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Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication

Abstract : Monitoring physical phenomena by using a network of sensors (autonomous but interconnected) is highly constrained in energy consumption, mainly for data transmission. In this context, this thesis proposes signal processing tools to reduce communications without compromising computational accuracy in subsequent calculations. The complexity of these methods is reduced, so as to consume only little additional energy. Our two building blocks are compression during signal acquisition (Compressive Sensing) and CoarseQuantization (1 bit). We first study the Compressed Correlator, an estimator which allows for evaluating correlation functions, time-delay, and spectral densities directly from compressed signals. Its performance is compared with the usual correlator. As we show, if the signal of interest has limited frequency content, the proposed estimator significantly outperforms theconventional correlator. Then, inspired by the coarse quantization correlators from the 50s and 60s, two new correlators are studied: The 1-bit Compressed and the Hybrid Compressed, which can also outperform their uncompressed counterparts. Finally, we show the applicability of these methods in the context of interest through the exploitation of real data.
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Augusto Zebadúa. Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication. Traitement du signal et de l'image [eess.SP]. Université Grenoble Alpes, 2017. Français. ⟨NNT : 2017GREAT085⟩. ⟨tel-01761603⟩

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