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Distributed binary geometric sensor arrays for low-data-throughput human gait biometrics

Distributed binary geometric sensor arrays for low-data-thro...

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sensor array and multichannel signal processing workshop

作者： Tianyao Yue Qi Hao David J. Brady Department of Electrical and Computer Engineering Duke University Durham NC USA Department of Electrical and Computer Engineering University of Alabama Tuscaloosa AL USA

We present a novel sensing paradigm of measuring human gait. The goal of the research is to achieve low-cost gait biometrics systems with minimum data throughput for various sensing modalities. The binary measurements of the system are achieved by using both (1) periodic and (2) pseudo-random sampling structures. As a result, either static or dynamic gait features can be estimated from a one-bit data stream. The simulation results demonstrate the gait information acquisition capability of the proposed binary sensing technology.

关键词： sensors Geometry Biometrics Estimation Humans Trajectory Legged locomotion

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Amplify-and-forward compressed sensing as a PHY-layer secrecy solution in wireless sensor networks

Amplify-and-forward compressed sensing as a PHY-layer secrec...

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sensor array and multichannel signal processing workshop

作者： J.E. Barceló-Lladó Antoni Morell Gonzalo Seco-Granados Dpt. Telecom. and Syst. Engineering Universitat Autònoma Barcelona Bellaterra Barcelona Spain

Results in distributed compressed sensing show that this technique can be applied to wireless sensor networks in order to reduce the power consumption and the amount of channel uses. In this paper we extend such results with the study of the physical layer secrecy performance. In particular, we focus on an amplify-and-forward compressed sensing scheme (AF-CS) for the case when malicious eavesdropping nodes are listening. We demonstrate that this scheme achieves perfect secrecy in presence of one eavesdropper (and also for a small number of them). We also show that a very high number of eavesdropping nodes are required to perfectly recover the signal in comparison to other distributed compressed sensing schemes in the literature.

关键词： Wireless sensor networks sensors Compressed sensing Physical layer Channel estimation Relays Vectors

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A distributed algorithm for two-way multiple-relay networks

A distributed algorithm for two-way multiple-relay networks

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sensor array and multichannel signal processing workshop

作者： C. Lameiro J. Vía I. Santamaria Communications Engineering Department (DICOM) University of Cantabria Santander Spain

In this paper we propose an efficient transmission strategy for the two-way relay channel (TWRC) with multiple relays, when these are multiple-input multiple-output (MIMO) transceivers that apply the amplify-and-forward (AF) protocol. Although the optimal beamforming strategy is known, it requires a central node, with channel state information (CSI) of the entire network, to compute all the beamforming matrices, which is impractical. To reduce the overhead, in this paper we present a distributed algorithm for the computation of the relay beamforming matrices. The proposed algorithm divides the problem in two stages. First, each relay computes its own beamforming matrix in parallel using only local CSI. Next, a distributed beamforming is applied to make the signals add up coherently at the nodes. Although the proposed algorithm is suboptimal, we show through simulations that it performs very close to the optimal achievable rate region.

关键词： Relays array signal processing MIMO Protocols signal processing algorithms signal to noise ratio Upper bound

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A new beamformer based on phase dispersion to improve 2D sparse array response

A new beamformer based on phase dispersion to improve 2D spa...

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sensor array and multichannel signal processing workshop

作者： Oscar Martinez-Graullera David Romero-Laorden Alberto Ibañez Luis G. Ullate CAEND (UPM-CSIC) Madrid Spain

This work studies a new beamformer for non-periodic sparse bidimensional ultrasonic array transducer. It is based on a weighting factor based on signals phase dispersion to enhance the image points where the phase coherence between the received signals is high. A method to measure the phase dispersion based on Fourier analysis and its integration in the beamformer are presented. Its performance is evaluated using a non-periodic 2D sparse array based on Fermat spiral with 96 elements and a diameter of 60λ. Compared with conventional Delay-And-Sum beamformer the proposed solution reduces the aperture lateral resolution and improves its dynamic range, achieving up to 50dB of contrast in the image and less 1° in lateral resolution.

关键词： arrays Dispersion Apertures Coherence Dynamic range Spirals Image resolution

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Limited field-of-view multimodal sensor adaptation for data association

Limited field-of-view multimodal sensor adaptation for data ...

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sensor array and multichannel signal processing workshop

作者： Sean M. O'Rourke A. Lee Swindlehurst Center for Pervasive Communications and Computing University of California Irvine Irvine CA USA

We have investigated the utility of field-of-view adaptation for multimodal sensing in cluttered multi-target environments. Measurement data from multiple integrated sensors are collected at a fusion center, which employs a soft association procedure to integrate them into the estimation procedure. A variance penalty model for the limited fields-of-view property is incorporated into the state estimation procedure. This model also forms the basis of an optimization problem that determines the best next-step sensing parameters for the changing target environment. Numerical simulations demonstrate the benefit of the proposed method for both tracking and association metrics compared to a non-adaptive tracker.

关键词： sensors Target tracking Clutter Radio frequency Noise measurement Noise Vectors

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An L1-norm linearly constrained LMS algorithm applied to adaptive beamforming

An L1-norm linearly constrained LMS algorithm applied to ada...

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sensor array and multichannel signal processing workshop

作者： José F. de Andrade Marcello L. R. de Campos José A. Apolinário Program of Electrical Engineering (PEE)-COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Department of Electrical Engineering (SE/3) Military Institute of Engineering Rio de Janeiro Brazil

We propose in this work an L 1 -norm Linearly-Constrained Least-Mean-Square (L 1 -CLMS) algorithm. In addition to the linear constraints present in the CLMS algorithm, the L 1 -CLMS algorithm takes into account an L 1 -norm penalty on the filter coefficients. The performance of the L 1 -CLMS algorithm is evaluated for a time-varying system identification under Gaussian noise and for an adaptive beamforming scenario. The effectiveness of the L 1 -CLMS algorithm is demonstrated by comparing, via computer simulations, its results with the CLMS algorithm. When employed in a sensor array, the L 1 -norm constraint increases the convergence rate making the proposed algorithm a good candidate for adaptive beamforming applications.

关键词： signal processing algorithms arrays Convergence array signal processing Heuristic algorithms Azimuth Vectors

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Hierarchical particle filtering for target tracking in multi-modal sensor networks

Hierarchical particle filtering for target tracking in multi...

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sensor array and multichannel signal processing workshop

作者： Phani Chavali Arye Nehorai Preston M. Green Department of Electrical and Systems Engineering Washington University in St. Louis Saint Louis MO USA

We propose a filtering method, called hierarchical particle filtering, for multi-modal sensor networks in which the unknown state vector is observed, through the measurements, in a hierarchical fashion. We partition the state space and the measurement space into lower dimensional subspaces. At each stage, we find an estimate of one partition using the measurements from the corresponding partition, and the information from the previous stages. We use hierarchical particle filtering for joint initiation, termination and tracking of multiple targets using multi-modal measurements. Numerical simulations demonstrate that the proposed filtering method accurately identifies the number and the categories of targets, and produces a lower mean-squared error (MSE) compared to the MSE obtained using a standard particle filter.

关键词： Target tracking Vectors Atmospheric measurements Particle measurements Cameras Multimodal sensors Standards

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Effective rate analysis of MISO Rician fading channels

Effective rate analysis of MISO Rician fading channels

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sensor array and multichannel signal processing workshop

作者： Michail Matthaiou George C. Alexandropoulos Hien Quoc Ngo Erik G. Larsson Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden Broadband Wireless and Sensor Networks Group Athens Information Technology Center Athens Greece Department of Electrical Engineering Linköping University Linkoping Sweden

The delay constraints imposed by future wireless applications require a suitable metric for assessing their impact on the overall system performance. Since the classical Shannon's ergodic capacity fails to do so, the so-called effective rate was recently established as a rigorous alternative. Yet, most prior relevant works have considered only the typical case of Rayleigh fading which allows for tractable manipulations. In this paper, we relax this assumption by considering the more general Rician fading model for multiple-input single-output (MISO) systems. A new, analytical expression for the exact effective rate is derived, along with tractable expressions for the key parameters dictating the effective rate performance in the high and low signal-to-noise (SNR) regimes.

关键词： Rician channels Delay signal to noise ratio Quality of service Wideband Rayleigh channels

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Low-overhead cooperative beamforming under imperfect quantized SNR of source-to-relay links

Low-overhead cooperative beamforming under imperfect quantiz...

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sensor array and multichannel signal processing workshop

作者： Jwo-Yuh Wu Chung-Hsuan Hu Tsang-Yi Wang Department of Electrical Engineering National Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9781467310703

We consider a cooperative beamforming system with the amplify-and-forward protocol. To reduce the signaling overhead, each relay quantizes the SNR of the source-to-relay (S-R) link into one bit, which is then transmitted through a binary symmetric channel (BSC) with a known crossover probability to the destination. Given the set of the error-corrupted one-bit messages received at the destination, the beamforming design criterion is the maximization of the expected receive SNR, averaged over the conditional bit-flipping distributions of BSCs. We derive an analytic expression for the considered SNR metric, which is a complicated function of the beamforming weights. To facilitate analysis, we further derive a tractable lower bound for the conditional average SNR. By conducting maximization with respect to this lower bound, a closed-form sub-optimal beamformer can be obtained as a solution to a generalized eigenvalue problem. Computer simulations are used to illustrate the performance of the proposed scheme.

关键词： signal to noise ratio array signal processing Relays Wireless sensor networks Protocols Measurement Computer simulation

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Dictionary based reconstruction and classification of randomly sampled sensor network data

Dictionary based reconstruction and classification of random...

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sensor array and multichannel signal processing workshop

作者： Grigorios Tsagkatakis Panagiotis Tsakalides Institute of Computer Science (ICS) Foundation for Research and Technology Hellas Crete Greece Department of Computer Science University of Crete Heraklion Crete Greece

In this paper, we propose a method for recovering and classifying WSN data while minimizing the number of samples that need to be acquired, processed, and transmitted. The problem is formulated according to the recently proposed framework of Matrix Completion (MC), which asserts that a low rank matrix can be recovered from a small number of randomly sampled entries. The application of MC in WSN data is motivated by the assumption that sensory data exhibit intra-sensor correlations and that these data can be represented using known examples. We formulate the problem as that of recovering the low rank measurement matrix by encoding the contributions of known examples, the dictionary elements, for reconstructing and classifying the data. Experimental results using artificial data suggest that the proposed scheme is able to accurately reconstruct and classify the sensory data from a small number of measurements.

关键词： Wireless sensor networks Dictionaries Correlation Noise Accuracy Minimization Noise measurement

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