Conversor configurável analógico para informação.

Abstract

In analog-to-digital converters (ADC) based on Nyquist Theorem, the basic parameter to guide acquisition is the bandwidth of the signal. The information processing and redundancy removal are performed after the digital representation obtained from the signal. Compressed Sensing was proposed as a digitalization technique that exploits the sparsity of the signal in a given domain to capture only its information content, at a rate that may be lower than that advocated by the Nyquist Theorem. The hardware architectures to implement Compressed Sensing are called Analog to Information Converters (AIC). The AICs proposed in the bibliography exploit the sparsity of the signal in a given domain, and therefore each architecture is specific for a class of signals. This thesis proposes a configurable AIC, based on known architectures, capable of acquiring signals from several classes, changing its configuration parameters. A computational model was developed to analyze the dynamic behavior of AIC and proposed hardware parameters, as well as the physical implementation of the proposed architecture. It was verified the adaptability of the proposed architecture from the obtained results, since it was possible to perform the acquisition of more than one class of signals.