HiDDaProTImA
High-dimensional data processing: from theory to imaging applications
Total Cost €
0EC-Contrib. €
0Partnership
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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 contact info |
| 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.
| # | ||||
|---|---|---|---|---|
| 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 |
|---|---|---|---|
| 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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