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HiDDaProTImA

High-dimensional data processing: from theory to imaging applications

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EC-Contrib. €

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Partnership

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Project "HiDDaProTImA" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE 

Organization address
address: TRINITY LANE THE OLD SCHOOLS
city: CAMBRIDGE
postcode: CB2 1TN
website: www.cam.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://www.damtp.cam.ac.uk/research/afha/people/francesco/hiddaprotima.html
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-02-01   to  2018-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) coordinator 183˙454.00

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 Project objective

The unstoppable increase in the volume of data stored, transmitted and interpreted by fixed and mobile devices strongly calls for the study of efficient solutions in processing the information contained in high-dimensional signals. Such need has been reflected in the recent flourishing of research efforts from the statistics, machine learning, computer science and signal processing communities. Within this multidisciplinary research ground, the proposed project will address the central question that can be formulated as -- what is the maximum level of information contained in large datasets that we can process from a small number of features, and how is it possible to achieve such limit in practice?

Recent advances in information processing have demonstrated that a promising mathematical tool to tackle this question is represented by the Bayesian approach, in which statistical models inferred from training samples accurately describe the data. In fact, the Bayesian framework offers fundamental advantages in modeling high-dimensional signals in terms of mathematical tractability of performance limits as well as enhanced capabilities in information processing.

Beyond the study of performance limits, the proposed project will involve case studies and applications in image processing. The researcher will be able to establish active collaborations with various research groups, in different department of Cambridge University, that test their research results on actual imaging devices.

This project will also form the proposer to his future independent research activity and it will provide him with new mathematical skills and practical implementation expertise with actual imaging systems. On the other hand, Cambridge University will benefit from the cross pollination of ideas brought by the researcher and his collaborators in top institutions in Europe and the US.

 Publications

year authors and title journal last update
List of publications.
2017 Francesco Renna, Jorge Oliveira, Miguel T. Coimbra
A Data-Driven Feature Extraction Method for Enhanced Phonocardiogram Segmentation
published pages: , ISSN: , DOI:
Proceeding of the Computing in Cardiology Conference 2019-07-22
2016 Hugo Reboredo, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues
Bounds on the Number of Measurements for Reliable Compressive Classification
published pages: 5778-5793, ISSN: 1053-587X, DOI: 10.1109/TSP.2016.2599496
IEEE Transactions on Signal Processing 64/22 2019-07-22
2016 Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues
Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information
published pages: 6459-6492, ISSN: 0018-9448, DOI: 10.1109/TIT.2016.2606646
IEEE Transactions on Information Theory 62/11 2019-07-22
2016 Francesco Renna, Joseph Doyle, Vasileios Giotsas, Yiannis Andreopoulos
Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing
published pages: 2537-2552, ISSN: 1520-9210, DOI: 10.1109/TMM.2016.2600438
IEEE Transactions on Multimedia 18/12 2019-07-22

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The information about "HIDDAPROTIMA" are provided by the European Opendata Portal: CORDIS opendata.

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