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Spatial polarimetric time-frequency distributions and applications to direction-of-arrival estimation

Spatial polarimetric time-frequency distributions and applic...

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conference on advanced signal processing algorithms architectures and implementations xiII

作者： Zhang, YM Amin, MG Obeidat, BA Villanova Univ Ctr Adv Commun Villanova PA 19085 USA

ISBN: (纸本)0819450782

Time-frequency distributions (TFDs) have evolved to be a powerful technique for nonstationary signal analysis and synthesis. With the use of a multi-sensor array, spatial time-frequency distributions (STFDs) have been developed and successfully applied to high-resolution direction-of-arrival (DOA) estimation and blind recovery of the source waveforms. In this paper, the polarimetric dimension is introduced to the STFDs resulting in the spatial polarimetric time-frequency distributions (SPTFDs) as a platform for the processing of non-stationary polarized signals. In the SPTFD platform, polarized signals are decomposed (projected) into two orthogonal polarization components, such as horizontal and vertical, and later processed in a manner where their polarization characteristics are exploited. This empowers the STFDs with additional degrees of freedom and improves the robustness of the signal and noise subspaces, and therefore, serving to enhance DOA estimation, signal recovery, and source separation performance. To demonstrate the advantages of the SPTFDs, the polarimetric time-frequency MUSIC (PTF-MUSIC) method for DOA estimation is proposed based on the SPTFD platform and is shown to outperform the time-frequency, polarimetric, and conventional MUSIC methods.

关键词： time-frequency distributions array signal processing polarization smart antennas MUSIC

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Image processing and low discrepancy sequences

Image processing and low discrepancy sequences

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conference on advanced signal processing algorithms, architectures, and implementations IX

作者： Nair, D Wenzel, L Natl Instruments Austin TX 78727 USA

ISBN: (纸本)0819432938

It is well known that high-dimensional integrals can be solved with Monte Carlo algorithms. Recently, it was discovered that there is a relationship between low discrepancy sets and the efficient evaluation of higher-dimensional integrals. Theory suggests that for midsize dimensional problems, algorithms based on low discrepancy sets should out perform all other existing methods by an order of magnitude in terms of the number of sample points used to evaluate the integrals. We show that the field of image processing can potentially take advantage of specific properties of low discrepancy sets. To illustrate this, we applied the theory of low discrepancy sequences to some relatively simple image processing and computer vision related operations such as the estimation of gray level image statistics, fast location of objects in a binary image and the reconstruction of images from a sparse set of points. Our experiments show that compared to standard methods, the proposed new algorithms are faster and statistically more robust. Classical low discrepancy sets based on the Halton and Sobol' sequences were investigated thoroughly and showed promising results. The use of low discrepancy sequences in image processing for image characterization, understanding and object recognition is a novel and promising area for further investigation.

关键词： sampling random sampling low-discrepancy sequences image statistics image reconstruction

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Approximation by structured lower rank matrices

Approximation by structured lower rank matrices

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SPIE conference on advanced signal processing algorithms, architectures, and implementations VIII

作者： Chu, MT Funderlic, RE Plemmons, RJ N Carolina State Univ Dept Math Raleigh NC 27695 USA

ISBN: (纸本)0819429163

We consider two general procedures for constructing the nearest approximation of a given matrix by one with any lower rank and any linear structure. Nearness can be measured in any matrix norm. Structured low rank matrices arise in various applications, including image enhancement and model reduction. In practice, the empirical data collected in the matrix often either do not maintain the specified structure or do not induce the desirable rank. It is an important task to search for the nearest structured lower rank approximation of a given matrix. The techniques developed in this paper can easily be implemented for numerical computation. In particular, it is shown that the computations can be approached using efficient optimization packages. The special case of Toeplitz structure using the Frobenius matrix norm is discussed in detail to illustrate the ideas, and numerical tests are reported. The procedures developed herein can be generalized to consider a much broader range of approximation problems.

关键词： Matrices signal processing MATLAB Optimization (mathematics) Image enhancement Computer programming Computer science Fourier transforms Image processing Mathematics

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Time-varying frequencies of a signal

Time-varying frequencies of a signal

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conference on advanced signal processing - algorithms, architectures, and implementations VII

作者： Loughlin, PJ Univ. of Pittsburgh (United States)

ISBN: (纸本)0819425842

We consider the definition and interpretation of instantaneous frequency and other time-varying frequencies of a signal, and related concepts of instantaneous amplitude, instantaneous bandwidth and the time-varying spectrum of a signal. A definition for the average frequency at each time is given, and we show that spectrograms and Cohen-Posch time-frequency distributions can yield this result for the first conditional moment in frequency. For some signals this result equals the instantaneous frequency, but generally instantaneous frequency is not the average frequency at each time in the signal. We discuss monocomponent versus multicomponent signals, and give an estimate of the time-varying spectrum given the instantaneous frequencies and bandwidths of the components. We also consider the role of the complex signal in defining instantaneous amplitude, frequency and bandwidth, and ways to obtain a complex signal satisfying certain physical properties, given a real signal (or its time-varying spectrum). Depending upon the physical properties desired (e.g., the instantaneous amplitude of a magnitude-bounded signal should itself be bounded), one obtains different complex representations - and hence different instantaneous amplitudes, frequencies and bandwidths - of the given signal.

关键词： instantaneous frequency, bandwidth Hilbert transform analytic signal time-frequency conditional moments maximum entropy density

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Progressive image decoding based on the inverse discrete cosine transform

Progressive image decoding based on the inverse discrete cos...

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conference on advanced signal processing - algorithms, architectures, and implementations VII

作者： Winograd, JM Nawab, SH Boston Univ. (United States)

ISBN: (纸本)0819425842

A new architecture is presented for incremental computation of the two-dimensional inverse discrete cosine transform (2D-IDCT) in the context of progressive image decoding. This architecture offers advantages over existing DCT inversion techniques for systems used in conjunction with progressive image coding schemes or those that must operate in environments with severe and/or time-varying resource constraints (e.g. real-time and low-power systems). The use of a bit-serial distributed arithmetic approach comprised of parallel input-pruned 2D-IDCT processing elements enables low-quality image reconstructions to be quickly and efficiently obtained using only a subset of the DCT coefficient bit stream. Initial approximate reconstructions can be improved in subsequent stages of incremental refinement according to the availability of processing resources or DCT coefficient data. Analysis is presented of image degradation at successive stages, illustrating uniform improvement according to quantitative and perceptual criteria.

关键词： progressive image decoding discrete cosine transform incremental refinement approximate processing

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A near canonic double-based number system (DBNS) with applications in digital signal processing 6

A near canonic double-based number system (DBNS) with applic...

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conference on advanced signal processing algorithms, architectures, and implementations VI

作者： Dimitrov, V SadeghiEmamchaie, S Jullien, GA Miller, WC UNIV WINDSOR VLSIRES GRPWINDSORON N9B 3P4CANADA

ISBN: (纸本)0819422347

In this paper we explore a new number system which uses a double base. The representation of the numbers has a very simple geometric interpretation, allowing potentially fast implementation of the basic arithmetic operations. The transformation of the integers into minimal form, however, leads to some problems associated with transcendental number theory, and we identify and open the discussion on these problems. An intriguing implementation vehicle, which has some of the properties associated with symbolic substitution in optical computing, is the use of Cellular Neural Networks (CNNs) to perform digit reduction. Brief details are presented on CNN implementation, and a system-level example is shown in order to justify the applicability of the proposed theory in digital signal processing. © 1996 SPIE. All rights reserved.

关键词： number systems computer arithmetic number theory diophantine equations symbolic substitution Cellular Neural Networks digital filtering

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VHDL token-based performance modeling for 2D and 3D infrared search and track processing

VHDL token-based performance modeling for 2D and 3D infrared...

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SPIE conference on advanced signal processing algorithms, architectures, and implementations VIII

作者： Pauer, EK Pettigrew, MN Myers, CS Madisetti, VK Signal Proc Ctr Nashua NH 03061 USA

ISBN: (纸本)0819429163

This study develops and evaluates a new VHDL-based performance modeling capability for multiprocessor systems.* The framework for this methodology involved modeling the following system aspects: processor characterization, task modeling, network characterization, and data set size. Initially, all aspects are specified at an abstract) level, and eventually become specified at a detailed level through the process of verification and refinement of design assumptions. Processor characterization involves modeling the processor's speed, instruction set, and memory hierarchy. Task modeling is concerned with the execution time and instruction mix of software tasks within the system. Network characterization models bus protocols, topology, and bandwidths. Data set size refers to how much data is represented by the tokens used in the models. In this study, we applied and evaluated this methodology using both two-dimensional (2D) and three-dimensional (3D) infrared search and track (IRST) algorithms. Two different candidate processors were investigated: IBM PowerPC 604 and Texas Instruments TMS320C80. For the 2D IRST algorithm, the abstract and detailed performance modeling results were obtained far both processors using partitioned data and pipelined algorithmic approaches. For the 3D IRST algorithm, abstract performance models for pipelined and parallelized implementations on the PowerPC were developed. These models examined the feasibility of the implementations, the potential risk areas, and laid the groundwork for detailed performance modeling.

关键词： performance modeling VHDL IRST architectures simulation

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NRL FLEX radar signal-processing architecture: an update

NRL FLEX radar signal-processing architecture: an update

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advanced signal processing algorithms, architectures, and implementations III

作者： Alter, James J. Evins, James B. Popick, Glen L. Scott, Russell A. Letellier, J.P. Naval Research Lab. Washington DC USA

ISBN: (纸本)081940943X

The NRL FLEX processor architecture is designed for real-time radar signal processing and was described at last year's conference in the context of its original application, the Point Defense Demonstration Radar project at NRL. This paper describes the current status of the processor and the application of the same architecture to a new problem which requires a different board configuration to satisfy a different set of latency and data flow requirements.

关键词： Radar systems

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signal processing algorithms by permutation test in radar application 6

Signal processing algorithms by permutation test in radar ap...

引用

conference on advanced signal processing algorithms, architectures, and implementations VI

作者： AlvarezVaquero, F SanzGonzalez, JL UNIV POLITECN MADRID ETSI TELECOMUNDEPT SIGNAL SYST & RADIOCOMMUNE-28040 MADRIDSPAIN

ISBN: (纸本)0819422347

A hypothesis H is parametric if every distribution from the process defined by H belongs to a family of distributions characterized by a finite number of parameters; on the other hand, if the distribution can not be defined by a finite number of parameters, the hypothesis is nonparametric. In this paper, we analyze a detector based on the optimum permutation test, applied to nonparametric radar detection which provide good performances without a large computational work, and we compare it with the parametric test and rank test in the Neyman-Pearson sense. The computational complexity of the detector is high and its implementation in real time is difficult, due to the number of operations increase with the factorial of the number of samples. Also, we present an algorithm that reduces the computational work required. We also present the detectability characteristic of the optimum permutation test against rank test and parametric test under Gaussian noise environments and different types of target models (nonfluctuating, Swerling I and Swerling II). The detection probability versus signal-to-noise ratio is estimated by Monte-Carlo simulations for different parameter values (N pulse, M reference samples and false alarm probability P_fa).

关键词： nonparametric permutation test rank test radar detection detectability target models

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An algorithm for weighted least squares positive time-frequency distributions

An algorithm for weighted least squares positive time-freque...

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conference on advanced signal processing - algorithms, architectures, and implementations VII

作者： Pitton, JW MathSoft Seattle WA 98109 1700 Westlake Ave N United States

ISBN: (纸本)0819425842

An algorithm for computing positive time-frequency distributions (TFDs) for nonstationary signals is presented. This work extends the earlier work of the author and his colleagues in computing positive TFDs [11, 15]. A general approach to the problem of computing these signal-dependent distributions is derived. The method is based on an evolutionary spectrum formulation of positive TFDs. Following earlier work [15], a relationship is derived between positive TFDs and the ambiguity function of the observed signal. In particular, it is shown that the TFD is approximately equal to the two-dimensional Fourier transform of the ambiguity function. A method for computing the positive TFD is then presented based on minimizing the squared error in this approximation subject to the TFD being positive and satisfying the time and frequency marginals. The squared error may be weighted non-uniformly, resulting in a constrained weighted least-squares optimization problem. A solution to this optimization problem based on an alternating projections framework is presented, and an example is provided. The resulting TFD provides excellent time-frequency resolution while maintaining positivity and satisfying the marginals.

关键词： signal processing time-frequency analysis time-varying signals optimization alternating projections

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