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Robust hand gesture detection and recognition using 4D millimeter-wave radar in a ubiquitous scene

Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Cai, Jiayi Yang, Zhaocheng Chu, Ping Guo, Juntao Zhou, Jianhua State Key Laboratory of Radio Frequency Heterogeneous Integration College of Electronics and Information Engineering Shenzhen University Nanhai Road 3688 Shenzhen Guangdong518060 China

In current research on HGR using radar sensors, hand gestures are typically confined to a smaller region. However, in ubiquitous scenarios, unrestricted human body movements and unexpected hand gesture motions usually occur, which results in a large false alarms and recognition performance degradation. To address this issue, we propose a robust hand gesture detection and recognition method in ubiquitous scenarios using Frequency-Modulated Continuous Wave (FMCW) Multiple-Input Multiple-Output (MIMO) radar. The core idea is to progressively define and classify motions in a cascaded manner, gradually filtering out non-specific movements, reducing false positives, and enhancing the applicability of HGR. Specifically, we first propose a suspected hand gesture motion detection method to help identify suspicious hand gestures. Then, the velocity and position features of the mutated signal and the stable signal are extracted. A mutated signal motion recognition method based on a single-layer long short-term memory (LSTM) network is used to effectively distinguish non-hand gesture motions from hand gestures. Finally, the two-dimensional trajectory features are extracted, and cascaded with a LSTM network combined a Gaussian probability model is developed to enhance the ability of open-set recognition. Experimental results show that the proposed method can achieve the recognition accuracy of 99.53% for designed hand gestures, the false alarm rate of 1.5% for unexpected hand gestures and 0.11% for non-hand gesture motions. © 2025 Elsevier Ltd

关键词： Gesture recognition

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Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction

Boundary-Driven Table-Filling with Cross-Granularity Contras...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Qingling Wen, Wushao Qin, Jinghui Sun Yat-Sen University China Guangdong University of Technology China Guangdong Shuye Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798350368741

The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling process in an end-to-end manner, focusing primarily on word-level interactions while often overlooking sentence-level representations. This limitation hampers the model's ability to capture global contextual information, particularly when dealing with multi-word aspect and opinion terms in complex sentences. To address these issues, we propose boundary-driven table-filling with cross-granularity contrastive learning (BTF-CCL) to enhance the semantic consistency between sentence-level representations and word-level representations. By constructing positive and negative sample pairs, the model is forced to learn the associations at both the sentence level and the word level. Additionally, a multi-scale, multi-granularity convolutional method is proposed to capture rich semantic information better. Our approach can capture sentence-level contextual information more effectively while maintaining sensitivity to local details. Experimental results show that the proposed method achieves state-of-the-art performance on public benchmarks according to the F1 score. © 2025 IEEE.

关键词： ASTE CNN cross-granularity contrastive learning fine-grained sentiment analysis table-filling

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP International Federation for Information Processing 2025.

关键词： Convex optimization

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Sentiment and User Churn Analysis for Online Platform Business using Yelp Studies 24

Sentiment and User Churn Analysis for Online Platform Busine...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Peng, Sijia Chen, Jie Chen, Cong Dai, Ming School of Ocean Information Engineering Hainan Tropical Ocean University Hainan Sanya China Department of Mathematics Hainan Tropical Ocean University Hainan Sanya China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Guangdong Shenzhen China

ISBN: (纸本)9798400718120

In the digital era, online platforms are increasingly subjected to scrutiny with regard to user churn, which has a profound influence on their revenue, market share, and brand reputation. This paper examines restaurant user review data on Yelp and explores the relationship between users’ review behaviour, different sentiment categories, and user *** paper presents a user stratification analysis and an engagement analysis to identify patterns among different user groups. The TextBlob tool was employed for sentiment analysis, with reviews categorised into five sentiment categories: highly positive, moderately positive, neutral, moderately negative, and highly negative. Subsequently, a method was established to calculate churn rates and a Chi-square test was conducted to explore the correlation between sentiment categories and user churn. Finally, a correlation analysis was conducted to examine the linear relationships between review characteristics and churn, thereby elucidating the determinants of user churn *** study reveals that elite users and the frequency of comments influence user engagement and churn. The Chi-square test results indicate a significant correlation between different user review sentiment categories and churn rates. Platforms can reduce user churn and retain elite users by organising contests, rewarding active users or introducing community-building features. These actions can lay a solid foundation for future online business success. © 2024 Copyright held by the owner/author(s).

关键词： Churn Rate

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On Efficient Single-Source Personalized PageRank Computation in Online Social Networks

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3598-3612页

作者： Wei, Victor Junqiu Jiang, Di Zhang, Jason Chen Macau University of Science and Technology Taipa999078 China WeBank Company Ltd. Ai Group Shenzhen518054 China Hong Kong Polytechnic University Hung Hom Hong Kong

The Single-Source Personalized PageRank (SSPPR) problem is widely used in information retrieval and recommendation systems. Traditional algorithms assume full knowledge of the network, making them inapplicable to online social networks (OSNs), where the topology is unknown, and users can only explore the network step by step via APIs. The only feasible approach for SSPPR in OSNs is Monte Carlo (MC) simulation, but traditional MC methods rely on static sampling, which lacks flexibility, delays feedback, and overestimates the number of required random walks. To address these limitations, we propose PANDA (Single-Source Personalized PageRank on OSNs with Rademacher Average), a progressive sampling algorithm. PANDA iteratively samples random walks in batches, estimating accuracy dynamically using Rademacher Average from statistical learning theory. This data-dependent approach allows for early termination once the desired accuracy is met. Additionally, PANDA features a dynamic sampling schedule to optimize efficiency. Empirical studies show that PANDA significantly outperforms existing methods, achieving the same accuracy with far greater efficiency. © 2025 IEEE.

关键词： Recommender systems

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The DRTS-Net Framework: Enhanced Probabilistic Health Perception for Wind Turbine Bearings under Complex Operating Conditions With Improved Interpretability

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yang, Tongguang Li, Guanchen Huo, Jinzhao Li, Xiaoyong Zhou, Xianwen Han, Qingkai Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Johns Hopkins University Department of Mechanical Engineering BaltimoreMD21218 United States Foshan University School of Mechatronic Engineering and Automation Foshan528225 China SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety MOE Key Laboratory of Theoretical Chemistry of Environment South China Normal University School of Environment Guangzhou510006 China SCNU Qingyuan Institute of Science and Technology Innovation Company Ltd. Qingyuan511517 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang110819 China

Health perception in mechanical systems is a crucial aspect of prognostics and health management (PHM). Deep learning models have been successfully applied to health perception in wind turbine bearings. However, due to the time-varying, redundant, and noisy characteristics of time-series data representing bearing wear, existing models face significant challenges, particularly in terms of generalization and interpretability. Moreover, although most current models have provided health perceptions, they overlook the strong stochastic nature of bearing degradation data, and thus fail to deliver reliable predictions of uncertainty information. To address these technical challenges, this study introduces a novel Deep residual temporal-spatial framework (DRTS-Net) for probabilistic health perception of wind turbine bearing wear states. This article creatively designs spatiotemporal information fusion module for extracting features from bearing degradation data. Inspired by improvements in residual network structures, the DRTS-Net integrates an attention mechanism and a soft thresholding mechanism from the deep residual shrinkage network to optimize the feature extraction process. This integration aims to mitigate the interference from minor redundant features in the degradation data. The framework incorporates Gaussian process regression in the output layer to yield the probability distribution of single-health perception values for bearing features. A comparative study was conducted using the same bearing health state dataset against current mainstream deep learning methods for bearing health assessment. Results demonstrate that the proposed DRTS-Net achieves superior performance in high-precision health perception, high-reliability probabilistic health perception. © 1963-2012 IEEE.

关键词： Stochastic systems

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Listwise ranking predictor for evolutionary neural architecture search

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Swarm and Evolutionary Computation 2025年 96卷

作者： Li, Nan Ma, Lianbo Wang, Rui Cheng, Shi Sun, Yanan Xue, Bing Zhang, Mengjie College of Software Northeastern University Liaoning Shenyang110819 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington 6140 New Zealand Foshan Graduate School of Innovation Guangdong Foshan528000 China College of System Engineering National University of Defense Technology Hunan Changsha410073 China School of Computer Science Shaanxi Normal University Shaanxi Xian710119 China School of Computer Science Sichuan University Sichuan Chengdu610000 China

In evolutionary neural architecture search (ENAS), the accuracy predictors (i.e., regression models) have been successfully applied to save computational costs for the evaluation of network architectures. However, the accuracy of these predictors is largely limited by the small amount of evaluated architectures that may be difficult to obtain. Such accuracy predictors with prediction bias often lead to an inaccurate ranking, misleading the selection of ENAS. To alleviate the above limitations, we design an efficient and novel listwise ranking predictor (LRP) for ENAS to directly predict the ranking of each architecture instead of the numerical accuracy value of each architecture. Specifically, the training data is constructed by the proposed random encoding-combination (REC) strategy, which can generate substantial training data using the small number of evaluated architectures (data level). These specially constructed training data are used to train LRP, which can convert the complex regression task into a ranking task to reduce ranking bias (model level). The proposed NAS method is compared with state-of-the-art NAS methods on widely-used benchmark datasets and practical application. Experimental results demonstrate that LRP can alleviate the ranking disorder problem and outperform others in terms of both effectiveness and efficiency. © 2025 Elsevier B.V.

关键词： Linear regression

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research on water quality detection integrating spectral analysis and automated control

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Spectrochimica Acta - part A: Molecular and Biomolecular Spectroscopy 2025年 339卷 126260页

作者： Huang, Xiaoman Xiong, Juntao Lin, Huaiyin Pan, Zijian Wang, Kailin Zhang, Mingyue Zhu, Zeyu Ou, Yanggang College of Mathematics and Informatics South China Agricultural University CO Guangdong510642 China College of Electronic Engineering South China Agricultural University CO Guangdong510642 China College of Engineering South China Agricultural University CO Guangdong510642 China College of Marine Sciences South China Agricultural University CO Guangdong510642 China

Water quality conditions have a significant impact on the aquaculture environment, however, high cost, low efficiency, and lack of portable testing equipment limit the field application of water quality testing. In this paper, a highly integrated, cost-effective, and efficient water quality testing device is designed and fabricated based on spectral analysis and automated control technology. The device maintains high stability under complex environments and long-time operating conditions, and realizes simultaneous multi-parameter detection and automation of the detection process through IoT control and embedded design, thus facilitating timely monitoring and intervention in the fisheries environment. Using low concentrations of ammonia nitrogen and nitrite as research objects, the data collected by a multi-channel spectral sensor was used to classify the water health level, and the experimental results showed that the accuracy of the device's detection was as high as 96.8 percent, while the manufacturing cost was only $60. Overall, this interdisciplinary approach combining spectral analysis, device development, and machine learning provides a promising solution for efficient water quality monitoring and management in aquaculture. © 2025 Elsevier B.V.

关键词： Ammonia

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LipGen: Viseme-Guided Lip Video Generation for Enhancing Visual Speech Recognition

LipGen: Viseme-Guided Lip Video Generation for Enhancing Vis...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Bowen Zhou, Dongliang Li, Xiaojie Zhang, Xingyu Xie, Liang Wu, Jianlong Yin, Erwei Harbin Institute of Technology Shenzhen China National Institute of Defense Technology Innovation Academy of Military Sciences Beijing China Tianjin China

ISBN: (纸本)9798350368741

Visual speech recognition (VSR), commonly known as lip reading, has garnered significant attention due to its wide-ranging practical applications. The advent of deep learning techniques and advancements in hardware capabilities have significantly enhanced the performance of lip reading models. Despite these advancements, existing datasets predominantly feature stable video recordings with limited variability in lip movements. This limitation results in models that are highly sensitive to variations encountered in real-world scenarios. To address this issue, we propose a novel framework, LipGen, which aims to improve model robustness by leveraging speech-driven synthetic visual data, thereby mitigating the constraints of current datasets. Additionally, we introduce an auxiliary task that incorporates viseme classification alongside attention mechanisms. This approach facilitates the efficient integration of temporal information, directing the model's focus toward the relevant segments of speech, thereby enhancing discriminative capabilities. Our method demonstrates superior performance compared to the current state-of-the-art on the lip reading in the wild (LRW) dataset and exhibits even more pronounced advantages under challenging conditions. © 2025 IEEE.

关键词： Generative model lip reading viseme labeling visual speech recognition

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Optimization-Based Geometric Enhancement and Motion Estimation for Non-Cooperative Spacecrafts

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Journal of Field Robotics 2025年第0期

作者： Zhang, Chi Han, Yu Liang, Qiaokang Peng, Jianqing School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou China College of Electrical and Information Engineering Hunan University Changsha China

The state estimation of non-cooperative spacecrafts is a crucial prerequisite for on-orbit services. Aiming at the challenges in the fusion-based scheme with monocular vision and sparse point cloud, an optimization-based method of geometric enhancement and motion estimation is proposed in this paper. First, with the novel idea of geometric shape representation using simple features, a real-time segmentation framework is established. Differing from segmentation models, it can guarantee both complete segmentation and high inference speed. Second, given the assumption of local shared planes, a new label-free algorithm of point cloud densification is developed with an explainable model. To improve its efficiency, a curvature-guided strategy is employed to sample depth-incomplete points conducive to feature enhancement. Compared with sparse point clouds, it shows higher pose observation accuracy. Third, a truncation compensator is built to fit the high-order terms of a nonlinear state transition model with online optimization, which mitigates the impairment in a priori estimation. Combined with the adaptive extended Kalman filter, the motion can be estimated with fewer errors. Finally, the proposed method is validated through comparative simulations and ground experiments. © 2025 Wiley Periodicals LLC.

关键词： Extended Kalman filters

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