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Underground imaging based on edge-preserving regularization

Underground imaging based on edge-preserving regularization

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International Conference on Information Intelligence and Systems

作者： H. Feng D.A. Castanon W.C. Karl Multi-Dimensional Signal Processing Laboratory Department of Electrical and Computer Engineering Boston University Boston MA USA

Develops approaches for imaging weak-contrast buried objects using data from a ground penetrating radar array. An approximate physical model relating the collected data to the underground objects is developed. This model uses ray optics to represent the air/soil interface, and a Born approximation to model the weak contrast backscattering from buried objects. In order to address both modeling errors and ill-posedness, the proposed image reconstruction algorithms use regularization based on a total variation norm with orientation preference. The algorithms are tested on data generated by nonlinear finite difference time domain electromagnetic simulations.

关键词： Buried object detection Optical imaging Ground penetrating radar Nonlinear optics Soil Scattering Approximation methods Backscatter Image reconstruction Testing

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Analysis of stochastic gradient identification of Hermite polynomial systems with memory

Analysis of stochastic gradient identification of Hermite po...

引用

5th International Symposium on signal processing and Its Applications, ISSPA 1999

作者： Celka, P. Bershad, N.J. Vesin, J.M. Department of Electrical Engineering Signal Processing Laboratory Swiss Federal Institute of Technology Lausanne 1015 Switzerland Department of Electrical and Computer Engineering School of Engineering University of California Imine CA 92697 United States

ISBN: (纸本)1864354518

This paper presents analytical and Monte Carlo results for a stochastic gradient adaptive scheme which identifies an orthogonal polynomial-type nonlinear system with memory. The analysis includes recursions for the mean and fluctuation behavior of the adaptive parameters as well computation of the mean-square error increase due to the adaptation process. © 1999 IEEE.

关键词： Mean square error

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Round-off error free fixed-point design of polynomial FIR predictors 33

Round-off error free fixed-point design of polynomial FIR pr...

引用

33rd Asilomar Conference on signals, Systems, and computers, ACSSC 1999

作者： Tanskanen, J.M.A. Dimitrov, V.S. Institute of Intelligent Power Electronics Department of Electrical and Communications Engineering Helsinki University of Technology Espoo Finland Laboratory of Signal Processing and Computer Technology Department of Electrical and Communications Engineering Helsinki University of Technology Espoo Finland

ISBN: (纸本)0780357000

We present a novel method for designing polynomial FIR predictors for fixed-point environments. Our method yields filters that perform exact prediction of polynomial signals even with short coefficient word lengths. Under ordinary coefficient truncation or rounding, the prediction capability degrades, or may be totally lost. With the proposed method, the filters are designed so that the predictive properties are exactly preserved in fixed-point implementations. The proposed filter design method is based on integer programming (IP) and can be directly applied to any fixed-point FIR design specifications which can be formulated in a form of linear constraints on the filter coefficients. © 1999 IEEE.

关键词： Polynomials

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Channel equalization by feedforward neural networks

Channel equalization by feedforward neural networks

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： B. Lu B.L. Evans Embedded Signal Processing Laboratory Department of Electrical and Computer Engineering University of Technology Austin TX USA

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either multilayer perceptron (MLP) or radial basis function (RBF) neural networks can outperform linear equalizers. In this paper, we compare the performance of MLP vs. RBF equalizers in terms of symbol error rate vs. SNR. We design a reduced complexity neural network equalizer by cascading an MLP and a RBF network. In simulation, the new MLP-RBF equalizer outperforms MLP equalizers and RBF equalizers.

关键词： Neural networks Feedforward neural networks Equalizers Neurons Backpropagation algorithms Least squares approximation Convergence Simulated annealing Bayesian methods Finite impulse response filter

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Optimal design of real and complex minimum phase digital FIR filters

Optimal design of real and complex minimum phase digital FIR...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： N. Damera-Venkata B.L. Evans Embedded Signal Processing Laboratory Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

We present a generalized optimal minimum phase digital FIR filter design algorithm that supports (1) arbitrary magnitude response specifications, (2) high coefficient accuracy, and (3) real and complex filters. The algorithm uses the discrete Hilbert transform relationship between the magnitude spectrum of a causal real sequence and its minimum phase delay phase spectrum given by Cizek (1970). We extend the transform pair to the complex case and show that the algorithm gives arbitrary coefficient accuracy. We present design examples that exceed the coefficient accuracy of the optimal real minimum phase filters reported by Chen and Parks (1986) and reduce the length of the optimal complex linear phase filters designed by Karam and McClellan (1995).

关键词： Finite impulse response filter Digital filters Nonlinear filters Algorithm design and analysis Polynomials signal processing algorithms Delay Discrete transforms Chebyshev approximation Cepstrum

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Analysis of stochastic gradient identification of polynomial nonlinear systems with memory

Analysis of stochastic gradient identification of polynomial...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： P. Celka N.J. Bershad J.M. Vesin Department of Electrical Engineering Signal Processing Laboratory Swiss Federal Institute of Technology Switzerland Electrical and Computer Engineering Department University of California Irvine USA

This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise.

关键词： Stochastic systems Polynomials Nonlinear systems Nonlinear filters Least squares approximation signal analysis Algorithm design and analysis signal processing algorithms Mean square error methods signal to noise ratio

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Fully constrained least-squares based linear unmixing [hyperspectral image classification]

Fully constrained least-squares based linear unmixing [hyper...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： D. Heinz C.-I. Chang M.L.G. Althouse Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

A fully constrained least-squares linear unmixing approach to hyperspectral image classification is presented. It is derived from an unconstrained least-squares based orthogonal subspace projection. It is similar to a method developed by Shimabukuro and Smith (1991) in the least-squares sense, but significantly different from their method in the way of implementing the constraints. Since there is no closed form solution available, an efficient algorithm is developed for finding a fully constrained solution, which can be viewed as a generalization of Shimabukuro and Smith's method. The effectiveness of this algorithm is demonstrated through computer simulations and real data experiments.

关键词： Vectors Subspace constraints Pixel Image processing Equations Hyperspectral imaging Remote sensing signal processing Laboratories computer science

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Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery

Linearly constrained minimum variance beamforming approach t...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chein-I Chang Hsuan Ren Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Subspace projection and maximum likelihood have been used for hyperspectral image classification. One of their disadvantages is the requirement of a priori knowledge about material signatures resident in images which may be difficult to obtain in many practical applications. In order to resolve this problem, an approach, called Constrained Energy Minimization (CEM) was developed for target detection where the only required knowledge is the target signature of interest. It was derived based on the Minimum Variance Distortionless Response (MVDR) in array processing. However, CEM has been also shown to be very sensitive to the knowledge being used to describe the target signature. This paper develops a more general approach, called Linearly Constrained Minimum Variance (LCMV) beamforming which includes CEM as a special case. Unlike CEM which considers a single constraint, LCMV uses multiple signatures to extend the capability of CEM to various target classification. More specifically, LCMV can be implemented as an estimator for target classification while CEM can be used as a detector for target detection.

关键词： Array signal processing Object detection Finite impulse response filter Hyperspectral sensors Hyperspectral imaging Nonlinear filters Image classification Vectors Power generation Layout

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Coding distortion caused by a phase difference between the LP filter and its residual

Coding distortion caused by a phase difference between the L...

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IEEE Workshop on Speech Coding Proceedings

作者： M. Tammi V.T. Ruoppila S. Kuusisto J. Saarinen Signal Processing Laboratory Tampere University of Technology Tampere Finland Department of Electrical and Computer Engineering University of Sherbrooke QUE Canada

Several speech coding algorithms modify the time scale of the residual signal to facilitate efficient coding of pitch information. Time scaling, however, results in a phase difference between the coded residual signal and the time-variant linear prediction (LP) filter used for synthesis in the decoder. In this paper, we examine the coding distortion induced by this phase difference. Moreover, we show that it may cause audible artifacts to the synthesized speech even if lossless coding of all parameters is employed. These artifacts occur particularly at onsets when the frequency response of successive LP filters changes rapidly. A waveform interpolation coder is used to illustrate the effects of the phase mismatch.

关键词： Phase distortion Filters Speech synthesis signal synthesis Speech coding Speech analysis Interpolation signal processing algorithms Filtering signal analysis

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Analysis of stochastic gradient identification of Hermite polynomial systems with memory

Analysis of stochastic gradient identification of Hermite po...

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International Symposium on signal processing and Its Applications (ISSPA)

作者： P. Celka N.J. Bershad J.M. Vesin Department of Electrical Engineering Signal Processing Laboratory Swiss Federal Institute of Technology Lausanne Switzerland Department of Electrical and Computer Engineering School of Engineering University of California Irvine CA USA

关键词： Stochastic systems Polynomials Fluctuations Nonlinear filters Adaptive signal processing Taylor series Stochastic processes signal processing algorithms Vectors Australia

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