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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms, Architectures, and Implementations XVII"

共 168 条记录，以下是141-150 订阅

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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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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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Optimum microphone placement for array sound capture

Optimum microphone placement for array sound capture

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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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Wavelet windowed time-frequency distribution decompositions

Wavelet windowed time-frequency distribution decompositions

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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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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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Optimizing time-frequency distributions for automatic classification

Optimizing time-frequency distributions for automatic classi...

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

作者： Atlas, L Droppo, J McLaughlin, J Univ. of Washington (United States) MIT Lincoln Lab. (United States)

ISBN: (纸本)0819425842

An entirely new set of criteria for the design of kernels (generating functions) for time-frequency representations (TFRs) is presented. These criteria aim only to produce kernels (and thus, TFRs) which will enable more accurate classification. We refer to these kernels, which are optimized to discriminate among several classes of signals, as signal class dependent kernels, or simply class dependent kernels. The genesis of the class dependent kernel is to be found in the area of operator theory, which we use to establish a direct link between a discrete-time, discrete-frequency TFR and its corresponding discrete signal. We see that many similarities, but also some important differences, exist between the results of the continuous-time operator approach and our discrete one. The differences between the continuous representations and discrete ones may not be the simple sampling relationship which has often been assumed. From this work, we obtain a very concise, matrix-based expression for a discrete-time/discrete-frequency TFR which is simply the product of the kernel with another matrix. This simple expression opens up the possibility to optimize the kernel in a number of ways. We focus, of course, on optimizations most suitable for classification, and ultimately wind up with the class dependent kernel. When applied to simulated sonar transient signals, we find that our approach does a good job of discriminating within very similar classes of transients and is especially sensitive to differences in time variation across classes.

关键词： Time-frequency analysis Matrices Fourier transforms Convolution Detection theory Distance measurement signal generators signal processing Silicon Stochastic processes

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Estimating the frequency interval of a regularly spaced multi-component harmonic line signal in coloured noise

Estimating the frequency interval of a regularly spaced mult...

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

作者： Frazer, GJ Anderson, SJ Defence Science and Technology Organisation (Australia)

ISBN: (纸本)0819425842

The radar returns from some classes of time-varying point targets can be represented by the discrete-time signal plus noise model: x(t) = s(t) + [nu(t) + eta(t)] = (P-1)Sigma(i=0) A(i)e(j2 pi fi/fst) + nu(t) + eta(t), t is an element of 0,...,.N - 1, f(i) = kf(I) + f(o) where the received signal x(t) corresponds to the radar return from the target of interest from one azimuth-range cell. The signal has an unknown number of components, P, unknown complex amplitudes A(i) and frequencies f(i). The frequency parameters f(o) and f(I) are unknown, although constrained such that f(o) < fI/2 and parameter k is an element of {-u,...,-2,-1,0,1,2,...,upsilon) is constrained such that the component frequencies f(i) are bound by [-fs/2,fs/2). The noise term nu(t), is typically coloured, and represents clutter, interference and various noise sources. It is unknown, except that Sigma(t)\nu(t)\(2) < infinity;in general, nu(t) is not well modelled as an auto-regressive process of known order. The additional noise term eta(t) represents time-invariant point targets in the same azimuth-range cell. An important characteristic of the target is the unknown parameter, f(I), representing the frequency interval between harmonic lines. It is desired to determine an estimate of f(I) from N samples of x(t). We propose an algorithm to estimate f(I) based on Thomson's harmonic line F-Test, which is part of the multi-window spectrum estimation method and demonstrate the proposed estimator applied to target echo time series collected using an experimental HF skywave radar.

关键词： HF radar multiple-window methods harmonic line detection frequency interval estimation

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Time-frequency analysis of a motor-generator 6

Time-frequency analysis of a motor-generator

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

作者： Rohrbaugh, R USN CTR SURFACE WARFAREADV SIGNAL PROC BRANCHBREMERTONWA 98314

Time-frequency methods are applied for bearing analysis of a motor-generator. We show that these methods reveal features that are not seen by traditional methods, such as the power spectrum. These features may be used... 详细信息

ISBN: (纸本)0819422347

关键词： Time-frequency analysis Modulation Amplitude modulation Manufacturing Analytical research Corrosion Data modeling Quantum wells signal processing Warfare

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Window design for signal-dependent spectrogram using optimal kernel techniques 6

Window design for signal-dependent spectrogram using optimal...

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

作者： Carson, CC Baraniuk, RG UNIV CALIF BERKELEY DEPT ELECT ENGN & COMP SCIBERKELEYCA 94720

ISBN: (纸本)0819422347

Time-frequency distributions (TFDs) have proven useful in a wide variety of nonstationary signal processing applications. While sophisticated optimal bilinear TFDs have been developed to extract the maximum possible time- frequency information from signals, certain applications dictate simpler linear, running-FFT processing techniques. In this paper, we propose a signal-dependent short-time Fourier transform/spectrogram that enjoys many of the advantages of optimal bilinear TFDs yet retains the simplicity and efficiency of running-FFT processing. In addition, we extend the optimal kernel design problem to linear spaces of signals.

关键词： time-frequency analysis spectrogram Wigner distribution optimal kernel design

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