the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically...
ISBN:
(纸本)0780358716
the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically bursting, and fast-spiking neurons;the use of a multilayer feedforward neural network for mineral prospectively mapping;discrimination of seismic signals using fuzzy entropy and a new FLVQ method;weightless neural network based monitoring of screw fastenings in automated assembly;multiple descent cost competitive learning and data-compressed 3-D morphing;fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks;an optimised decision tree induction algorithm for real world data domains;a neural mechanism of hyperaccurate detection of phase advance and delay in electrosensory system of weakly electric fish;a neural model of control system for foraging trips of honeybees;a diagnostic tool for tree based supervised classification learning algorithms;optimization of non-quadratic cost functions associated with Hopfield-like neural networks: impact of initial coefficient values;evolution versus training: an investigation into combining genetic algorithms and neural networks;comparison between syntactic pattern recognition and the randomized Hough transform;automated segmentation of the lateral ventricle of MR brain by fuzzy inference;identification of a landmark in a roentgenographic cephalogram by employing the wavelet neurons;connectionist incremental learning by analogy;and approximating discrete mapping of chaotic dynamical system based on on-line EM algorithm.
the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically...
ISBN:
(纸本)0780358716
the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically bursting, and fast-spiking neurons;the use of a multilayer feedforward neural network for mineral prospectively mapping;discrimination of seismic signals using fuzzy entropy and a new FLVQ method;weightless neural network based monitoring of screw fastenings in automated assembly;multiple descent cost competitive learning and data-compressed 3-D morphing;fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks;an optimised decision tree induction algorithm for real world data domains;a neural mechanism of hyperaccurate detection of phase advance and delay in electrosensory system of weakly electric fish;a neural model of control system for foraging trips of honeybees;a diagnostic tool for tree based supervised classification learning algorithms;optimization of non-quadratic cost functions associated with Hopfield-like neural networks: impact of initial coefficient values;evolution versus training: an investigation into combining genetic algorithms and neural networks;comparison between syntactic pattern recognition and the randomized Hough transform;automated segmentation of the lateral ventricle of MR brain by fuzzy inference;identification of a landmark in a roentgenographic cephalogram by employing the wavelet neurons;connectionist incremental learning by analogy;and approximating discrete mapping of chaotic dynamical system based on on-line EM algorithm.
the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically...
ISBN:
(纸本)0780358716
the proceedings contain 213 papers. the topics discussed include: learning and recall of temporal sequences in network of CA3 pyramidal cells and a basket cell;spiking neuron models, for regular-spiking, intrinsically bursting, and fast-spiking neurons;the use of a multilayer feedforward neural network for mineral prospectively mapping;discrimination of seismic signals using fuzzy entropy and a new FLVQ method;weightless neural network based monitoring of screw fastenings in automated assembly;multiple descent cost competitive learning and data-compressed 3-D morphing;fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks;an optimised decision tree induction algorithm for real world data domains;a neural mechanism of hyperaccurate detection of phase advance and delay in electrosensory system of weakly electric fish;a neural model of control system for foraging trips of honeybees;a diagnostic tool for tree based supervised classification learning algorithms;optimization of non-quadratic cost functions associated with Hopfield-like neural networks: impact of initial coefficient values;evolution versus training: an investigation into combining genetic algorithms and neural networks;comparison between syntactic pattern recognition and the randomized Hough transform;automated segmentation of the lateral ventricle of MR brain by fuzzy inference;identification of a landmark in a roentgenographic cephalogram by employing the wavelet neurons;connectionist incremental learning by analogy;and approximating discrete mapping of chaotic dynamical system based on on-line EM algorithm.
this paper adresses the definition and properties of list scheduling in the context of scheduling a cyclic set of n non-preemptive and non-reentrant-dependent tasks on m identical processors when the reduced precedenc...
this paper adresses the definition and properties of list scheduling in the context of scheduling a cyclic set of n non-preemptive and non-reentrant-dependent tasks on m identical processors when the reduced precedence graph is assumed to be strongly connected. It is first shown that the average cycle time of an arbitrary list schedule is at most (2 - 1/m) times the absolute minimum average cycle time. K-periodic list schedules are then shown to be the list schedules associated with special priority mappings called K-periodic linear orders. Moreover, given a list that offers the performance ratio rho in the non-cyclic case and whose structure is quasi K-periodic in the cyclic case, it is shown that each of its corresponding K-periodic linear orders provides a list schedule withthe same guarantee. Since the well-known Coffman-Graham's list is shown to be quasi K-periodic, its performance may be transferred to UET cyclic problems. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
Optical mapping is a novel technique for generating the restriction map of a DNA molecule by observing many single, partially digested, copies of it, using fluorescence microscopy. the real-life problem is complicated...
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this paper considers scheduling algorithms for an infrastructure network where: (i) multiple channels are available per cell and (ii) mobile reservation and basestation scheduling protocol is used for medium access. T...
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Time-frequency distribution (TFDs) based on Cohen's class has significant potential for the analysis of nonstationary signals. However, high computational requirement limits its use in applications where hardware ...
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Time-frequency distribution (TFDs) based on Cohen's class has significant potential for the analysis of nonstationary signals. However, high computational requirement limits its use in applications where hardware resources are costly. this paper proposes an algorithm which provides mechanism to trade-off computational complexity against exactness for the computation of generalized discrete TFDs (GDTFDs). the approach employs a linear signal expansion by means of the wavelet transform to make the signal sparse, thus simplifying subsequent analysis and processing. It then restricts the accuracy of the computation to some predefined measures using the data dependent wavelet shrinkage via hard thresholding. the paper discusses the complexity of the approach and shows its efficiency under some mild constraints. the trade-off flexibility allows the implementation of the high computational tasks in a power-efficient and cost-effective way tailored to specific application demands.
this paper describes the project of a processor for the calculation of the direct/inverse two-dimensional discrete cosine transform to be employed in videoconference applications. this processor makes use of the separ...
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this paper describes the project of a processor for the calculation of the direct/inverse two-dimensional discrete cosine transform to be employed in videoconference applications. this processor makes use of the separability technique as calculation method and of an architecture based on distributed arithmetic, in which multipliers are replaced by accumulation-and-shift blocks. the processor was implemented on the AMS 0.8 /spl mu/m technology with semi-custom approach in order to realize an IP macro-cell to be integrated in multimedia ICs. It features a very low-complexity (15 kgates) for an overall area of 33 mm/sup 2/ and a maximum frequency of 36 MHz. Besides, the processor is fully compliant with accuracy specifications in H.263 recommendation.
Discusses the reconstruction of chaotic dynamics by using a normalized Gaussian network (NGnet). the NGnet is trained by an online expectation maximization (EM) algorithm in order to learn the discrete mapping of the ...
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Discusses the reconstruction of chaotic dynamics by using a normalized Gaussian network (NGnet). the NGnet is trained by an online expectation maximization (EM) algorithm in order to learn the discrete mapping of the chaotic dynamics. We also investigate the robustness of our approach to two kinds of noise processes: system noise and observation noise. It is shown that a trained NGnet is able to reproduce a chaotic attractor, even under various noise conditions. the trained NGnet also shows good prediction performance. When only part of the dynamical variables are observed, the NGnet is trained to learn the discrete mapping in the delay coordinate space. It is shown that the chaotic dynamics is able to be learned withthis method under the two kinds of noise.
In our previous paper, we appliedthe idea of perfect-reconstruction cosine-modulated filter banks to develop efficient transmultiplexers for multicarrier VDSL transmission systems of the discrete wavelet multitone ty...
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In our previous paper, we appliedthe idea of perfect-reconstruction cosine-modulated filter banks to develop efficient transmultiplexers for multicarrier VDSL transmission systems of the discrete wavelet multitone type. However, it is sometimes judicious to relax perfect-reconstruction conditions by allowing amplitude and/or aliasing distortions. In this paper, we present a nearly perfect-reconstruction cosine-modulated transmultiplexer approach for VDSL modems. the effects of design parameters (roll-off, filter length, maximum allowable amplitude distortion, and maximum allowable aliasing distortion) on the performance of the transmultiplexer systems are evaluated through design examples.
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