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Intra-topic variability normalization based on linear projection for topic classification 15

Intra-topic variability normalization based on linear projec...

15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016

作者： Liu, Quan Guo, Wu Ling, Zhen-Hua Jiang, Hui Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China HefeiAnhui China Department of Electrical Engineering and Computer Science York University 4700 Keele Street TorontoONM3J 1P3 Canada iFLYTEK Research Hefei China

ISBN: (纸本)9781941643914

This paper proposes a variability normalization algorithm to reduce the variability between intra-topic documents for topic classification. Firstly, an optimization problem is constructed based on linear variability removable assumption. Secondly, a new feature space for document representation is found by solving the optimization problem with kernel principle component analysis (KPCA). Finally, effective feature transformation is taken through linear projection. As for experiments, state-of-the-art SVM and KNN algorithm are adopted for topic classification respectively. Experimental results on a free-style conversational corpus show that the proposed variability normalization algorithm for topic classification achieves 3.8% absolute improvement for micro-F1 measure. ©2016 Association for Computational Linguistics.

关键词： Linear transformations

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Reconfigurable accelerator design platform for ultrasonic signal processing and imaging applications

Reconfigurable accelerator design platform for ultrasonic si...

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IEEE International Ultrasonics Symposium

作者： Paul Gal Jafar Saniie Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago U.S.A.

ISBN: (纸本)9781467398985

Ultrasonic testing and imaging systems have evolved from a basic single transducer system to arrays capable of 3-dimensional scans. These systems need to be flexible and also reconfigurable in order to acquire and process a vast amount of data. The system must also allow for a variety of parameters like the data acquisition rate, signal lengths and processing characteristics which need to be reconfigurable for adaptability to different applications. This study presents a reconfigurable hardware/software co-design system including specific hardware accelerators to acquire and perform ultrasonic signal processing efficiently. For this study, the architecture of the developed ultrasonic system is comprised of two different functional components. One component is specially designed to process the data while the other component gathers, compresses and transfers data which can later be restored and analyzed. For data transfer a simple and consequently efficient real-time operating system and Ethernet connective is developed. This is all implemented on the ZEDBoard Zynq SoC for maximum flexibility and performance. The data handling component is built with support for gathering, compressing, reconstructing, and transferring data to other platforms via Ethernet communication. The different variations of the designs allow the system designers the choice of applying the best solution for their specific applications.

关键词： Acoustics Discrete wavelet transforms Adders Signal processing Field programmable gate arrays Real-time systems Image reconstruction Field programmable gate arrays discrete wavelet transform adders signal processing configurability real-time systems image reconstruction Acoustics ultrasonic signal ethernet communication hardware accelerator

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Coherent oscillations of driven rf SQUID metamaterials

arXiv

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arXiv 2017年

作者： Trepanier, Melissa Zhang, Daimeng Mukhanov, Oleg Koshelets, V.P. Jung, Philipp Butz, Susanne Ott, Edward Antonsen, Thomas M. Ustinov, Alexey V. Anlage, Steven M. Department of Physics CNAM University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Hypres Inc. 175 Clearbrook Road ElmsfordNY10523 United States Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow125009 Russia Physikalisches Institut Karlsruhe Institute of Technology Germany Russian Quantum Center National University of Science and Technology MISIS Moscow119049 Russia

Through experiments and numerical simulations we explore the behavior of rf SQUID (radio frequency superconducting quantum interference device) metamaterials, which show extreme tunability and nonlinearity. The emergent electromagnetic properties of this metamaterial are sensitive to the degree of coherent response of the driven interacting SQUIDs. Coherence suffers in the presence of disorder, which is experimentally found to be mainly due to a dc flux gradient. We demonstrate methods to recover the coherence, specifically by varying the coupling between the SQUID meta-atoms and increasing the temperature or the amplitude of the applied rf flux. Copyright © 2017, The Authors. All rights reserved.

关键词： Metamaterials

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A comparative study of singular spectrum analysis and empirical mode decomposition for ultrasonic NDE

A comparative study of singular spectrum analysis and empiri...

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IEEE International Ultrasonics Symposium

作者： Yufeng Lu Jafar Saniie Electrical and Computer Engineering Department Bradley University Peoria Illinois 61625 United States Embedded Computing and Signal Processing Laboratory Electrical and Computer Engineering Department Illinois Institute of Technology Chicago 60616 United States

ISBN: (纸本)9781467398985

In this investigation, a comparative study of two time-series analysis techniques, singular spectrum analysis (SSA) and empirical mode decomposition (EMD), is carried out for ultrasonic NDE applications. Unlike transform-based approaches, the SSA and EMD are fully driven in the time domain by the data itself. The SSA method captures the trend of signals via eigenvalues from a trajectory matrix. Whereas EMD searches intrinsic mode functions (IMF) via local maxima and minima, then the instantaneous frequency of IMFs is used to disclose the signal trends. Simulation shows that the trend tracking becomes problematic for defect assessment when signal trends become too complicated. As a remedy, a technique combing these two methods is proposed. Experimental and simulation results reveal the effectiveness of the collective effort from EMD and SSA. The study demonstrates that the data-driven nature is unique and attractive to characterize non-stationary ultrasonic signals where scattering, absorption and dispersion effects become dominant.

关键词： Acoustics Market research Ultrasonic imaging Eigenvalues and eigenfunctions Transforms Noise measurement Trajectory Ultrasonography Noise measurement Spectrum Analysis market research Eigenfunctions trajectory Transform Acoustics SSA CALR gene NDE1 gene Nondestructive Testing Defect assessment TIME-DOMAIN

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Perormance evaluation of radiative transfer models of satellite atmospheric microwave sounding for data assimilation

Perormance evaluation of radiative transfer models of satell...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gongwei Li Ke Chen Albin J. Gasiewski Kun Zhang Qingxia Li Li Zhang Ye He Anjie Cao Science and Technology on Multi-spectral Information Processing Laboratory Huazhong University of Science and Technology Department of Electrical Computer and Energy Engineering University of Colorado Shanghai Institute of Satellite Engineering

ISBN: (纸本)9781509033331

To assimilate satellite-based passive microwave observation over heavy clouds and precipitation into numerical weather prediction (NWP) systems and thus to improve the performance of it has become an intensely studied topic. These attempts rely on the development of a radiative transfer (RT) model that accounts for particle scattering and accurately simulates the observation process at an acceptable computational speed. In this paper, two existing RT models (the RTTOV and the DOTLRT) are tested and the accuracies are evaluated by comparing the simulated brightness temperature with real observational data of a satellite-based sounder ATMS. RTTOV is much more well-known and widely applied, however, since these two models use different solvers to deal with the scattering effect, whether it is possible to improve the performance of RT model is still open to discussion.

关键词： Atmospheric modeling Brightness temperature Data models Computational modeling Satellites Microwave theory and techniques Storms

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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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Ultrasonic reflectivity function estimation using Cepstrum Sparse Deconvolution

Ultrasonic reflectivity function estimation using Cepstrum S...

引用

IEEE International Ultrasonics Symposium

作者： Juan Lu Ramazan Demirli Jafar Saniie Embedded Computing and Signal Processing Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States Center for Advanced Communications Villanova University Pennsylvania United States

ISBN: (纸本)9781467398985

The ultrasonic signals backscattered from multilayered materials using a pulse-echo method are inherently low-resolution due to the band-limited nature of the interrogating transducer. To improve resolution, the Wiener Filter (WF) based solutions have been used traditionally, although they provide limited improvement and are highly sensitive to noise. More recently, sparse deconvolution techniques (e.g., l1-regularized Least Squares Deconvolution (l1-LSD)) have proved to be effective in improving the resolution;however, they are highly dependent on pulse invariance. It is common for an ultrasonic wavelet to change its shape as it propagates through the materials due to frequency dependent attenuation, scattering and dispersion. Consequently, these changes in pulse shape limit the application of these algorithms which need a prior knowledge of pulse-wave of the system. In this paper we present an adaptive and highly efficient deconvolution technique, Cepstrum Sparse Deconvolution (CSD), which does not require any prior information of the system or the detected object but compensates for the pulse-wave variation by estimating the fundamental pulse-wave shape directly from the backscattered signal. This pulse-wave estimation is based on cepstrum processing of the signal prior to the deconvolution process. Applications and comparisons are made to evaluate the performance of CSD, WF and l1-LSD through experimental and simulated ultrasound data which contain varying-shape wavelets with respect to amplitude, center frequency, arrival time, bandwidth, and phase parameters. The Normalized Mean Absolute Score (NMAS) index, a measure of distance between the actual reflectivity function and the estimated function is used to evaluate these algorithms' performance. The results show that CSD is the fastest algorithm and offers highest resolution among these three algorithms even though the pulse-wave may have gone through a significant change.

关键词： l1-LSD Sparse Deconvolution Cepstrum processing Wiener Filter

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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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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Throughput-delay tradeoff for wireless multichannel multi-interface random networks

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Canadian Journal of electrical and computer engineering 2015年第2期38卷 162-169页

作者： Ma, Xiaolin Li, Fangmin Liu, Jian Liu, Xinhua Key Laboratory of Fiber Optical Sensing Technology and Information Processing School of Information Engineering Wuhan University of Technology Wuhan430070 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Capturing throughput-delay tradeoff in wireless networks has drawn considerable attention, as it could bring better usage experience by considering different requirements of throughput/delay demands. Traditional works consider only typical single-channel single-interface networks, whereas multichannel multi-interface (MCMI) networks will become mainstream since they provide concurrent transmissions in different channels, which in turn helps each node to obtain better performance. Unlike previous works, this paper investigates the throughput-delay tradeoff for MCMI random networks. Two queuing systems, i.e., the M/M/m queuing system and the m M/M/1 queuing system, are established for MCMI nodes, and a parameter in routing implementation named routing deviation is also considered in the analytical model. This paper studies concurrent transmission capacity (CTC) using the physical interference model and also explores the impact on CTC of different physical parameters. Moreover, the relations between throughput and delay are achieved using two queuing systems in MCMI random networks respectively. The deterministic results obtained with a group of real network configuration parameters demonstrate that the proposed tradeoff model could be applied to the real network scenarios. © 2015 IEEE.

关键词： Queueing networks

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