Title: Non-Oscillatory Pattern Learning for Non-Stationary Signals,
Abstract: This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for
several oscillatory data analysis problems including signal decomposition,
super-resolution, and signal sub-sampling. To the best of our knowledge, the
proposed NOP is the first algorithm for these problems with fully
non-stationary oscillatory data with close and crossover frequencies, and
general oscillatory patterns. NOP is capable of handling complicated situations
while existing algorithms fail; even in simple cases, e.g., stationary cases
with trigonometric patterns, numerical examples show that NOP admits
competitive or better performance in terms of accuracy and robustness than
several state-of-the-art algorithms.