Abstract
BIOSWIM (Body Interface System based on Wearable Integration Monitoring) is a joint multidisciplinary effort involving a number of Portuguese R&D teams. It seeks a pervasive monitoring solution for physiological and biomechanical signals from a swimmer under normal training conditions, both in and out of the water. A wearable inertial monitoring unit (WIMU) was developed in order to serve as the biomechanical data processing unit of the system. The preliminary version of the WIMU has a commercially available microcontroller and transceiver set, as well as a tri-axial accelerometer and a bi-axial gyroscope serving as sensors. Testing in and out of the water has provided promising data and contributed to design modifications. These also took into account input from athletes, trainers, and physicians. Future work includes the integration of the WIMU within the complete BIOSWIM swimsuit system, complemented by truly integrated EMG and ECG textile sensors and a chemical monitoring unit. © Springer-Verlag Berlin Heidelberg 2013.

Abstract

Abstract
As experimental research reveals the biological mechanisms behind the processing done by the retina, complete models of the retina become more and more possible. This paper presents a temporal model of primate photoreceptors inspired by the mechanisms discovered in other species. It implements light adaptation based on pigment bleaching and biochemical reactions. The simulation provides similar results to experiments made in impulse, contrast and sensitivity response curves of primate cones and rods.

Abstract
Face detection is a problem in image processing that has been extensively studied in the last decades. This fact is justified due to the innumerous applications this subject has in computer vision. In this paper we present a technique to detect a face in an image where several persons may be. The images were recorded from news broadcasts from different networks. First, a model based on normalized RGB color space was built to define a skin color model: skin regions were selected from several face images and extracted statistical characteristics. Second, each pixel of the image was classified in two categories: skin and not skin. Third, the face region was identified. A MATLAB function was created to detect faces of any size, achieving high detection rates.

Abstract
The Cellular Neural Networks (CNN) model is now a paradigm of cellular analogue programmable multidimensional processor array with distributed local logic and memory. CNNs consist of many parallel analogue processors computing in real time. One desirable feature is that these processors arranged in a two dimensional grid only have local connections, which lend themselves easily to VLSI implementations. In this paper, we present a new algorithm for motion estimation using CNN. We start from a mathematical viewpoint (i.e., statistical regularisation based on Markov Random Field, (MRF)) and proceed by mapping the algorithm onto a cellular neural network. Because of the temporal dynamics inherent in the cells of the CNN it is well suited to processing time-varying images. A robust motion estimation algorithm is achieved by using a spatio-temporal neighbourhood for modelling pixel interactions.

Abstract
This paper presents the implementation of an optical flow algorithm on a pipeline image processor The overall optical flow computation method is presented and evaluated on a common set of image sequences. Results are compared to other implementations according to two different error measures. Due to its deterministic architecture, this implementation achieves very low computation delays that allow it to operate at standard video frame-rate and resolutions. It compares favorably to recent implementations in parallel hardware.