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作者： Chang, Chein-I Xiong, Wei Chen, Shih-Yu Information and Technology College Dalian Maritime University Dalian China School of Physics and Optoelectronic Engineering Xidian University Xian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a convex cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Spectroscopy

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Design and modeling of a compact rotational nonlinear spring

Design and modeling of a compact rotational nonlinear spring

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Hamed Jalaly Bidgoly Majid Nili Ahmadabadi Mohammad Reza Zakerzadeh Cognitive Robotics Laboratory The Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Dynamic and Vibration Laboratory School of Mechanical Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9781509037636

In this paper, we propose a new method for implementing a rotational nonlinear spring with user defined profile, based on the combination of a linear spring with a nonlinear transmission mechanism. The proposed structure consists of a non-circular cam, a roller which moves along outer circumference of the cam, and a stretched translational linear spring which is connected between center of the cam and center of the roller. We obtain a set of differential equations to design shape of the cam for any given torque-angle profile. Also, it will be shown that profiles with both positive and negative values can be implemented by the proposed method. At last, the cam of some popular nonlinear springs are designed, including constant, cubic, hyperbolic tangent and sinusoidal springs.

关键词： computer aided manufacturing Camelopardalis Hookean spring springs Device Spring TRANSFER MECHANISM Modeling nonlinearity Rotational SCROLL Differential equations Spring season

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Bandwidth Allocation with Minimum Rate Constraints in Cluster-based Femtocell Networks

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International Journal of Automation and computing 2015年第1期12卷 77-82页

作者： Kai Ma Liang Li Jie Yang Zhi-Xin Liu Xin-Bin Li Xin-Ping Guan School of Electrical Engineering Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education

Inter-femtocell interference becomes serious when femtocells are densely deployed. To mitigate the inter-femtocell interference, this paper proposes a cluster-based bandwidth allocation algorithm. We create femtocell clusters by constructing a weighted interference graph and allocate bandwidth to each cluster based on a Nash bargaining solution(NBS). Simulation results show that the cluster-based bandwidth allocation algorithm can reduce the inter-femtocell interference and meet the minimum rate constraint of each cluster.

关键词： Femtocell cluster inter-femtocell interference bandwidth allocation Nash bargaining solution(NBS)

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Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors 7

Neuromorphic sampling on the SpiNNaker and parallella chip m...

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7th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2016

作者： Mendat, Daniel R. Chin, Sang Furber, Steve Andreou, Andreas G. Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States School of Computer Science University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9781467378352

We present a bio-inspired, hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models using energy aware hardware. We have developed algorithms and programming data flows for two recently developed multiprocessor architectures, the SpiNNaker and parallella. We employ a neurally inspired sampling algorithm that abstracts the functionality of neurons in a biological network and exploits the neural dynamics to implement the sampling process. This algorithm maps nicely on the two hardware systems. Speedups as high as 1000 fold are achieved when performing inference using this approach, compared to algorithms running on traditional engineering workstations. © 2016 IEEE.

关键词： Markov processes

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Interfacial Defects: Probing and Manipulating the Interfacial Defects of InGaAs Dual‐Layer Metal Oxides at the Atomic Scale (Adv. Mater. 2/2018)

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Advanced Materials 2018年第2期30卷

作者： Xing Wu Chen Luo Peng Hao Tao Sun Runsheng Wang Chaolun Wang Zhigao Hu Yawei Li Jian Zhang Gennadi Bersuker Litao Sun Kinleong Pey Shanghai Key Laboratory of Multidimensional Information Processing Department of Electrical Engineering East China Normal University 500 Dongchuan Road Shanghai 200241 China SEU‐FEI Nano‐Pico Center Key Laboratory of MEMS of Ministry of Education School of Electronic Science and Engineering Southeast University Nanjing 210096 P. R. China Institute of Microelectronics School of Electronics Engineering and Computer Science Peking University 5 Yiheyuan Road Haidian District Beijing 100871 China The Aerospace Corporation P.O. Box 92957 M2/244 Los Angeles CA 90009 USA Singapore University of Technology and Design 8 Somapah Road 487372 Singapore

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A study on sampling of STFT modifications in time and frequency domains for DNN-based speech dereverberation

A study on sampling of STFT modifications in time and freque...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Bo Wu Kehuang Li Minglei Yang Chin-Hui Lee National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Xidian University Xian Shaanxi CN

ISBN: (纸本)9781509024018

We investigate the effects of time and frequency sampling on short-time Fourier transform modifications to be used for speech dereverberation based on deep neural networks (DNNs). We first show that by adopting a linear activation function at the output layer and globally normalizing the target features into zero mean and unit variance, better performances can be obtained than existing DNN approaches. Then we show that the quality of dereverberated speech could be degraded with denser sampling in time for longer reverberation times, even at the price of increased computational complexities, requiring an adaptive time sampling strategy. On the other hand, the difference between the unwrapped phases of reverberant and anechoic speech becomes negligible with a dense sampling in frequency, implying a reduced speech distortion. Therefore, there is a great potential to enhance DNN based acoustic signal processing if the conventional sampling strategy can be carefully adjusted.

关键词： Speech Time-frequency analysis Reverberation Training Discrete Fourier transforms

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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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Existence and switching behavior of bright and dark kerr solitons in whispering-gallery mode resonators with zero group-velocity dispersion

arXiv

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arXiv 2017年

作者： Talla Mbé, Jimmi H. Milián, Carles Chembo, Yanne K. Laboratory of Electronics Automation and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Laboratory of Modelling and Simulation in Engineering Biomimetics and Prototypes Department of Physics University of Yaoundé I P. O. Box 812 Yaoundé Cameroon Polytechnic School of Yaoundé P.O. Box 8390 Yaoundé Cameroon Centre de Physique Théorique CNRS Ecole Polytechnique PalaiseauF-91128 France Optics Department FEMTO-ST Institute CNRS & Univ. Bourgogne-Franche-Comté 15B Avenue des Montboucons Besançon Cedex25030 France Atlanta Mirror Lab School of Electrical and Computer Engineering 777 Atlantic Dr NW AtlantaGA30332-0250 United States

We use the generalized Lugiato-Lefever model to investigate the phenomenon of Kerr optical frequency comb generation when group-velocity dispersion is null. In that case, the first dispersion term that plays a leading role is third-order dispersion. We show that this term is sufficient to allow for the existence of both bright and dark solitons. We identify the areas in the parameter space where both kind of solitons can be excited inside the resonator. We also unveil a phenomenon of hysteretic switching between these two types of solitons when the power of the pump laser is cyclically varied. Copyright © 2017, The Authors. All rights reserved.

关键词： Group velocity dispersion

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Dual-Tone Radio Interferometric Positioning Systems for Multi-Target Localization Using a Single Mobile Anchor

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China Communications 2015年第1期12卷 25-35页

作者： LI Li'an WANG Yiyin MA Xiaoli CHEN Cailian GUAN Xinping Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Electrical & Computer Engineering Georgia Institute of Technology

In this paper, a low-cost dualtone radio interferometric positioning system using a single mobile anchor is proposed to locate multiple targets at the same time. We name it mDRIPS. In mDRIPS, each target continuously transmits a dual-tone signal with its dedicated frequencies, and the mobile anchor receives the signals at different positions along its trajectory, Neither time synchronization between the mobile anchor and the targets nor time synchronization among the targets is required. We take the instability of targets clocks into consideration and develop an ESPRIT-type algorithm to estimate frequencies of each dual-tone signal. Furthermore, the time of arrival （TOA） of each target signal is extracted from the phase estimates of the received dual-tone signals. After measuring several TOAs at different locations along the anchor＇s trajectory, each target can be located. Since the frequency difference of the two tones of each dualtone signal is designed to be smaller than the channel coherence bandwidth, the same fading effect on these two tones can be eliminated. Moreover, the integer ambiguity problem due to phase wrapping is investigated, and a localization algorithm to deal with a simplified ambiguity problem is proposed. Numerical results demonstrate the efficiency of the proposed mDRIPS.

关键词： ranging localization mobile anchor radio interferometry oscillator instability

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Unsupervised Single-Channel Speech Separation via Deep Neural Network for Different Gender Mixtures

Unsupervised Single-Channel Speech Separation via Deep Neura...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference

作者： Yannan Wang Jun Du Li-Rong Dai Chin-Hui Lee National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui P. R. China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA. USA

ISBN: (纸本)9781509024018

In this study, we propose a regression approach via deep neural network (DNN) for unsupervised speech separation in a single-channel setting. We rely on a key assumption that two speakers could be well segregated if they are not too similar to each other. A dissimilarity measure between two speakers is then proposed to characterize the separation ability between competing speakers. We demonstrate that the distance between speakers of different genders is large enough to warrant a possible separation. We finally propose a DNN architecture with dual outputs, one representing the female speaker group and the other characterizing the male speaker group. Trained and tested on the Speech Separation Challenge corpus our experimental results show that the proposed DNN approach achieves large performance gains over the state-of-the-art unsupervised techniques without using specific knowledge about the mixed target and interfering speakers and even outperforms the supervised GMM-based method.

关键词： Deep Neural Network Different Gender Mixtures GMM-based

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