Recent experimental and computational studies suggest that linearly correlated sets of parameters (intrinsic and synaptic properties of neurons) allow central pattern-generating networks to produce and maintain their rhythmic activity regardless of changing internal and external conditions

and tracked. In the second stage the facial features are extracted for further processing. In last stage the most crucial parameter is monitored which is eye’s status. In the last stage it is determined that whether the eyes are closed or open. On the basis of this result the warning is issued to the driver

Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a

andmolecular scale.Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model that helps to identify those reaction parameters of the molecular interaction network that influence the tumour progression on the cellular scale the most. In particular, those parameters are identified

multicomponent clinical material like serum can influence kinetic parameters of the BChE. The observed inhibitory effect is dependent on serum dilution factor used in the assay.

The unsuitability of the ‘‘CFU’ ’ parameter and the usefulness of cultivation-independent quantification of Campylobacter on chicken products, reflecting the actual risk for infection, is increasingly becoming obvious. Recently, real-time PCR methods in combination with the use of DNA intercalators

parameters for PPP projects successful planning and implementation

con-tribution is to give a simple and general formulation of the notion that a constraint system is a system of partial infor-mation (a la the information systems of Scott). Parameter passing and hiding is handled by borrowing ideas from the cylindric algebras of Henkin, Monk and Tarski to introduce

distributions over the parameters remains a difficult problem. Moreover, learning the structure of models with latent variables, for which the Bayesian approach is crucial, is yet a harder problem. In this paper I present the Variational Bayes framework, which provides a solution to these problems

The mean-shift algorithm is an efficient technique for track-ing 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this pa