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International Conference on Information Fusion

作者： Huajun Liu Hui Zhang Christoph Mertz School of Computer Science and Engineering Nanjing University of Science and Technology Robotics Institute Carnegie Mellon University School of Computer Science and Engineering Nanjing University of Science and Technology Robotics Institute Carnegie Mellon University

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

The Long Short-Term Memory (LSTM) neural network based data association algorithm named as DeepDA for multi-target tracking in clutter is proposed to deal with the NP-hard combinatorial optimization problem in this paper. Different from the classical data association methods involving complex models and accurate prior knowledge on clutter density, filter covariance or associated gating etc, data-driven deep learning methods have been extensively researched for this topic. Firstly, data association mathematical problem for multitarget tracking on unknown target number, missed detection and clutter, which is beyond one-to-one mapping between observations and targets is redefined formally. Subsequently, an LSTM network is designed to learn the measurement-to-track association probability from radar noisy measurements and existing tracks. Moreover, an LSTM-based data-driven deep neural network after a supervised training through the BPTT and RMSprop optimization method can get the association probability directly. Experimental results on simulated data show a significant performance on association ratio, target ID switching and time-consuming for tracking multiple targets even they are crossing each other in the complicated clutter environment.

关键词： Radar tracking Clutter Target tracking Optimization Radar measurements Laser radar

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Multi-hop reasoning over sparse knowledge graphs with deep reinforcement learning

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Expert Systems with Applications 2025年 290卷

作者： Liu, Yiyang Fu, Lejun Fu, Yukai Wu, Tianxing Bai, Hongfei State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China School of Computer Science and Engineering Southeast University Nanjing211189 China

In recent years, knowledge reasoning has demonstrated substantial progress in inferring missing information, effectively alleviating the limitations caused by the incompleteness of knowledge graphs (KGs). Dealing with entities that have sufficient paths between them, multi-hop reasoning methods have achieved notable success and demonstrated interpretability, but they still exhibit deficiencies when dealing with sparse KGs. To overcome the insufficiency of edges in sparse KGs and the absence of effective guidance in multi-hop reasoning methods, a hybrid multi-hop collaborative Multi-Agent Sparse Knowledge Graph Reasoning model (MASKGR) is proposed. Attention-based dynamic completion (ADC) and variable rule guidance(VRG) are used to enhance the reasoning capability of deep reinforcement learning (RL) in sparse KGs. ADC focuses on high-value relations based on attention weights and combines embedding-based methods to predict the tail entities of these relations, expanding the original action space and improving the exploration capability of agents. Moreover, VRG greatly improves the ability of MASKGR to use rule-guided agent learning by utilizing the variable rule ratio and the variable loss reshaping. ADC can be applied to both single-agent and multi-agent in other models, while VRG is specifically designed for multi-agent. Comprehensive experiments have been conducted on four datasets: FB15K-237-10 %, FB15K-237-50 %, NELL23K, and WD-singer. Specifically, MASKGR achieves relative improvements of 1.3 % in Mean Reciprocal Rank (MRR) and 1.1 % in the proportion of correct tail entity rankings in the top 3 (Hit@3) on the FB15K-237-10 %, 2.4 % in MRR and 1.3 % in Hit@3 on the NELL23K, and 2.7 % in MRR and 3.6 % in Hit@3 on the WD-singer. The results indicate that MASKGR outperforms state-of-the-art multi-hop reasoning models based on reinforcement learning. © 2025

关键词： Deep reinforcement learning

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Design of a Highly Reliable SRAM Cell with Advanced Self-Recoverability from Soft Errors

Design of a Highly Reliable SRAM Cell with Advanced Self-Rec...

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International Test Conference in Asia (ITC-Asia)

作者： Zhengda Dou Albin Yan Jun Zhou Yuanjie Hu Yan Chen Tianming Ni Jie Cui Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University/Anhui Engineering Laboratory of IoT Security Technologies Hefei China College of Electrical Engineering Anhui Polytechnic University Wuhu China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier/CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728189444

ISBN: (纸本)9781728189451

In this paper, a highly reliable SRAM cell, namely SESRS cell, is proposed. Since the cell has a special feedback mechanism among its internal nodes and has more access transistors compared to a standard SRAM cell, the SESRS cell provides the following advantages: (1) it can self-recover from single node upsets (SNUs) and double-node upsets (DNUs); (2) it can reduce power consumption by 49.78% and silicon area by 7.92%, compared with the only existing SRAM cell which can self-recover from all possible DNUs. Simulation results validate the robustness of the proposed SESRS cell. Moreover, compared with the state-of-the-art hardened SRAM cells, the proposed SESRS cell can reduce read access time by 61.93% on average.

关键词： SRAM cells Transistors Integrated circuit reliability Logic gates Standards Robustness Reliability engineering

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Orientation Control of an Electromagnetically Actuated Soft-Tethered Colonoscope Based on 2OR Pseudo-Rigid-Body Model

Orientation Control of an Electromagnetically Actuated Soft-...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yehui Li Weibing Li Wenci Xin Xue Zhang Yitian Xian Philip Wai Yan Chiu Zheng Li The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Science and Multi-Scale Medical Robotics Center Ltd The Chinese University of Hong Kong Hong Kong

Colorectal cancer incidence has been steadily rising worldwide. Magnetic colonoscopes provide new approaches to conduct colon inspection and treatment. This paper presents a novel electromagnetically actuated soft-tethered colonoscope to achieve precise and stable orientation control. An inflated balloon is designed to eliminate the unpredictable disturbance of the floating tether. A 2OR Pseudo-Rigid-Body (PRB) model of the soft tether is developed to analyze the relationship between the tether deflection and applied force and torque. A closed-loop control framework is constructed with visual position feedback. Experiments are first conducted to validate the assumption of the PRB model and the efficacy of the magnetic field model. Then, trajectory tracking tasks and disturbance rejection tests are performed to validate the feasibility of the proposed solution and closed-loop control. Results show that the colonoscope can stably and accurately orient to the desired orientation with an absolute mean position error of less than 0.5 mm and an average velocity of 3.5 mm/s. The distal tip can quickly re-stabilize to the desired orientation even when a large disturbance exists.

关键词： Location awareness Visualization Torque Trajectory tracking Biological system modeling Position control Medical services

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Multiple-object tracking based on monocular camera and 3-D lidar fusion for autonomous vehicles

Multiple-object tracking based on monocular camera and 3-D l...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Chen, Hao Xue, Chunyue Liu, Shoubin Sun, Yuxiang Chen, Yongquan Shenzhen Institute of Artificial Intelligence and Robotics for Society China Institute of Robotics and Intelligent Manufacturing Chinese University of Hong Kong Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9781728163215

This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios. © 2019 IEEE.

关键词： Optical radar

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Correction to: A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

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Complex & intelligent systems 2023年第2期9卷 2205页

作者： Tanzeela Shakeel Shaista Habib Wadii Boulila Anis Koubaa Abdul Rehman Javed Muhammad Rizwan Thippa Reddy Gadekallu Mahmood Sufiyan School of System and Technology University of Management and Technology Lahore Pakistan. Robotics and Internet of Things Lab Prince Sultan University Riyadh 12435 Saudi Arabia. Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan. Department of Computer Science Kinnaird College for Women Lahore Pakistan. School of Information Technology and Engineering Vellore Institute of Technology Vellore India.

[This corrects the article DOI: 10.1007/s40747-022-00767-w.].

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AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier

arXiv

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arXiv 2022年

作者： Heiliger, Lars Marinov, Zdravko Hasin, Max Ferreira, André Fragemann, Jana Pomykala, Kelsey Murray, Jacob Kersting, David Alves, Victor Stiefelhagen, Rainer Egger, Jan Kleesiek, Jens Institute for Ai in Medicine University Hospital Essen Essen Germany University Medicine Essen Essen Germany Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Center Algoritmi University of Minho Braga Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Nuclear Medicine University Hospital Essen Essen Germany Essen Germany West German Cancer Center Essen Germany HIDSS4Health - Helmholtz Information Data Science School for Health Heidelberg Karlsruhe Germany

Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDGPET/CT scans are routinely used for staging and re-staging of cancer, as the radiolabeled fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately, these regions with high metabolism are not specific to tumors and can also represent physiological uptake by normal functioning organs, inflammation, or infection, making detailed and reliable tumor segmentation in these scans a demanding task. This gap in research is addressed by the AutoPET challenge, which provides a public data set with FDG-PET/CT scans from 900 patients to encourage further improvement in this field. Our contribution to this challenge is an ensemble of two state-of-the-art segmentation models, the nn-Unet and the Swin UNETR, augmented by a maximum intensity projection classifier that acts like a gating mechanism. If it predicts the existence of lesions, both segmentations are combined by a late fusion approach. Our solution achieves an average Cross Validation Dice score of 72.12% on patients diagnosed with lung cancer, melanoma, and lymphoma in our cross-validation. Code: https://***/heiligerl/AutoPET-Challenge-Submission. © 2022, CC BY.

关键词： Tumors

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Learning transferable push manipulation skills in novel contexts

arXiv

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arXiv 2020年

作者： Howard, Rhys Zito, Claudio Intelligent Robotics Lab School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates

This paper is concerned with learning transferable forward models for push manipulation that can be applying to novel contexts and how to improve the quality of prediction when critical information is available. We propose to learn a parametric internal model for push interactions that, similar for humans, enables a robot to predict the outcome of a physical interaction even in novel contexts. Given a desired push action, humans are capable to identify where to place their finger on a new object so to produce a predictable motion of the object. We achieve the same behaviour by factorising the learning into two parts. First, we learn a set of local contact models to represent the geometrical relations between the robot pusher, the object, and the environment. Then we learn a set of parametric local motion models to predict how these contacts change throughout a push. The set of contact and motion models represent our internal model. By adjusting the shapes of the distributions over the physical parameters, we modify the internal model’s response. Uniform distributions yield to coarse estimates when no information is available about the novel context. We call this an unbiased predictor. A more accurate predictor can be learned for a specific environment/object pair (e.g. low friction/high mass), called a biased predictor. The effectiveness of our approach is demonstrated in a simulated environment in which a Pioneer 3-DX robot equipped with a bumper needs to predict a push outcome for an object in a novel context, and we support those results with a proof of concept on a real robot. We train on two objects (a cube and a cylinder) for a total of 24,000 pushes in various conditions, and test on six objects encompassing a variety of shapes, sizes, and physical parameters for a total of 14,400 predicted push outcomes. Our experimental results show that both biased and unbiased predictors can reliably produce predictions in line with the outcomes of a carefully tuned physi

关键词： Forecasting

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

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