Unlimited Sensing: Redefining Digital Acquisition, Representation and Signal Processing (video)

The digitization of information has profoundly transformed human lives. At the core of this process lies quantization—the rounding of time and amplitude by analog-to-digital converters, a concept taught widely in engineering education. The Shannon–Nyquist theorem (1949) proves that time quantization can be lossless, but amplitude quantization introduces quantization noise, exposing a fundamental trade-off between digital resolution and dynamic range. Can we recover a signal from quantization noise alone? Remarkably, the answer is “yes!” Even more surprising is that this recovery follows the well-known Nyquist criterion. This approach, which redefines quantization noise (QN) as the digital signal itself, is at the core of the Unlimited Sensing Framework (USF). Rooted in an intriguing mathematical principle, the USF exploits the fact that, for smooth functions, their fractional parts (the QN) encode their integer parts (the digital signal). By leveraging this insight, the USF fundamentally redefines digital acquisition, breaking the range-resolution barrier in conventional methods and creating new opportunities across all application areas involving digital sensing. This talk presents a first-principles introduction to USF, spanning theory, algorithms, hardware, and experiments, and highlights breakthroughs in sub-Nyquist sampling, radar/communications, and computational imaging via modulo non-linearities.