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sEMG-vision Tra: A Gesture Recognition Method Based on Surface EMG Signal-Vision Fusion

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sEMG-vision Tra: A Gesture Recognition Method Based on Surfa...

IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Weili Ding Shuhui Zhang Han Liu Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao People's Republic of China College of Computer Science and Software Engineering Shenzhen University Shenzhen People's Republic of China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

To address the challenges of gesture recognition accuracy within human-computer interaction technologies, and to overcome the limitations of single-modal biometric feature recognition - which often falls short in practical applications, this paper introduces a multimodal feature fusion classification algorithm, sEMG-vision Tra. This algorithm aims to integrate different types of biometric data, thereby enhancing the robustness and effectiveness of gesture recognition. Initially, we utilize the Mediapipe framework to extract skeletal point data from images of the human hand. The next process involves calculating distance and angle features between skeletal points, as well as extracting time-domain features from surface electromyographic (sEMG) signals. Following this, features from these two modalities are concatenated along the feature dimension and then linearly projected onto the target dimension. To effectively capture the intricate interrelationships among these features, we employ a multi-layer Transformer encoder for encoding the combined features. The encoded features are subsequently mapped to the final output dimension. The effectiveness of our approach is demonstrated through validation on a custom dataset. This algorithm's key contribution lies in the fusion of two distinct modal features, images and EMG signals, to achieve a more holistic and precise feature representation. By this, it significantly enhances our ability to capture the essential information for applications like gesture recognition or human-computer interaction. The algorithm showcases exceptional performance, achieving a noteworthy accuracy rate of 95.56% on a custom dataset.

关键词： Human computer interaction Biometrics Visualization Accuracy Gesture recognition Feature extraction Transformers Electromyography Classification algorithms Time-domain analysis

Iterative Equalization of CPM With Unitary Approximate Message Passing

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arXiv 2024年

作者： Liu, Zilong Song, Yi Guo, Qinghua Sun, Peng Gong, Kexian Wang, Zhongyong The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The Key Laboratory of Grain Information Processing and Control Ministry of Education The College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection in frequency selective fading channels. This paper proposes an iterative receiver tailored for the detection of CPM signals over frequency selective fading channels. This design leverages the factor graph framework to integrate equalization, demodulation, and decoding functions. The equalizer employs the unitary approximate message passing (UAMP) algorithm, while the unitary transformation is implemented using the fast Fourier transform (FFT) with the aid of a cyclic prefix (CP), thereby achieving low computational complexity while with high performance. For CPM demodulation and channel decoding, with belief propagation (BP), we design a message passing-based maximum a posteriori (MAP) algorithm, and the message exchange between the demodulator, decoder and equalizer is elaborated. With proper message passing schedules, the receiver can achieve fast convergence. Simulation results show that compared with existing turbo receivers, the proposed receiver delivers significant performance enhancement with low computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Fast Fourier transforms

Research on Multi Beam Sounding Based on Spatial Position Relationship and Backtracking Algorithm

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Research on Multi Beam Sounding Based on Spatial Position Re...

control, Electronics and computer Technology (ICCECT), IEEE International Conference on

作者： Bensen Xian Bo Liu Yufen Li Baicheng Zhu Qiuyan Chen Jinlong Diao Wenkai Sun Jiahuan Mo Shaoyuan Bai Ya'nan Zhang College of Environmental Science and Engineering Guilin University of Technology Guilin China School of Computer Science and Engineering Guilin University of Technology Guilin China School of Mathematics and Statistics Guilin University of Technology Guilin China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of Technology Guilin China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

The multi beam depth measurement system is an efficient underwater terrain measurement method, which has wide applications in marine terrain surveys, underwater archaeology, and other fields. Therefore, exploring the problem of multi beam survey lines is of great research significance for the exploration of marine disciplines. This article mainly uses SketChup for drawing. With the help of mathematical geometry and triangle theory, a seawater depth calculation model, a coverage width calculation model, and an optimal survey line design model are constructed. The indicator values of each measurement point are solved using Python. This paper establishes a mathematical model for the coverage width of multi beam depth measurement and the overlaps rate between adjacent bands. Firstly, the spatial structure is analyzed as a planar Fig. Then, this paper uses a plan for geometric analysis to obtain right angled triangles with similar ratios. Through physical analysis, a seawater depth measurement model was established. Then, geometric analysis is performed using the obtained planar Fig to obtain a right angled triangle with similar ratios. Then, a depth measurement model for seawater is obtained through physical angle analysis. The coverage width measurement model and the repetition rate measurement model are established using the sine theorem and equal substitution. Finally, using Python, we calculate the index values for the listed positions with an open angle of 120°, a slope of 1.5°, and a center point seawater depth of 70m. For example, the depth, coverage width, and overlap rate of the measuring line at a distance of 200m from the center point are 64.763m, 224.885m, and 13.837%, respectively.

关键词： Surveys Geometry Analytical models Area measurement Sea measurements Position measurement Length measurement

Low-cost IoT-Based sensors dashboard for monitoring the state of health of mobile harbor cranes: Hardware and software description

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Heliyon 2024年第22期10卷 e40239页

作者： Awad, A.H. Alsabaan, Maazen Ibrahem, Mohamed I. Saraya, M.S. Elksasy, M.S.M. Ali-Eldin, Amr M.T. Abdelsalam, Mohamed M. Computers Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura 35516 Egypt Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia School of Computer and Cyber Sciences Augusta University Augusta 30912 GA United States Faculty of Engineering Delta University for Science and Technology Gamasa 7731168 Egypt

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is “immersed” in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the pa

关键词： IoT Low-cost Mobile harbor Crane Raspberry pi

TS-CGNet: Temporal-Spatial Fusion Meets Centerline-Guided Diffusion for BEV Mapping

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arXiv 2025年

作者： Hong, Xinying Li, Siyu Zeng, Kang Shi, Hao Peng, Bomin Yang, Kailun Li, Zhiyong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China

Bird’s Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlusions and handling complex environments, with a notable decline in perceptual performance under adverse weather conditions or low-light scenarios. Therefore, this paper proposes TS-CGNet, which leverages Temporal-Spatial fusion with Centerline-Guided diffusion. This visual framework, grounded in prior knowledge, is designed for integration into any existing network for building BEV maps. Specifically, this framework is decoupled into three parts: Local mapping system involves the initial generation of semantic maps using purely visual information;The Temporal-Spatial Aligner Module (TSAM) integrates historical information into mapping generation by applying transformation matrices;The Centerline-Guided Diffusion Model (CGDM) is a prediction module based on the diffusion model. CGDM incorporates centerline information through spatial-attention mechanisms to enhance semantic segmentation reconstruction. We construct BEV semantic segmentation maps by our methods on the public nuScenes and the robustness benchmarks under various corruptions. Our method improves 1.90%, 1.73%, and 2.87% for perceived ranges of 60×30m, 120×60m, and 240×60m in the task of BEV HD mapping. TS-CGNet attains an improvement of 1.92% for perceived ranges of 100×100m in the task of BEV semantic mapping. Moreover, TS-CGNet achieves an average improvement of 2.92% in detection accuracy under varying weather conditions and sensor interferences in the perception range of 240×60m. The source code will be publicly available at https://***/krabs-H/TS-CGNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

A Battery Capacity Prediction Method Applying to On-Service Vehicles Using Charging Data and Decomposition Framework

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SSRN

SSRN 2024年

作者： Zhao, Xinwei Liu, Yonggui Xiao, Bin Key Laboratory of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Information Engineering School Guangzhou Vocational College of Technology & Business Guangzhou511442 China

Lithium-ion batteries are valuable as the primary power source for electric vehicles (EVs). However, their capacity degradation and reliability directly affect driving of on-service EVs, accurately forecasting battery capacity is still a tricky challenge due to complex battery systems and limitations of computing power. To overcome such barriers, a concise battery capacity prediction method is proposed based on charging data and decomposition framework in this paper. First, charging hot-spot intervals are counted to provide a reliable foundation for screening high-quality and low-quantity data. Then, the seasonal-trend decomposition method with independent analysis is designed and applied to extract significant correlation features associated with capacity degradation. Subsequently, a temporal convolution network with the decomposed mode is developed to forecast battery capacity, in conjunction with the use of decoupling mechanism to rectify the analytical interference and misinformation caused by data mixing. Finally, experiment results demonstrate that the proposed method can accurately predict capacity of on-service EVs. Furthermore, this approach alleviates the computational burden without compromising prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless Communications and Open Problems

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China Communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming

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arXiv 2023年

作者： Adams, Steven Patanè, Andrea Lahijanian, Morteza Laurenti, Luca Delft Center for Systems and Control Technical University of Delft Delft2628 CD Netherlands School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Departement of Aerospace Engineering Sciences and Computer Science University of Colorado Boulder BoulderCO80303 United States

In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points T ⊂ Rn, BNN-DP computes lower and upper bounds on the BNN’s predictions for all the points in T. The framework is based on an interpretation of BNNs as stochastic dynamical systems, which enables the use of Dynamic Programming (DP) algorithms to bound the prediction range along the layers of the network. Specifically, the method uses bound propagation techniques and convex relaxations to derive a backward recursion procedure to over-approximate the prediction range of the BNN with piecewise affine functions. The algorithm is general and can handle both regression and classification tasks. On a set of experiments on various regression and classification tasks and BNN architectures, we show that BNN-DP outperforms state-of-the-art methods by up to four orders of magnitude in both tightness of the bounds and computational efficiency. © 2023, CC BY.

关键词： Dynamic programming

Saliency-Aware Regularized Graph Neural Network

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arXiv 2024年

作者： Pei, Wenjie Xu, WeiNa Wu, Zongze Li, Weichao Wang, Jinfan Lu, Guangming Wang, Xiangrong Department of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen518172 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518066 China

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification;2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released. © 2024, CC BY.

关键词： Graph neural networks