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We propose a prediction/detection scheme for automatic forest fire surveillance by means of passive infrared sensors. Prediction takes advantages of the highly correlated environment in the infrared band to improve signal to noise ratio. We have observed that, in general, data are non-Gaussian distributed; thence nonlinear prediction allows improvements in the predictor performance. In particular,...
We consider the use of the Wiener system to perform nonlinear prediction. In this paper we propose a technique to retain the simplicity of the linear prediction by including a memoryless nonlinear function. The design of this later is approached from a Bayesian perspective: we look for the conditional mean of the predicted value, given the output of the linear predictor. Two techniques are proposed:...
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