  In this paper, we introduce a time-stampless adaptive nonuniform 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. We focus on
two TANS schemes for discrete-time stochastic signals: a greedy method, and a
method based on dynamic programming. We analyze the performances of these
schemes by computing (or bounding) their trade-offs between sampling rate and
expected reconstruction distortion for autoregressive and Markovian signals.
Simulation results support the analysis of the sampling schemes. We show that,
by opportunistically adapting to local signal characteristics, TANS may lead to
improved power efficiency in some applications.
