Recovering Lost Information in the Digital World

The conversion of physical analog signals to the digital domain for further processing inevitably entails loss of information.

The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power.

In this talk, we present a framework for sampling and processing a wide class of wideband analog signals at rates far below Nyquist by exploiting signal structure and the processing task.

We then show how these ideas can be used to overcome fundamental resolution limits in optical microscopy, ultrasound imaging and more. We demonstrate the theory through several demos of real-time sub-Nyquist prototypes and devices operating beyond the standard resolution limits combining high spatial resolution with short integration time.