Abstract
Analysis of biomedical time series plays an essential role in clinical management and basic investigation. However, conventional monitors streaming data in real-time show only the most recent values, not referenced to past dynamics. We describe a chromatic approach to bring the 'memory' of the physiologic system's past behavior into the current display window. The method employs the estimated probability density function of a time series segment to colorize subsequent data points. For illustrative purposes, we selected open-access recordings of continuous: (1) fetal heart rate during the pre-partum period, and (2) heart rate and systemic blood pressure from a critical care patient during a spontaneous breathing trial. The colorized outputs highlight changes from the 'baseline' reference state, the latter defined as the mode value assumed by the signal, i.e. the maximum of its probability density function. A colorization method may facilitate the recognition of relevant features of time series, especially shifts in baseline dynamics and other trends (including transient and longer-term deviation from baseline values) which may not be as readily noticed using traditional displays. This method may be applicable in clinical monitoring (real-time or off-line) and in research settings. Prospective studies are needed to assess the utility of this approach.

Abstract
This chapter proposes a sampled-data model predictive control (MPC) architecture to solve the decentralized cooperative path-following (CPF) problem of multiple unmanned aerial vehicles (UAVs). In the cooperative path-following proposed scenario, which builds on previous work on CPF, multiple vehicles are required to follow pre-specified paths at nominal speed profiles (that may be path dependent) while keeping a desired, possibly time-varying, geometric formation pattern. In the proposed framework, we exploit the potential of optimization-based control strategies with significant advantages on explicitly addressing input and state constraints and on the ability to allow the minimization ofmeaningful cost functions. An example consisting of three fixed wing UAVs that are required to follow a given desired maneuver illustrates the proposed framework.We highlight and discuss some features of the UAVs trajectories. © Springer International Publishing Switzerland 2015.

Abstract
Background: Mammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services. Objective: Our goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy, effectiveness, and user satisfaction. Methods: A stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre-and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis. Results: A total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+ 34). The intervention was completed by 40 participants (11+ 29), with 4 (2+ 2) discontinued interventions, and 10 (7+ 3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<. 001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=. 004) but was unclear in students (18 pp vs 5 pp; P=. 098). Nonetheless, differences in students' posttest results were found (88% vs 63%; P=. 003), which were absent in pretest (63% vs 63%; P=. 106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<. 001), both in students (25 pp vs 3 pp; P=. 004) and radiographers (27 pp vs 2 pp; P<. 001). Overall, 85% were satisfied with the course, and 88% considered it successful. Conclusions: This e-learning course is effective, especially for radiographers, which highlights the need for continuing education.

Abstract

Abstract
Video surveillance systems must process and manage a growing amount of data captured over a network of cameras for various recognition tasks. In order to limit human labour and error, this paper presents a spatial-temporal fusion approach to accurately combine information from Region of Interest (RoI) batches captured in a multi-camera surveillance scenario. In this paper, feature-level and score-level approaches are proposed for spatial-temporal fusion of information to combine information over frames, in a framework based on ensembles of GMM-UBM (Universal Background Models). At the feature-level, features in a batch of multiple frames are combined and fed to the ensemble, whereas at the score-level the outcome of ensemble for individual frames are combined. Results indicate that feature-level fusion provides higher level of accuracy in a very efficient way.

Abstract
This paper presents an experiment concerning the development of two mobile devices applications with the App Inventor 2 for Android operating systems. These applications are intended to support teaching and learning activities in Industrial Automation and Control courses, particularly concerning logic control, logic controller programming and process control. While the reported applications: eLogicum and Automatum are yet in an early development stage, this paper aims to motivate teachers and students to a wider use of mobile devices in the context of university teaching and learning processes. Both applications are reported, focusing in the more relevant development issues concerning the App Inventor 2 use.