Computing Semilinear Sparse Models for Approximately Eventually Periodic Signals
This work addresses signal processing challenges for applications dealing with irregularly transitioning signals, but it appears incremental as it integrates existing methods without clear broad advancements.
The paper tackles the problem of modeling discrete-time signals that are initially aperiodic and later become approximately periodic by developing semilinear sparse models that combine sparse representation methods, linear autoregressive models, and GRU neural networks, with prototypical implementations presented.
Some elements of the theory and algorithmics corresponding to the computation of semilinear sparse models for discrete-time signals are presented. In this study, we will focus on approximately eventually periodic discrete-time signals, that is, signals that can exhibit an aperiodic behavior for an initial amount of time, and then become approximately periodic afterwards. The semilinear models considered in this study are obtained by combining sparse representation methods, linear autoregressive models and GRU neural network models, initially fitting each block model independently using some reference data corresponding to some signal under consideration, and then fitting some mixing parameters that are used to obtain a signal model consisting of a linear combination of the previously fitted blocks using the aforementioned reference data, computing sparse representations of some of the matrix parameters of the resulting model along the process. Some prototypical computational implementations are presented as well.