This paper presents an approach to distributed state estimation‐based control of nonlinear MIMO systems, capable of incorporating delayed measurements in the estimation algorithm while also being robust to packet losses. First, the paper examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated sta...

Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Despite significant progress in the general theory and implementation, sequential Bayesian filters have been sparsely applied to ocean acoustics. The foundations of sequential Bayesian filtering with emphasis on practical issues are first presented covering...

In this paper, we focus on the insertion of feature filtering into natural language parsers. First, we present a logic-programming modelling of feature filtering mechanisms. We give a logical-based specification, valid for different theories of features (including their evolution from the lexicon, expressed in terms of axioms, and including control tools). The logical-based language and our for...

In this paper we present a method for the temporal tracking of fluid flows velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In ...

Algorithms of filtering, edge detection, and extraction of details and their implementation using cellular neural networks (CNN) are developed in this paper. The theory of CNN based on universal binary neurons (UBN) is also developed. A new learning algorithm for this type of neurons is carried out. Implementation of low-pass filtering algorithms using CNN is considered. Separate processing of ...

Gaussian convolution is of fundamental importance in linear scale-space theory and in numerous applications. We introduce iterated extended box filtering as an efficient and highly accurate way to compute Gaussian convolution. Extended box filtering approximates a continuous box filter of arbitrary non-integer standard deviation. It provides a much better approximation to Gaussian convolution t...

Bayesian spam filtering is a classification method based on the theory of probability and statistics, and the Bayesian spam filtering based on Mapreduce can solve the defect of the traditional Bayesian spam filtering that consumes large amounts of system resources and network resources when the mail set is pre-training. It needs to classify mails manually in the pre-training phase of mail set, ...

In this paper we attempt to give a historical account of the main ideas leading to the development of non-linear filtering and stochastic control as we know it today. The paper contains six sections. In Section 2 we present a development of linear filtering theory, beginning with Wiener-Kolmogoroff filtering and ending with Kalman filtering. The method of development is the innovations method a...

In this paper we introlduce a robust and nonlinear filtering framework: Weighted Myriad Filtering. Much like the Gaussian assumption has motivated the development of linear filtering theory, the formulation of myriad filters is motivated by the statistical properties of a-stable processes. Weighted Myriad Fi1ter.s have a solid theoretical basis, are inherently more powerful than weighted median...