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Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

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Chinese Physics B 2023年第3期32卷 608-612页

作者：王莹祁峰张子旭汪晋宽 School of Communication Science and Engineering Northeastern UniversityShenyang 110819China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of SciencesShenyang 110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China University of Technology Sydney NSW 2007Australia

Terahertz(THz)imaging has drawn significant attention because THz wave has a unique capability to transient,ultrawide spectrum and low photon ***,the low resolution has always been a problem due to its long wavelength,limiting their application of fields practical *** this paper,we proposed a complex one-shot super-resolution(COSSR)framework based on a complex convolution neural network to restore superior THz images at 0.35 times wavelength by extracting features directly from a reference measured sample and groundtruth without the measured *** with real convolution neural network-based approaches and complex zero-shot super-resolution(CZSSR),COSSR delivers at least 6.67,0.003,and 6.96%superior higher imaging efficacy in terms of peak signal to noise ratio(PSNR),mean square error(MSE),and structural similarity index measure(SSIM),respectively,for the analyzed ***,the proposed method is experimentally demonstrated to have a good generalization and to perform well on measured *** COSSR provides a new pathway for THz imaging super-resolution(SR)reconstruction below the diffraction limit.

关键词： terahertz image processing complex convolution neural network

D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement

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arXiv 2024年

作者： Yan, Danqi Gao, Qing Qian, Yuepeng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China

Three-dimensional (3D) human pose estimation using a monocular camera has gained increasing attention due to its ease of implementation and the abundance of data available from daily life. However, owing to the inherent depth ambiguity in images, the accuracy of existing monocular camera-based 3D pose estimation methods remains unsatisfactory, and the estimated 3D poses usually include much noise. By observing the histogram of this noise, we find each dimension of the noise follows a certain distribution, which indicates the possibility for a neural network to learn the mapping between noisy poses and ground truth poses. In this work, in order to obtain more accurate 3D poses, a Diffusion-based 3D Pose Refiner (D3PRefiner) is proposed to refine the output of any existing 3D pose estimator. We first introduce a conditional multivariate Gaussian distribution to model the distribution of noisy 3D poses, using paired 2D poses and noisy 3D poses as conditions to achieve greater accuracy. Additionally, we leverage the architecture of current diffusion models to convert the distribution of noisy 3D poses into ground truth 3D poses. To evaluate the effectiveness of the proposed method, two state-of-the-art sequence-to-sequence 3D pose estimators are used as basic 3D pose estimation models, and the proposed method is evaluated on different types of 2D poses and different lengths of the input sequence. Experimental results demonstrate the proposed architecture can significantly improve the performance of current sequence-to-sequence 3D pose estimators, with a reduction of at least 10.3% in the mean per joint position error (MPJPE) and at least 11.0% in the Procrustes MPJPE (P-MPJPE). Copyright © 2024, The Authors. All rights reserved.

关键词： Diffusion

An intelligent interactive system based on LLM to combine CAD API development with FEA

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An intelligent interactive system based on LLM to combine CA...

2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Mengqiang Yu Yujie Sheng Zhongxu Wan Shuyu Yang Muping Sun Hongxia Cai School of Mechatronic Engineering and automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China School of Communication & Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

关键词： Electric potential Databases Terminology Interactive systems Large language models Software Natural language processing Robots Optimization Python

An intelligent Deterministic Scheduling Method for Ultralow Latency communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

Distributed Autonomous Swarm Formation for Dynamic Network Bridging

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Distributed Autonomous Swarm Formation for Dynamic Network B...

IEEE Conference on Computer communications Workshops, INFOCOM Wksps

作者： Raffaele Galliera Thies Mohlenhof Alessandro Amato Daniel Duran Kristen Brent Venable Niranjan Suri Institute for Human & Machine Cognition (IHMC) Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Fraunhofer Institute for Communication Information Processing and Ergonomics (FKIE) Wachtberg Germany US Army Research Laboratory (ARL) Adelphi MD USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications. In contexts such as disaster response, swarm operations require coordinated behavior and mobility control to be handled in a distributed manner, with the quality of the agents' actions heavily relying on the communication between them and the underlying network. In this paper, we formulate the problem of dynamic network bridging in a novel Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where a swarm of agents cooperates to form a link between two distant moving targets. Furthermore, we propose a Multi-Agent Reinforcement Learning (MARL) approach for the problem based on Graph Convolutional Reinforcement Learning (DGN) which naturally applies to the networked, distributed nature of the task. The proposed method is evaluated in a simulated environment and compared to a centralized heuristic baseline showing promising results. Moreover, a further step in the direction of sim-to-real transfer is presented, by additionally evaluating the proposed approach in a near Live Virtual Constructive (LVC) UAV framework.

关键词： Training Robot kinematics Markov decision processes Disasters Conferences Reinforcement learning Autonomous aerial vehicles

Low-Terahertz Radar Image Analysis for Road Obstacles 33

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Low-Terahertz Radar Image Analysis for Road Obstacles

第33届中国控制与决策会议

作者： Hongming Wu Feng Qi Jinkuan Wang School of communication science and engineering Northeastern UniversityLiaoning Province Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

ISBN: (纸本)9781665431293

At present,laser radar is main studied in the in automotive ***,Driverless cars have higher request for different environment and light *** is even difficult for lidar to find tiny targets on the road,such as pits and *** the paper,The low-terahertz high-resolution imaging radar system operating frequency is 94GHz,Using the frequency-modulated continuous-wave(FMCW) technique with 3 mm *** simulated the pits of different depths on the road,analyzed the changes in the electromagnetic field,and used the low-terahertz radar to perform experiments.

关键词： frequency-modulated continuous-wave(FMCW) low-terahertz radar high-resolution imaging road pit

A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp

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arXiv 2024年

作者： Xu, Yansong Wang, Xiaohui Li, Junlin Zhang, Xiaoqian Li, Feng Gao, Qing Fu, Chenglong Leng, Yuquan The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees’ grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects’ feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It’s subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand’s position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient R2 of grasping process of 0.911, a Root Mean Squared Error (RMSE) of 2.47°, a success rate of grasping of 95.43%, and an average duration of grasping process of 3.07±0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35%. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand’s functionality and provides valuable insights for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Electromyography

A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

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arXiv 2022年

作者： Yang, Liu Lu, Yinzhi Yang, Simon X. Guo, Tan Liang, Zhifang The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes. © 2022, CC BY.

关键词： Uncertainty analysis

COLLABORATIVE AUTO-ENCODING FOR BLIND IMAGE QUALITY ASSESSMENT

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arXiv 2023年

作者： Zhou, Zehong Zhou, Fei Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE’s decoder for reconstructing distorted images, thus effectively forcing DAE’s encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. The codes are available at: https://***/Macro-Zhou/NRIQA-VISOR/. © 2023, CC BY.

关键词： Supervised learning