We live in an analog world, but data processing is usually performed by digital computers.
The transition from the analog (continuous time) to the digital world is called sampling.
In most analog-to-digital converters (ADCs) today, sampling is based on the Shannon-Nyquist theorem, which requires sampling at a rate that is at least twice the highest signal frequency.
As the bandwidth of the signal increases, it demands the increase in sampling frequency, which raises a number of critical issues that affect system design:
There is a need for expensive wideband ADCs which require excessive hardware solutions and consume a lot of power.
Computer systems need more memory and more computing power in order to process the sampled data. In many cases, much of the sampled information is compressed and reduced in later stages of the processing.
Sub-Nyquist sampling offers a new way of smart and effective sampling of wideband signals by performing analog preprocessing prior to sampling. The idea is to exploit the same structure that is used in the digital chain in order to drastically reduce the sampling rate and only sample the information in the signal that is actually needed. Thus, instead of sampling at a high rate and then compressing the data, it is possible to sample the signal at a low rate to begin with. Low sampling rate also enables low-rate digital processing and reduces required system memory and power.
This technology has many potential applications in a large variety of fields such as communications, radar systems, medical imaging, optical systems, super-resolution microscopy and more.
At the event we will present algorithms and systems developed in the area of sub-Nyquist sampling.