The CALCHAS project aims to develop an innovative signal processing and machine learning framework for the joint analysis of time-series of EO measurements from multiple sources, including satellite and in-situ observations. The performance of the analysis will be judged based on the resolution and accuracy of key geophysical parameters, focusing on the case of surface soil moisture.
The objective of PHySIS is to develop, test, and evaluate novel signal processing technologies for real-time processing of hyperspectral data cubes. Our aim is to extend recent theoretical and algorithmic developments in the field of sparsity-enforcing recovery, compressive sensing, and matrix completion, in order to build and exploit sparse representations adapted to the hyperspectral signals of interest.
 The DEDALE interdisciplinary project intends to develop the 
next generation of data analysis methods for such data set in order to probe the fine structureand extract  information in high dimensional data sets, in astrophysics and remote sensing.
The project have three main scientific directions:
i) Introduce new models and methods to analyze and restore complex, multivariate, manifold-based signals,
ii) Exploit the current knowledge in optimization and operations research to build efficient numerical data processing algorithms in the large-scale settings,
iii) Show the reliability of the proposed methods in two different applications: one in cosmology and one in remote sensing.

In CS-ORION, our focus is on the design, testing, and evaluation of compressive sensing (CS) architectures for enhancing the high-quality video acquisition and delivery capabilities of remote sensing devices that will enable them to provide efficient remote imaging in aerial and terrestrial surveillance. Our goal is to consider a long-term, multi-layer approach that combines expertise from statistical signal processing, data representation theory, and video coding and transmission techniques, for enabling robust and high-quality remote imaging.

HYDROBIONETS will address: a) the distributed acquisition of spatio-temporal biological signals, including the specific design of BioMEMs and their stable integration to motes; b) in-network cooperative processing and distributed intelligence to achieve essential tasks such as inference, detection, and decision-making; c) networked dense control to ensure adequate water quality, productivity and energy efficiency of water treatment plants. The results of this project will be demonstrated in real large-scale industrial water treatment and desalination plants, provided directly by partner ACCIONA, a worldwide leader in the water industry.