Abstract
This paper reports the design of a new remotely operated underwater vehicle (ROV), which has been developed at the Underwater Systems and Technology Laboratory (USTL) - University of Porto. This design is contextualized on the KOS project (Kits for underwater operations). The main issues addressed here concern directional drag minimization, symmetry, optimized thruster positioning, stability and layout of ROV components. This design is aimed at optimizing ROV performance for a set of different operational scenarios. This is achieved through modular configurations which are optimized for each different scenario.

Abstract
The need to generate random wave fields with a pre-determined spectrum is a common one. One of the typical applications requiring this capability is the need to interface spectral and deterministic models. The spectra and integral parameters produced as output of spectral models must often be transformed into wave fields which can be used as input to deterministic models (e.g. Navier-Stokes based numerical models). Also, the instantaneous statistics of the produced wave field must be controlled. This is typically the case when the objective is to evaluate the influence of these statistics in the appearance of certain wave phenomena. In this article we propose a parametric based approach to the wave field generation. Not only is this a computationally efficient way of generating local elevation sequences, but it also enables perfect control of both the instantaneous and spectral characteristics of the generated waves. Two examples of applications will be given. One, where the developed generator will be used to investigate the influence of the spectral model and the instantaneous statistics of the wave field in extreme wave generation. A second example will be the generation of a boundary field for a Navier-Stokes based model starting from the spectral output of the SWAM model.

Abstract
Spatial and temporal sea level variability in the North Atlantic is investigated from Topex/Poseidon (T/P) altimetry data. Time series of sea level anomalies on a regular 5 degrees grid are analysed. Non-linear denoising through thresholding in the wavelet transform domain is carried out for each series in order to remove noise while preserving non-smooth features. Principal Component Analysis (PCA) is used to obtain a spatio-temporal description of the sea level field, To avoid modal mixing and improve interpretation of the principal modes, PCA is implemented separately for seasonal and trend components of the sea level field obtained from a wavelet-based multiresolution analysis. The leading pattern of the seasonal field reflects the dominance of a stable annual cycle over the study area and the change in the seasonal regime approaching the equator with contribution of the semi-annual cycle and phase-shift in the annual cycle in the tropical Atlantic. The leading pattern of the trend field is a broad spatial pattern associated with North Atlantic Oscillation (NAO), reflecting the influence of atmospheric conditions on interannual sea level variability.

Abstract
In this paper we introduce a new identification algorithm for MIMO bilinear systems driven by white noise inputs. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state space approximations, thus considered a Picard based method. The key to the algorithm is the fact that the bilinear terms behave like white noise processes. Using a linear Kalman filter, the bilinear terms can be estimated and combined with the system inputs at each iteration, leading to a linear system which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a bilinear model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.

Abstract
In this paper we introduce an identification algorithm for MIMO bilinear systems subject to deterministic inputs. The new algorithm is based on an expanding dimensions concept, leading to a rectangular, dimension varying, linear system. In this framework the observability, controllability, and Markov parameters are similar to those of a time-varying system. The fact that the system is time invariant, leads to an equaivaleet linear deterministic subspace algorithm. Provided a rank condition is satisfied, the algorithm will produce unbiased parameter estimates. This rank condition can be guaranteed to hold if the ratio of the number of outputs to the number of inputs is larger than the system order. This is due to the typical exponential blow-out in the dimensions of the Hankel data matrices of bilinear systems, in particular for deterministic inputs since part of the input subspace cannot be projected out. Other algorithms in the literature, based on Walsh functions, require that the number of outputs is at least equal to the system order. For ease of notation and clarification, the algorithm is presented as an intersection based subspace algorithm. Numerical results show that the algorithm reproduces the system parameters very well, provided the rank condition is satisfied. When the rank condition is not satisfied, the algorithm will return biased parameter estimates, which is a typical bottleneck of bilinear system identification algorithms for deterministic inputs. © 2005 IEEE.

Abstract
In this paper we describe the algorithms used in the external tracking system of the Isurus AUV. By listening to the acoustic signals exchanged between the vehicle and the beacons of the acoustic navigation network, the tracking system is able to obtain distance measurements from the vehicle to each beacon, that are then used to compute the vehicle horizontal position. Several error sources make these measurements inadequate to be used for computing the vehicle position by a simple triangulation technique. The tracking algorithms described here are able to reject highly erroneous measurements, producing position estimates with a satisfactory degree of accuracy. Copyright © 2004 IFAC