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IEEE Digital signal processing Workshop

作者： Yifeng Wu Richard A. Black Brian D. Jeffs Karl F. Warnick Department of Electrical and Computer Engineering Brigham Young University Provo UT USA National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781467391702

Radio astronomy instrumentation uses phased array feeds to provide radio telescopes with wider fields of view and enhanced beam control for detection and interference suppression. The standard assumption in Radio Astronomy is that receiver amplifiers operate in a linear region. In the presence of strong radio-frequency interference (RFI), however, it is possible to drive the amplifiers non-linear. This can cause out-of-band RFI to become non-linear and mix harmonics into the filter passband. In this scenario, classical RFI-mitigating beamformers may not be very good at suppressing the interference. This paper analyzes the effect of several beamformers in suppressing interference resulting from non-linear amplifiers. Experimental results show that a subspace projection beamformer is able to suppress the interference despite the nonlinear RFI.

关键词： Interference Array signal processing Radio astronomy Telescopes Feeds Receivers

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Sum-Rate and Power Scaling of Massive MIMO Systems with Channel Aging

Sum-Rate and Power Scaling of Massive MIMO Systems with Chan...

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作者： Kong, Chuili Zhong, Caijun Papazafeiropoulos, Anastasios K. Matthaiou, Michail Zhang, Zhaoyang Institute of Information and Communication Engineering Zhejiang University Hangzhou310027 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Communications and Signal Processing Group Imperial College London London United Kingdom School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law;instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively. © 2015 IEEE.

关键词： Channel state information

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Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiliang Zhang Hui Jiang Lirong Dai Google MPI for Intelligent Systems National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York UniversityToronto OntarioCanada

In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.

关键词： PCA mixture models neural networks orthogonal projection unsupervised learning von Mises-Fisher (vMF) model

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IRCI free range reconstruction for co-located MIMO-OFDM radar

IRCI free range reconstruction for co-located MIMO-OFDM rada...

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IEEE China Summit & International Conference on signal and Information processing (ChinaSIP)

作者： Yunhe Cao Xiang-Gen Xia Shenghua Wang National Laboratory of Radar Signal Processing Xidian University Xi'an P.R. China Department of Electrical and Computer Engineering University of Delaware Newark DE USA

ISBN: (纸本)9781479919499

A MIMO-OFDM range reconstruction method based on cyclic prefix (CP) and its corresponding MIMO-OFDM waveform design for co-located MIMO radar systems are proposed. Our proposed MIMO-OFDM waveform design achieves the maximum signal-to-noise ratio (SNR) gain after the range reconstruction and its peak-to-average power ratio (PAPR) in the discrete time domain is also optimal, i.e., 0dB, when Zadoff-Chu sequences are used in the discrete frequency domain as the weighting coefficients for the subcarriers. Finally, simulation results are presented to verify the effectiveness of the proposed method.

关键词： OFDM Receiving antennas MIMO radar Transmitting antennas Radar antennas Scattering

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AN OVERVIEW OF ROBUST COMPRESSIVE SENSING OF SPARSE signalS IN IMPULSIVE NOISE

AN OVERVIEW OF ROBUST COMPRESSIVE SENSING OF SPARSE SIGNALS ...

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European signal processing Conference

作者： Ana B. Ramirez Rafael E. Carrillo Gonzalo Arce Kenneth E. Barner Brian Sadler Universidad Industrial de Santander Signal Processing Laboratory (LTS5) Ecole Polytechnique Federale de Lausanne (EPFL) Department of Electrical and Computer Engineering University of Delaware US Army Research Laboratory

ISBN: (纸本)9781479988518

While compressive sensing (CS) has traditionally relied on l_2 as an error norm, a broad spectrum of applications has emerged where robust estimators are required. Among those, applications where the sampling process is performed in the presence of impulsive noise, or where the sampling of the high-dimensional sparse signals requires the preservation of a distance different than l_2. This article overviews robust sampling and nonlinear reconstruction strategies for sparse signals based on the Cauchy distribution and the Lorentzian norm for the data fidelity. The derived methods outperform existing compressed sensing techniques in impulsive environments, thus offering a robust framework for CS.

关键词： Compressed sensing Sampling methods Robust signal reconstruction Nonlinear estimation Impulsive noise Impulse noise Compressed sensing nonlinear estimation sampling methods signals Spectra applications sampling process Noise

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Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture

Markov Chain Monte Carlo inference on graphical models using...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Daniel R. Mendat Sang Chin Steve Furber Andreas G. Andreou Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland Applied Physics Laboratory Johns Hopkins University Laurel Maryland School of Computer Science The University of Manchester UK Center for Language and Speech Processing Johns Hopkins University Baltimore Maryland

We present a combined hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models in a brain-inspired, energy-aware manner. By combining massively-parallel neuromorphic hardware architecture (SpiNNaker) with algorithms we've have developed for the event-based framework employed in SpiNNaker, we achieve large speedups when performing inference as compared to a traditional PC. We present results from two sampling approaches both well suited to the SpiNNaker architecture. Neural sampling, the first of the two approaches relies directly on simulating networks of spiking neurons while the second, Gibb's sampling is more flexible but still takes advantage of the hardware's event-handling capabilities.

关键词： Neurons Graphical models Markov processes Bayes methods Roads computer architecture MATLAB

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A Secure and Efficient Scheme for Digital Gift Certificates

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Journal of Information Hiding and Multimedia signal processing 2015年第3期6卷 416-429页

作者： Liu, Yanjun Chang, Chin-Chen Sun, Chin-Yu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Department of Electrical Engineering Anhui University 111 Jiu-Long road Hefei230039 China Department of Computer Science and Information Engineering Asia University 500 Liou-feng Road Taichung413 Taiwan Department of Information Engineering and Computer Science Feng Chia University 100 Wen-hwa Road Taichung407 Taiwan Department of Computer Science National Tsing-Hua University 101 Kuang-Fu Road Hsinchu300 Taiwan

With the increasingly rapid developments in e-commerce, schemes for digital gift certificates have become prevalent electronic payment systems due to their practicality and simplicity. In 2002, Chan and Chang introduced the concept of digital gift certificates and proposed the first scheme associated with them. Recently, Chang and Chang (2005) proposed a more practical scheme, called DGC-US (digital gift certificates with unconstrained spending), which can reduce the computational cost of all entities involved in Chan and Chang‘s scheme, especially the computational cost of the electronic department store. However, the asymmetric encryption/decryption operations required in the DGC-US scheme still waste a significant amount of time. In this paper, we propose a novel authentication scheme for digital gift certificates based on Chebyshev chaotic maps and the blind signature. Our proposed scheme can satisfy the essential security requirements and withstand various well-known attacks. Our analyses have shown that our proposed scheme maintains all of the merits of the DGC-US scheme but is more efficient in terms of computational cost. © 2015, Ubiquitous International. All rights reserved.

关键词： Authentication

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PHYSIOLOGICALLY-MOTIVATED FEATURE EXTRACTION FOR SPEAKER IDENTIFICATION

PHYSIOLOGICALLY-MOTIVATED FEATURE EXTRACTION FOR SPEAKER IDE...

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IEEE International Conference on Acoustics, speech and signal processing

作者： Jianglin Wang Michael T. Johnson Speech and Signal Processing Laboratory Department of Electrical and Computer Engineering Marquette University Milwaukee USA

ISBN: (纸本)9781479928941

This paper introduces the use of three physiologically-motivated features for speaker identification, Residual Phase Cepstrum Coefficients (RPCC), Glottal Flow Cepstrum Coefficients (GLFCC) and Teager Phase Cepstrum Coefficients (TPCC). These features capture speaker-discriminative characteristics from different aspects of glottal source excitation patterns. The proposed physiologically-driven features give better results with lower model complexities, and also provide complementary information that can improve overall system performance even for larger amounts of data. Results on speaker identification using the YOHO corpus demonstrate that these physiologically-driven features are both more accurate than and complementary to traditional mel-frequency cepstral coefficients (MFCC). In particular, the incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks.

关键词： speaker recognition Cepstrum Loudspeakers Feature extraction Mel frequency cepstral coefficient Complementary System performance trisphenylcarbamoylcellulose

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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THE ELECTROMAGNETIC ARTICULOGRAPHY MANDARIN ACCENTED ENGLISH (EMA-MAE) CORPUS OF ACOUSTIC AND 3D ARTICULATORY KINEMATIC DATA

THE ELECTROMAGNETIC ARTICULOGRAPHY MANDARIN ACCENTED ENGLISH...

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IEEE International Conference on Acoustics, speech and signal processing

作者： An Ji Jeffrey J. Berry Michael T. Johnson Speech and Signal Processing Laboratory Department of Electrical and Computer Engineering Marquette University Milwaukee USA Speech and Swallowing Laboratory Department of Speech Pathology and Audiology Marquette University Milwaukee USA

ISBN: (纸本)9781479928941

There is a significant need for more comprehensive electromagnetic articulography (EMA) datasets that can provide matched acoustics and articulatory kinematic data with good spatial and temporal resolution. The Marquette University Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus provides kinematic and acoustic data from 40 gender and dialect balanced speakers representing 20 Midwestern standard American English L1 speakers and 20 Mandarin Accented English (MAE) L2 speakers, half Beijing region dialect and half are Shanghai region dialect. Three dimensional EMA data were collected at a 400 Hz sampling rate using the NDI Wave system, with articulatory sensors on the midsagittal lips, lower incisors, tongue blade and dorsum, plus lateral lip corner and tongue body. Sensors provide three-dimensional position data as well as two-dimensional orientation data representing the orientation of the sensor plane. Data have been corrected for head movement relative to a fixed reference sensor and also adjusted using a biteplate calibration system to place the data in an articulatory working space relative to each subject's individual midsagittal and maxillary occlusal planes. speech materials include isolated words chosen to focus on specific contrasts between the English and Mandarin languages, as well as sentences and paragraphs for continuous speech, totaling approximately 45 minutes of data per subject. A beta version of the EMA-MAE corpus is now available, and the full corpus is in preparation for public release to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training.

关键词： Acoustics orientation data Pronunciation Dialect ELECTROMAGNETIC beta version Isolated words English English language Paragraphs calibration system Head Movements kinematics Acoustic data Loudspeakers

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