1 research outputs found
Achieving Super-Resolution with Redundant Sensing
Analog-to-digital (quantization) and digital-to-analog (de-quantization)
conversion are fundamental operations of many information processing systems.
In practice, the precision of these operations is always bounded, first by the
random mismatch error (ME) occurred during system implementation, and
subsequently by the intrinsic quantization error (QE) determined by the system
architecture itself. In this manuscript, we present a new mathematical
interpretation of the previously proposed redundant sensing (RS) architecture
that not only suppresses ME but also allows achieving an effective resolution
exceeding the system's intrinsic resolution, i.e. super-resolution (SR). SR is
enabled by an endogenous property of redundant structures regarded as "code
diffusion" where the references' value spreads into the neighbor sample space
as a result of ME. The proposed concept opens the possibility for a wide range
of applications in low-power fully-integrated sensors and devices where the
cost-accuracy trade-off is inevitable