An analog hardware solution for compressive sensing reconstruction using gradient-based method

Abstract

This work proposes an analog implementation of gradient-based algorithm for compressive sensing signal reconstruction. Compressive sensing has appeared as a promising technique for efficient acquisition and reconstruction of sparse signals in many real-world applications. It starts from the assumption that sparse signals can be exactly reconstructed using far less samples than in standard signal processing. In this paper, we consider the gradient-based algorithm as the optimal choice that provides lower complexity and competitive accuracy compared with existing methods. Since the efficient hardware implementations of reconstruction algorithms are still an emerging topic, this work is focused on the design of hardware that will provide fast parallel algorithm execution for real-time applications, overcoming the limitations imposed by the large number of nested iterations during the signal reconstruction. The proposed implementation is simple and fast, executing 400 iterations in 1 ms which is sufficient to obtain highly accurate reconstruction results.

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

“…This can be found out using the l 0 , l 1 , and l 2 norms, though l 1 -norm yields a precise result, as it is a nonprogrammable hard issue; it is rarely used and the l 2 -norm is not recommended, as it creates significant errors. Since l 1 norm has less error, it is the most suitable and commonly used norm for this optimization problem [53][54][55][56][57][58]. It is denoted as in the following equation:…”

Multi-base compressive sensing procedure with application to ECG signal reconstruction

“…In certain situations, CS solutions can be also used to recover parts of signals that are discarded due to disturbances and noise [2,3]. The measurements are usually represented as a small subset of original data samples acquired through a random selection process [4][5][6][7][8]. In order to provide a successful and unique signal recovery, CS reconstruction algorithms require a certain basis in which the considered signal is sparse [8].…”