Abstract
This paper presents an efficient parameter extraction method applied to GaN high electron mobility transistors (HEMTs) for mm-wave applications. The procedure is based on S-parameters measurements at cold bias condition to extract the extrinsic parameters of a 19-element small-signal model. Hybrid technique of particle-swarm-optimization and direct fitting has been developed and implemented. The extraction procedure has been optimized to consider measurements uncertainty and improve the reliability of the extraction. The model has been validated by S-parameters measurements at different bias conditions and wide frequency range. A very good agreement between simulations and measurements has been obtained.

Abstract
In the latest years, the number of elderly people that has been living alone and need regular support has highly increased. Meal intake monitoring is a well-known strategy that enables premature detection of health problems. There are several attempts to develop automatic meal intake monitoring systems, but they are inadequate to monitor elderly people at home. In this context, we propose an automatic meal intake monitoring system that helps tracking people's eating behaviors, and is adequate for elderly remote monitoring at home due to its nonintrusive features. The system uses the MS Kinect sensor that provides the coordinates of the user's sitting skeleton during his meals. It analyzes the coordinates, detects eating gestures, and classifies them using Hidden Markov Models (HMMs) to estimate the user's eating behavior. A demonstrative prototype for detection and classification of gestures was implemented and tested. The detection module got satisfactory percentages of sensitivity, having a minimum of 72.7% and a maximum of 90%. The Classification module was tested with 3 proposed methods and the best method had a good average percentage of success (approximately 83%) in the classification of Soup and Main dish; regarding the left hand transporting Liquids, the results were less successful. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Abstract
This paper presents experience obtained in modelling and simulation of stakeholder-driven interactions for improved transport service design. The presented results describe value-aware, service model driven design artefacts supporting smart transport service development. The Socio-Technical System Engineering process is used in order to generate modelling and simulation artefacts, based on an executable representation of requirements. As a case study, the paper presents an improved design approach for a city transport information service to support travellers with valuable information regarding planning a trip in a city. This attempt to integrate agent-based modelling and simulation experience into the development of smart transport services emphasises the role of the development platform that provides tools for model analysis, validation, simulation, and real-time animation. The development platform's role in transposing the above mentioned aspects in practice is emphasized and integration guidelines of the STSE process steps with the IBM Rational Rhapsody (R) development platform are described.

Abstract

Abstract
This paper aims to discuss both the ICT and grid architectures of the Évora Demonstrator under the project SENSIBLE. The demonstrator is focused on testing grid management functions under normal and emergency operation in a rural low voltage grid, taking advantage of electrochemical, electromechanical and thermal storage technologies as well as renewable energy sources (photovoltaics) that will be deployed at both distribution grid and at clients' electrical installation. In addition, the community engagement strategy is presented since it is crucial for the full implementation of the project.