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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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An algorithm for weighted least squares positive time-frequency distributions

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

引用

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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A Jacobi method for signal subspace computation

A Jacobi method for signal subspace computation

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Paul, S Gotze, J Institute for NTCD TU Munich D-80290 Munich Germany

ISBN: (纸本)0819425842

The Jacobi method for singular value decomposition is well-suited for parallel architectures. Its application to signal subspace computations is well known. Basically the subspace spanned by singular vectors of large singular values are separated from subspace spanned by those of small singular values. The Jacobi algorithm computes the singular values and the corresponding vectors in random order. This requires sorting the result after convergence of the algorithm to select the signal subspace. A modification of the Jacobi method based on a linear objective function merges the sorting into the SVD-algorithm at little extra cost. In fact, the complexity of the diagonal processor cells in a triangular array get slightly larger. In this paper we present these extensions, in particular the modified algorithm for computing the rotation angles and give an example of its usefulness for subspace separation.

关键词： subspace methods singular value decomposition Jacobi method parallel architecture optimization on manifolds Lie groups

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Fast blind signal separation for multi-user interference rejection in smart antenna arrays

Fast blind signal separation for multi-user interference rej...

引用

conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Sarajedini, A Chau, PM Univ. of California/San Diego (United States)

ISBN: (纸本)0819425842

Spread spectrum modulation techniques such as frequency-hopped code division multiple access (FH-CDMA) are an efficient way to allow multi-user transmission over a limited bandwidth. Recently, there has been a push to increase user capacity over the same bandwidth. In particular, smart antenna arrays have been studied for their capacity gain potential. In this regard, we propose the use of blind signal separation for recovery of signals received through an antenna array. To implement this technique requires the antenna array vectors to be stationary which does not hold in FH-CDMA, so we also propose a new method for frequency compensation. We present the theoretical details of the frequency compensation and compare its performance to no frequency compensation. We then present the blind signal separation algorithm applied to a complex antenna array matrix and complex signals with noise and show in simulation that the blind signal separation works.

关键词： blind signal separation frequency compensation frequency-hopped CDMA spread spectrum

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Optimum microphone placement for array sound capture

Optimum microphone placement for array sound capture

引用

conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Rabinkin, DV Renomeron, RJ French, JC Flanagan, JL Rutgers Univ. (United States)

ISBN: (纸本)0819425842

Microphone arrays can be used for high-quality sound pick up in reverberant and noisy environments. The beamforming capabilities of microphone array systems allow highly directional sound capture, providing superior signal-to-noise ratio (SNR) when compared to single microphone performance. There are two aspects in microphone array system performance: The ability of the system to locate and track sound sources, and its ability to selectively capture sound from those sources. Both aspects of system performance are strongly affected by the spatial placement of microphone sensors. A method is needed to optimize sensor placement based on geometry of the environment and assumed sound source behavior. The objective of the optimization is to obtain the greatest average system SNR using a specified number of sensors. A method is derived to evaluate array performance for a given array configuration defined by the above mentioned metrics. An overall performance function is described based on these metrics. A framework for optimum placement of sensors under the practical considerations of possible sensor placement and potential location of sound sources is also characterized.

关键词： signal to noise ratio Sensors Error analysis Interference (communication) Acoustics Monte Carlo methods signal attenuation Statistical analysis Environmental sensing signal processing

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Modulo-addressing utilization in automatic software synthesis for digital signal processors

Modulo-addressing utilization in automatic software synthesi...

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1997 IEEE International conference on Acoustics, Speech, and signal processing (ICASSP 97)

作者： Willems, M Keding, H Zivojnovic, V Meyr, H Aachen Univ of Technology Aachen Germany

ISBN: (纸本)0818679204

Digital signal Processors (DSPs) have become key components for the implementation of digital signal processing systems. With DSPs moving into new application domains and the increasing complexity of modern DSP architectures, efficient programming support receives major interest. Therefore, an optimizing compiler becomes a must for future DSP-architectures. Todays DSP compilers result in significant overheads both in memory consumption and program execution time compared to hand-written assembly code. This is mainly due to an inefficient compiler support of the DSP specific architectural features, such as the modulo-addressing capability which is an enabling feature for a large class of DSP algorithms. Within this paper we analyze why existing compilers fail short in supporting the module-addressing mode and present a compiler concept that allows the efficient utilization of this feature. We describe how an advanced compiler optimization strategy allows a near optimum support of the module-addressing mode, and point out why this concept is favorable to DSP-specific language extensions.

关键词： Program compilers

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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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Wavelet windowed time-frequency distribution decompositions

Wavelet windowed time-frequency distribution decompositions

引用

conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Williams, WJ Sang, TH ONeill, JC Zalubas, EJ Univ. of Michigan (United States)

ISBN: (纸本)0819425842

This paper outlines means of combining and reconciling concepts associated with Cohen's class of distributions and with the wavelet transform. Both have their assets and their liabilities. Previous work has shown that one can decompose any time-frequency distribution (TFD) in Cohen's class into a weighted sum of spectrogram. A set of orthogonal analysis windows which also have the scaling property in common with wavelets is proposed. Successful application of this theory offers very fast computation of TFDs, since very few analysis windows may be needed and fast algorithms can be used. In addition, the decomposition idea offers the possibility of shaping the analysis such that good local and global properties as well as a number of desirable TFD properties are retained. Finally, one may view the result in terms of conventional Cohen's class concepts or, alternatively, in terms of wavelet concepts and potentially combine powerful insights and concepts from both points of view. Preliminary results applied to radar backscatter are provided. Performance curves for several wavelet types are also provided.

关键词： time-frequency wavelets signal decomposition radar fast algorithms

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Blind source separation using joint signal representations for arbitrary variables.

Blind source separation using joint signal representations f...

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Belouchrani, A Amin, MG Villanova Univ. (United States)

ISBN: (纸本)0819425842

Blind source separation is an emerging field of fundamental research with a broad range of applications. It is motivated by practical problems that involve several source signals and several sensors. Each sensor receives an instantaneous linear mixture of the source signals. The problem of the blind source separation consists then of recovering the original waveforms of the sources without any knowledge of the mixture structure. So far, the problem of the blind source separation has been solved using statistical information available on the source signals. A blind source separation approach for non-stationary signals based on time-frequency representations (TFR) have been recently introduced by the authors (SPIE 1996). Herein, we generalize the TFR based blind source separation approach to arbitrary variables, including time and frequency. 'Spatial joint arbitrary variable distributions' are introduced and used for blind source separation via joint diagonalization techniques.

关键词： blind source separation time-frequency representation joint signal representation spatial joint arbitrary variable distributions joint diagonalization

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Computing the signal subspaces of a sparse and/or structured matrix

Computing the signal subspaces of a sparse and/or structured...

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Fierro, RD California State Univ./San Marcos (United States)

ISBN: (纸本)0819425842

Many techniques involve the computation of singular subspaces associated with an extreme cluster of singular values of an m x n data matrix A. Frequently A is sparse and/or structured, which usually means matrix-vector multiplications involving A and its transpose can be done with much less than O(mn) flops, and A and its transpose can be stored in static data structures with much less than O(mn) storage locations. Standard complete orthogonal decompositions may be unattractive due to the computational and dynamic storage overhead associated with the initial preprocessing of the data. We describe an efficient Matlab implementation of the low-rank ULV algorithm for extracting reliable and accurate approximations to the singular subspaces associated with the cluster of large singular values without altering the matrix. The user can choose any principal singular vector estimator to underwrite the algorithm, may call a specialized routine to compute matrix-vector products involving A and its transpose, and can choose the desired level of accuracy of a residual. The main computational savings stems from preserving A and avoiding the explicit formation of unwanted information.

关键词： singular value decomposition singular subspaces signal subspaces ULV decomposition fast algorithms Householder transformations

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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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