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Hawkes processes are a class of auto-regressive point processes that are commonly used in modeling data in which events tend to cluster and influence the likelihood of future events. Because of their ability to model and explain how events or processes can influence each other, Hawkes processes (and their multivariate extensions) have been applied in a variety of practical applications such as analyzing financial time series, communication networks, and biological networks, to name just a few.

We have been exploring the integration of sparse recovery methods into the ray space transform over the past years and now demonstrate the potential and benefits of beamforming and upscaling signals in the integrated ray space and sparse recovery domain. A primary advantage of the ray space approach derives from its robust ability to integrate information from multiple arrays and viewpoints. Nonetheless, for a given viewpoint, the ray space technique requires a dense array that can be divided into sub-arrays enabling the plenacoustic approach to signal processing.

In this paper, we present a novel Image Fusion Model
(IFM) for ECG heart-beat classification to overcome the
weaknesses of existing machine learning techniques that rely
either on manual feature extraction or direct utilization of 1D
raw ECG signal. At the input of IFM, we first convert the
heart-beats of ECG into three different images using Gramian
Angular Field (GAF), Recurrence Plot (RP) and Markov
Transition Field (MTF) and then fuse these images to create