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The interest on the use of renewable energy resources is increasing, especially wind and hydro powers. To this aim, fuzzy control techniques represent viable strategies that can be employed for this purpose, thanks to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Based on past investigations carried out by the authors about the control of wind turbines and hydroelectric power plants, this paper provides some guidelines on the design and application of the control strategies suitable to these energy conversion systems. The working conditions of these energy conversion systems are also taken into account to highlight the reliability and robustness characteristics of the developed control strategies.

This paper evaluates the small- and large-signal characteristics of a single pole double thru (SPDT) RF antenna switch including its insertion loss, isolation and non-linear behavior. It is fabricated on two different types of high resistivity (HR) Silicon-on- Insulator (SOI) substrates: one standard (HR-SOI) and one trap-rich (RFeSI80). Using a special test structure, the contribution of substrate and active devices is separated for both in small- and large-signal. It is shown that by using trap-rich substrate technology, a reduction of more than 17 dB of 2nd harmonic is achieved compared with HR SOI substrate.

There is an increasing interest in NoSQL (Not Only SQL) systems developed in the area of Big Data as candidates for implementing multidimensional data warehouses due to the capabilities of data structuration/storage they offer. In this paper, we study implementation and modeling issues for data warehousing with document-oriented systems, a class of NoSQL systems. We study four different mappings of the multidimensional conceptual model to document data models. We focus on formalization and cross-model comparison. Experiments go through important features of data warehouses including data loading, OLAP cuboid computation and querying. Document-oriented systems are also compared to relational systems.

How efficiently decoding affective information when computational resources and sensor systems are limited? This paper presents a framework for analysis of affective behavior starting with a reduced amount of visual information related to human upper-body movements. The main goal is to individuate a minimal representation of emotional displays based on non-verbal gesture features. The GEMEP (Geneva multimodal emotion portrayals) corpus was used to validate this framework. Twelve emotions expressed by ten actors form the selected data set of emotion portrayals. Visual tracking of trajectories of head and hands was performed from a frontal and a lateral view. Postural/shape and dynamic expressive gesture features were identified and analyzed. A feature reduction procedure was carried out, resulting in a four-dimensional model of emotion expression, that effectively classified/grouped emotions according to their valence (positive, negative) and arousal (high, low). These results show that emotionally relevant information can be detected/measured/obtained from the dynamic qualities of gesture. The framework was implemented as software modules (plug-ins) extending the EyesWeb XMI Expressive Gesture Processing Library and was tested as a component for a multimodal search engine in collaboration with Google within the EU-ICT I-SEARCH project.

International audience; The plethora of data warehouse solutions has created a need comparing these solutions using experimental benchmarks. Existing benchmarks rely mostly on the relational data model and do not take into account other models. In this paper, we propose an extension to a popular benchmark (the Star Schema Benchmark or SSB) that considers non-relational NoSQL models. To avoid data post-processing required for using this data with NoSQL systems, the data is generated in different formats. To exploit at best horizontal scaling, data can be produced in a distributed file system, hence removing disk or partition sizes as limit for the generated dataset. Experimental work proves improved performance of our new benchmark.

International audience; Metamorphic robots are robots that can change their shape by reorganizing the connectivity of their modules to adapt to new environments, perform new tasks, or recover from damages. In this paper we present a decentralized method for structural evolving of a class of lattice-based simulated metamorphic robots in a static environment. These robots are considered as a set of crystalline (compressible) modules that are able to connect or disconnect one from each another or even exchange information and energy with the neighbor modules in order to form various structures/patterns dynamically. Our approach is splitted in two layers: in the first layer a genetic algorithm is used to generate a number of well suited target configurations based on current information perceived from environment, while in the second layer a PacMan-like algorithm is used to make a plan for modules movement to transform the robot from its current pattern to the target pattern emerged in first layer.

Phase noise plays an important role in the performances of electronic oscillators. Traditional approaches describe the phase noise problem as a purely diffusive process. In this paper we develop a novel phase model reduction technique for phase noise analysis of nonlinear oscillators subject to stochastic inputs. We obtain analytical equations for both the phase deviation and the probability density function of the phase deviation. We show that, in general, the phase reduced models include non Markovian terms. Under the Markovian assumption, we demonstrate that the effect of white noise is to generate both phase diffusion and a frequency shift, i.e. phase noise is best described as a convection-diffusion process. The analysis of a solvable model shows the accuracy of our theory, and that it gives better predictions than traditional phase models.

International audience; This paper describes the first step of a research project with the aim of predicting students' performance during an online curriculum on a LMS and keeping them from falling behind. Our research project aims to use data mining, machine learning and artificial intelligence methods for monitoring students in e-learning trainings. This project takes the shape of a partnership between computer science / artificial intelligence researchers and an IT firm specialized in e-learning software. We wish to create a system that will gather and process all data related to a particular e-learning course. To make monitoring easier, we will provide reliable statistics, behaviour groups and predicted results as a basis for an intelligent virtual tutor using the mentioned methods. This system will be described in this article. In this step of the project, we are clustering students by mining Moodle log data. A first objective is to define relevant clustering features. We will describe and evaluate our proposal. A second objective is to determine if our students show different learning behaviours. We will experiment whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. Experiments in clustering were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced.

This paper intends to be a literature review in the field of emotions detection using spectral analysis of neurological signals. It also shows the great boom in Brain Computer Interfaces (BCI) applications. Explains the research methodology used for this type of projects, and finally it highlights the results of several studies that have been done in this area.