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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Improved Particle swarm optimization Least Squares Support Vector Machine method for train wireless time delay prediction

Improved Particle swarm optimization Least Squares Support V...

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2023 International Conference on Smart Transportation and City engineering, STCE 2023

作者： Xu, Huigang Zhang, Liyan Li, Changxian School of Automation and Electrical Engineering Dalian Jiaotong University Dalian116028 China School of Computer and Communication Engineering Dalian Jiaotong University Dalian116028 China

ISBN: (纸本)9781510673540

In the process of high-speed movement of multiple unit trains, the train wireless communication delay has a very important impact on driving safety. If the delay is too long, the train will not be able to control, traction and brake normally. Therefore, a wireless delay prediction model based on Singular Spectrum Analysis (SSA)-Quantum Particle Swarm Optimization (QPSO) to optimize Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, to lower the complexity of the sequence, the measured time series is broken down into components corresponding to different eigenvalues after singular value processing. During the decomposition process, the window length is selected using the Cao method. Secondly, each sub sequence is trained by QPSO-LSSVM model to determine the optimal parameters of LSSVM. Finally, each predicted subsequence is superimposed to get the final predicted results. The simulation results show that the proposed method has higher prediction accuracy and minimum prediction error compared to SSA-PSO-LSSVM, EMD-QPSO-LSSVM, and QPSO-LSSSVM methods. © 2024 SPIE.

关键词： Timing circuits

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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

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IEEE Internet of Things Journal 2025年第13期12卷 25240-25253页

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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Dense Small Object Tracking via DeepSort Integrated with YOLOv.X-based Detection 13

Dense Small Object Tracking via DeepSort Integrated with YOL...

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13th International Conference on control, automation and Information Sciences, ICCAIS 2024

作者： Zhang, Mengfan Liu, Weifeng Xia, Xingyu School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China School of Automation Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798331542047

In order to address the problem of incomplete feature extraction, missed tracking, and incorrect tracking in dense small object tracking tasks, This paper proposes a DeepSort method based on YOLOv.X for input detection. This method uses YOLOv5 as the detection input for DeepSort to obtain more complete detection box information for dense small objects;Then, the feature extraction and fusion parts in YOLOv5 are reused to replace the appearance feature extraction network in DeepSort, improving the algorithm's performance in extracting features from dense small objects;On this basis, the CIoU algorithm is used as a measure of the correlation between unmatched detection boxes and unmatched prediction boxes during the tracking process to avoid unnecessary trajectory loss. The experimental results show that compared with the original DeepSort tracking algorithm, the improved algorithm improved the MOTA index by 3.6%, the MOTP index by 2.9%, and reduced the error ID conversion index by 269 times. © 2024 IEEE.

关键词： Object tracking

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A discrete-time looped functional approach and its application to discrete-time systems with cyclically varying delays

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Science China(Information Sciences) 2025年

作者： Wen-Hu CHEN Chuan-Ke ZHANG Chen-Rui WANG Ke-You XIE Yong HE Hong-Bing ZENG School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Electrical and Information Engineering Hunan University of Technology

This paper investigates the stability analysis problem of discrete-time systems with cyclically varying *** previous studies, the delay investigated in this paper cyclically alternates between increasing and decreasing changes within its upper and lower bounds. First, to reasonably describe the cyclic variation characteristics of the delay, a discretetime looped functional(DTLF) approach and a refined allowable delay set(ADS) are proposed. Then, based on the constructed DTLF with delay-product terms and using matrix-separation-based inequality to estimate its forward difference,delay-variation-interval-dependent stability conditions considering two different ADSs are derived. Finally, the effectiveness and superiority of the obtained conditions are demonstrated through two numerical examples.

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MobileNet-YOLOv2 Mask Wearing Detection Method Fused With Attention Mechanism 2

MobileNet-YOLOv2 Mask Wearing Detection Method Fused With At...

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2nd International Conference on Intelligent Perception and computer Vision, CIPCV 2024

作者： Lin, Qianmin Li, Jie Lu, Changjia Chen, Haojie Zhou, Chengxian Xiamen University of Technology School of Electrical Engineering and Automation Xiamen China Xiamen University of Technology School of Engineering and Automation Xiamen China Xiamen University of Technology School of Electrical Engineering and Automatio China Electric Power Equipment and Intelligent Control Xiamen Key Laboratory of Frontier Xiamen China

ISBN: (纸本)9798350360097

In view of the problem of slow detection speed and low accuracy in manual detection and existing mask wearing detection algorithms in special public places, this paper proposes a MobileNet-YOLOv2 mask wearing detection method fused with attention mechanism. We have adjusted the backbone network structure of the original YOLOv2 algorithm, replacing the backbone network Darknet-19 with MobileNet, and using deep separable convolution for feature extraction, greatly reducing the number of model parameters and computational complexity, and improving detection speed. Introducing the convolutional attention mechanism module CBAM enables the feature extraction network to focus more on key image regions and improve detection accuracy. Collect and establish 850 facial mask wearing images to train and test the proposed algorithm model. The experimental result shows that the average detection accuracy of wearing a mask reaches 90%, while the detection accuracy of not wearing a mask reaches 80%. Compared to the DarknetYOLOv2 and SSD algorithms, the detection speed has been improved by 6.3 and 6.5 times, and the model size has been reduced by 77.5%, 89.1%, and 89.2%, respectively, compared to Darknet-YOLOv2, R-CNN, and Faster-RCNN. © 2024 IEEE.

关键词： Feature extraction

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Doppler Measurement-Based Positioning and Tracking Methods with External Radiation Radar 2

Doppler Measurement-Based Positioning and Tracking Methods w...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liu, Weifeng He, Linqing Tian, Zhengwang Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi'an China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

This paper introduces a new approach for addressing the complex problem of Multi-target tracking in external radiation radar systems equipped with Multi-static Doppler measurements. Drawing from research on sensor arrays and mobile sensors, the method utilizes the Generalized Labelled Multi-Bernoulli (GLMB) filter to navigate the intricacies of Multi-target tracking across sensor arrays. The study validates the effectiveness of the GLMB and the computationally efficient Gibbs-GLMB filters in a setup involving three transmitters and one receiver. Experimental results, assessed with the Optimal Sub-Pattern Assignment (OSPA) metric, suggest that the Gibbs-GLMB filter accelerates computation while potentially compromising on measurement precision, which could lead to reduced accuracy in specific cases. The investigation also examines the influence of sensor geometry on tracking results, indicating that there is an association between tracking performance and sensor arrangement. © 2024 IEEE.

关键词： Radar target recognition

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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SE-CNN Attention Structure for Quantitative EEG-Based Assessment of VR Motion Sickness 23

SE-CNN Attention Structure for Quantitative EEG-Based Assess...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Xu, Mengdie Yang, Banghua Liu, Mutian Huang, Yiling Xia, Xinxing Zhang, Jie Shanghai University School of Mechanical and Electrical Engineering and Automation Research Center of Brain-Computer Engineering Shanghai China

ISBN: (纸本)9781450399029

The applications of virtual reality (VR) technology are currently numerous and promising, but motion sickness (MS) problems are affecting the development of the VR market. Questionnaires are commonly used to subjectively assess motion sickness, but they are applied before and after the user experiences VR and cannot assess the user’s motion sickness in real time. In this work, this paper proposes a convolutional neural network (CNN) structure incorporating squeeze and stimulus (SE) attention mechanisms, with subjective questionnaire scores used as markers of electroencephalographic signal (EEG) for real-time prediction of VR motion sickness scores. In this thesis, EEG signals were collected from 16 subjects in a virtual reality environment and the experimental data were fed into the network structure to train the model. The experimental results showed that the root mean square error (RMSE) was 25.69, the Pearson linear correlation coefficient (PLCC) was greater than 0.85, and the Spearman rank correlation coefficient (SROCC) was also greater than 0.85, indicating a significant relationship between the prediction and subjective assessment scores (p © 2023 Copyright held by the owner/author(s).

关键词： Virtual reality

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Hybrid Water Quality Prediction With Multimodal Low-Rank Fusion and Localized Attention

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IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, Mengchu Beijing University of Technology College of Computer Science Beijing Laboratory of Smart Environmental Protection Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short- or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality’s nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates convNeXt V2, multimodal bottleneck transformer, low-rank multimodal fusion, iTransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2014 IEEE.

关键词： Rain

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