ABSTRACT Over the past few years, there has been a steady increase in the use of aircraft vehicles, in particular unmanned aerial vehicles (UAV). UAV navigation is generally controlled by a human pilot. But the challenge for the scientific community is to carry out autonomous navigation. Some solutions have been proposed for the UAV autonomous navigation. Studies indicate as a solution to use data fusion and/or image processing navigation. Kalman Filter (KF) can be employed as a data fuser, but the KF has disadvantages. An alternative to the KF is based on artificial intelligence. Here, the KF is replaced by a self-configured neural network. This work investigates a way to select data for training the neural fuser, based on cross-validation techniques. The results are compared to the data fusion done by a KF.

Estimativa do perfil de concentração de clorofila, em águas naturais, a partir da radiação emergente na superfície de um corpo d'agua, com o uso de rede neural artificial do tipo Perceptron de Múltiplas Camadas. A concentração de clorofila está relacionada com os coeficientes de absorção e espalhamento via modelos bio-ópticos. O treinamento da rede é formulado como um problema de otimização, no qual a atualização das variáveis livres da rede (pesos, viés e parâmetros de cada função de ativação) é feita através do método quasi-Newton.

In this work the average profile of chlorophyll concentration is estimated from the emitted radiation at the surface of natural waters. This is performed through the use an Artificial Neural Network of Multilayer Perceptron type to act as the inverse operator. Bio-optical models are used to correlate the chlorophyll concentration with the absorption and scattering coefficients. The network training is formulated as an optimization problem, in which the update of the free variables of network (weights, viéses and each slope of the activation functions) is performed through the quasi-Newton method.

Tsallis' non-extensive entropy is used as a regularization operator. The parameter ''q'' (non-extensivity parameter) has a central role in the Tsallis' thermostatiscs formalism. Here, several values of q are investigated in inverse problems, using q < 1 and q > 1. Two standard regularization techniques are recovered for special q-values: (i) q = 2 is the well known Tikhonov regularization; (ii) q = 1 is the standard Boltzmann-Gibbs-Shannon formulation for entropy. The regularization feature is illustrated in an inverse test problem: the estimation of initial condition in heat conduction problem. Two methods are studied for determining the regularization parameter, the maximum curvature for the L-curve, and the Morozov's discrepancy principle. The new regularization of higher order is applied to the retrieval of the atmospheric vertical profile from satellite data.