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SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Ranchagodage Don, Sanjaya Arunapriya Muthuchidambaranathan, P. Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias Autónoma TechLab Universidade Autónoma Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka 10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located in a transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation

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Medical Image Analysis 2025年

作者： Shengdong Zhang Fan Jia Xiang Li Hao Zhang Jun Shi Liyan Ma Shihui Ying Department of Mathematics School of Science Shanghai University Shanghai 200444 China Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah Salt Lake City 84102 UT USA School of Computer Science and Technology East China Normal University Shanghai 200444 China School of Communication and Information Engineering Shanghai University Shanghai 200444 China School of Computer Engineering and Science Shanghai University Shanghai 200444 China School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai 200444 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai 200072 China School of Mechanics and Engineering Science Shanghai University Shanghai 200072 China

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMS-Net), for the FSS task. Specifically, motivated by the effectiveness of pixel-to-prototype comparison in prototypical FSS methods and the capability of deep priors to model complex spatial structures, we leverage our learned Mumford-Shah model (LMS model) as a mathematical foundation to integrate these insights into a unified framework. By reformulating the LMS model into prototype update and mask update tasks, we propose an alternating optimization algorithm to solve it efficiently. Further, the iterative steps of this algorithm are unfolded into corresponding network modules, resulting in LMS-Net with clear interpretability. Comprehensive experiments on three publicly available medical segmentation datasets verify the effectiveness of our method, demonstrating superior accuracy and robustness in handling complex structures and adapting to challenging segmentation scenarios. These results highlight the potential of LMS-Net to advance FSS in medical imaging applications. Our code will be available at: https://***/SDZhang01/LMSNet .

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Deep Learning Techniques for In-Crop Weed Identification: A Review

arXiv

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arXiv 2021年

作者： Hu, Kun Wang, Zhiyong Coleman, Guy Bender, Asher Yao, Tingting Zeng, Shan Song, Dezhen Schumann, Arnold Walsh, Michael School of Computer Science The University of Sydney NSW2006 Australia School of Life and Environmental Sciences The University of Sydney NSW2006 Australia Australian Centre for Field Robotics The University of Sydney NSW2008 Australia College of Information Science and Technology Dalian Maritime University Dalian116000 China College of Mathematics and Computer Science Wuhan Polytechnic University Hubei430023 China Department of Computer Science and Engineering Texas A&M University College StationTX77843-3127 United States Citrus Research and Education Center University of Florida FL33850-2299 United States

Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem. © 2021, CC BY.

关键词： Weed control

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Understanding Dynamic Scenes using Graph Convolution Networks

Understanding Dynamic Scenes using Graph Convolution Network...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sravan Mylavarapu Mahtab Sandhu Priyesh Vijayan K Madhava Krishna Balaraman Ravindran Anoop Namboodiri Center for Visual Information Technology KCIS IIIT Hyderabad Robotics Research Center KCIS IIIT Hyderabad School of Computer Science McGill University and Mila IIT Madras

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving monocular camera. The input to MRGCN is a multi-relational graph where the graph's nodes represent the active and passive agents/objects in the scene, and the bidirectional edges that connect every pair of nodes are encodings of their Spatio-temporal *** show that this proposed explicit encoding and usage of an intermediate spatio-temporal interaction graph to be well suited for our tasks over learning end-end directly on a set of temporally ordered spatial relations. We also propose an attention mechanism for MRGCNs that conditioned on the scene dynamically scores the importance of information from different interaction *** proposed framework achieves significant performance gain over prior methods on vehicle-behavior classification tasks on four datasets. We also show a seamless transfer of learning to multiple datasets without resorting to fine-tuning. Such behavior prediction methods find immediate relevance in a variety of navigation tasks such as behavior planning, state estimation, and applications relating to the detection of traffic violations over videos.

关键词： Convolution Performance gain Encoding Automobiles Task analysis Vehicle dynamics Videos

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POMDP-based candy server: Lessons learned from a seven day demo 29

POMDP-based candy server: Lessons learned from a seven day d...

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29th International Conference on Automated Planning and Scheduling, ICAPS 2019

作者： Hoerger, Marcus Song, Joshua Kurniawati, Hanna Elfes, Alberto School of Information Technology and Electrical Engineering University of Queensland Australia Research School of Computer Science Australian National University Australia Robotics and Autonomous Systems Group Data61 CSIRO

ISBN: (纸本)9781577358077

An autonomous robot must decide a good strategy to achieve its long term goal, despite various types of uncertainty. The Partially Observable Markov Decision Processes (POMDPs) is a principled framework to address such a decision making problem. Despite the computational intractability of solving POMDPs, the past decade has seen substantial advancement in POMDP solvers. This paper presents our experience in enabling on-line POMDP solving to become the sole motion planner for a robot manipulation demo at IEEE SIMPAR and ICRA 2018. The demo scenario is a candy-serving robot: A 6-DOFs robot arm must pick-up a cup placed on a table by a user, use the cup to scoop candies from a box, and put the cup of candies back on the table. The average perception error is ∼3cm (≈ the radius of the cup), affecting the position of the cup and the surface level of the candies. This paper presents a strategy to alleviate the curse of history issue plaguing this scenario, the perception system and its integration with the planner, and lessons learned in enabling an online POMDP solver to become the sole motion planner of this entire task. The POMDP-based system were tested through a 7 days live demo at the two conferences. In this demo, 150 runs were attempted and 98% of them were successful. We also conducted further experiments to test the capability of our POMDP-based system when the environment is relatively cluttered by obstacles and when the user moves the cup while the robot tries to pick it up. In both cases, our POMDP-based system reaches a success rate of 90% and above. © 2019 Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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A Survey on Capsule Networks: Evolution, Application, and Future Development

A Survey on Capsule Networks: Evolution, Application, and Fu...

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Jiawei Li Qichen Zhao Nan Li Lin Ma Xuan Xia Xiaoguang Zhang Ning Ding Nannan Li College of Computer Science and Technology Xi'an Jiaotong University Xi’an P. R. China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen P. R. China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen P. R. China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen P. R. China International Institute of Next Generation Internet Macau University of Science and Technology Macau P. R. China

ISBN: (纸本)9781665413282

Capsule networks have drawn increasing attention since the concept is proposed in 2011. They hold advantages over convolutional neural networks (CNNs) on modeling part-to-whole relationships between entities and learning viewpoint invariant representations. A variety of improved designs and applications related to capsule networks have been explored within the past decade. This paper presents a relatively thorough survey of literatures corresponding to the evolution of the capsule networks and their applications. Different design of capsule networks from Hinton’s group as well as other researchers are reviewed. Applications of capsule networks in various scenarios and tasks are elaborated, including hyperspectral image processing, medical image processing, facial image processing, text classification, object detection, image segmentation, few-shot learning, etc. The relation and comparison of capsule networks, CNNs, and transformers, and the trend of future development of capsule networks are discussed. We believe this review provides a good reference to researchers who are interested in capsule networks.

关键词： Learning systems Image segmentation Text categorization Training data Object detection Transformers Market research

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MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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A Sextuple Cross-Coupled SRAM Cell Protected against Double-Node Upsets

A Sextuple Cross-Coupled SRAM Cell Protected against Double-...

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Asian Test Symposium (ATS)

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

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

In this paper, we propose a sextuple cross-coupled SRAM cell, namely SCCS18T, protected against double-node upsets. Since the proposed SCCS18T cell forms a large feedback loop for value retention and error interception, the cell can provide self-recoverability from any single-node upsets (SNUs) and partial double-node upsets (DNUs). Moreover, the proposed cell has optimized operation speed due to the use of six access transistors. Simulation results show that the SCCS18T cell can save approximately 65% read access time at the cost of 49% power dissipation and 50% silicon area on average, compared with typical hardened SRAM cells.

关键词： Transistors SRAM cells Simulation Radiation hardening (electronics) Power dissipation Latches Integrated circuit reliability

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Binary Channel Fuzzy Self-Adjusted Neural Network for Solving Time-Changing QP Problems

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IEEE Transactions on Neural Networks and Learning Systems 2025年 PP卷 PP页

作者： Luo, Yamei Ren, Qingyi Zheng, Zihao Chen, Siyuan Ma, Xin Liu, Yu Li, Xiaoli Yu, Junzhi Zhang, Zhijun The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern University of Science and Technology Institute for Robotics Shenzhen518055 China Peking University College of Engineering Beijing100871 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Laboratory Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong 723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established;then, using Lagrange’s law, the time-changing QP problem is transformed into a time-changing matrix equation;and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN). © 2012 IEEE.

关键词： Recurrent neural networks

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Self-supervised video representation learning by uncovering spatio-temporal statistics

arXiv

引用

arXiv 2020年

作者： Wang, Jiangliu Jiao, Jianbo Bao, Linchao He, Shengfeng Liu, Wei Liu, Yun-Hui The CUHK T Stone Robotics Institute The Hong Kong Centre for Logistics Robotics Hong Kong The Department of Engineering Science University of Oxford United Kingdom Tencent AI Lab United States The School of Computer Science and Engineering South China University of Technology China

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://***/laura-wang/video_repres_sts. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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