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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger

Texture Discrimination using a Flexible Tactile Sensor Array...

引用

IEEE Sensors

作者： Sriramana Sankar Alisa Brown Darshini Balamurugan Harrison Nguyen Mark Iskarous Talya Simcox Deepesh Kumar Andrei Nakagawa Nitish Thakor Department of Biomedical Engineering Johns Hopkins University Baltimore USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore USA Baltimore Polytechnic Institute Baltimore USA Singapore Institute for Neurotechnology National University of Singapore Singapore

Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3×3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.

关键词： Soft biomimetic finger Flexible tactile sensor array Neuromorphic model Supervised learning

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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

引用

Journal of Physics: Conference Series 2021年第1期1828卷

作者： Michael Pritchard Abraham Itzhak Weinberg John A R Williams Felipe Campelo Harry Goldingay Diego R Faria Aston Robotics Vision and Intelligent Systems Laboratory (ARVIS Lab) College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Aston Institute of Photonic Technologies Aston University Birmingham B4 7ET UK

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

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Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

arXiv

引用

arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

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Underwater acoustic intensity field reconstruction by kriged compressive sensing 13

Underwater acoustic intensity field reconstruction by kriged...

引用

13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018

作者： Sun, Jie Yu, Jiancheng Zhang, Aiqun Song, Aijun Zhang, Fumin State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Electrical Engineering University of Alabama TuscaloosaAL35487 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781450361934

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea. © 2018 Association for Computing Machinery.

关键词： Kriging

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Adaptive leader-follower formation control and obstacle avoidance via deep reinforcement learning

arXiv

引用

arXiv 2019年

作者： Zhou, Yanlin Lu, Fan Pu, George Ma, Xiyao Sun, Runhan Chen, Hsi-Yuan Li, Xiaolin Wu, Dapeng National Science Foundation Center for Big Learning Large-scale Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Department of Mechanical and Aerospace Engineering University of Florida GainesvilleFL United States Amazon Robotics North Reading AI Institute Tongdun Technology

We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control into a perception module and a controller module, we can train a DRL agent without sophisticated physics or 3D modeling. In addition, the modular framework averts daunting retrains of an image-to-action end-to-end neural network, and provides flexibility in transferring the controller to different robots. First, we train a convolutional neural network (CNN) to accurately localize in an indoor setting with dynamic foreground/background. Then, we design a new DRL algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. We also show that MPG is robust at tracking varying leader movements and can naturally be extended to problems of formation control. Leveraging reward shaping, features such as collision and obstacle avoidance can be easily integrated into a DRL controller. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Motion Planning In High-Dimensional Spaces

arXiv

引用

arXiv 2018年

作者： Petrović, Luka University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems Mobile Robotics

—Motion planning is a key tool that allows robots to navigate through an environment without collisions. The problem of robot motion planning has been studied in great detail over the last several decades, with researchers initially focusing on systems such as planar mobile robots and low degree-of-freedom (DOF) robotic arms. The increased use of high DOF robots that must perform tasks in real time in complex dynamic environments spurs the need for fast motion planning algorithms. In this overview, we discuss several types of strategies for motion planning in high dimensional spaces and dissect some of them, namely grid search based, sampling based and trajectory optimization based approaches. We compare them and outline their advantages and disadvantages, and finally, provide an insight into future research opportunities. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion planning

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Scalable block-diagonal locality-constrained projective dictionary learning

arXiv

引用

arXiv 2019年

作者： Zhang, Zhao Jiang, Weiming Zhang, Zheng Li, Sheng Liu, Guangcan Qin, Jie School of Computer Science School of Artificial Intelligence Hefei University of Technology China School of Computer Science and Technology Soochow University China School of Information Technology and Electrical Engineering University of Queensland Australia Department of Computer Science University of Georgia United States School of Information and Control Nanjing University of Information Science and Technology China Computer Vision Laboratory ETH Zurich Switzerland

We propose a novel structured discriminative blockdiagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning a structured discriminative dictionary and a block-diagonal representation without using costly l0/l1-norm. Besides, it avoids extra time-consuming sparse reconstruction process with the well-trained dictionary for new sample as many existing models. More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class. To enhance the performance, we incorporate a locality constraint of atoms into the DL procedures to keep local information and obtain the codes of samples over each class separately. A block-diagonal discriminative approximation term is also derived to learn a discriminative projection to bridge data with their codes by extracting the special block-diagonal features from data, which can ensure the approximate coefficients to associate with its label information clearly. Then, a robust multiclass classifier is trained over extracted block-diagonal codes for accurate label predictions. Experimental results verify the effectiveness of our algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

arXiv

引用

arXiv 2021年

作者： Untracht, Gavrielle R. Matos, Rolando Dikaios, Nikolaos Bapir, Mariam Durrani, Abdullah K. Butsabong, Teemapron Campagnolo, Paola Sampson, David D. Heiss, Christian Sampson, Danuta M. Optical+Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Surrey Biophotonics Advanced Technology Institute School of Physics School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Department of Biochemical Sciences Department of Clinical and Experimental Medicine School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Mathematics Research Center Academy of Athens Athens106 79 Greece Surrey and Sussex Healthcare NHS Trust East Surrey Hospital Surrey RedhillRH1 5RH United Kingdom Surrey Biophotonics Centre for Vision Speech and Signal Processing School of Biosciences and Medicine The University of Surrey GuildfordGU2 7XH United Kingdom

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease. © 2021, CC BY-NC-SA.

关键词： Optical tomography

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The medical segmentation decathlon

arXiv

引用

arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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