A massively parallel signal and image processing architecture is considered. The architecture is comprised of 2D arrays of cells that simulate the response of retina neurons. The results of simulations are compared to...
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ISBN:
(纸本)0819416207
A massively parallel signal and image processing architecture is considered. The architecture is comprised of 2D arrays of cells that simulate the response of retina neurons. The results of simulations are compared to previously published experimental results and the system is applied to detection of spatio-temporal features in sequences of images representative of pulse- doppler radar images. By arranging the output layer so that the cells respond to various key input features an array of feature extraction cells can be obtained. The system is characterized by developing an image space to feature space mapping.
Much research has focused on estimating the parameters of a sum of exponentially damped sinusoids. In some applications, such as nuclear magnetic resonance signalprocessing, only a few of the sinusoids are of interes...
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ISBN:
(纸本)0819416207
Much research has focused on estimating the parameters of a sum of exponentially damped sinusoids. In some applications, such as nuclear magnetic resonance signalprocessing, only a few of the sinusoids are of interest. This paper presents some new frequency-selective techniques for estimating the parameters of sinusoids within a specified frequency region using a subspace and SvD-based estimation algorithm and an FIR filter matrix. The applicable estimation algorithms in this technique are the linear prediction method, the matrix pencil method, Kung et al.'s method and its total-least-squares variant, called the HTLS method. The benefits of the frequency-selective technique combined with the HTLS estimation method are confirmed through simulations.
We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a ...
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ISBN:
(纸本)0819416207
We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals. Due to the ill-posedness of scale-limited extrapolation, a regularization process is introduced for noisy data extrapolation.
In this paper we consider the direction-of-arrival (DOA) estimation of Gaussian signals in non- Gaussian noise of unknown spatial correlations. The proposed method involves two primary steps. Firstly, the third-order ...
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ISBN:
(纸本)0819416207
In this paper we consider the direction-of-arrival (DOA) estimation of Gaussian signals in non- Gaussian noise of unknown spatial correlations. The proposed method involves two primary steps. Firstly, the third-order cumulant function of the random wavefield received by an array of sensors is estimated and then projected onto the correlation domain to exploit the correlations of noise across the sensors in an array. Secondly, the estimated noise correlation matrix is then utilized in a correlation-based eigenstructure method, such as MUSIC, for DOA estimation. As expected, the proposed method improves the performance of MUSIC which requires the knowledge of noise correlation. Numerical simulation results for both the MUSIC and proposed algorithms are presented and compared.
In this paper, a highly scalable hardware accelerator design for digital signalprocessing is presented. The key features of this accelerator are minimum I/O operations, highly scalable massive parallelism, easy progr...
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ISBN:
(纸本)0819416207
In this paper, a highly scalable hardware accelerator design for digital signalprocessing is presented. The key features of this accelerator are minimum I/O operations, highly scalable massive parallelism, easy programming, and modularity and regularity. With a very large register file (> 1000) per processing element, the reuse factor per datum in this accelerator can be increased significantly (as compared to traditional DSP architectures). System performance is improved because the amount of data transfer between the on-chip cache and the off-chip cache/memory is reduced by the same factor. Since the basic building block of this accelerator is simply a vLSI chip with several processing elements, scalable massive parallelism can be achieved by connecting multiple chips together in a SIMD `vector- like' fashion. Finally, programming of this accelerometer is not difficult because it is operated under the SIMD `vector-like' mode. With the expected vLSI technology in the next few years, the throughput of one single accelerator chip can approach GFLOPs performance. Hence, the high computing power needed by digital signalprocessing applications can be provided by just connecting a small number of this chip together.
An algorithm is proposed to compute samples of any bilinear joint time-frequency representation of the Cohen's class. The computation is performed on a decimated sampling grid, mapping N = 3BD signal samples into ...
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ISBN:
(纸本)0819416207
An algorithm is proposed to compute samples of any bilinear joint time-frequency representation of the Cohen's class. The computation is performed on a decimated sampling grid, mapping N = 3BD signal samples into N = K × L critical samples of the joint representation in the time-frequency domain. This is in contrast with the usual approaches that perform the computation on a much denser grid, mapping N signal samples into N × N samples in the time-frequency plane. The algorithm is based on the discrete Zak transform and represents an extension of the work by Auslander et al. on fast computation of the ambiguity function. For a number of popular representations, the algorithm is shown to have computational complexity about the same as an ordinary FFT.
This paper presents an overview of adaptive nonlinear filters equipped with bilinear system models. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedb...
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ISBN:
(纸本)0819416207
This paper presents an overview of adaptive nonlinear filters equipped with bilinear system models. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. The paper first describe stochastic gradient adaptive bilinear filters. The class of recursive least-squares adaptive bilinear filters are discussed next. Stability issues associated with bilinear system models and adaptive bilinear filters are also discussed in the paper. The paper concludes with an experimental comparison of the performance of linear, truncated second-order volterra, and bilinear system models in a nonlinear channel equalization problem.
Algorithm-based fault tolerance (ABFT) has been proposed as a cost-effective approach to concurrent error detection. So far, the application of ABFT has been limited to computationally intensive applications that lend...
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ISBN:
(纸本)0819416207
Algorithm-based fault tolerance (ABFT) has been proposed as a cost-effective approach to concurrent error detection. So far, the application of ABFT has been limited to computationally intensive applications that lend easily to high-level fault modeling. In this paper we extend the application of ABFT to non-computationally intensive applications. To that end, we first develop a fault model for such systems. Based on the fault model, we develop ABFT schemes for a set of graph theoretic as well as set theoretic problems. Application of the new schemes is illustrated with examples.
In this paper we consider cyclostationary signalprocessing techniques implemented via acousto-optics (AO). Cyclic processing methods are reviewed and motivated, including the cyclic correlation and the cyclic spectru...
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ISBN:
(纸本)0819416207
In this paper we consider cyclostationary signalprocessing techniques implemented via acousto-optics (AO). Cyclic processing methods are reviewed and motivated, including the cyclic correlation and the cyclic spectrum. We show how a 1D AO spectrum analyzer can be used to detect the presence, and estimate the value, of cycle frequencies. The cyclic correlation can then be computed at cycle frequencies of interest using a 1D time-integrating correlator. Next we consider the problem of computing the (2D) cyclic correlation for all cycle frequencies and lags simultaneously. This is accomplished via an AO triple-product processor, configured in a manner similar to that used for ambiguity function generation. The cyclic spectrum can be obtained in a post-processing step by Fourier transforming the cyclic correlation in one dimension. We then consider higher order extensions of the cyclic correlation and show how a 2D slice of the 3D cyclic triple-correlation can be computed using an AO four-product processor.
Existing approaches to blind channel estimation and deconvolution (equalization) focus exclusively on channel or inverse-channel impulse response estimation. It is well-known that the quality of the deconvolved output...
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ISBN:
(纸本)0819416207
Existing approaches to blind channel estimation and deconvolution (equalization) focus exclusively on channel or inverse-channel impulse response estimation. It is well-known that the quality of the deconvolved output depends crucially upon the noise statistics also. Typically it is assumed that the noise is white and the signal-to-noise ratio is known. In this paper we remove these restrictions. Both the channel impulse response and the noise model are estimated from the higher-order (fourth, e.g.) cumulant function and the (second-order) correlation function of the received data via a least-squares cumulant/correlation matching criterion. It is assumed that the noise higher-order cumulant function vanishes (e.g., Gaussian noise, as is the case for digital communications). Consistency of the proposed approach is established under certain mild sufficient conditions. The approach is illustrated via simulation examples involving blind equalization of digital communications signals.
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