A hardware platform to test analog-to-information conversion and non-uniform sampling

Abstract

The Nyquist-Shannon sampling theorem tells us that in order to fully recover a band-limited signal previously converted to discrete data points, said signal must have been sampled at a frequency greater than twice its bandwidth. This theorem puts a burden on circuits like ADCs, in the sense that the higher the bandwidth of a signal, the faster the ADC must be by a factor of at least 2. This in turn translates into higher power consumption. The problem can be mitigated to a certain extent by the use of zero-crossing based ADCs which consume much less power than conventional op-amp based ones, while maintaining the same performance levels. However, the burden still remains, and with the increase in the use of biologically implantable devices, the need for the utmost power efficiency is essential. This is where the theory of compressed sensing seems to offer an alternate solution. Instead of solving the problem with the brute force approach of increasing power consumption to meet performance, compressed sensing promises to increase the effective figure of merit (FOM) by exploiting certain characteristics in the signal's structure. Compressed sensing tells us, that a signal that meets certain criteria, does not need to be sampled at twice its bandwidth in order to be fully recoverable. This means that an ADC no longer has to operate at the Nyquist rate to guarantee that the signal will not be distorted and as a result its power consumption can be reduced considerably. This allows for more robust and energy efficient data acquisition circuits. This means more efficient and longer lasting implantable monitoring devices along with the ability to perform on-site data processing.