Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to-information converter systems.