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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： K. Premaratne P.H. Bauer Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical Engineering Laboratory of Image and Signal Analysis University of Notre Dame Notre Dame IN USA

This paper analyzes the problem of global asymptotic stability of delta-operator formulated discrete-time systems implemented in fixed-point arithmetic. It is shown that the free response of such a system tends to produce period one limit cycles if conventional quantization arithmetic schemes are used. Explicit necessary conditions for global asymptotic stability are derived, and these demonstrate that, in almost all cases, fixed-point arithmetic does not allow for global asymptotic stability in delta-operator formulated discrete-time systems that use a short sampling time.< >

关键词： Limit-cycles Asymptotic stability Quantization Fixed-point arithmetic Sampling methods Floating-point arithmetic Laboratories Signal analysis Bandwidth Feedback

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Fixed-point implementation of multi-dimensional delta-operator formulated discrete-time systems: difficulties in convergence

Fixed-point implementation of multi-dimensional delta-operat...

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IEEE Southeastcon

作者： P.H. Bauer K. Premaratne Department of Electrical Engineering Laboratory of Image and Signal Analysis University of Notre Dame Notre Dame IN USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA

The convergence properties of linearly stable multi-dimensional systems are investigated for the case of delta-operator implementations in fixed-point format. It is shown that zero-convergence is almost never achieved, if the sampling time is small. Using a one-dimensional analysis, it is demonstrated that zero-convergence cannot be guaranteed along the axis of the first hyper-quadrant for a first hyper-quadrant causal system. This limits the use of delta-operators for solving partial differential equations in discrete time with fixed-point arithmetic.< >

关键词： Quantization Convergence Multidimensional systems Differential equations Nonlinear equations Signal analysis Digital signal processing Asymptotic stability Laboratories image sampling

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Maximum likelihood parameter estimation for non-Gaussian prior signal models

Maximum likelihood parameter estimation for non-Gaussian pri...

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IEEE International Conference on image Processing

作者： R.R. Schultz R.L. Stevenson A. Lumsdaine Laboratory for Image and Signal Analysis Electrical Engineering University of Notre Dame Notre Dame IN USA Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. These models are generally dependent upon two parameters, one controlling the size of reconstructed discontinuities, and the other controlling data smoothing. The authors propose a maximum likelihood technique for automatically estimating these parameters, resulting in the optimization of an expression dependent upon the prior model partition function. An exact expression is derived for the 1D signal model partition function, while an approximation is proposed for the 2D image model partition function. Parameters estimated from degraded signals result in high quality restorations.< >

关键词： Maximum likelihood estimation Parameter estimation image restoration Signal restoration Automatic control Size control Gaussian distribution Bayesian methods image reconstruction Smoothing methods

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PARTITION-FUNCTION ESTIMATION OF GIBBS RANDOM-FIELD imageS USING MONTE-CARLO SIMULATIONS

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IEEE TRANSACTIONS ON INFORMATION THEORY 1993年第4期39卷 1322-1332页

作者： POTAMIANOS, GG GOUTSIAS, JK Department of Electrical and Computer Engineering. image Analysis and Communications Laboratory. The Johns Hopkins University

A Monte Carlo simulation technique, for estimating the partition function of a general Gibbs random field image, is proposed. By expressing the partition function as an expectation, an importance sampling approach for estimating it using Monte Carlo simulations is developed. As expected, the resulting estimators are unbiased and consistent. Computations can be performed iteratively, by using simple Monte Carlo algorithms, with remarkable success, as demonstrated by simulations. The work concentrates on binary, second-order Gibbs random fields, defined on a rectangular lattice. However, the proposed methods can be easily extended to more general Gibbs random fields. Their potential contribution to optimal parameter estimation and hypothesis testing problems, for general Gibbs random field images, via a likelihood approach, is anticipated.

关键词： GIBBS RANDOM FIELDS IMPORTANCE SAMPLING MONTE-CARLO SIMULATIONS PARTITION FUNCTION CALCULATIONS

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Adaptive control of redundant multilink robot using fussy logic

Adaptive control of redundant multilink robot using fussy lo...

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Applications of Fuzzy Logic Technology 1993

作者： Su, Xiongmin Mitra, Sunanda Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University LubbockTX79409-3102 United States

The kinematics model of multilink robot manipulator involves solution of the inverse problem. The use of fuzzy logic based linguistic rules demonstrates superior performance over classical, computationally intensive and yet approximate solutions for the target position control and tracking control of multilink robot arm. A new approach to fuzzy distance and restriction measures is used to obtain the appropriate orientations of the links for avoiding obstacles in the robot trajectories. This approach eliminates the classical task of solving highly coupled, nonlinear equations describing the illposed inverse problems of multilink robot motion at a much less demanding computational time. Such clear advantage of fuzzy logic based adaptive controller are illustrated by simulation results of guidance of a multilink robot in target positioning and trajectories tracking. The simulation results involve a threelink robot arm with capability of moving from one position to any desired position and tracking defined trajectories accurately. A modified fuzzy rule based distance measure allows the robot to follow trajectories without hitting the obstacles in the path. The simulation results indicate the advantage of fuzzy logic based adaptive controllers in multiple criteria decision-making tasks. © 1993 SPIE. All rights reserved.

关键词： Trajectories

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An analysis of Monte Carlo methods for likelihood estimation of Gibbsian images

An analysis of Monte Carlo methods for likelihood estimation...

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1993 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： G. Potamianos J. Goutsias Department of Electrical and Computer Engineering Image Analysis and Communications Laboratory Johns Hopkins University Baltimore MD USA

A unified analysis of two Monte Carlo algorithms for estimating the likelihood function of Gibbs random field images is presented. It is shown that such an estimation reduces to estimating the partition functions of suitably chosen Gibbs random fields. The first algorithm requires drawing samples from a mutually compatible Gibbs random field and provides unbiased and consistent estimators of these partition functions. The second algorithm uses samples which are approximately drawn from the Gibbs distribution, and results in asymptotically unbiased and consistent estimators of the partition functions. A measure of the computational complexity of these algorithms which makes it possible to compare them is introduced. It is concluded that the first algorithm is superior, especially for models with strong interactions.< >

关键词： image analysis Partitioning algorithms Monte Carlo methods Computational complexity Testing Laboratories image processing Parameter estimation Scattering Algorithm design and analysis

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Comments on "A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images" [with reply]

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IEEE Transactions on Information Theory 1993年第1期39卷 310-311页

作者： R.M. Neal J.K. Goutsias Department of Computer Science University of Toronto Toronto Canada Department of Electrical and Computer Engineering Image Analysis and Communications Laboratory Johns Hopkins University Baltimore MD USA

The commenter contends that, contrary to the conclusions of the above-titled paper by J.K. Goutsias (see ibid., vol.37, no.6, p.1618-28, Nov. 1991), the relative entropy of the initial distribution for a Monte Carlo simulation is not monotonically related to the degree of convergence after some given number of steps, and optimal single-step convergence is not achieved by the Gibbs sampler with systematic site ordering. In his reply, Goutsias explains the intent of his work, but expresses agreement with many of the comments. He provides a corrected statement of Theorem 3.< >

关键词： Algorithm design and analysis Monte Carlo methods Analytical models Convergence Entropy image converters Equations Sampling methods image processing image analysis

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Identification of noise outliers in clustering by a fuzzy neural network

Identification of noise outliers in clustering by a fuzzy ne...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： S. Pemmaraju S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

Since most real data sets encountered in cluster analysis are contaminated with background noise or outliers, it is essential to detect and isolate these noise samples from the data set. A few noisy points can affect the clustering procedure by severely biasing the algorithm. An ideal solution to this problem is to identify all the outliers and form a separate noise cluster. To do so, a technique is required by which the noise points are automatically identified and removed from the pattern data. The authors present a modified adaptive fuzzy leader clustering (AFLC) algorithm that has been used to detect and eliminate the outliers from the data structure and create a separate cluster of the outliers. The AFLC algorithm has an adaptive resonance theory (ART) like architecture with fuzzy learning embedded into it. Test results of the algorithm when applied to real and noisy data sets are presented.< >

关键词： Intelligent networks Fuzzy neural networks Clustering algorithms Background noise Subspace constraints image analysis Data structures Neural networks computer vision Laboratories

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A new method for estimating dimensions and 3-D reconstruction of coronary arterial trees from biplane angiograms

A new method for estimating dimensions and 3-D reconstructio...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： T. Kayikcioglu S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

The coronary arteries may be represented and visualized efficiently by a generalized cylinder (GC) model with elliptical cross-sections. By using this model and utilizing quadratic expression of an ellipse, the intensity distribution of an ellipse in any image plane, which is perpendicular to the plane of the arterial cross-section, is computed. A nonlinear parametric model for observed intensity distribution of an arterial cross-section is applied to estimate not only cross-sectional areas but also parameters of the ellipse which are essential in 3-D reconstruction of an arterial segment. This model takes into account background intensity, noise and blurring. Performance of this model has been tested on computer-generated and actual data for various background types and noise values and compared to those of other methods for estimating arterial dimensions. The computed cross-sectional areas from our model for computer-generated as well as actual data demonstrate much less variability than those reported in recent literature. 3-D reconstruction of arterial segments from actual biplane angiograms is presented.< >

关键词： Three dimensional displays Arteries Background noise Diseases Parameter estimation Visualization Parametric statistics image reconstruction Shape Bones

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Integrated adaptive fuzzy clustering (IAFC) algorithm

Integrated adaptive fuzzy clustering (IAFC) algorithm

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Y.S. Kim S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

An integrated adaptive fuzzy clustering (IAFC) algorithm using a structure similar to that found in the Adaptive Resonance Theory (ART-1) neural network, is presented. The IAFC incorporates a new learning rule and a new similarity measure to eliminate some structural problems inherent in other fuzzy ART-type neural networks. The new learning rule utilizes a fuzzy membership value, a function of the number of iterations, and a fuzzy within-cluster membership value. The new similarity measure incorporates a fuzzy membership value to the Euclidean distance. This incorporation of the new learning rule and the new similarity measure guarantees the convergence of weights in the IAFC algorithm and provides more flexibility to the shapes of the clusters formed by this algorithm. The critical parameters in the operation of IAFC are discussed. The performance of IAFC is evaluated in the classification of real data and compared with other recent neuro-fuzzy clustering algorithms.< >

关键词： Clustering algorithms Neural networks Fuzzy neural networks Euclidean distance Partitioning algorithms Fuzzy sets Resonance Shape measurement Convergence Subspace constraints

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