A Lowrate Variable-Bias Integrate-and-Fire Time Encoding Machine

Abstract

Integrate-and-fire time-encoding machines (IF-TEMs) are an alternative to conventional uniform sampling, where the signals are measured based on their variations. While IF-TEMs excel in energy efficiency through their event-driven sampling approach, they often oversample significantly to ensure accurate signal reconstruction. The adaptive IF-TEM method proposed in the literature addresses oversampling but lacks theoretical guarantees. In this work, we proposed a variable-bias IF-TEM that uses signals’ bandwidth and energy to reduce oversampling. We derived theoretical guarantees and ensured that the method always results in perfect reconstruction while reducing oversampling. We presented simulation results to support the claims and show that the proposed method results in lower error and fewer samples than the existing techniques.