Optimized Compressed Sensing Decoder | L. Prono | PitchD 20
Channel: IEEE PoliTO Student Branch
Date: 2020-06-18

PitchD – the PhD’s pitch: our PhD IEEE Student Members explain to students, collegues and professors their research.

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An Optimized Compressed Sensing Decoder based on Deep Neural Support Prediction

Mr. Luciano Prono, Dept. of Electronics and Telecommunications (DET), Politecnico di Torino

Compressed Sensing (CS) is a widely researched technique employed to lower energy consumption of signal acquisition. It is especially suited for naturally sparse signals such as human body biosignals. These type of signals can be linearly mapped on a lower dimensional space through a simple vector-matrix multiplication. From this, the recovery of these signals can be partitioned in two steps: support estimation phase (i.e., the retrieval of the positions of the coefficients in the sparse vector) and coefficient estimation phase. Support estimation can be performed with an oracle based approach on a deep neural network. The output of the oracle can be then used to estimate the coefficient by solving the Least Mean Square (LMS) problem. This approach allows the definition of an encoder-decoder pair with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG.