Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

Reatores de fusão: confinamento magnético e confinamento inercial

Dissertação para a obtenção do grau de Mestre em Engenharia Eletrotécnica Ramo de Energia

Complementary filter design with three frequency bands: robot attitude estimation

This paper extents the by now classic sensor fusion complementary filter (CF) design, involving two sensors, to the case where three sensors that provide measurements in different bands are available. This paper shows that the use of classical CF techniques to tackle a generic three sensors fusion problem, based solely on their frequency domain characteristics, leads to a minimal realization, stable, sub-optimal solution, denoted as Complementary Filters3 (CF3). Then, a new approach for the estimation problem at hand is used, based on optimal linear Kalman filtering techniques. Moreover, the solution is shown to preserve the complementary property, i.e. the sum of the three transfer functions of the respective sensors add up to one, both in continuous and discrete time domains. This new class of filters are denoted as Complementary Kalman Filters3 (CKF3). The attitude estimation of a mobile robot is addressed, based on data from a rate gyroscope, a digital compass, and odometry. The experimental results obtained are reported.

Generation of high-affinity monoclonal antibodies of IgG class against native β-d-glucans from basidiomycete mushrooms

β-d-glucans from basidiomycete strains are powerful immunomodulatory agents in several clinical conditions. Therefore, their assay, purification and characterization are of great interest to understand their structure-function relationship. Hybridoma cell fusion was used to raise monoclonal antibodies (Mabs) against extracellular β-d-glucans (EBGs) from Pleurotus ostreatus. Two of the hybridoma clones (1E6-1E8-B5 and 3E8-3B4) secreting Mabs against EBGs were selected. This hybridoma cell line secreted Mabs of the IgG class which were then purified by hydroxyapatite chromatography to apparent homogeneity on native and SDS-PAGE. Mabs secreted by 1E6-1E8-B5 clone were found to recognize a common epitope on several β-d-glucans from different basidiomycete strains. This Mab exhibited high affinity constant (KA) for β-d-glucans from several mushroom strains in the range of 3.20 × 109 ± 3.32 × 103-1.51 × 1013 ± 3.58 × 107 L/mol. Moreover, they reacted to some heat-treated β-d-glucans in a different mode when compared with the native forms; these data suggest that this Mab binds to a conformational epitope on the β-d-glucan molecule. The epitope-binding studies of Mabs obtained from 1E6-1E8-B5 and 3E8-3B4 revealed that the Mabs bind to the same epitope on some β-d-glucans and to different epitopes in other antigen molecules. Therefore, these Mabs can be used to assay for β-d-glucan from basidiomycete mushrooms. © 2015 Elsevier Ltd. All rights reserved.