Marques de biblioteca: Robert Brian Tate

Explicació del motius que Robert Brian Tate podia tenir per a triar l’humanista quatrecentista Joan Margarit i Pau com a figura del seu ex-libris

Identity based encription with tate pairing

L'objectiu principal del projecte és l'estudi, la implementació d'algoritmes i protocols amb criptografia basada en la identitat. Aquesta o Identity Based Encryption (IBE) s'utilitza per simplificar el procés de comunicacions segures, com per exemple el correu electrònic. IBE permet a les polítiques de seguretat ser codificades directament sense la necessitat d'usar certificats. Aquests esquemes van ser proposats inicialment per A. Shamir a l'any 1984 i han estat objecte d'estudi per D. Boneh, S. Galbraith, etc. En aquest farem l'estudi dels emparellaments de Werl i Tate a través de l'algorisme de Miller, que ens permetrà implementar aquests emparellaments sobre corbes el·líptiques supersingulars.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

Peer-reviewed

Classifier combination for in vivo magnetic resonance spectra of brain tumours

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.