A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products...

Computer measurement systems play an important role on process automation and Industry 4.0 implementation strategies. They can be easily integrated modern production systems, enabling real time test control of multiple product characteristics that need to monitored. If for one side the big data provided by these is asset analytics optimization, other hand, high frequency sampling, commonly used...

This paper revisits the topic of how linear functions of observations having zero expectation, play an important role in our statistical understanding of the effect of addition or deletion of a set of observations in the general linear model. The effect of adding or dropping a group of parameters is also explained well in this manner. Several sets of update equations were derived by previous re...

In the ordinary linear models, regressing the residuals against lagged values has been suggested as an approach to test the hypothesis of zero autocorrelation among residuals. In this paper we extend these results to the both equally and unequally replicated functionally measurement error models. We consider the equally and unequally replicated cases separately, because in the first case the re...

A novel solution to the estimation of catchment rainfall at a sub-hourly resolution from measured streamflow is introduced and evaluated for two basins with markedly different flow pathways and rainfall regimes. It combines a continuous-time transfer function model with regularised derivative estimates obtained using a recursive method with capacity for handling missing data. The method has gen...

In this paper we are interested in the problem of learning an over-complete basis and a methodology such that the reconstruction or inverse problem does not need optimization. We analyze the optimality of the presented approaches, their link to popular already known techniques s.a. Artificial Neural Networks,k-means or Oja’s learning rule. Finally, we will see that one approach to reach the opt...

Reinforcement learning is a machine learning answer to the optimal control problem. It consists in learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function. An important subtopic of reinforcement learning is to compute an approximation of this value function when the system is too large ...