Text analysis, via natural language processing (NLP) methods, aims at high-level information extraction. Applications vary from document classification to automatic answering machine. 

Agents' dynamics are considered using various motion models. They are  appplied to moving humans and vessels.  

Tracking represents a set  of techniques that, fusing information from various sensors, aims at identifying and predicting  objects'  motion. 

Data gathered from various sensor modalities  can be used to model and classify complex 3D scenes.  

Probabilistic Graphical Models with message propagation are a very powerful paradigm to build robust models for  real phenomena.

Artificial neural networks in various configurations are showing  great potential in a number of applications. 

The increasing volume of available data in the Healthcare domain suggests to use powerful machine leaning techniques for classification and prediction. 

Estimation theory aims at obtaining  hidden information  from measured empirical data using  stochastic models.

The group research actives include the development of  experimental platforms that can collect real data gathered from mobile terminals and sensors.

Current telecommunication systems are implemented on powerful digital platforms. Software-Defined Radio (SDR) is the paradigms that includes algorithm for coding and modulation on wireless channels.

The multipath channel is modelled with a stochastic lattice-based approach. 

Various algorithms have been studied for analysis, modification and synthesis of signals and images. Application domains include acoustics, image processing and  biological data. 

Genetic algorithms are a population-based search paradigm in stochastic optimization, inspired by the natural selection process.

A collection of other topics.