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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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STMDNet: A Lightweight Directional Framework for Motion Pattern Recognition of Tiny Targets

arXiv

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arXiv 2025年

作者： Xu, Mingshuo Luan, Hao Hao, Zhou Daniel Peng, Jigen Yue, Shigang The School of Mathematics and Computing Science University of Leicester LeicesterLE1 7RH United Kingdom The Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tuanjin 300222 China The Machine Life and Intelligence Research Center Guangzhou University Guangzhou510006 China

Recognizing motions of tiny targets—only few dozen pixels—in cluttered backgrounds remains a fundamental challenge when standard feature-based or deep learning methods fail under scarce visual cues. We propose STMDNet, a model-based computational framework to Recognize motions of tiny targets at variable velocities under low-sampling frequency scenarios. STMDNet designs a novel dual-dynamics-and-correlation mechanism, harnessing ipsilateral excitation to integrate target cues and leakage-enhancing-type contralateral inhibition to suppress large-object and background motion interference. Moreover, we develop the first collaborative directional encoding-decoding strategy that determines the motion direction from only one correlation per spatial location, cutting computational costs to one-eighth of prior methods. Further, simply substituting the backbone of a strong STMD model with STMDNet raises AUC by 24%, yielding an enhanced STMDNet-F. Evaluations on real-world low-sampling frequency datasets show state-of-the-art results, surpassing the deep learning baseline. Across diverse speeds, STMDNet-F improves mF1 by 19%, 16%, and 8% at 240Hz, 120Hz, and 60Hz, respectively, while STMDNet achieves 87 FPS on a single CPU thread. These advances highlight STMDNet as a next-generation backbone for tiny target motion pattern recognition and underscore its broader potential to revitalize model-based visual approaches in motion detection. Code is available at https://***/MingshuoXu/STMDNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal encoding

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Multiple feature sample classification via joint sparse representation

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Journal of Computational Information systems 2015年第14期11卷 5125-5133页

作者： Zha, Changjun Xu, Tailong Key Laboratory of Machine Vision and Intelligence Control Technology Hefei University Hefei China

Based on the characteristics of wireless monitoring networks and profiling sensors, we present a method of acquiring the profile of moving objects. First, principal component analysis is used to extract profile sample features, and then a sink node collects all of the nodes' sample features to give a signal fusion model. This model enables a novel algorithm to be proposed that identifies test signals containing multiple sample features. Using profile feature information, the algorithm constructs a dictionary from which test samples can be sparsely represented. Then, according to the error in the sparse reconstruction, the algorithm identifies the class of profile samples acquired by the monitoring node. Unlike traditional classification algorithms, which rely on single feature samples, our approach can identify test samples consisting of multiple sample features. A numerical simulation and experimental results demonstrate that the proposed algorithm is superior to traditional methods. ©, 2015, Journal of Computational Information systems. All right reserved.

关键词： Classification (of information)

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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Tbd-Y: Automatic Tea Bud Detection with Synergistic Object-Spatial Attention and Global-Local Attention Guided Feature Fusion

SSRN

引用

SSRN 2025年

作者： Liu, Zhongyuan Zhuo, Li Dong, Chunwang Li, Jiafeng Li, Yang Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Tea Research Institute Shandong Academy of Agricultural Sciences Jinan250100 China Key Laboratory of Tea Quality and Safety Control Ministry of Agriculture and Rural Affairs Tea Research Institute Chinese Academy of Agricultural Sciences Hangzhou China Shandong Academy of Agricultural Sciences China

Automatic Tea Bud Detection (TBD) is a critical technology in intelligent tea-picking systems. Nevertheless, challenges, such as complex environments and the high visual similarity between tea buds and backgrounds, frequently result in false detection and missed detection, especially for small tea buds. To address these issues, in this paper, an automatic TBD method is proposed, which is built upon the YOLOv11 object detection framework, named TBD-Y. Firstly, a Synergistic Object-Spatial Attention (SOSA) mechanism is proposed, which incorporates the proposed Local Context Attention (LCA) mechanism o enhance the features in both spatial and regional dimensions. It enables the network to focus more on the tea bud regions, and suppress the interference from background noise. Secondly, a Global-local Attention Guided Feature Fusion (GAGFF) strategy is designed. It consists of two branches: one branch enhances low-resolution, high-level features containing rich global semantic information, while the other branch strengthens high-resolution features that preserve low-level visual details. The fusion of these two branches improves the representation capability of the features. The SOSA and GAGFF are integrated into the YOLOv11 framework, constructing three variants of the TBD model with different parameter scales, named TBD-Y-L, TBD-Y-M, and TBD-Y-S. Experimental results on the self-built TBD dataset and the publicly available Global Wheat Head Dataset 2021 (GWHD_2021) demonstrate that the proposed TBD-Y-L outperforms the existing methods, achieving a superior detection accuracy. Furthermore, the TBD-Y-S model exhibits improved detection accuracy compared to YOLOv11-L, while maintaining lower model parameters and computational complexity. © 2025, The Authors. All rights reserved.

关键词： Object detection

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Symplectic Wigner Distribution in the Linear Canonical Transform Domain: Theory and Application

arXiv

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arXiv 2025年

作者： He, Yangfan Zhang, Zhichao School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing Institute of Technology Nanjing211167 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science Application of Hainan Province Hainan Normal University Haikou571158 China Key Laboratory of Numerical Simulation of Sichuan Provincial Universities School of Mathematics and Information Sciences Neijiang Normal University Neijiang641000 China

This paper devotes to combine the chirp basis function transformation and symplectic coordinates transformation to yield a novel Wigner distribution (WD) associated with the linear canonical transform (LCT), named as the symplectic WD in the LCT domain (SWDL). It incorporates the merits of the symplectic WD (SWD) and the WD in the LCT domain (WDL), achieving stronger capability in the linear frequency-modulated (LFM) signal frequency rate feature extraction while maintaining the same level of computational complexity. Some essential properties of the SWDL are derived, including marginal distributions, energy conservations, unique reconstruction, Moyal formula, complex conjugate symmetry, time reversal symmetry, scaling property, time translation property, frequency modulation property, and time translation and frequency modulation property. Heisenberg’s uncertainty principles of the SWDL are formulated, giving rise to three kinds of lower bounds attainable respectively by Gaussian enveloped complex exponential signal, Gaussian signal and Gaussian enveloped chirp signal. The optimal symplectic matrices corresponding to the highest time-frequency resolution are generated by solving the lower bound optimization (minimization) problem. The time-frequency resolution of the SWDL is compared with those of the SWD and WDL to demonstrate its superiority in LFM signals time-frequency energy concentration. A synthesis example is also carried out to verify the feasibility and reliability of the theoretical analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Wigner-Ville distribution

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Complete Gait Phases Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

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IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Hangzhou China Zhejiang University of Technology College of Mechanical Engineering Hangzhou China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Fuyang District Hangzhou311400 China Chinese Academy of Sciences Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk-stand cycle as well as continuous daily walking were recognized based on muscle synergy and PSO-CNN-LSTM model. Firstly, the complete stand-walk-stand walking cycle were divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Secondly, a muscle weight matrix and an activation sequence matrix were calculated by using non-negative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk-stand cycle by applying above mentioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

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Parameter-level Mask-based Adapter for 3D Continual Learning with Vision-Language Model

Parameter-level Mask-based Adapter for 3D Continual Learning...

引用

2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Qian, Longyue Zhang, Mingxin Zhang, Qian Zhang, Lin Li, Teng Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250100 China

ISBN: (纸本)9781665481090

As 3D object classification becomes increasingly essential in various applications, the need for models to continuously adapt to new scenarios is more crucial than ever. However, existing continual learning methods primarily concentrate on 2D tasks, making their direct application to 3D tasks challenging due to the inherent domain gap. To address this issue, we propose Parameter-level Mask-based Adapter (PMA) for 3D continual learning with Vision-Language Models. Our approach leverages the power of Contrastive Language-Image Pre-Training (CLIP) to extract rich multimodal features by projecting 3D point clouds into multi-angle depth maps, which are then processed by the 2D encoder aligned with rendered images. The core of our method is a parameter-level adapter with learnable masks that effectively integrates 3D point cloud features with 2D features. This mechanism flexibly selects the most relevant subsets of weights for each task, allowing subsequent tasks to utilize the learned weights without the need for updates, which effectively balances stability and plasticity during continual learning. Experimental results show that our method significantly outperforms existing state-of-the-art techniques. © 2024 IEEE.

关键词： Contrastive Learning

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