Pedro M. Ferreira

Learn More
The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair(More)
Online learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by using a sliding-window policy that enforces the novelty of(More)
Heating, Ventilating and Air Conditioning (HVAC) systems are used to provide adequate comfort to occupants of spaces within buildings. One important aspect of comfort, the thermal sensation, is commonly assessed by computation of the Predicted Mean Vote (PMV) index. Model-based predictive control may be applied to HVAC systems in existing buildings in order(More)
The paper addresses the problem of controlling an heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the(More)
Neural and neuro-fuzzy models are powerful nonlinear modelling tools. Different structures, with different properties, are widely used to capture static or dynamical nonlinear mappings. Static (non-recurrent) models share a common structure: a nonlinear stage, followed by a linear mapping. In this paper, the separability of linear and nonlinear parameters(More)
A method based on the information theory concept of entropy is presented for selecting subsets of data for offline model identification. By using entropy-based data selection instead of random equiprobable sampling before training models, significant improvements are achieved in parameter convergence, accuracy and generalisation ability. Furthermore, model(More)
Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable(More)
This study describes research to design a seismic detection system to act at the level of a seismic station, providing a similar role to that of STA/LTA ratio-based detection algorithms. In a first step, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), trained in supervised mode, were tested. The sample data consisted of 2903 patterns(More)