Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. Unfortunately, linear systems arising in image processing are highly ill-conditioned and precond...
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ISBN:
(纸本)0819450782
Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. Unfortunately, linear systems arising in image processing are highly ill-conditioned and preconditioners often give bad results, since the noise components on the data are strongly amplified already at the early iterations. In this work, we propose filtering strategies which allow to obtain preconditioners with regularization features for Toeplitz systems of image deblurring. Regularization preconditioners are able to speed up the convergence in the space less sensitive to the noise and, simultaneously, they slow down the restoration from components mainly corrupted by noise. A 2-d numerical simulation concerning astronomical image deblurring confirms the effectiveness of the arguments.
A comprehensive theory for time-frequency based signal detection has been developed during the past decade. The time-frequency detectors proposed in literature are linear structures operating on the time-frequency rep...
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ISBN:
(纸本)0819450782
A comprehensive theory for time-frequency based signal detection has been developed during the past decade. The time-frequency detectors proposed in literature are linear structures operating on the time-frequency representation of the signals and are equivalent to quadratic receivers that are defined in the,time domain. In this paper, an information theoretic approach for signal detection on the time-frequency plane is introduced. In recent years, Renyi entropy has been proposed as an effective measure for quantifying signal complexity on the time-frequency plane and some important properties of this measure have been proven. In this paper, a new approach that uses the entropy functional as the test statistic for signal detection is developed. The minimum error detection algorithm is derived and the performance of this new signal detection method is demonstrated through examples.
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly...
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ISBN:
(纸本)0819445584
Multiple input multiple output (MIMO) communications have been a hot research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly at the receiver. We focus on the situation where both the transmitter and the receiver know the channel information. We consider a transmit diversity scheme that maximizes the signal to noise ratio at the receiver. We analyze its performance in terms of capacity, duality and asymptotic behavior. By simulation, we compare this scheme with Alamouti's transmit diversity to show the advantage of utilizing the channel side information to improve the performance of the wireless systems.
This Volume 5205 of the conference proceedings contains 59 papers. Topics discussed include time frequency and time scale analysis, adaptive sensor network, wireless communication, image processing, exploitation of st...
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This Volume 5205 of the conference proceedings contains 59 papers. Topics discussed include time frequency and time scale analysis, adaptive sensor network, wireless communication, image processing, exploitation of structured in imaging and signalprocessing, matrixalgorithms, signalprocessing applications, computer arithmetic and arithmetic and architectures for real time applications.
Algorithmic engineering provides a rigorous framework for describing and manipulating the type of building blocks commonly used to define parallel algorithms and architectures for digital signalprocessing. So far, th...
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ISBN:
(纸本)081940943X
Algorithmic engineering provides a rigorous framework for describing and manipulating the type of building blocks commonly used to define parallel algorithms and architectures for digital signalprocessing. So far, the concept has only been illustrated by means of some relatively simple examples. These relate to the use of QR decomposition by Givens rotations for the purposes of adaptive filtering and beamforming. In this paper we present a much more challenging example whereby the techniques of algorithmic engineering are used to derive the QRD-based lattice algorithm for multi-channel least squares linear prediction. The elegant simplicity of this derivation, which comprises a sequence of straightforward diagrammatic manipulations, serves to demonstrate the potential power of algorithmic engineering as a formal design technique.
The primary basis for adaptive radar algorithm design is that (1) a Binary Hypothesis formulation with unknown parameters is an adequate test and (2) that radar interference is composed of combinations of thermal nois...
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ISBN:
(纸本)0819425842
The primary basis for adaptive radar algorithm design is that (1) a Binary Hypothesis formulation with unknown parameters is an adequate test and (2) that radar interference is composed of combinations of thermal noise, self induced clutter, and extraneous noise. This is the typical generalized likelihood formulation that yield the CFAR characteristic for the assumed conditions. implementations have shown that such formulations yield inadequate performance in complex clutter environments. As compensation measure, a secondary CFAR process then addresses the potential violation of this assumption by large ''target-like'' interference such as large Clutter discretes or a large number of targets interfering with each other. In order to detect small targets, an approach based on the Likelihood Statistic provides a technique for optimally suppressing the neighboring large signals. Performance is characterized as a function of a generalized distance and relative signal power ratios in the Joint Space-Time domain.
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extract...
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ISBN:
(纸本)9780819468451
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
Some recent results in the theory of adaptive array detection are presented. Detection curves are given for various detection algorithms. These detection algorithms are suitable for nulling out noise or interference s...
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ISBN:
(纸本)0819406945
Some recent results in the theory of adaptive array detection are presented. Detection curves are given for various detection algorithms. These detection algorithms are suitable for nulling out noise or interference sources in which the noise statistics are to be estimated from target- free data. They share the constant false alarm rate property, so that their false alarm rate can be set without knowledge of the noise covariance matrix. Also considered is a detection algorithm employing diagonal loading.
This paper presents modifications of the continuous Hopfield and Hartline-Ratliff networks for use in signal restoration and parameter estimation. The particular parameter estimation problem of interest is concerned w...
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ISBN:
(纸本)081940943X
This paper presents modifications of the continuous Hopfield and Hartline-Ratliff networks for use in signal restoration and parameter estimation. The particular parameter estimation problem of interest is concerned with the estimation of the directions of arrival of an unknown number of plane waves in unknown noise. Restoration of linearly distorted noisy images is considered as an example of regularized restoration of a signal with known dynamic range.
This brief presents efficient single-rate architectures for the one-dimensional orthonormal discrete wavelet transform (DWT). This brief makes two contributions, First, we show that architectures that are based on the...
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This brief presents efficient single-rate architectures for the one-dimensional orthonormal discrete wavelet transform (DWT). This brief makes two contributions, First, we show that architectures that are based on the quadrature mirror filter (QMF) lattice structure require approximately half the number of multipliers and adders than corresponding direct-form structures. Second, we present techniques for mapping the 1-D orthonormal DWT to folded and digit-serial architectures which are based on the QMF lattice structure. For folded architectures, we discuss two techniques for mapping the QMF lattice structure to hardware. For digit-serial architectures, we show that any two-channel subband system can be implemented using digit-serial processing techniques by utilizing the polyphase decomposition Using this result, we describe an orthonormal DWT architecture which uses the QMF lattice structure and digit-serial processing techniques, The proposed folded and digit-serial QMF lattice structures are attractive choices for implementations of the orthonormal DWT which require low area and low power dissipation.
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