Abstract
Ao longo dos anos, a espessura da camada de plasma tem sido determinada com o auxílio de métodos manuais. Apesar destes métodos serem bastante fiáveis, estes são morosos e podem introduzir erros do utilizador nos dados. No presente trabalho, foi desenvolvido um método automático de processamento de imagem para a determinação da espessura camada de plasma de uma forma automática.

Abstract
Blood is an opaque, heterogeneous, non-Newtonian fluid composed by a yellowish homogeneous fluid – the plasma – and a series of cellular elements. Red blood cells (RBCs) in microvessels and microchannels has tendency to undergo axial migration due to the parabolic velocity profile which results in a high shear stress around wall that forces the RBCs to move towards the center induced by the tank treading motion of the RBC membrane [1]. As a result there is a formation of a cell free layer (CFL) with extremely low concentration of cells around the walls of the microchannel [1-3]. This phenomenon is commonly observed in both in vitro [2, 3] and in vivo [4] experiments and has been extensively studied in small straight glass tubes [2, 5]. However, to the best of our knowledge, there are very few quantitative studies on the effect of complex geometries (such as bifurcations and confluences) on the CFL flow behaviour. The main objective of this study is to develop a MatLab script able to measure automatically the RBCs trajectories, at the CFL interface, and CFL thickness in microchannels containing series of bifurcations.

Abstract

Abstract
Most of the original image feature detectors are not able to cope with large photometric variations, and their extensions that should improve detection eventually increase the computational cost and introduce more noise to the system. Here we extend the original SURF algorithm increasing its invariance to illumination changes. Our approach uses the local space average color descriptor as working space to detect invariant features. A theoretical analysis demonstrates the impact of distinct photometric variations on the response of blob-like features detected with the SURF algorithm. Experimental results demonstrate the effectiveness of the approach in several illumination conditions including the presence of two or more distinct light sources, variations in color, in offset and scale.

Abstract
Intelligent wheelchairs can become an important solution to assist physically impaired individuals who find it difficult or impossible to drive regular powered wheelchairs. However, when designing the hardware architecture several projects compromise the user comfort and the wheelchair normal usability in order to solve robotic problems. In this paper we describe the main concepts regarding the design of the IntellWheels intelligent wheelchair. Our approach has a user-centered perspective, in which the needs and limitations of physically impaired users are given extensive attention at each stage of the design process. Finally, our design was evaluated through a public opinion assessment. A statistical analysis suggested that the design was effective to mitigate the visual and ergonomic impacts caused by the addition of sensorial and processing capabilities on the wheelchair. © 2013 IEEE.

Abstract
In recent years, computer vision has been widely used on industrial environments, allowing robots to perform important tasks like quality control, inspection and recognition. Vision systems are typically used to determine the position and orientation of objects in the workstation, enabling them to be transported and assembled by a robotic cell (e.g. industrial manipulator). These systems commonly resort to CCD (Charge-Coupled Device) Cameras fixed and located in a particular work area or attached directly to the robotic arm (eye-in-hand vision system). Although it is a valid approach, the performance of these vision systems is directly influenced by the industrial environment lighting. Taking all these into consideration, a new approach is proposed for eye-on-hand systems, where the use of cameras will be replaced by the 2D Laser Range Finder (LRF). The LRF will be attached to a robotic manipulator, which executes a pre-defined path to produce grayscale images of the workstation. With this technique the environment lighting interference is minimized resulting in a more reliable and robust computer vision system. After the grayscale image is created, this work focuses on the recognition and classification of different objects using inherent features (based on the invariant moments of Hu) with the most well-known machine learning models: k-Nearest Neighbor (kNN), Neural Networks (NNs) and Support Vector Machines (SVMs). In order to achieve a good performance for each classification model, a wrapper method is used to select one good subset of features, as well as an assessment model technique called K-fold cross-validation to adjust the parameters of the classifiers. The performance of the models is also compared, achieving performances of 83.5% for kNN, 95.5% for the NN and 98.9% for the SVM (generalized accuracy). These high performances are related with the feature selection algorithm based on the simulated annealing heuristic, and the model assessment (k-fold cross-validation). It makes possible to identify the most important features in the recognition process, as well as the adjustment of the best parameters for the machine learning models, increasing the classification ratio of the work objects present in the robot's environment.