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International Conference on Information and Automation (ICIA)

作者： Min Wang Xinyu Wu Duxin Liu Can Wang Ting Zhang Pingan Wang Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation The Chinese University of Hong Kong Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences

This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR) signals to predict human motion in the binding exoskeleton, the non-binding exoskeleton robot collect the Inertial Measurement Unit (IMU) signals of the pilot. Seven basic motions are studied, each motion is divided into four phases except the standing-still motion which only has one motion phase. The human motion prediction algorithm adopts Support Vector machine (SVM) to classify human motion phases and Hidden Markov Model (HMM) to predict human motion. The experimental data demonstrate the effectiveness of the proposed algorithm.

关键词： Hidden Markov models Prediction algorithms Exoskeletons Support vector machines Accuracy Classification algorithms Training

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Robust localization system for an autonomous mower

Robust localization system for an autonomous mower

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IEEE International Conference on Robotics and Biomimetics

作者： Huiwen Guo Xinyu Wu Ruiqing Fu Wei Feng Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences

ISBN: (纸本)9781467396769

This paper presents a robust vision-based localization system for an autonomous mower, which is significant for both the meadow map building and the successful area covering. Instead of setting the monocular camera toward the scene, which suffers from the disturbance of moving objects, less mark points or variation of illumination, we equip the camera toward the ground with constant illumination compensation. To achieve the localization of the mower, point features are extracted and matched between pairs of frames. Motion is incremental obtained by calculate the rotation and translation transformation of matched feature point pairs. As the angle accumulated error has greater contribution to the location error, angular acceleration sensor is adopted to compensate the angle error especially in the steep turning case. Experiments on meadow with our mowers demonstrate the robustness of our localization system.

关键词： Cameras Robot sensing systems Mobile robots Lighting Acceleration Robustness

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On study of a wheel-track transformation robot

On study of a wheel-track transformation robot

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IEEE International Conference on Robotics and Biomimetics

作者： Jianbing Hu Ansi Peng Yongsheng Ou Guolai Jiang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781467396769

Wheeled robots usually move fast in relatively even surfaces while losing their major mobility on rough terrains. However, the tracked robots are superior to the wheel-types when they are to move on irregular terrains in spite of lower speed. In this paper, a Wheel-Track Transformation robot which combines the advantages of both mobile types by turning into wheel mode or track mode is designed. The robot consists of a robot platform equipped with an extra support mechanism, two wheels with transformation units and an universal wheel. Each transformation unit is composed of a rotating board and a double two-bar linkage mechanism. Three key mechanical components and several locomotion strategies on rough terrains are presented. Meanwhile, a detailed quasi-dynamic analysis is conducted to obtain proper torque capabilities of motors. Three prototypes of the proposed mechanism were manufactured and their fundamental mobility was evaluated by experiments.

关键词： Mobile robots Wheels Couplings Force Torque Tracking

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States 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 Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Detecting Anomaly Using the Scene Modeling Based on Time Delayed Statistical Data

Detecting Anomaly Using the Scene Modeling Based on Time Del...

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IEEE International Conference on Information and Automation

作者： Lixin Chen Huiwen Guo Min Wang Yen-Lun Chen Xinyu Wu Wei Feng Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781467391054

We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera view and acquire the crowd flow model by the K-means clustering algorithm. Secondly, we analyze the crowd flow model based on the time delayed statistical data between two camera views. And then we acquire the relative location among the entrances and exits in the different regions. Thirdly, we analyze the crowd transferring probabilistic model on the global scene based on the log-likelihood function and Dirichlet distribution to detect the crowd anomaly. We set up the empirical threshold value of probability P_c. If the probability of detected model is less than P_c, the detected model is marked as the crowd anomaly. Our approach is evaluated on the simulated data set and the real data set in far-field scenes. Experimental results show the anomaly detection is precise.

关键词： Crowd anomaly detection Non-overlapping view K-means clustering algorithm Log-likelihood function Dirichlet distribution

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Effects of Contralateral Acoustic Stimulation on Otoacoustic Emissions Induced by Swept Tones

Effects of Contralateral Acoustic Stimulation on Otoacoustic...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Shixiong Chen Yanbing Jin Lisheng Xu Guanglin Li Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 518055 China Sino-Dutch Biomedical and Information Engineering School Hunnan Campus of Northeastern University Shenyang Liaoning 110167 China

ISBN: (纸本)9781424492695

The medial olivocochlear complex (MOC) is an auditory nucleus that projects efferent nerve fibers to control the behaviors of both sides of the cochlea. Otoacoutsic emissions (OAEs) are by-products the activities of the outer hair cells (OHCs) in the cochlea and could be used as a noninvasive way to study the efferent control of the MOC. However, existing results regarding the efferent control are quite controversial and often restricted to a rather limited frequency range. In this study, a new method of measuring stimulus frequency otoacoustic emissions (SFOAEs) with the presence of a contralateral acoustic stimulation (CAS) was proposed to study the efferent control over the cochlea. SFOAEs were measured with swept tones with time varying frequencies so that SFOAE spectra with and without the presence of the CAS could be compared with high frequency-resolution. The results showed that there was consistent decrease in the amplitude of the swept-tone SFOAEs across a wide frequency range from 0.5 to 8 kHz when the CAS was presented, suggesting an outstanding attenuation of OHC activities by the efferent control from the MOC. The SFOAE decrease with the presence of the CAS might provide a new approach to measure the strength of the efferent control and to evaluate the functional status of the central auditory pathway.

关键词： MOC OAEs OHCs MARGIN OF CONTROL (CRmCALITY) CONDITION ASSESSMENT SURVEYS spontaneous otoacoustic emission efferent nerve OHC frequency ranges Cochlea auditory stimulation nerve fiber Auditory Pathways Stimulus Frequency wide frequency range

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Design and realization of 3D NOC multicast router base on multicast rotational routing arithmetic 5

Design and realization of 3D NOC multicast router base on mu...

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5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Wang, Wei Li, Runfeng Fang, Fang Chen, Tian Ren, Fuji Liu, Jun Wu, Xi Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine 230009 China

ISBN: (纸本)9781479926961

Traditional 3D NoC router may encounter the situations of congestion and hot spot. And it also doesn't have the function of multicast. In this paper, we present a new routing arithmetic called Multicast Rotational Routing Arithmetic (MRRA). It can handle several destination addresses at the same time, recombine packets, sent the same data to the different destination nodes, and avoid the congestion. The proposed algorithm is implemented by the new multicast 3D NoC router which is also proposed in this paper. The 3D NoC router is designed in Verilog HDL, simulated by Modelsim, synthesized and prototyped by Synplify and FPGA respectively. The experiment results show that the multicast router of MRRA can achieve a very good performance in terms of throughput and delay. The increased area utilization also can be tolerated. © 2014 IEEE.

关键词： Network-on-chip

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Semantic polarity detection of Chinese multiword expression in microblogging based on discriminative latent model

Semantic polarity detection of Chinese multiword expression ...

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作者： Sun, Xiao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

Extracting semantic polarity of Chinese Multiword Expression, especially some newly generated Multiword Expression from internet(such as weibo or microblog), is an important task for sentiment analysis of web texts or other real word text as some Multiword Expressions can express more integrative sentiments than words units. This paper proposes a method that contains a novel latent discriminative algorithm, which attempts to attack this problem by integrating discriminative model and latent value model. Although Chinese Multiword Expressions consist of multiple words, the semantic polarity of the Multiword Expression is not just simple integration of polarities of the component words, as some words can invert the affective polarity so the Multiword Expressions can have totally opposite semantic polarity, such as ironic texts. In order to capture the property of such Multiword Expressions, hidden semi-CRF which includes a latent valuable layer, which can be used to address dual-sequence labeling tasks synchronously, is adopted. The method is tested experimentally by adopting a manually labeled set of positive and negative Multiword Expressions from microblog or other internet resources, and the experiments have shown very promising results, which is comparable to the best value ever reported. © 2014-IOS Press and the authors. All rights reserved.

关键词： Sentiment analysis

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Chinese microblogging emotion classification based on support vector machine 5

Chinese microblogging emotion classification based on suppor...

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5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Sun, Xiao Li, Chengcheng Ye, Jiaqi Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781479926961

Through the analysis and study of the emotional characteristics in Chinese micro-blog, this paper proposed a multidimensional sentiment classification method based micro-blog emotion classification, emoticon in micro-blog is adopted to train the support vector machine and divide micro-blog into seven types of emotions: happiness, fondness, sorrow, anger, fear, detestation and surprise. We used micro-blog emoticon to initial screen large-scale unmarked data, and automatically labeled them into seven types, then used this emotional corpus as training set to train the emotion classifier to classify micro-blog data into multiple emotion categories. The experimental results show that precision of unigram model for each type can reach 63.7%. Meanwhile, different feature selection methods for support vector machines and Naive Bayes classifier experiment have been adopted in the experiment, the precision and recall has reached more than 71%. © 2014 IEEE.

关键词： Support vector machines

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