| Paper ID | SPTM-17.4 |
| Paper Title |
EVENT-DRIVEN MODULO SAMPLING |
| Authors |
Dorian Florescu, Imperial College London, United Kingdom; Felix Krahmer, Technische Universität München, Germany; Ayush Bhandari, Imperial College London, United Kingdom |
| Session | SPTM-17: Sampling, Multirate Signal Processing and Digital Signal Processing 3 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In contrast to Shannon sampling theory, where measurements are recorded at equally-spaced time instants, event-driven sampling records values at non-uniform instants dependent on the input.However, both sampling schemes are subject to input dynamic range constraints. This represents a fundamental bottleneck, which can only be alleviated via adjustments in the encoder architecture. Here we explore an alternative strategy based on the recent work onUnlimited Sampling theory, which uses a modulo non-linearity to guarantee a predefined input amplitude range. We propose a cascade model comprising a modulo non-linearity in series with an integrate-and-fire (IF) event-driven encoder. The modulo component does no tact on inputs within the IF dynamic range, thus our model is fully compatible with the existing IF methodology. For inputs out side the IF dynamic range, the modulo output is discontinuous, and it currently cannot be recovered from the IF output with existing methods. We introduce theoretical conditions for which the input of the proposed cascade model can be recovered with arbitrary precision.Through numerical simulations, we show the performance of the reconstruction algorithm. The proposed methodology paves the way for a new generation of event-driven models suitable for a much wider range of applications |
