The work described in this paper explores the use of Poisson point processes and stochastic arithmetic to perform signalprocessing functions. Our work is inspired by the asynchrony and fault tolerance of biological n...
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ISBN:
(纸本)0780370414
The work described in this paper explores the use of Poisson point processes and stochastic arithmetic to perform signalprocessing functions. Our work is inspired by the asynchrony and fault tolerance of biological neural systems. The essence of our approach is to code the input signal as the rate parameter of a Poisson point process, perform stochastic computing operations on the signal in the arrival or "pulse" domain, and decode the output signal by estimating the rate of the resulting process. An analysis of the Poisson pulse frequency modulation encoding error is performed. Asynchronous, stochastic computing operations are applied to the impulse stream and analyzed. A special finite impulse response (FIR) filtering scheme is proposed that preserves the Poisson properties and allows filters to be cascaded without compromising the ideal signal statistics.
This paper presents an overview of some of the recent theory and application of stochastic minimal graphs in the context of entropy estimation for imaging applications. stochastic graphs which span a set of extracted ...
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ISBN:
(纸本)0780367251
This paper presents an overview of some of the recent theory and application of stochastic minimal graphs in the context of entropy estimation for imaging applications. stochastic graphs which span a set of extracted image features can be constructed to yield consistent estimators of Jensen's entropy difference for between pairs of images. Unlike traditional plug-in entropy estimates based on density estimation, stochastic graph methods provide direct estimates of these quantities. We review the stochastic graph approach to entropy estimation, compare convergence rates to that of plug-in estimators, and discuss a geo-registration application.
We present evidence that several higher-order statistical properties of natural images and signals can be explained by a stochastic model which simply varies scale of an otherwise stationary Gaussian process. We discu...
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ISBN:
(纸本)0262122413
We present evidence that several higher-order statistical properties of natural images and signals can be explained by a stochastic model which simply varies scale of an otherwise stationary Gaussian process. We discuss two interesting consequences. The first is that a variety of natural signals can be related through a common model of spherically invariant random processes, which have the attractive property that the Joint densities can be constructed from the one dimensional marginal. The second is that in some cases the non-stationarity assumption and only second order methods can be explicitly exploited to find a linear basis that is equivalent to independent components obtained with higher-order methods. This is demonstrated on spectro-temporal components of speech.
In this paper we investigate how to improve the robustness of a speech recognizer in a noisy, mismatched environment when only a single or a few test utterances are available for compensating the mismatch. A new hiera...
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ISBN:
(纸本)0780370414
In this paper we investigate how to improve the robustness of a speech recognizer in a noisy, mismatched environment when only a single or a few test utterances are available for compensating the mismatch. A new hierarchical tree-based transformation is proposed to enhance the conventional stochastic matching algorithm in the cepstral feature space. The tree-based hierarchical transformation is estimated in two criteria: i) maximum likelihood (ML) using the current test utterance;ii) Sequential maximum a posterior (MAP) using the current and previous utterances. Recognition results obtained using a hands-free database show the proposed feature compensation is robust. Significant performance improvement has been observed over the conventional stochastic matching.
Noise reduction has been a traditional problem in imageprocessing. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequenc...
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ISBN:
(纸本)0780370414
Noise reduction has been a traditional problem in imageprocessing. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequency signal details. However, the local space-scale information of the image is not adaptively considered by standard wavelet thresholding methods. In this paper, a new type of thresholding neural networks (TNN) is presented with a new class of smooth nonlinear thresholding functions being the activation function. Unlike the standard soft-thresholding function, these new nonlinear thresholding functions are infinitely differentiable. Then a new nonlinear 2-D space-scale adaptive filtering method based on the wavelet TNN is presented for noise reduction in images. The numerical results indicate that the new method outperforms the Wiener filter and the standard wavelet thresholding denoising method in both peak-signal-to-noise-ratio (PSNR) and visual effect.
We derive a Kalman filter based on data from a sliding window. This is used for a new approach to fault detection and diagnosis, where the state estimate from past data is compared to the state estimate of some of the...
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ISBN:
(纸本)0780370414
We derive a Kalman filter based on data from a sliding window. This is used for a new approach to fault detection and diagnosis, where the state estimate from past data is compared to the state estimate of some of the future data. We suggest a method to judge the quality of diagnosis in a simple way. For fault estimation in the diagnosis, the general concept of stochastic observability in linear systems is introduced. Its role on the design step is illustrated on a problem of estimating the true velocity of a car.
Audio signal recovery is a common problem in digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subbands architecture is presented for audio signal recovery, using neu...
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ISBN:
(纸本)0780370414
Audio signal recovery is a common problem in digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subbands architecture is presented for audio signal recovery, using neural nonlinear prediction based on adaptive spline neural networks. The experimental results show the mean square reconstruction error, and maximum error obtained with increasing gap length, from 200 to 5000 samples. The method gives good results allowing the reconstruction of over 100ms signal with low audible effects in overall quality.
SPECT (Single Photon Emission Computed Tomography) is used in nuclear medicine to determine the distribution of a radioactive isotope within a patient from tomographic views or projection data. These images are severe...
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ISBN:
(纸本)0780370414
SPECT (Single Photon Emission Computed Tomography) is used in nuclear medicine to determine the distribution of a radioactive isotope within a patient from tomographic views or projection data. These images are severely degraded due to the presence of noise and several physical factors like attenuation and scattering. In this paper we use, within the Bayesian framework, a Compound Gauss Markov Random Field (CGMRF) as prior model to reconstruct such images. In order to find the Maximum a Posteriori (MAP) estimate we propose a new iterative method, which is stochastic for the line process and deterministic for the reconstruction. The proposed method is tested and compared with other reconstruction methods on both synthetic and real SPECT images.
The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve this bound asymptotically. In this p...
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ISBN:
(纸本)0780370414
The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve this bound asymptotically. In this paper, we study the stochastic CRB on DOA estimation accuracy in the general case of arbitrary unknown noise field parametrized by a vector of unknowns. We derive explicit closed-form expressions for the CRB and examine its properties theoretically and by representative numerical examples.
Resolution enhancement for video sequences has always been an attractive application in multimedia signalprocessing. "Superresolution" methods, that combine non-redundant information from a set of low-resol...
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ISBN:
(纸本)0780367251
Resolution enhancement for video sequences has always been an attractive application in multimedia signalprocessing. "Superresolution" methods, that combine non-redundant information from a set of low-resolution images, are beginning to be applied to the most popular video compression standard, MPEG. Bayesian approaches, which are very successful for raw video, largely fail for MPEG video, since they do not incorporate the compression Process into their models. This compression process introduces quantization noise, which is comparable to the additive noise that is used in the Bayesian models. In this paper we present an analytical derivation that combines the quantization and additive noises in a stochastic framework for MPEG-compressed video. This is a general framework in the sense that different video acquisition models, source statistics, implementation techniques can be used with it.
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