Section: Application Domains
This short section is only concerned with the list of concrete application domains developed by our team project on Bayesian inference and unsupervised learning, nonlinear filtering and rare event analysis. Most of these application areas result from fruitful collaborations with other national institutes through a series of four recently selected ANR research projects and one INRIA-INRA joint research project.
Three application domains are directly related to evolutionary computing, particle filtering and Bayesian inference are currently investigated by our team project, two of them are 2008's ANR research projects :
Multi-target tracking. Multi-target tracking deals with the task of estimating the states of a set of moving targets from a set of measurements obtained sequentially. These measurements may either arise from one of the targets or from clutter and the measurement-to-target association is generally unknown. This problem can then be recast as a dynamic clustering one where the clusters are the clutter and the different targets. The targets actually move over time, some targets may appear/disappear over time and the number of targets is generally unknown and time-varying. We are running this research project with the DCNS-SIS division in Toulon.
Forecasting and Data assimilation : This new application domain concerns the application of the particle filter technology and more general sequential Monte Carlo methods to data assimilation problems arising in forecasting. The ALEA team project is involved in the ANR 2008 selected project PREVASSEMBLE with Météo France Toulouse, the INRIA Rennes and the LMD in Paris.
Virtual prairie: This application domain of evolutionary computing is concerned with the design of ecological systems, mixed-species models and prairial ecosystems. For more details, we refer the reader to the web site of the Virtual Prairie project The ALEA project is a partner of the 2008 ANR SYSCOM project named MODECOL.
Three other application domains are directed related to rare event analysis using particle stochastic simulations techniques. These projects are currently investigated by our team project, two of them are 2008's ANR research projects :
Watermarking of digital contents:
The terminology watermarking refers to a set of techniques for imbedding/hiding information in a digital audio or video file, such that the change is not noticed, and very hard to remove. In order to be used in an application, a watermarking technique must be reliable. Protection false alarms and failures of traceability codes are practically not achievable without using rare event analysis. This application domain area of particle rare event technology is the subject of joint ANR 2007 research project with the IRISA-INRIA in Rennes and the LIS INPG in Grenoble. For more details, we refer the reader to the web site of the Nebbiano project. Security and Reliability in Digital Watermarking
Epidemic propagations analysis:
This project aims at developing stochastic mathematical models for the spread of transmissible infectious diseases, together with dedicated statistical methodologies, with the intent to deliver efficient diagnostic/prediction tools for epidemiologists. This application domain area of particle rare event technology is the subject of joint ANR 2008 research project with Telcom Paristech, the Laboratoire Paul Painlevé in Lille 1 and the University of Paris 5 (cf. Programme Systèmes Complexes et Modélisation Mathématique, list of 2008 selected projects ).
Statistical eco-microbiology predictions:
This project aims at developing stochastic models and algorithms for the analysis of bacteriology ecosystems, especially in food safety. The objective is to predict and control critical risk of proliferations. This is the subject of joint research project with the INRA of Paris and Montpellier (Appel d'Offre INRIA-INRA 2008 : Systèmes Complexes).