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IEEE International Conference on robotics and Automation

作者： Eduard Vidal Juan David Hernandez Klemen Istenic Marc Carreras Underwater Robotics Research Center (CIRS) University of Girona Spain Department of Computer Science Rice University Houston USA

Achieving full autonomous robotic environment exploration in the underwater domain is very challenging, mainly due to noisy acoustic sensors, high localization error, control disturbances of the water and lack of accurate underwater maps. In this work we present a robotic exploration algorithm for underwater vehicles that does not rely on prior information about the environment. Our method has been greatly influenced by many robotic exploration, view planning and path planning algorithms. The proposed method constitutes a significant improvement over our previous work [1]: Firstly, we refine our exploration approach to improve robustness;Secondly, we propose an alternative map representation based on the quadtree data structure that allows different relevant queries to be performed efficiently, reducing the computational cost of the viewpoint generation process;Thirdly, we present an algorithm that is capable of generating consistent maps even when noisy sonar data is used. The aforementioned contributions have increased the reliability of the algorithm, allowing new real experiments performed in artificial structures but also in more challenging natural environments, from which we provide a 3D reconstruction to show that with this algorithm full optical coverage is obtained.

关键词： Cameras Sonar Planning Robot sensing systems Inspection Three-dimensional displays

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GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint

GPU-based fast hyperspectral image classification using join...

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作者： Pan, Lei Li, Heng-Chao Ni, Jun Chen, Chen Chen, Xiang-Dong Du, Qian The School of Information Science & Technology Southwest Jiaotong University Chengdu610031 China The College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China The Center of Research in Computer Vision University of Central Florida OrlandoFL32816 United States The Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States

Due to the fact that neighboring hyperspectral pixels often belong to the same class with high probability, spatial correlation between pixels has been widely used in hyperspectral image classification. In this paper, a novel joint sparse representation classifier with spectral consistency constraint (JSRC-SCC) is proposed. Specifically, to efficiently exploit contextual structure information, a local adaptive weighted average value is reallocated as the central pixel of a window through spatial filtering, and then, representation coefficients are estimated by the joint sparse representation model, which is imposed by the spectral consistency constraint under l1-minimization. For the purpose of fast classification, graphics processing units are adopted to accelerate this model. Experimental results on two classical hyperspectral image data sets demonstrate the proposed method can not only produce satisfying classification performance, but also shorten the computational time significantly. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Graphics processing unit

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Development of mobile-based application for practical wood identification

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IOP Conference Series: Earth and Environmental science 2020年第1期572卷

作者： M R Arifin E P B Sugiarto R Wardoyo Y Rianto Computer Vision Research Group Research Center for Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia Department of Informatics Faculty of Engineering Universitas Sangga Buana YPKP Bandung Indonesia Center for The Utilization and Innovation of Science and Technology Indonesian Institute of Sciences (LIPI) Cibinong Indonesia

Wood Identification is an important task, especially for wood anatomist. The identification process is required in many fields, such as custom ports, forest survey, and wood industries. However, expertise-in wood identification is limited due to a few wood anatomists. To enable wood identification can be performed in a broader area, a mobile-based application is proposed to realise these purposes. The paper presents the development of a mobile-based application for wood identification. A dataset of Indonesian commercial wood images of cross-section surface was collected from Xylarium Bogoriense, The Ministry of Environment and Forestry of The Republic of Indonesia. The images were collected using a smartphone camera at optical magnification level around 200 times and a minimum resolution of 12 megapixels. The collected images were then used to develop a deep learning-based algorithm to classify wood species. These images are considered as the training datasets, whereas testing dataset is collected in another session. The final model obtained through the training process is then stored in a cloud-based server at research center for Informatics, Indonesian Institute of sciences. A mobile application, namely AIKO stands of Indonesian words-Alat Identifikasi Kayu Otomatis (Automatic Tool for Wood Identification), is developed to enable image acquisition and images transferring to the server. AIKO application sends the acquired image to the trained identification model in the server. The output of the identification model is the species name of the observed images. Besides species/botanical name, AIKO also provides information on trade names, specific gravity and density, durability class, strength class, commercial timber classification, and recommended utilization.

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Associations between Aβ deposition and neurometabolic alterations in Alzheimer's disease: Insights from hybrid 3D MRSI-PET imaging

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70332页

作者： Miao Zhang Jialin Hu Yaoyu Zhang Ziyu Meng Yibo Zhao Yudu Li Wen Jin Xiao-Hang Qian Lijun Wang Guanyu Ye Xu-Feng Jiang Ya-Dong Li Jian Ye Huidong Tang Jun Liu Parashkev Nachev Zhi-Pei Liang Biao Li Yao Li Department of Nuclear Medicine Ruijin Hospital School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy Shanghai Jiao Tong University Shanghai China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA. National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Geriatrics Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Medical Center on Aging of Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurology and Institute of Neurology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurovascular Center Changhai Hospital Naval Medical University Shanghai China. Department of Gastroenterology Songjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders State Key Laboratory of Eye Health Shanghai Jiao Tong University School of Medicine Shanghai China. Shanghai Key Laboratory of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China. High-Dimensional Neurology Group Institute of Neurology University College London London UK.

INTRODUCTION:Understanding the neurometabolic changes associated with amyloid-β (Aβ) deposition is important for early Alzheimer's disease (AD) diagnosis, but their spatial relationships remained unexplored due to technical limitations. METHODS:We investigated the relationship between Aβ deposition and neuronal and glial metabolites using high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) (8-min scan, 2 × 3 × 3 mm resolution) and Aβ-positron emission tomography (Aβ-PET) imaging. N-acetylaspartate, myo-inositol, and creatine maps were obtained from 174 participants: 39 controls, 65 mild cognitive impairment (MCI), and 70 AD patients. RESULTS:N-Acetylaspartate levels were negatively correlated with Aβ, while myo-inositol levels were positively correlated globally. Regional associations with Aβ include N-acetylaspartate reductions in frontal cortex, anterior cingulate cortex, and precuneus, and myo-inositol increases in precuneus, lateral temporal, and lateral parietal cortices. Combined MRSI and PET biomarkers achieved the highest diagnostic accuracy for MCI and AD . DISCUSSION:Hybrid high-resolution 3D MRSI and Aβ-PET imaging provides valuable insights into Aβ's impact on neurometabolic changes, improving early AD diagnosis. HIGHLIGHTS:Hybrid 3D magnetic resonance spectroscopic imaging-positron emission tomography (MRSI-PET) imaging reveals Aβ deposition impact on neurometabolism in Alzheimer's disease (AD). N-acetylaspartate (NAA) as a neuronal metabolic marker is negatively associated with Aβ globally and locally. Myo-inositol (mI) as a glial metabolic marker is positively associated with Aβ globally and locally. Combining 3D magnetic resonance spectroscopic imaging (MRSI) and PET biomarkers improves diagnostic accuracy for mild cognitive impairment (MCI) and AD.

关键词： alzheimer's disease amyloid‐β magnetic resonance spectroscopic imaging myo‐inositol n‐acetylaspartate

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Design optimization of a cable-driven parallel robot in upper arm training-rehabilitation processes 6th

Design optimization of a cable-driven parallel robot in uppe...

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6th International Symposium on Multibody Systems and Mechatronics, MuSMe 2017

作者： Hernandez, Eusebio Valdez, S. Ivvan Carbone, Giuseppe Ceccarelli, Marco Secc. de Estudios de Posgrado e Investigacion ESIME UP TICOMAN IPN Mexico City Mexico Department of Computer Science Center for Research in Mathematics Mexico City Mexico Laboratory of Robotics and Mechatronics University of Cassino and Southern Lazio Meridionale Cassino Italy

ISBN: (纸本)9783319675664

This paper presents an optimized design of a cable driven parallel manipulator which is intended in rehabilitation or exercise of patients with shoulder problems like illness, traumatic events or for the elderly who need to exercise their limbs. Cable based parallel manipulators have characteristics that make them suitable for rehabilitation-exercise purposes like large workspace, re-configurable architecture, portability and low cost. From these purposes, upper-limb movements are analyzed and different prescribed workspaces are defined. After kinematic and wrench analysis, the Jacobian matrix of the cable driven manipulator is derived, which is used as a quantitative representation of dexterity along the workspace. An optimization model is presented to simultaneously fulfill the prescribed workspace and to improve dexterity by selecting proper length cables and other structural parameters. Numerical examples delineate effectiveness of an Estimation of Distribution Algorithm (EDA), where correlation among variables are inserted in the optimization process. © 2018, Springer International Publishing AG.

关键词： Cables

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Automated Recognition of Sleep Arousal Using Multimodal and Personalized Deep Ensembles of Neural Networks

Automated Recognition of Sleep Arousal Using Multimodal and ...

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computers in Cardiology (CinC)

作者： Andrea Patane Shadi Ghiasi Enzo Pasquale Scilingo Marta Kwiatkowska Department of Computer Science University of Oxford Bioengineering and Robotics Research Center “Enrico Piaggio” & Department of Information Engineering University of Pisa Pisa Italy

ISBN: (纸本)9781728109244

Background and Aim: Monitoring physiological signals during sleep can have substantial impact on detecting temporary intrusion of wakefulness, referred to as sleep arousals. To overcome the problems associated with the cubersome visual inspection of these events by experts, sleep arousal recognition algorithms have been proposed. Method: As part of the Physionet/Computing in Cardiology Challenge 2018, this study proposes a deep ensemble neural network architecture for automatic arousal recognition from multi-modal sensor signals. Separate branches of the neural network extract features from electro-encephalography, electrooculography, electromyogram, breathing patterns and oxygen saturation level; and a final fully-connected neural network combines features computed from the signal sources to estimate the probability of arousal in each region of interest. We investigate the use of shared-parameter Siamese architectures for effective feature calibration. Namely, at each forward and backward pass through the network we concatenate to the input a user-specific template signal that is processed by an identical copy of the network. Result: The proposed architecture obtains an AUPR score of 0.40 on the test set of the official phase of Physionet/CbiC Challenge 2018. A score of 0.45 is obtained by means of 10 -fold cross-validation on the training set.

关键词： Sleep Electroencephalography Training Electromyography computer architecture Electrooculography Neural networks

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Automatic Scale Estimation of Structure from Motion based 3D models using laser scalers

arXiv

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arXiv 2019年

作者： Istenic, Klemen Gracias, Nuno Arnaubec, Aurlien Escartn, Javier Garcia, Rafael Underwater Robotics Research Center Computer Vision and Robotics Institute University of Girona Edifici P-IV Campus de Montilivi Girona17071 Spain Coronis Computing S.L. Science and Technological Park of UdG Carrer Pic de Peguera 15 Girona17003 Spain IFREMER Ctr Mediterranee Unit Syst. Marins La Seyne Sur MerF-83507 Universit de Paris Institut de Physique du Globe de Paris CNRS F-75005 France

Recent advances in structure-from-motion techniques are enabling many scientific fields to benefit from the routine creation of detailed 3D models. However, for a large number of applications, only a single camera is available for the image acquisition, due to cost or space constraints in the survey platforms. Monocular structure-from-motion raises the issue of properly estimating the scale of the 3D models, in order to later use those models for metrology. The scale can be determined from the presence of visible objects of known dimensions, or from information on the magnitude of the camera motion provided by other sensors, such as GPS. This paper addresses the problem of accurately scaling 3D models created from monocular cameras in GPS-denied environments, such as in underwater applications. Motivated by the common availability of underwater laser scalers, we present two novel approaches which are suitable for different laser scaler configurations. A fully-calibrated method enables the use of arbitrary laser setups, while a partially-calibrated method reduces the need for calibration by only assuming parallelism on the laser beams, with no constraints on the camera. The proposed methods have several advantages with respect to the existing methods. By using the known geometry of the scene expressed by the 3D model, along with some parameters of the laser scaler geometry, the need for laser alignment with the optical axis of the camera is removed. Furthermore, the extremely error-prone manual identification of image points on the 3D model, currently required in image-scaling methods, is eliminated as well. The performance of the methods and their applicability was evaluated on both data generated from a realistic 3D model and data collected during an oceanographic cruise in 2017. Three separate laser configurations have been tested, encompassing nearly all possible laser setups, to evaluate the effects of terrain roughness, noise, camera perspective angle and camer

关键词： Cameras

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Learning a saliency evaluation metric using crowdsourced perceptual judgments

arXiv

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arXiv 2018年

作者： Xia, Changqun Li, Jia Su, Jinming Borji, Ali State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China International Research Institute for Multidisciplinary Science Beihang University Beijing China Center for Research in Computer Vision Computer Science Department University of Central Florida OrlandoFL United States

In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions. As a result, saliency model benchmarking often requires several evaluation metrics to simultaneously assess saliency models from multiple perspectives. However, most computational metrics are not designed to directly measure the perceptual similarity of saliency maps so that the evaluation results may be sometimes inconsistent with the subjective impression. To address this problem, this paper first conducts extensive subjective tests to find out how the visual similarities between saliency maps are perceived by humans. Based on the crowdsourced data collected in these tests, we conclude several key factors in assessing saliency maps and quantize the performance of existing metrics. Inspired by these factors, we propose to learn a saliency evaluation metric based on a two-stream convolutional neural network using crowdsourced perceptual judgements. Specifically, the relative saliency score of each pair from the crowdsourced data is utilized to regularize the network during the training process. By capturing the key factors shared by various subjects in comparing saliency maps, the learned metric better aligns with human perception of saliency maps, making it a good complement to the existing metrics. Experimental results validate that the learned metric can be generalized to the comparisons of saliency maps from new images, new datasets, new models and synthetic data. Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

arXiv

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arXiv 2020年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Ahn, Namhyuk Bai, Dongwoon Cai, Jie Cao, Yun Chen, Junyang Cheng, Kaihua Chun, SeYoung Deng, Wei El-Khamy, Mostafa Man Ho, Chiu Ji, Xiaozhong Kheradmand, Amin Kim, Gwantae Ko, Hanseok Lee, Kanghyu Lee, Jungwon Li, Hao Liu, Ziluan Liu, Zhi-Song Liu, Shuai Lu, Yunhua Meng, Zibo Navarrete Michelini, Pablo Micheloni, Christian Prajapati, Kalpesh Ren, Haoyu Hyeok Seo, Yong Siu, Wan-Chi Sohn, Kyung-Ah Tai, Ying Muhammad Umer, Rao Wang, Shuangquan Wang, Huibing Haoning Wu, Timothy Wu, Haoning Yang, Biao Yang, Fuzhi Yoo, Jaejun Zhao, Tongtong Zhou, Yuanbo Zhuo, Haijie Zong, Ziyao Zou, Xueyi Wang, Li-Wen Cani, Marie-Paule Siu, Wan-Chi Yang, Huan Fu, Jianlong Shi, Yukai Chen, Junyang Lee, Kanghyu Park, Jaihyun Lee, Junyeop Min, Jeongki Lee, Bokyeung Ko, Hanseok Yoo, Jaejun Sohn, Kyung-Ah Micheloni, Christian Hu, Fengshuo Wang, Yanhong Lu, Yunhua Peng, Jinjia Wang, Huibing Zhuo, Haijie Lee, Junyeop Min, Jeongki Lee, Bokyeung Park, Jaihyun Ko, Hanseok Tai, Lab Ying Jilin Li, Youtu Lab Liu, Shuai Yang, Biao Liu, Xing Chen, Shuaijun Zhao, Lei Wang, Zhan Lin, Yuxuan Jia, Xu Gao, Qinquan Deng, Wei Kheradmand, Amin El-Khamy, Mostafa Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Patel, Heena Chudasama, Vishal Upla, Kishor Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Meng, Zibo Ho, Chiu Man Computer Vision Lab ETH Zurich Switzerland LIX - Computer science laboratory at the cole polytechnique Palaiseau France Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong Shanghai Jiao Tong University China Microsoft Research Beijing China Guangdong University of Technology China Intelligent Signal Processing Laboratory Korea University Korea Republic of Ajou University Korea Republic of EPFL University of Udine Italy BOE Technology Group Co. Ltd Dalian Maritime University China Guangdong OPPO Mobile Telecommunications Corp. Ltd Department of Video Information Processing Korea University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Tencent Youtu Lab Yun Cao Tencent Youtu Tencent Youtu Lab Chengjie Wang Tencent Tencent Youtu Lab Feiyue Huang Tencent Youtu Lab Peking University China North China University of Technology China Huawei Technologies Co. Ltd China Fuzhou University Tong Tong Fuzhou University Imperial Vision Technology China Fuzhou University Imperial Vision Technology China Imperial Vision Technology SOC R&D Samsung Semiconductor Inc. United States Ulsan national institute of science and technology Korea Republic of Sardar Vallabhbhai National Institute Of Technology Surat India Norwegian University of Science and Technology Gjøvik Norway InnoPeak Technology

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r.t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Machine learning–based detection model of early Alzheimer's disease using wearable device and gait assessment

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Alzheimer's & Dementia 2021年第S5期17卷

作者： YoungHoon Jeon Thi Kieu Khanh Ho Jaeyong Kang Byeong C. Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea Department of Software Korea National University of Transportation Chungju South Korea Department of Neurology Chonnam National University Medical School Gwangju South Korea Department of Biomedical Science Chosun University Gwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Daegu South Korea Gwangju Alzheimer’s Disease and Related Dementia Cohort Research Center Chosun University Gwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Chungju South Korea Department of AI Robotics Engineering Korea National University of Transportation Chungju South Korea Department of Biomedical Engineering Korea National University of Transportation Chungju South Korea

Background A method for diagnosing early stages of Alzheimer's disease (AD) is developed by analyzing human gait and training a machine learning classifier using signals obtained from inertial measurement unit. This patient-friendly and cost-effective method enables periodic monitoring, contributing to savings in treatment costs and effort. Method 145 subjects (77 healthy controls, 68 Mild Cognitive Impairment) were employed to perform seven types of gait paradigms, including normal and complex walking. The gait of the employed subjects was measured by 7 shimmer devices, which consist of a three-dimensional accelerometer and gyroscope. The acquired signal data is converted into 35 features including gait cycle. The extracted features are used to train an ensemble classifier based on machine learning with an oversampling method. In order to find the optimal combination in the ensemble model, an elimination technique was used to compare performance by removing single classifiers one by one. For performance comparison, the dataset is divided into training set/test set by leave-one-patient-out cross-validation, and several metrics such as accuracy and F1 score are used. Result The classifier for MCI detection classified severity to an accuracy of about 0.7 for 145 subjects. The combination of oversampling techniques and different single classifiers contributes to improved performance. In the seven walking experimental paradigms, the accuracy of the classification of normal walking without subtasks is about 0.67. O the other hand, the classification of walking with associative word-speaking or counting numbers shows significant results with accuracy of 0.7413 and 0.7338, respectively. Conclusion Our proposed method predicts severity through efficient walking with less space-time constraints. Subtasks that stress the brain in walking contribute to the performance improvement of the ensemble model with oversampling. These results demonstrate that our proposed method with g

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