the highlands and lowlands of Asia have been experiencing the dramatic human ***,the threat degree of human expansion to biodiversity is poorly ***,the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental(Asia),national(48 Asian countries),and hotspot(6,502 Asian terrestrial PAs established before 2000)scales is investigated by integrating multiple large-scale *** results show that human expansion poses widespread threat to biodiversity in Asia,especially in Southeast Asia,with Malaysia,Cambodia,and Vietnam having the largest threat degrees(~1.5 to 1.7 times of the Asian average level).Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries(most Southeast Asian countries).The regions with threats to biodiversity are present in~75%terrestrial PAs(including 4,866 PAs in 26 countries),and human expansion in PAs triggers higher threat degrees to biodiversity than that in *** findings provide novel insight for the Sustainable Development Goal 15(SDG-15 Life on Land)and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of *** reduce the threat degree,Asian developing countries should accelerate economic transformation,and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries(i.e.,trade leading to the loss of wildlife habitats)to alleviate human expansion,especially in PAs and highlands.

关键词： Human terrestrial integrating

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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Local Weighted Neighborhood Rough Sets for Weakly Labeled Data

Local Weighted Neighborhood Rough Sets for Weakly Labeled Da...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Guo, Yanting Hu, Meng Liu, Qiang Tsang, Eric C.C. Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Innovation Engineering China

ISBN: (纸本)9798350303780

Local rough set models are efficient rough data analysis methods for weakly labeled data in the rough set field. However, the existing local models do not distinguish the importance of different attributes to decisions when describing and defining binary relations, resulting in inaccurate information granules or classes, which affects the accuracy of concept approximation. In this paper, we choose local neighborhood rough sets as the representative model, and then propose Local Weighted Neighborhood Rough Sets (LWNRS). First, the importance of attributes to decisions is quantified by combining the supervised information of labeled data and the structural information of unlabeled data, and an attribute weighted method based on semi-supervised dynamic optimization is proposed. Meanwhile, the weighted neighborhood relation and neighborhood classes of objects are defined under weakly labeled data, and the WLNRS model is constructed. In addition, the weighted dependency is proposed to measure the data uncertainty, and the relationships among WLNRS and other relevant local and global models are deeply explored. Finally, the efficiency and accuracy of WLNRS in concept approximation are verified by comparison. © 2023 IEEE.

关键词： Rough set theory

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Exponential Synchronization for Mobile Robotic Formation: A Hybrid Dynamic Event-Triggered Intermittent control Method

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Chang, Zhenyu Guo, Ying Zong, Guangdeng Qi, Wenhai Zhao, Xudong School of Control Science and Engineering Tiangong University Tianjin China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering Qufu Normal University Rizhao China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

To address the formation tracking issue of mobile robotic systems, this paper constructs a novel hybrid dynamic event-triggered intermittent control strategy, which can achieve the exponential synchronization of the systems. Considering the limited control resources, the intermittent control method is introduced into the distributed control strategy to save resources, and the existing intermittent control model is reconstructed to describe the system model better. A hybrid dynamic event-triggered mechanism is developed by combining the dynamic event-triggered method with the time sampling strategy, eliminating the Zeno phenomenon. The control time sequences in the developed intermittent control strategy are automatically selected by the hybrid dynamic event-triggered sequences, rather than artificially designed in advance, which reduces a certain degree of design complexity. The developed control strategy effectively saves control resources while alleviating the burden of network communication. Sufficient conditions for achieving exponential synchronization formation tracking are provided, and the exponential convergence of the formation error is demonstrated through the proposed lemma. Finally, a control task of multi-mobile robots formation is presented to verify the effectiveness of the theoretical analysis. IEEE

关键词： Synchronization

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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${\sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

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IEEE Transactions on Mobile Computing 2024年第3期24卷 1496-1512页

作者： Yongpan Zou Jianhao Weng Wenting Kuang Yang Jiao Victor C. M. Leung Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Information Hub Hong Kong University of Science and Technology Guangzhou China

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose ${\sf Img2Acoustic}$ , a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement ${\sf Img2Acoustic}$ on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.

关键词： Training Gesture recognition Acoustics Data models Sensors Feature extraction Target recognition Mobile computing Image recognition Few shot learning

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