Making the Invisible Visible: Computational Sensing and Imaging via Folding Non-Linearities

By leveraging a radically new digital sensing approach, the CoSI-Fold program strives to achieve a quantitative breakthrough in performance while unlocking qualitatively new and previously unthinkable applications.The emerging field of computational sensing has opened up new horizons, such as the imaging of the Black Hole.

However, mainstream digital sensing still relies on decades-old ideas from Claude Shannon's seminal 1948 paper, which laid the foundations of sampling theory and catalyzed the ""Digital Revolution."" Despite immense progress, capturing high-dynamic-range (HDR) signals at high digital resolution (HDRes) while maintaining low power consumption (LPC) remains a significant challenge.

Implementing Shannon's method via analog-to-digital converters (ADCs) introduces fundamental bottlenecks, including quantization noise and saturation due to limited dynamic range (LDR).

These limitations are conspicuously evident in cases like the 1986 Chernobyl Accident, where excessively HDR radiation went unrecorded due to LDR dosimeters, resulting in loss of lives.

Bottlenecks in current digital pipelines clearly necessitate a paradigm shift.The CoSI-Fold program capitalizes on an alternative methodology that simultaneously unlocks HDR and HDRes capabilities with LPC.

CoSI-Fold harnesses a synergistic melding of novel hardware and mathematical algorithms, representing the core principle of computational sensing.

The analog-domain folding of HDR inputs eliminates the well-known clipping problem while also reducing the quantization noise floor.

Mathematically principled algorithms then computationally unfold the LDR folded samples.The quantum leap brought by CoSI-Fold lies in the comprehensive convergence of theoretical, algorithmic, and hardware innovations it introduces.

Our feasibility tests indicate the potential to revolutionize the fields of Audio and Acoustics, Biomedical and Healthcare Monitoring, and Computational Imaging, which are the focus of CoSI-Fold.