Embedded systems have entered a new era in which system designers have to consider more and more strict energy consumption constraints. This thesis reviewsprevious studies of the processor energy consumption estimation. Particularly, wefocus on instruction-level energy consumption for embedded processors and explorethe energy consumption model for manycore architecture in real traffic pattern.The purpose of this thesis project is to estimate energy consumption and constructan energy model using an instruction-set simulator for embedded processors. OpenVirtual Platforms (OVP) and Epiphany Single Core Simulator (ESCS) are used toobtain an instruction sequence for a given software. Then, the functionality of energyconsumption estimation is integrated into OVP.Our energy consumption estimation approach categorizes instructions in fourgroups and uses base energy cost of each category to calculate the total energyconsumption for an application that runs on Epiphany core and ARM Cortex M0.The energy consumption estimation for Epiphany core has been successfully testedby BEEBS benchmarks. Details of the derivation process, results and analysis ofenergy consumption estimation methods are provided.

Existem atualmente diversas propostas para integração de dispositivos móveis em uma grade computacional, porém vários problemas são observados em tais ambientes. Esta dissertação mantém o foco em um problema, a restrição sobre a quantidade de energia despendida na execução das aplicações, ao utilizar esses dispositivos móveis como provedores de recursos em uma grade computacional que fornece processamento para aplicações paralelas. Para tanto, este trabalho propõe um método para estimar o consumo de energia das aplicações considerando que elas utilizam um determinado conjunto de operações as quais estão presentes na grande maioria das aplicações paralelas (operações matemáticas e alocação de memória). Com base no método proposto, dois dispositivos móveis foram estudados e foi criada uma representação do consumo de energia utilizando-se de métodos de regressão. Para validar os modelos, duas aplicações foram analisadas e o consumo de energia real foi comparado ao consumo estimado. O modelo criado apresentou resultados próximos ao medido, mostrando um aumento entre 6% e 14,24% em relação ao resultado medido. / Nowadays, there are different proposals to integrate mobile devices in a computational grid, although several problems are introduces. This dissertation focus on the energy limitation problem when using mobile devices to provide resources, such as processing power to run parallel applications. It also proposes a method to estimate energy consumption for a task that needs to be executed in this environment. To achieve this goal two mobile devices were used as a test case and a representation of its energy consumption was created running benchmarks and using regression techniques. To validate the model created, two applications were executed and had the measured values compared to the estimated ones. The estimation showed a raise between 6 and 14.24 percent.

Existem atualmente diversas propostas para integração de dispositivos móveis em uma grade computacional, porém vários problemas são observados em tais ambientes. Esta dissertação mantém o foco em um problema, a restrição sobre a quantidade de energia despendida na execução das aplicações, ao utilizar esses dispositivos móveis como provedores de recursos em uma grade computacional que fornece processamento para aplicações paralelas. Para tanto, este trabalho propõe um método para estimar o consumo de energia das aplicações considerando que elas utilizam um determinado conjunto de operações as quais estão presentes na grande maioria das aplicações paralelas (operações matemáticas e alocação de memória). Com base no método proposto, dois dispositivos móveis foram estudados e foi criada uma representação do consumo de energia utilizando-se de métodos de regressão. Para validar os modelos, duas aplicações foram analisadas e o consumo de energia real foi comparado ao consumo estimado. O modelo criado apresentou resultados próximos ao medido, mostrando um aumento entre 6% e 14,24% em relação ao resultado medido. / Nowadays, there are different proposals to integrate mobile devices in a computational grid, although several problems are introduces. This dissertation focus on the energy limitation problem when using mobile devices to provide resources, such as processing power to run parallel applications. It also proposes a method to estimate energy consumption for a task that needs to be executed in this environment. To achieve this goal two mobile devices were used as a test case and a representation of its energy consumption was created running benchmarks and using regression techniques. To validate the model created, two applications were executed and had the measured values compared to the estimated ones. The estimation showed a raise between 6 and 14.24 percent.

Ce travail de thèse s’inscrit dans le domaine de l’informatique affective et plus précisément de l’intelligence artificielle et de l’exploration d’architecture. L’objectif de ce travail est de concevoir un système complet de détection des émotions en utilisant des signaux physiologiques. Ce travail se place donc à l’intersection de l’informatique pour la définition d’algorithme de détection des émotions et de l’électronique pour l’élaboration d’une méthodologie d’exploration d’architecture et pour la conception de nœuds de capteurs. Dans un premier temps, des algorithmes de détection multimodale et instantanée des émotions ont été définis. Deux algorithmes de classification KNN puis SVM, ont été implémentés et ont permis d’obtenir un taux de reconnaissance des émotions supérieurs à 80%. Afin de concevoir un tel système alimenté sur pile, un modèle analytique d’estimation de la consommation à haut niveau d’abstraction a été proposé et validé sur une plateforme réelle. Afin de tenir compte des contraintes utilisateurs, un outil de conception et de simulation d’architecture d’objets connectés pour la santé a été développé, permettant ainsi d’évaluer les performances des systèmes avant leur conception. Une architecture logicielle/matérielle pour la collecte et le traitement des données satisfaisant les contraintes applicatives et utilisateurs a ainsi été proposée. Doté de cette architecture, des expérimentations ont été menées pour la Mnémothérapie musicale. EMOTICA est un système complet de détection des émotions utilisant des signaux physiologiques satisfaisant les contraintes d’architecture, d’application et de l’utilisateur. / This thesis work is part of the field of affective computing and more specifically artificial intelligence and architectural exploration. The goal of this work is to design a complete system of emotions detection using physiological signals. This work is therefore situated at the intersection of computer science for the definition of algorithm of detection of emotions and electronics for the development of an architecture exploration methodology for the design of sensor nodes. At first, algorithms for multimodal and instantaneous detection of emotions were defined. Two algorithms of classification KNN then SVM, were implemented and made it possible to obtain a recognition rate of the emotions higher than 80%. To design such a battery-powered system, an analytical model for estimating the power consumption at high level of abstraction has been proposed and validated on a real platform. To consider user constraints, a connected object architecture design and simulation tool for health has been developed, allowing the performance of systems to be evaluated prior to their design. Then, we used this tool to propose a hardware/software architecture for the collection and the processing of the data satisfying the architectural and applicative constraints. With this architecture, experiments have been conducted for musical Mnemotherapy. EMOTICA is a complete system for emotions detection using physiological signals satisfying the constraints of architecture, application and user.