This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited, The method, which we call conditional entropy-constrained residual VQ, employs ...
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This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited, The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors, When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way, It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature, This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.
Multitrack codes that have recently been proposed for the magnetic storage channel promise larger capacities than conventional run-length limited (RLL) single-track codes. This paper proposes an approach that combines...
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Multitrack codes that have recently been proposed for the magnetic storage channel promise larger capacities than conventional run-length limited (RLL) single-track codes. This paper proposes an approach that combines modulation and error-correcting capabilities with resilience to intertrack interference (ITI) in the magnetic storage channel with constraints on the minimum run-length for each track, in conjunction with the minimum run-length constraint between tracks and the maximum run-length constraint satisfied by at least one track. Comparison is made with previously proposed approaches.
An algorithm is described for the joint estimation of motion and disparity vector fields from stereoscopic image sequences. Markov random fields (MRF) are used to model local interaction processes. Interaction of neig...
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An algorithm is described for the joint estimation of motion and disparity vector fields from stereoscopic image sequences. Markov random fields (MRF) are used to model local interaction processes. Interaction of neighbouring-motion disparity vectors across a discontinuity line is prohibited via hidden Markov fields signalling discontinuities in the vector fields. Occlusion processes are also used to mark occluded image locations, which may yield ambiguous matches. The coherence of motion and disparity vector fields is exploited by means of the epipolar constraint and the so called 'loop constraint'. A computationally efficient, suboptimal, hierarchical, deterministic relaxation algorithm is proposed.
The authors introduce a new bit-serial algorithm for stack filtering, designated as the bit-serial window partitioning algorithm. It is shown that the proposed algorithm can achieve very important savings over the con...
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The authors introduce a new bit-serial algorithm for stack filtering, designated as the bit-serial window partitioning algorithm. It is shown that the proposed algorithm can achieve very important savings over the conventional bit-serial binary-tree search algorithm, in terms of the computational speed. This improved efficiency is obtained by evaluating the Boolean function at thresholds corresponding to the sample values within the filter window, and by taking advantage of the ordering information associated with the threshold sequences.
An on-line iterative method for signal recovery from nonuniform samples is presented. This method uses the singular value decomposition (SVD) approach to estimate both the uniform samples and the error in their estima...
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An on-line iterative method for signal recovery from nonuniform samples is presented. This method uses the singular value decomposition (SVD) approach to estimate both the uniform samples and the error in their estimation. Two examples of its application are given.
Recently, a new class of adaptive filters called generalized adaptive neural filters (GANF's) emerged. They share many things in common with stack filters and include ail stack filters as a subset. The GANF's ...
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Recently, a new class of adaptive filters called generalized adaptive neural filters (GANF's) emerged. They share many things in common with stack filters and include ail stack filters as a subset. The GANF's allow a very efficient hardware implementation once they are trained, However, the training process can be slow. This paper discusses structural modifications to allow for faster training. In addition, these modifications can lead to an increase in the filter's robustness, given a limited amount of training data. This paper does not attempt to justify use of a GANF;it only presents an alternative implementation of the filter.
In this paper we propose a rule-based inductive learning algorithm called Multiscale Classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by ...
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In this paper we propose a rule-based inductive learning algorithm called Multiscale Classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by successively splitting the feature space in half. The algorithm has several significant differences from existing mts-based approaches: learning is incremental, the tree is non-binary, and backtracking of decisions is possible to some extent. The paper first provides background on current machine learning techniques and outlines some of their strengths and weaknesses. It then describes the MSC algorithm and compares it to other inductive learning algorithms with particular reference to ID3, C4.5, and back-propagation neural networks. Its performance on a number of standard benchmark problems is then discussed and related to standard learning issues such as generalization, representational power, and over-specialization.
In this paper we review the Receiver Operating Characteristic (ROC) curve, and the chi(2) test statistic, in relation to the analysis of a confusion matrix. We then show how these two methods are related, and propose ...
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In this paper we review the Receiver Operating Characteristic (ROC) curve, and the chi(2) test statistic, in relation to the analysis of a confusion matrix. We then show how these two methods are related, and propose an extension to the ROC curve so that it shows contours of chi(2) values. These contours can be used to provide further insight into the appropriate setting of the decision threshold for a particular application.
The performance of nonhierachical circuit switched networks at moderate load conditions is improved when alternate routes are made available, Alternate routes, however, introduce instability under heavy and overloaded...
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The performance of nonhierachical circuit switched networks at moderate load conditions is improved when alternate routes are made available, Alternate routes, however, introduce instability under heavy and overloaded conditions, and under these load conditions network performance is found to deteriorate, To alleviate this problem, a control mechanism is used where, a fraction of the capacity of each link is reserved for direct routed calls, In this work, a traffic management scheme is developed to enhance the performance of a mesh-connected, circuit-switched satellite communication network, The network load is measured and the network is continually adapted by reconfiguring the map to suit the current traffic conditions, The routing is performed dynamically. The reconfiguration of the network is done by properly allocating the capacity of each link and placing an optimal reservation on each link, The optimization is done by using two neural network-based optimization techniques: simulated annealing and mean field annealing. A comparative study is done between these two techniques, The results from the simulation study show that this method of traffic management performs better than the pure dynamic routing with a fixed configuration.
This paper treats algorithms for feature extraction and clustering of multichannel EEG transients occurring in epilepsy, so called spikes. Hermite functions with a variable width parameter is used as features. We stud...
This paper treats algorithms for feature extraction and clustering of multichannel EEG transients occurring in epilepsy, so called spikes. Hermite functions with a variable width parameter is used as features. We study nonlinear optimization of a series expansion for multichannel spikes. For the clustering problem, the nearest mean (NM) algorithm, generalized to matrix features, is used. The number of classes is assumed to be known a priori. The series expansion gives good signal description while reducing information. A simulation to estimate the space resolution capability of the algorithms indicates that perfect clustering requires approximately one head radius distance between the dipoles, which each generate one cluster. The NM algorithm was used to cluster two sets of clinically recorded spikes, and the clustering was compared to the manual clustering obtained by a neurophysiologist. For both spike sets evaluated, the clusters obtained by the algorithms had high accordance with the result of the neurophysiologist. (C) 1996 Academic Press, Inc.
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