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arXiv 2023年

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Liu, Tongliang Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Australia JD Explore Academy *** China College of Computing & Data Science Nanyang Technological University Singapore

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection, recognition, and script identification simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. Extensive experiments on public benchmarks demonstrate that our simple approach achieves better training efficiency compared with Transformer-based models and outperforms the previous state-of-the-art. For example, on ICDAR 2019 ReCTS for Chinese text, our method boosts the 1-NED metric to a new record of 78.3%. On ICDAR 2019 MLT, DeepSolo++ achieves absolute 5.5% H-mean and 8.0% AP improvements on joint detection an

关键词： Semantics

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Iterative learning algorithm of upper limb rehabilitation robot based on BP neural network

Iterative learning algorithm of upper limb rehabilitation ro...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Zaixiang Pang Xiaomeng Deng Linan Gong Nan Wang Changchun University of Technology School of Engineering training center Changchun China Changchun University of Technology School of Computer science and engineering Changchun China Changchun Vocational Institute of Technology School of Mechanical and Electrical Engineering Changchun China Jilin Weimiao Medical Technology Co. LTD Changchun China

At present, in the trajectory tracking control of upper limb rehabilitation robot, there are nonlinear and uncertain problems of patients' spasticity disturbance. Therefore, an iterative learning control algorithm based on Back Propagation neural network is proposed in this paper. Based on the repeatability of the rehabilitation robot system, a multi-layer feedforward neural network trained by error reverse propagation algorithm is combined with iterative learning. The algorithm can effectively compensate the effect of spasmodic interference on the system performance and improve the convergence rate of trajectory tracking errors. The structure of the iterative learning control system based on neural network and the iterative learning control algorithm based on Back Propagation neural network are described. The process of network selection and robot trajectory tracking iterative learning control is given. Through simulation experiments, the feasibility and effectiveness of the algorithm are verified, which solves the above problems and meets the control requirements of rehabilitation training.

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Physical-Layer Authentication Over Spatially Correlated MIMO Channels

Physical-Layer Authentication Over Spatially Correlated MIMO...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Kenta Miwa Kengo Ando Giuseppe Thadeu Freitas de Abreu Koji Ishibashi Advanced Wireless and Communication Research Center The University of Electro-Communications Tokyo Japan School of Computer Science and Engineering Constructor University Bremen Germany

We propose a novel physical-layer authentication (PLA) method that exploits spatio-temporal correlation features of multiple-input multiple-output (MIMO) channels. In particular, in the proposed method, the correlation due to the spatial structure and aging of the channel between two legitimate nodes (Alice and Bob), captured in the corresponding covariance matrix, is incorporated into the Mahalanobis distance between consecutive channel estimates and utilized to detect the attack by an impersonator. The authentication decision is carried out via hypothesis testing, with distributions and threshold derived analytically. computer simulations demonstrate that, for a given false alarm probability, the proposed method can detect an attacker with a higher probability than a conventional method based on Euclidean distances between channels.

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Autonomous Sensor System for Wind Turbine Blade Collision Detection

Autonomous Sensor System for Wind Turbine Blade Collision De...

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作者： Clocker, Kyle Hu, Congcong Roadman, Jason Albertani, Roberto Johnston, Matthew L. Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States Oregon State University School of Mechanical Industrial and Manufacturing Engineering CorvallisOR97331 United States National Wind Technology Center National Renewable Energy Laboratory GoldenCO80401 United States

This paper presents an automated blade collision detection system for use on wind turbines, toward the goal of supporting monitoring and quantitative assessment of wind energy impacts on wildlife. A wireless, multisensor module mounted at the blade root measures surface vibrations, and a blade-mounted camera provides image capture of colliding objects. Using sensor data recorded during field testing of the system on an operational wind turbine, we present the development, training, and testing of automated detection algorithms for collision detection using machine-learning approaches. In particular, we compare the use of a new two-step, anomaly-based classification algorithm with conventional adaptive boosting and amplitude-based detection techniques, where the two-step approach improves average precision for the experimental data set. This integrated sensor and classification systems demonstrates a new approach for automated, on-blade collision detection for wind turbines, with broad utility across structural health monitoring applications. © 2001-2012 IEEE.

关键词： Cameras

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Passenger Waiting Time Prediction Based on Density Spatiotemporal Clustering and Time Series Feature Processing 6

Passenger Waiting Time Prediction Based on Density Spatiotem...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Huang, Ruixi Xia, Dawen Geng, Jian Zhang, Wenyong Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili556011 China College of Big Data Engineering College of Microelectronics and Artificial Intelligence Kaili University Kaili556011 China Party School of the Anshun Municipal Committee of C.P.C Anshun561000 China Guizhou Traffic Technician and Transportation College Department of Automotive Engineering Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350353594

Accurate passenger waiting time prediction addresses the taxi supply-demand imbalance in Intelligent Transport Systems (ITS). To capture the dependencies of passenger waiting time on urban taxi distribution, we propose a Density Spatiotemporal Clustering and Time Series Feature Processing (DSC-TSFP) method that discovers potential feature relationships of taxi GPS trajectory data. Specifically, a Multilevel Spatiotemporal Density-Based Spatial Clustering of Applications with Noise (MST-DBSCAN) is proposed to cluster taxi GPS trajectory data to obtain waiting time series with spatiotemporal correlations. Furthermore, a time series processing module based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Normalization (CEEMDANN) is designed to enhance prediction accuracy by handling non-stationarity and numerical discrepancy, which solves the residual white noise problem in Intrinsic Modal Functions (IMFs) of EEMD and CEEMD. Next, a waiting time prediction module oriented to a Convolutional Long Short-Term Memory Network (ConvLSTM) is incorporated to compensate for the defects of spatially localized dependencies that LSTM cannot capture. Finally, a CEEMDANN-ConvLSTM model is constructed to predict the passenger waiting time by combining the time series processing and waiting time prediction module. Based on real-world taxi GPS trajectory data, experimental results demonstrate that DSC-TSFP outperforms comparable methods regarding prediction accuracy. © 2024 IEEE.

关键词： Global positioning system

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DeepRB: Deep Resource Broker Based on Clustered Federated Learning for Edge Video Analytics

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IEEE Transactions on Network and Service Management 2025年

作者： Zhang, Xiaojie Debroy, Saptarshi Wang, Peng Li, Keqin Hunan First Normal University School of Electronics and Information Engineering China Changsha410002 China City University of New York Department of Computer Science New YorkNY10016 United States Hunan University College of Information Science and Engineering National Supercomputing Center in Changsha Hunan Changsha410082 China State University of New York Department of Computer Science New PaltzNY12561 United States

Edge computing plays a crucial role in large-scale and real-time video analytics for smart cities, particularly in environments with massive machine-type communications (mMTC) among IoT devices. Due to the dynamic nature of mMTC, one of the main challenges is to achieve energy-efficient resource allocation and service placement in resource-constrained edge computing environments. In this paper, we introduce DeepRB, a deep learning-based resource broker framework designed for real-time video analytics in edge-native environments. DeepRB develops a two-stage algorithm to address both resource allocation and service placement efficiently. First, it uses a Residual Multilayer Perceptron ( ResMLP) network to approximate traditional iterative resource allocation policies for IoT devices that frequently transition between active and idle states. Second, for service placement, DeepRB leverages a multi-agent federated deep reinforcement learning (DRL) approach that incorporates clustering and knowledge-aware model aggregation. Through extensive simulations, we demonstrate the effectiveness of DeepRB in improving schedulability and scalability compared to baseline edge resource management algorithms. Our results highlight the potential of DeepRB for optimizing resource allocation and service placement for real-time video analytics in dynamic and resource-constrained edge computing environments. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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AppleQSM:Geometry-Based 3D Characterization of Apple Tree Architecture in Orchards

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Plant Phenomics 2024年第3期6卷 737-754页

作者： Tian Qiu Tao Wang Tao Han Kaspar Kuehn Lailiang Cheng Cheng Meng Xiangtao Xu Kenong Xu Jiang Yu School of Electrical and Computer Engineering Cornell UniversityIthacaNYUSA Institute of Statistics and Big Data Renming University of ChinaBeijingChina Department of Ecology and Evolutionary Biology Cornell UniversityIthacaNYUSA School of Integrative Plant Science Cornell UniversityIthacaNYUSA Center for Applied Statistics Renmin University of ChinaBeijingChina School of Integrative Plant Science Cornell University.IthacaNYUSA

The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple ***,2D imaging technologies were employed to delineate the architectural traits of apple trees,but their accuracy was hampered by occlusion and perspective *** study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization,utilizing point clouds collected by a terrestrial laser scanner(TLS).The pipeline consisted of four modules:(a)data preprocessing module,(b)tree instance segmentation module,(c)tree structure segmentation module,and(d)architectural trait extraction *** developed pipeline was used to analyze 84 trees of two representative apple cultivars,characterizing architectural traits such as tree height,trunk diameter,branch count,branch diameter,and branch *** results indicated that the established pipeline attained an R^(2)of 0.92 and 0.83,and a mean absolute error(MAE)of 6.1cm and 4.71mm for tree height and trunk diameter at the tree level,***,at the branch level,it achieved an R^(2)of 0.77 and 0.69,and a MAE of 6.86 mm and 7.48°for branch diameter and angle,*** accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding ***,bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.

关键词： architecture tree tion appleqsm character geometry-based orchards

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Systolic Array Based Convolutional Neural Network Inference on FPGA 15

Systolic Array Based Convolutional Neural Network Inference ...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Chua, Shi Hui Teo, T. Hui Tiruye, Mulat Ayinet Wey, I-Chyn Singapore University of Technology and Design Engineering Product Development Singapore Singapore University of Technology and Design Engineering Product Development Science Mathematics and Technology Singapore Artificial Intelligence Research Center School of Electrical and Computer Engineering College of Engineering Graduate Institute of Electrical Engineering Chang Gung University Chang Gung University Taiwan

ISBN: (纸本)9781665464994

Convolutional Neural Networks (CNNs) possess a particular edge over its predecessor, the Multi-Layer Perceptron (MLP). This is due to its weight sharing features that allows the CNN to use less parameters for the same number of outputs as compared to the MLP. Systolic arrays capitalize on the weight sharing property of CNNs to do data reuse while performing convolutional operations, in order to reduce the power consumption from the memory accesses. A kernel fitting systolic processing element array was designed with only positive multiplication to increase the throughput and power efficiency of the CNN accelerator, while using weight stationary dataflow to achieve data reuse in the systolic array. A cost-optimized lightweight solution is implemented through low-cost FPGA hardware so as to allow for greater accessibility. The CNN accelerator consumes 0.363 W power at 100 MHz operating frequency. A peak throughput of 10.98 GOps/s was achieved with peak performance density of 0.200 GOps/s/DSP and peak power efficiency of 30.26 GOps/s/W. Even with the added support for additional functions, proposed design achieved up to 1.59x better power efficiency compared to other systolic implementations and up to 6.17x better power efficiency compared to non-systolic implementations. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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An Energy-Aware AUV-Assisted Data Collection Scheme for Maximizing Network Lifetime in UWSNs

An Energy-Aware AUV-Assisted Data Collection Scheme for Maxi...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Tang, Jiarun Gu, Xuan Huang, Xiao Liu, Wei He, Jianhua Xu, Jing School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China China Ship Development and Design Center Wuhan China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798350368369

Utilizing an autonomous underwater vehicle (AUV) for data collection in underwater wireless sensor networks (UWSNs) is a promising approach. However, since underwater sensor nodes are typically battery-powered and difficult to replace, effective energy management is crucial for extending the network lifetime of UWSNs. Additionally, the limited energy of the AUV presents further challenges. To address these issues, this paper proposes an energy-aware AUV-assisted data collection scheme based on dynamic clustering and cluster head selection (DCCHS) to maximize network lifetime. Specifically, we utilize the energy center to define the cluster center and employ a bottom-up hierarchical clustering approach to address the node dynamic clustering problem under the AUV movement distance constraint. Subsequently, we introduce a cluster head (CH) selection algorithm based on iterative optimization, and adds auxiliary CHs near the AUV path to reduce the energy consumption of CHs. Simulation results demonstrate that the proposed DCCHS scheme significantly extends the network lifetime compared to existing schemes, particularly in scenarios with dense node deployment. © 2025 IEEE.

关键词： Power management (telecommunication)

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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