לוח שנה משולב

The conversion of physical analog signals to the digital domain for further processing inevitably entails loss of information.
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 the context of medical imaging sampling at high rates often translates to high radiation dosages, increased scanning times, bulky medical devices, and limited resolution.
In this talk, we present a framework for sampling and processing a wide class of wideband analog signals at rates far below the standard Nyquist rate, by exploiting signal structure and the processing task and show several demos of real-time sub-Nyquist prototypes. We consider applications of these ideas to a variety of problems including fast and quantitative MRI, wireless ultrasound, and super-resolution in microscopy and ultrasound which combines high spatial resolution with short integration time. We then show how the ideas of exploiting the task, structure and model can be used to develop interpretable model-based deep learning methods that can adapt to existing structure and are trained from small amounts of data.

TBA