the intention of the paper is first to show the applicability of the general categorical framework of open maps to the setting of true concurrent models with dense time. In particular, we define a category of timed ev...
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To minimize the absolute (or relative) error of signal reproduction the "uniform analog" of the truncate Fourier discrete spectrum is proposed. To put it more exactly, the spectrum withthe coefficients of t...
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To minimize the absolute (or relative) error of signal reproduction the "uniform analog" of the truncate Fourier discrete spectrum is proposed. To put it more exactly, the spectrum withthe coefficients of the uniform (minimax, Chebyshev) approximation needs to be used instead of the Fourier coefficients. the algorithm and application examples are described. A decrease in the signal reproduction error of 2 - 20 times is obtained.
Let a be a vector of real numbers. By an integer relation for a we mean a non-zero integer vector c such that ca(T) = 0. We discuss the algorithms for finding such integer relations from the user's point of view, ...
Let a be a vector of real numbers. By an integer relation for a we mean a non-zero integer vector c such that ca(T) = 0. We discuss the algorithms for finding such integer relations from the user's point of view, by presenting examples of their applications and by reviewing the available software implementations. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
In this work we present a novel strategy for the simultaneous design and training of multilayer discrete-Time Cellular Neural Networks. this methodology is applied to the detection of surface-laid antipersonnel mines ...
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In this work we present a novel strategy for the simultaneous design and training of multilayer discrete-Time Cellular Neural Networks. this methodology is applied to the detection of surface-laid antipersonnel mines in infrared imaging. the procedure is based on the application of Genetic algorithms for both network design and learning tasks.
the present paper addresses the question of the efficiency of Independent Component Analysis (ICA) as a statistical process for deriving optimal representational bases for the projection of spectrum and cepstrum in th...
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ISBN:
(纸本)7801501144
the present paper addresses the question of the efficiency of Independent Component Analysis (ICA) as a statistical process for deriving optimal representational bases for the projection of spectrum and cepstrum in the context of Automatic Speech Recognition (ASR). Several decorrelation strategies have been applied on the log-spectrum and cepstrum to fulfill the practical need of a diagonal covariance HMM for uncorrelated features. In our work we question the optimality of a fixed decorrelation strategy as DCT and follow an emerging trend in ASR that designs projection bases based on the statistics of speech. We differentiate our approach from the second order statistics of discrete Cosine Transform (DCT), Linear Discrimination Analysis (LDA) and Principal Component Analysis (PCA) by proposing an alternative data-driven approach based on Higher Order Statistics.
the direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. the estimation of densiti...
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Noise robust speech recognition has become an important area of research in recent years. the fact that human listeners can recognize speech in the presence of strong noise inspires researchers to imitate some aspects...
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ISBN:
(纸本)7801501144
Noise robust speech recognition has become an important area of research in recent years. the fact that human listeners can recognize speech in the presence of strong noise inspires researchers to imitate some aspects of human auditory perception in automatic speech recognition. this has led to sub-band based speech recognition in which the full-band speech is split into several sub-bands and where each sub-band is processed separately. the resulting multi-band features can be combined in various ways for carrying out speech recognition task. Reported results have shown the superiority of this technique for speech recognition in strong noise conditions. In this paper, we will briefly review the multi-band feature extraction. We will then propose a block discrete cosine transform (BDCT) with its kernel transformation matrix being derived from the decomposition of the kernel of the discrete cosine transform (DCT). We show that the BDCT approximates the DCT in keeping information in decorrelating a sequence. When the BDCT is applied to the mel frequency filter bank energies (FBEs) to replace the DCT to convert them to cepstral coefficients, a new kind of MFCCs is yielded. We call these new features Block discrete cosine transform based MFCCs (BMFCCs) and show that a sub-band processing idea is implicit in the BMFCCs since the BDCT automatically divides the mel frequency FBEs into two sub-bands. We will report various speech recognition results using the BMFCCs as well as the comparison withthe multi-band MFCCs and fullband MFCCs to elaborate the properties of the BMFCCs.
A new approach to characterizing the performance of point- correspondence algorithms is presented. Instead of relying on any"gro- und truth’, it uses the self-consistency of the outputs of an algorithm independe...
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this paper analyzes the performance of a parallel solver for discrete-time periodic Riccati equations based on a sequence of orthogonal reordering transformations of the monodromy matrices associated withthe equation...
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the proceedings contain 47 papers. the special focus in this conference is on Principles and Practice of Constraint Programming. the topics include: Constraints for interactive graphical applications;automatic generat...
ISBN:
(纸本)3540410538
the proceedings contain 47 papers. the special focus in this conference is on Principles and Practice of Constraint Programming. the topics include: Constraints for interactive graphical applications;automatic generation of propagation rules for finite domains;global constraints as graph properties on a structured network of elementary constraints of the same type;universally quantified interval constraints;constraint propagation for soft constraints;constraints, inference channels and secure databases;a simple method for identifying tractable disjunctive constraints;a language for audiovisual template specification and recognition;new tractable classes from old;expressiveness of full first order constraints in the algebra of finite or infinite trees;cutting planes in constraint programming;a constraint-based framework for prototyping distributed virtual applications;a scalable linear constraint solver for user interface construction;maintaining arc-consistency within dynamic backtracking;analysis of random noise and random walk algorithms for satisfiability testing;faster algorithms for bound-consistency of the sortedness and the all different constraint;a hybrid search architecture applied to hard random 3-sat and low-autocorrelation binary sequences;linear formulation of constraint programming models and hybrid solvers;a global constraint combining a sum constraint and difference constraints;optimal anytime constrained simulated annealing for constrained global optimization;using randomization and learning to solve hard real-world instances of satisfiability;branching constraint satisfaction problems for solutions robust under likely changes and the phase transition in distributed constraint satisfaction problems.
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