Objective
Separation/extraction of sources are wide concepts in information sciences, since sensors provide information mixing and an essential step consists in separating or extracting useful information from unuseful one, called noise. In this project, we consider three challenges.
The first one is the multimodality. Indeed, with the multiplication of kinds of sensors, in many areas like biomedical signal processing, hyperspectral imaging, etc. there are many ways for recording the same physical phenomenon leading thus to multimodal data. Multimodality has been studied in the framework of human-computer interface or in data fusion, but never at the signal level. The objective is to provide a general framework for modeling classical multimodal properties, like complementarity, redundancy, equivalence, etc. as of function of source signals.
The second challenge is nonlinearity. Indeed, there exist a few cases where the mixtures are essentially nonlinear, e.g. with chemical sensors. The main objective is to enlarge results on identifiability conditions for new classes of nonlinearities and priors on sources.
The third challenge is the data size. For high-dimension data (e.g. EEG or MRI in brain imaging), separating all the sources is neither tractable nor relevant, since one would like to only extract the useful sources. Conversely, for a small number of sensors, especially smaller than the number of sources, it is again necessary to only focus on the useful signals. The main objective is to develop generic approaches able to only extract useful signals, based on simple reference signal, modeling weak properties of the useful signal.
Finally, validation and relevant modeling must be based on actual signals and problems. In this project, theoretical results and algorithms will be developed in interaction with applications in biomedical engineering (brain-computer interface, EEG, fMRI), chemical engineering, audio-visual scene analysis and hyperspectral imaging.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
ERC-2012-ADG_20120216
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Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Host institution
Marne-la-Vall�
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.