检索结果-内蒙古大学图书馆

Collective circular motion and cooperative circumnavigation for nonholonomic mobile robots using range-based measurements

学校读者我要写书评

暂无评论

Collective circular motion and cooperative circumnavigation ...

AIAA Guidance, Navigation, and control Conference, 2016

作者： Cao, Yongcan Milutinovic, Dejan Casbeer, David Kingston, Derek Department of Electrical and Computer Engineering University of Texas San AntonioTX78249 United States Department of Computer Engineering University of California Santa CruzCA95064 United States Control Science Center of Excellence Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624103896

ISBN: (纸本)9781624103896

In this paper, we study two typical collective behaviors, namely, collective circular motion and cooperative circumnavigation, for a team of nonholonomic mobile robots when only range-based measurements are available. The main challenge is the development of control algorithms using range measurements rather than position measurements. For the collective circular motion, the goal is to design control algorithms such that a team of robots evenly spreads over the circle of a desired radius, which is centered at a stationary target. For the cooperative circumnavigation motion, the goal is to develop control strategies such that a robot can orbit around a stationary target at a desired distance that is larger than the maximum communication range between the robot and the target. To accomplish this goal, we first let a few other "relay" robots maintain the collective circular motion by using the developed control aglorithm and then compute the distance between the robot and the target using auxilliary range measurements between "relay" robots and the robot (or the target). Finally, the distance between the robot and the target is used to enable the circumnavigation of the robot around the target at the desired radius. To further demonstrate the effectiveness of the developed control algorithms and strategies, simulation examples are provided as a proof of concept. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Cooperative communication

Characterizing multi-photon quantum interference with practical light sources and threshold single-photon detectors

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Navarrete, Álvaro Wang, Wenyuan Xu, Feihu Curty, Marcos EI Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain Center for Quantum Information and Quantum Control Department of Physics Department of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Hefei Anhui230026 China

The experimental characterization of multi-photon quantum interference effects in optical networks is essential in many applications of photonic quantum technologies, which include quantum computing and quantum communication as two prominent examples. However, such characterization often requires technologies which are beyond our current experimental capabilities, and today's methods suffer from errors due to the use of imperfect sources and photodetectors. In this paper, we introduce a simple experimental technique to characterise multi-photon quantum interference by means of practical laser sources and threshold single-photon detectors. Our technique is based on well-known methods in quantum cryptography which use decoy settings to tightly estimate the statistics provided by perfect devices. As an illustration of its practicality, we use this technique to obtain a tight estimation of both the generalized Hong-Ou-Mandel dip in a beamsplitter with six input photons, as well as the three-photon coincidence probability at the output of a tritter. Copyright © 2017, The Authors. All rights reserved.

关键词： Photons

Robust and Scalable Power System State Estimation via Composite Optimization

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center and the Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200072 China

—In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

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

学校读者我要写书评

暂无评论

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

Towards Visual Mega-Analysis of Voxel-based Measurement in Brain Cohorts

学校读者我要写书评

暂无评论

Towards Visual Mega-Analysis of Voxel-based Measurement in B...

18th Eurographics/IEEE VGTC Conference on Visualization, EuroVis 2016

作者： Zhang, Guohao Kochunov, Peter Hong, Elliot Carr, Hamish Chen, Jian Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Center for Brain Imagining Research Maryland Psychiatric Research Center United States Institute for Computation and System Science University of Leeds United Kingdom

ISBN: (纸本)9783038680147

We present a visualization prototype for comparative analysis of factional anisotropy (FA) distributions constructed from three-dimensional (3D) brain diffusion tensor imaging (DTI) in brain cohorts. The prototype lets brain scientists examine meta-analysis (the pooled analysis of multiple smaller trials or multi-site studies) results for identifying differences in cohorts. Interactive side-by-side bar charts show multiple statistical results of FA comparisons in regions of interest (ROIs) defined by user-chosen atlas. An occlusion-free two-dimensional (2D) semantic merge tree further displays the global distribution of FA values. Two histograms on each tree arc reveal voxel-based FA distributions represented by that arc branch in cohorts. Interaction techniques support brushing-and-linking of local and global ROIs queries. ROIs can be defined from atlas or select through interaction. We report validation results in a case study and an interview. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Diffusion tensor imaging

An ultra-high-density 256-channel/25mm2 neural sensing microsystem using TSV-embedded neural probes

学校读者我要写书评

暂无评论

An ultra-high-density 256-channel/25mm2 neural sensing micro...

2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016

作者： Huang, Yu-Chieh Huang, Po-Tsang Wu, Shang-Lin Hu, Yu-Chen You, Yan-Huei Chen, Ming Huang, Yan-Yu Chang, Hsiao-Chun Lin, Yen-Han Duann, Jeng-Ren Chiu, Tzai-Wen Hwang, Wei Chen, Kuan-Neng Chuang, Ching-Te Chiou, Jin-Chern Institute of Electrical Control Engineering National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Department of Electronics Engineering National Chiao Tung University Hsinchu Taiwan Department of Biological Science and Technology National Chiao Tung University Hsinchu Taiwan BME RandD Center China Medical University Taiwan

ISBN: (纸本)9781479953400

Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, a 256-channel/25 mm2 neural sensing microsystem is presented based on through-silicon-via (TSV) 2.5D integration. This microsystem composes of dissolvable μ-needles, TSV-embedded μ-probes, 256-channel neural amplifiers, 11-bit area-power-efficient SAR ADCs and serializers. Based on the dissolvable μ-needles and TSV 2.5D integration, this microsystem can detect 256 ECoG/LFP signals within the small area of 5mm × 5mm. Additionally, the neural amplifier realizes 57.8dB gain with only 9.8μW for each channel, and the 9.7-bit ENOB of the SAR ADC at 32kS/s can be achieved with 0.42μW and 0.036 mm2. The overall power of this microsystem is only 3.79mW for 256-channel neural sensing. © 2016 IEEE.

关键词： Microsystems

3D Mesostructures: Fabrication and Deformation of 3D Multilayered Kirigami Microstructures (Small 11/2018)

学校读者我要写书评

暂无评论

Small 2018年第11期14卷

作者： Mohammad Humood Yan Shi Mengdi Han Joseph Lefebvre Zheng Yan Matt Pharr Yihui Zhang Yonggang Huang John A. Rogers Andreas A. Polycarpou Department of Mechanical Engineering College of Engineering Texas A&M University College Station TX 77843-3123 USA State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Center for Mechanics and Materials and Center for Flexible Electronics Technology AML Department of Engineering Mechanics Tsinghua University Beijing 100084 P. R. China Department of Materials Science and Engineering Fredrick Seitz Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 USA Bruker Nano Surfaces Eden Prairie MN 55344 USA Department of Chemical Engineering and Department of Mechanical and Aerospace Engineering University of Missouri Columbia MI 65211 USA Departments of Mechanical Engineering and Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA Department of Biomedical Engineering Department of Chemistry Department of Mechanical Engineering Department of Electrical Engineering and Department of Computer Science Center for Bio-Integrated Electronics Simpson Querrey Institute for Nano/Biotechnology Northwestern University Evanston IL 60208 USA

An event-triggered consensus approach for distributed clock synchronization

学校读者我要写书评

暂无评论

An event-triggered consensus approach for distributed clock ...

American control Conference

作者： Eloy Garcia Shaoshuai Mou Yongcan Cao David W. Casbeer Control Science Center of Excellence Air Force Research Laboratory WPAFB OH 45433 United States of America School of Aeronautics and Astronautics Purdue University West Lafayette IN 47907 United States of America Department of Electrical and Computer Engineering University of Texas at San Antonio 78249 United States of America

ISBN: (纸本)9781509045839

Distributed clock synchronization with intermittent communication is addressed in this paper. An event-triggered control and communication approach is presented in order to determine in a decentralized way the time instants at which each agent transmits information. The decentralized event-triggered synchronization of clocks is formulated as a single integrator consensus problem in the particular case and as double integrator consensus problem in the general case. The main contribution of this paper, besides the implementation of event-triggered mechanisms, is the feature that the virtual clocks are continuous. This is an important property because of the processes associated to each local clock, where missing deadlines or performing time dependent tasks more than once needs to be avoided.

关键词： clock recovery Event trigger local clock Timing devices integrators

A Data Driven Approach for Resting-state EEG signal Classificition of Schizophrenia with control Participants Using Random Matrix Theory

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Liu, Haichun Zhang, TianHong Ye, Yumeng Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Resting state electroencephalogram (EEG) abnormalities in clinically high-risk individuals (CHR), clinically stable first-episode patients with schizophrenia (FES), healthy controls (HC) suggest alterations in neural oscillatory activity. However, few studies directly compare these anomalies among each types. Therefore, this study investigated whether these electrophysiological characteristics differentiate clinical populations from one another, and from non-psychiatric controls. To address this question, resting EEG power and coherence were assessed in 40 clinically high-risk individuals (CHR), 40 first-episode patients with schizophrenia (FES), and 40 healthy controls (HC). These findings suggest that resting EEG can be a sensitive measure for differentiating between clinical *** paper proposes a novel data-driven supervised learning method to obtain identification of the patients mental status in schizophrenia research. According to Marchenko-Pastur Law, the distribution of the eigenvalues of EEG data is divided into signal subspace and noise subspace. A test statistic named LES that embodies the characteristics of all eigenvalues is adopted. different classifier and different feature(LES test function) are selected for experiments, we have shown that using von Neumann Entropy as LES test function combine with SVM classifier could obtain the best average classification accuracy during three classification among HC, FES and CHR of Schizophrenia group with EEG signal. It is worth noting that the result of LES feature extraction with the highest classification accuracy is around 90% in two classification(HC compare with FES) and around 70% in three classification. Where the classification accuracy higher than 70% could be used to assist clinical diagnosis. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions