System-level design of energy-efficient sensor-based human activity recognition systems: a model-based approach

This thesis contributes an evaluation of state-of-the-art dataflow models of computation regarding their suitability for a model-based design and analysis of human activity recognition systems, in terms of expressiveness and analyzability, as well as model accuracy. Different aspects of state-of-the-art human activity recognition systems have been modeled and analyzed. Based on existing methods, novel analysis approaches have been developed to acquire extra-functional properties like processor utilization, data communication rates, and finally energy consumption of the system

Nouvelles approches pour la conception d'outils CAO pour le domaine des systèmes embarqués

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

A Performance Analysis Tool for Scenario-Aware Streaming Applications

Dataflow models are often used for analysing streaming applications. The recently introduced scenario-aware extension of the synchronous dataflow model can capture the dynamism in computation and communication resource requirements of streaming applications that originates from different modes of operation (scenarios). This scenario-aware dataflow model uses a probabilistic approach to express the order in which scenarios (and different execution times within a scenario) occur. This paper discusses a tool for exhaustive and simulation-based analysis of various important performance metrics for scenario-aware streaming applications. 1