Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals
In this paper, the authors introduce a Time-stampless Adaptive Non-uniform Sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample values. Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process. They focus on two TANS schemes for discrete-time stochastic signals: a greedy method, and a method based on dynamic programming. They analyze the performances of these schemes by computing (or bounding) their trade-offs between sampling rate and expected reconstruction distortion for Markovian signals. Simulation results support the analysis of the sampling schemes.