Abstract
In this paper we derive a set of approximate but general bilinear Kalman filter equations for a multiinput multi-output bilinear stochastic system driven by general autocorrelated inputs. The derivation is based on a convergent Picard sequence of linear stochastic state-space subsystems. We also derive necessary and sufficient conditions for a steady-state solution to exist. Provided all the eigenvalues of a chain of structured matrices are inside the unit circle, the approximate bilinear Kalman filter equations converge to a stationary value. When the input is a zero-mean white noise process, the approximate bilinear Kalman filter equations coincide with those of the well known bilinear Kalman filter model operating under white noise inputs.

Abstract
In this paper we present a case study involving mathematical modeling, system identification, and controller design of a two tank fluid level system. The case study is motivated by a realistic application of a two tank problem. We address some fundamental control oriented issues such as physical plant design and identification, transformation from discrete-time to continuous-time, and finally the controller design. We also introduce a novel physical system identification algorithm consisting of subspace identification, followed by a similarity transformation computation to extract the physical parameters of the system. The controller design is done by Pole Placement.

Abstract
In the past decades urban areas have suffered large demographic pressures, forcing people and their housing compounds to migrate to peripheral regions were they often build without land planning concerns, and where many times they are subject to adverse natural conditions and exposed to natural hazards being landslides one of the main threats. Nowadays, geophysical methods assume a relevant role monitoring and surveying unstable slopes. We performed thirty seismic profiles with the aim of determine distribution of rock weathering through seismic refraction techniques, in Canelas, a small village in NW Portugal. Each profile was summarized with average values of velocity for each depth. Despite having a low density coverage for the area involved, the results seem to show that seismic refraction is an important tool to rapidly characterize weathering thicknesses, a very important factor to be taken into account in problems of slope stability.

Abstract
This paper describes a preliminary and innovative approach to integrated cooperative control and navigation of multi robots dynamic formations that encompasses the simultaneous tracking of opponent team players in robotic football games. Unlike traditional approaches that use self-localization to distribute object position estimates, a coordinated approach to cooperative formation navigation is proposed. The control architecture is based in a hierarchic hybrid systems approach, where distributed maneuvers allow simultaneous navigation and coordination. Our main contributions reside in an integrated control and navigation design framework yielding cooperative localization maneuvers and also some specific maneuver results on both formation estimation and global localization of two robots and two landmarks with bearing only measurements. © 2006 IEEE.

Abstract

Abstract
This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoreerressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.