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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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Enforcing Eigenvector Smoothness for a Compact DFT-Based Polynomial Eigenvalue Decomposition

Enforcing Eigenvector Smoothness for a Compact DFT-Based Pol...

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sensor array and multichannel signal processing workshop

作者： Fraser K. Coutts Keith Thompson Jennifer Pestana Ian K. Proudler Stephan Weiss Department of Electronic & Electrical Engineering University of Strathclyde Glasgow Scotland Department of Mathematics & Statistics University of Strathclyde Glasgow Scotland

A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue decomposition (PEVD). As an extension of the ordinary EVD to polynomial matrices, the PEVD will generate paraunitary matrices that diagonalise a parahermitian matrix. While iterative PEVD algorithms that compute a decomposition in the time domain have received a great deal of focus and algorithmic improvements in recent years, there has been less research in the field of frequency-based PEVD algorithms. Such algorithms have shown promise for the decomposition of problems of finite order, but the state-of-the-art requires a priori knowledge of the length of the polynomial matrices required in the decomposition. This paper presents a novel frequency-based PEVD algorithm which can compute an accurate decomposition without requiring this information. Also presented is a new metric for measuring a function's smoothness on the unit circle, which is utilised within the algorithm to maximise eigenvector smoothness for a compact decomposition, such that the polynomial eigenvectors have low order. We demonstrate through the use of simulations that the algorithm can achieve superior levels of decomposition accuracy to a state-of-the-art frequency-based method.

关键词： Matrix decomposition Eigenvalues and eigenfunctions signal processing algorithms Covariance matrices Approximation algorithms Measurement Linear programming

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Optimal signalling for Massive SIMO Channels

Optimal Signalling for Massive SIMO Channels

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sensor array and multichannel signal processing workshop

作者： Xiang-Chuan Gao Jian-Kang Zhang Jin Jin School of Information and Engineering ZhengZhou University China Department of Electrical and Computer Engineering McMaster University Canada

This paper focuses on a noncoherent flat Rayleigh fading wireless communication system with a single transmitter antenna and N receiver antennas, i.e., a single-input-multiple-output (SIMO) system, in which the channel coefficients vary in every one time slot. For such a system, a fast noncoherent maximum likelihood (ML) detection algorithm and a corresponding closed form symbol error probability (SEP) formula are derived. In addition, the lower and upper bounds on SEP are established to quantitatively characterize how quickly SEP decays when N goes to infinity, with two important concepts, full receiver diversity gain and geometrical coding gain, being introduced, showing that any nonnegative pulse amplitude modulation (PAM) constellation enables full receiver diversity for the massive SIMO system. Furthermore, we find that an optimal received constellation that maximizes the geometrical coding gain is the geometrical constellation. Comprehensive computer simulations validate our theoretical analysis and demonstrate that our proposed optimal constellation attains significant performance gains over the currently available constellation design the the same massive SIMO system.

关键词： Receiving antennas Transmitting antennas Detectors MIMO communication signal processing

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SVM-Based Tool to Detect Patients with Multiple Sclerosis Using a Commercial EMG sensor

SVM-Based Tool to Detect Patients with Multiple Sclerosis Us...

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sensor array and multichannel signal processing workshop

作者： Konstantin Akhmadeev Aya Houssein Saïd Moussaoui Einar A. HØgestØl Ingerid Tutturen Hanne F. Harbo Steffan D. Bos-Haugen Jennifer Graves David-Axel Laplaud Pierre-Antoine Gourraud LS2N UMR 6004 Université de Nantes Nantes Frances Ecole Centrale Nantes LS2N UMR 6004 Nantes France Department of Neurology University of Oslo Oslo Norway Department of Neurology Oslo University Hospital Oslo Norway Department of Neurology University of California San Francisco San Francisco CA USA INSERM Centre de Recherche en Transplantation et Immunologie UMR 1064 Nantes France

Multiple sclerosis (MS) is a major auto-immune disease that is the leading cause of non-traumatic impairment of the central nervous system (CNS) in young adults. Successful treatment of MS patients depends on accurate tools for both the MS diagnosis and the disability progression. In current and upcoming studies the authors aim to explore the capabilities of applying a commercial electromyographic and inertial sensor (MYO Armband by Thalmic Labs Inc.), coupled with a multichannel signal processing tool, to standard neurological examination of MS progression. In this pilot study we formulate a two-class “healthy control” - “having MS” classification problem. A dataset of electromyographic signals and inertial sensor measurements from 71 individuals (31 MS patients and 40 healthy controls) was acquired during standard neurological examination routine. Temporal and spectral features of the signals were extracted in order to train and validate a classification model. Finally, a Support Vector Machine classifier was obtained giving AUROC = 0.94, 95% CI = [0.88, 0.99]. We propose a set of signal descriptors that correlate with objective components of the neurological examination. The proposed signal acquisition and processing technique, being easy to integrate into the traditional neurological exam, may have high potential for aiding in quantifying MS progression.

关键词： Electromyography Feature extraction Protocols signal processing Support vector machines Multiple sclerosis Foot

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New Theory for Unmixing ILL-Conditioned Hyperspectral Mixtures

New Theory for Unmixing ILL-Conditioned Hyperspectral Mixtur...

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sensor array and multichannel signal processing workshop

作者： Chia-Hsiang Lin José M. Bioucas Dias Instituto de Telecomunicacoes Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Hyperspectral unmixing (HU), a blind source separation problem, aims at unambiguiously identifying the spectral signatures of the materials, as well as their abundances, from the measured hyperspectral mixtures. In real hyperspectral scenes, high correlation between the spectral signatures is commonly observed, making HU quite challenging. Although such ill-conditioning is critical for effective HU, it is often ignored in existing HU literature. To the best of our knowledge, existing preconditioning techniques, for reducing the condition number of the signature matrix, were developed based on the pure-pixel assumption, which can, however, be seriously violated in remote sensing. Under a relaxed purity assumption, with respect to the pure-pixel one, this paper proposes novel theory for unmixing ill-conditioned hyperspectral mixtures. Specifically, we exactly identify the John's ellipsoid (i.e., the maximum ellipsoid inscribed in the convex hull of the hyperspectral data vectors) via split augmented Lagrangian shrinkage algorithm (SALSA), and transform this ellipsoid into an Euclidean ball. This transformation brings the data vectors into a new space wherein the corresponding material signature vectors form a regular simplex, which is a very strong prior information. Based on this prior, we design an HU criterion, and prove its perfect identifiability under a very mild sufficient condition. Then, we demonstrate the feasibility of realizing our criterion via non-convex optimization and guarantee a stationary point solution.

关键词： Ellipsoids Hyperspectral imaging Optimization array signal processing sensor arrays

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Coalition Game for Emergency Vehicles Re-Routing in Smart Cities

Coalition Game for Emergency Vehicles Re-Routing in Smart Ci...

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sensor array and multichannel signal processing workshop

作者： Hayder M. Amer Hayder A. A. Al-Kashoash Andrew Kemp Lyudmila Mihaylova Martin Mayfield Automatic Control and Systems Engineering Department University of Sheffield UK Technical Institute Ourna Southern Technical University Basra Iraq Electronic and Electrical Engineering School University of Leeds UK Civil and Structural Engineering Department University of Sheffield UK

Traffic jam is considered as a difficult problem to deal with in many cities around the world due to the continuously increasing number of vehicles compared to the available infrastructure. Traffic congestion significantly influences drivers travel journey, fuel consumption and air pollution. However, the most important factor has affected the delay of emergency vehicles, such as ambulances and police cars, leading to increased road deaths and significant financial losses. To reduce this problem, we propose an advanced traffic control allows rapid emergency services response in smart cities. This can be achieved through a traffic management system capable of implementing path planning in road network monitoring and driving the emergency vehicle in the best possible way to reach the hazard zone. The performance of the proposed algorithm is compared with two other algorithms over Birmingham city centre test scenarios. Simulation results show that the proposed approach improves traffic efficiency of emergency vehicles by an overall average of 21.78%, 29.32%, 32.79% and 46.77% in terms of travel time, fuel consumption, CO 2 emission and average speed, respectively.

关键词： Roads Games signal processing algorithms Smart cities Emergency services Fuels

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Short Data Record Filtering for Adaptive Underwater Acoustic Communications

Short Data Record Filtering for Adaptive Underwater Acoustic...

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sensor array and multichannel signal processing workshop

作者： Adam Gannon Sarankumar Balakrishnan George Sklivanitis Dimitris A. Pados Stella N. Batalama Dept. of Electrical Engineering The State University of New York at Buffalo I-SENSE and Dept. of Computer and Electrical Engineering & Computer Science Florida Atlantic University College of Engineering and Computer Science Florida Atlantic University

Spread-spectrum signaling offers robust communication over the multipath underwater acoustic channel, while at the same time allows multiple users to simultaneously occupy the entire continuum of the device-accessible bandwidth. However, limitations in temporal and spatial coherence of the underwater acoustic channel affect the maximum spreading code length and therefore the effective link data rate. In this work, we propose adaptive code length optimization toward high-rate spread-spectrum underwater acoustic communications. We first evaluate the bit error rate (BER) performance of the estimated minimum variance distortionless response (MVDR) and auxiliary-vector (AV) linear filter receivers. We observe that the AV-filter based receiver significantly outperforms the estimated MVDR solution under limited data-record availability. We then investigate data rate trade-offs for optimized code lengths under pre-defined BER constraints in time-varying underwater acoustic channels. Simulation results verify that the proposed adaptive packetization scheme offers superior data rate performance under short data-record constraints imposed by the non-stationary nature of the underwater environment.

关键词： Receivers Underwater acoustics Interference signal to noise ratio Coherence Bit error rate array signal processing

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Restoration of Multilayered Single-Photon 3D Lidar Images

Restoration of Multilayered Single-Photon 3D Lidar Images

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sensor array and multichannel signal processing workshop

作者： Abderrahim Halimi Jose Bioucas-Dias Ximing Ren Stephen McLaughlin Aongus McCarthy Gerald S. Buller School of Engineering and Physical Sciences Heriot-Watt University Edinburgh U.K. Instituto de Telecomunicações and Instituto Superior Técnico Universidade de Lisboa Portugal

This paper proposes a new algorithm to restore 3D single-photon Lidar images obtained under challenging realistic scenarios which include imaging multilayered targets such as semi-transparent surfaces or imaging through obscurants such as scattering media (e.g., water, fog). The Data restoration and exploitation is achieved by minimising an appropriate cost-function accounting for the data Poisson statistics and the available prior knowledge regarding the depth and reflectivity estimates. The proposed algorithm takes into account (i) the non-local spatial correlations between pixels, by using a convex non-local total variation (TV) regularizer, and (ii) the clustered nature of the returned photons, by using a collaborative sparse prior. The resulting minimization problem is solved using the alternating direction method of multipliers (ADMM) that offers good convergence properties. The algorithm is validated using both synthetic and real data which show the benefit of the proposed strategy in the sparse regime due to a fast acquisition or in presence of a high background due to obscurants.

关键词： Photonics Image restoration Imaging Laser radar Surface emitting lasers Three-dimensional displays signal processing algorithms

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Self-Calibrating Nonlinear Reconstruction Algorithms for Variable Density Sampling and Parallel Reception MRI

Self-Calibrating Nonlinear Reconstruction Algorithms for Var...

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sensor array and multichannel signal processing workshop

作者： Loubna El Gueddari C. Lazarus H. Carrié A. Vignaud Ph. Ciuciu CEA/NeuroSpin Gif-sur Yvette France

Compressed Sensing has allowed a significant reduction of acquisition times in MRI, especially in the high resolution (e.g., 400 μm) context. However, in this setting CS must be combined with parallel reception as multichannel coil acquisitions maintain high input signal-to-noise ratio (SNR). To get rid of usual parallel imaging limitations (output SNR loss), non-Cartesian trajectories provide a gain in sampling efficiency in the CS context. In this paper, we propose a self-calibrating MRI reconstruction framework that handles variable density sampling. Low resolution sensitivity maps are estimated from the low frequency k-space content using an original and fast method while MR images are reconstructed using a nonlinear iterative algorithm, which promotes sparsity in the wavelet domain. As regards the optimization task, we compare three first-order proximal gradient methods: Forward Backward (FB), Fast Iterative Soft Thresolding Algorithm (FISTA) and Proximal Optimized Gradient Method (POGM) and evaluate their respective convergence speed. Comparison with state-of-the-art (i.e., ℓ l -ESPIRiT) suggests that our self-calibrating POGM-based algorithm outperforms current approaches both in terms of image quality and computing time on several acquired data collected at 7 Tesla and we will focus more specifically on prospective non-Cartesian 8-fold accelerated in vivo Human brain data.

关键词： Sensitivity Image reconstruction Magnetic resonance imaging Image resolution signal processing algorithms Acceleration

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2014 ieee 8th sensor array and multichannel signal processing workshop, SAM 2014

2014 IEEE 8th Sensor Array and Multichannel Signal Processin...

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2014 ieee 8th sensor array and multichannel signal processing workshop, SAM 2014

ISBN: (纸本)9781479914814

The proceedings contain 134 papers. The topics discussed include: decentralized direction finding using Lanczos method;on MSE performance of time-reversal MUSIC;optimal virtual array length under position imperfections;direction finding and array calibration based on sparse reconstruction in partly calibrated arrays;analytical esprit-based performance study: what can we gain from non-circular sources?;direction of arrival estimation of harmonic signal using single moving sensor;network design via modular set function optimization in localization problems;cooperative simultaneous localization and synchronization: toward a low-cost hardware implementation;preprocessing algorithm for source localisation in a multipath environment;a weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras;and improving the projection method for TOA source localization in the presence of sensor position errors.

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Multi-sensor signal processing on a PSD matrix manifold

Multi-sensor signal processing on a PSD matrix manifold

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2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Wong, Kon Max Zhang, Jian-Kang Jiang, Huiying School of Information Engineering Zhengzhou University Zhengzhou China Dept of Electrical and Computer Engineering McMaster University Hamilton Canada

ISBN: (纸本)9781509021031

We propose to use the power spectral density (PSD) matrices of the received signals of a multi-sensor system as the feature of processing. PSD matrices have structural constraints and they form a manifold in signal space. We introduce two new Riemannian distance (RD) measures on the PSD matrix manifold and developed algorithms to locate the means and medians of PSD matrices in terms of these RD. These concepts are then applied to the detection of narrow-band sonar signals in noise and the results are encouraging. © 2016 ieee.

关键词： Covariance matrix

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