IRMA

Information Role Models with Applications

 Coordinatore THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE 

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Edna
Cognome: Murphy
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 237˙684 €
 EC contributo 237˙684 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-IEF-2008
 Funding Scheme MC-IEF
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-06-01   -   2011-11-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Edna
Cognome: Murphy
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

UK (CAMBRIDGE) coordinator 237˙684.38

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

curie    bayesian    marie    optimal    model    wireless    difficult    receiver    observations    theory    estimator    communications   

 Obiettivo del progetto (Objective)

'Picking a role model is a natural way of deciding what to do by emulating a person we admire: children look up to superheroes; chemists and physicists follow Marie Curie. When faced with a difficult choice, solving the problem on our own may seem difficult. Instead, we seek shelter behind our role model and try to imagine what Superman or Marie Curie would have done. There is a parallel to this approach in statistical signal processing. Consider the design of an estimator based on low quality observations. Bayesian theory dictates rules for the optimal design of such an estimator. Suppose there exists a better estimator that has access to superior observations. We can use this better estimator as a role model, and train our estimator to imitate its outputs. Under certain conditions, we have proved that this role-model approach gives the same optimal solution as the Bayesian approach, albeit with a different design methodology that is easier to implement. The motivation for this approach is in adaptive receiver design for wireless communications. During the proposed fellowship, we plan to extend the theory for the role-model framework, to refine its application to receiver design, to study its connection with other problems in information theory, and to investigate possible interdisciplinary applications. For wireless communications, the project will result in optimized low-complexity receiver structures that can save power, increase radio coverage and data integrity, and reduce the cost of mobile handsets. Further applications of role models may include computer science, risk assessment, image processing, and others.'

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