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Applications of Compressive Sensing in Communications and Signal Processing
Compressive Sensing is a Signal Processing technique, which gave a break through in 2004. The main idea of CS is, by exploiting the sparsity nature of the signal (in any domain), we can reconstruct the signal from very fewer samples than required by Shannon-Nyquist sampling theorem. Reconstructing a sparse signal from fewer samples is equivalent to solving a under-determined system with sparsity constraints. Least square solution to such a problem yield poor `results because sparse signals cannot be well approximated to a least norm solution. Instead we use l1 norm(convex) to solve this problem which is the best approximation to the exact solution given by l0 norm(non-convex). In this paper we plan to discuss three applications of CS in estimation theory. They are, CS based reliable Channel estimation assuming sparsity in the channel is known for TDS-OFDM systems. Indoor location estimation from received signal strength (RSS) where CS is used to reconstruct the radio map from RSS measurements. Identifying that subspace in which the signal of interest lies using ML estimation, assuming signal lies in a union of subspaces which is a standard sparsity assumption according to CS theory. Index terms : Compressive Sensing, Indoor positioning, fingerprinting, radio map, Maximum likelihood estimation, union of linear subspaces, subspace recovery.
Este ejemplo representa el esquema de un amplificador diferencial construido con un amplificador operacional y cinco resistencias, el cual se usa para calcular la ganancia de la diferencia de dos señales independientes. Las notaciones son las siguientes:
v1: tensión de entrada 1.
v2: tensión de entrada 2.
RL: resistencia de carga.
vo: tensión de salida.
Este esquema es una adaptación del que se encuentra en el la página 76, Capítulo 1 del texto "Electrónica, 2da Edición" de Allan R. Hambley, publicado en idioma español por la editorial Pearson Educación.
Projet Personnel en Humanités
Rapport du Projet Personnel en Humanités de Kévin Bulmé ayant pour sujet l'étude des bienfaits sociaux des jeux vidéo sur les personnes et la société.
Multi-Tagging for Transition-based Dependency Parsing
This project focuses on a modification of a greedy transition based dependency parser. Typically a Part-Of-Speech (POS) tagger models a probability distribution over all the possible tags for each word in the given sentence and chooses one as its best guess. This is then pass on to the parser which uses this information to build a parse tree. The current state of the art for POS tagging is about 97% word accuracy, which seems high but results in a around 56% sentence accuracy. Small errors at the POS tagging phase can lead to large errors down the NLP pipeline and transition based parsers are particularity sensitive to these types of mistakes. A maximum entropy Markov model was trained as a POS multi-tagger passing more than its 1-best guess to the parser which was thought could make a better decision when committing to a parse for the sentence. This has been shown to give improved accuracy in other parsing approaches. We shown there is a correlation between tagging ambiguity and parsers accuracy and in fact the higher the average tags per word the higher the accuracy.