Abstract
A new methodology for fault detection on wearable medical devices is proposed. The basic strategy relies on correctly classifying the captured physiological signals, in order to identify whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the physiological signals, like the electrocardiogram (ECG) and blood pressure (BP), to increase the trust levels of the captured data after rejecting or correcting distorted vital signals from each sensor, and to provide additional information on the patient's condition by classifying the set of signals into normal or abnormal condition (e.g. arrhythmia, chest angina, and stroke). Once an abnormal situation is detected in one or several sensors the monitoring system runs a set of tests in a fast and energy efficient way to check if the wearer shows a degradation of his health condition or the system is reporting erroneous values.

Abstract
We present the first study for the automatic recognition of microglial cells' state using stacked denoising autoencoders. Microglia has a pivotal role as sentinel of neuronal diseases where its state (resting, transition or active) is indicative of what is occurring in the Central Nervous System. In this work we delve on different strategies to best learn a stacked denoising autoencoder for that purpose and show that the transition state is the most hard to recognize while an accuracy of approximately 64% is obtained with a dataset of 45 images.

Abstract

Abstract
The ability to adjust itself to users' profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning and training of user modelling techniques combined with existing system components. Based on the architecture for user modelling on Intelligent and Adaptive Scheduling Systems, this paper presents an analysis of using the mentioned architecture to characterize user's behaviours and a case study comparing the employment of different user classifiers. Bayesian and Artificial Neural Networks were selected as the elements of the computational study and this paper presents a description on how to prepare them to deal with user information.

Abstract
The aim of this research paper is to formulate a road map for implementation of lean techniques within SMEs product development (PD) teams, identifying barriers to change and explaining possible drivers for successful implementation. The research methodology necessary to develop the road map - the IPID cycle - is a combination of different approaches. It combines a literature review, an initial quantitative study with questionnaires, informal interviews and direct observation of companies' practices of five different manufacturing companies organised in a consortium. Like any other case study methodology, it suffers from generalisation issues, but we expect that the proposed road map is applicable whenever managers need to introduce changes inside their PD teams. Also, we found that a consortium arrangement can be highly positive to the implementation of the proposed road map. Finally, we found that the application of the road map to SMEs PD teams is somewhat different from big companies' implementations and discuss the observed particularities of these findings. Copyright © 2016 Inderscience Enterprises Ltd.

Abstract
Context recognition is an indispensable functionality of context-aware applications that deals with automatic determination and inference of contextual information from a set of observations captured by sensors. It enables developing applications that can respond and adapt to user's situations. Thus much attention has been paid to developing innovative context recognition capabilities into context-aware systems. However, some existing studies rely on wearable sensors for context recognition and this practice has limited the incorporation of contexts into practical applications. Additionally, contexts are usually provided as low-level data, which are not suitable for more advanced mobile applications. This article explores and evaluates the use of smartphone's built-in sensors and classification algorithms for context recognition. To realize this goal, labeled sensor data were collected as training and test datasets from volunteers' smartphones while performing daily activities. Time series features were then extracted from the collected data, summarizing user's contexts with 50% overlapping slide windows. Context recognition is achieved by inducing a set of classifiers with the extracted features. Using cross validation, experimental results show that instance-based learners and decision trees are best suitable for smart phone -based context recognition, achieving over 90% recognition accuracy. Nevertheless, using leave one -subject-out validation, the performance drops to 79%. The results also show that smartphone's orientation and rotation data can be used to recognize user contexts. Furthermore, using data from multiple sensors, our results indicate improvement in context recognition performance between 1.5% and 5%. To demonstrate its applicability, the context recognition system has been incorporated into a mobile application to support context-aware personalized media recommendations.