Rapid parameter estimation of discrete decaying signals using autoencoder networks
This addresses the problem of slow signal analysis in fields like spectroscopy or medical imaging by providing a faster alternative, though it is incremental as it applies existing neural network methods to a specific signal processing task.
The paper tackled rapid parameter estimation for discrete decaying signals by using dense autoencoder networks, achieving analysis rates of 75 kHz, orders-of-magnitude faster than conventional methods while maintaining precision similar to algorithmic approaches.
In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cramér-Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, we explore the limitations of our approach, demonstrating that analysis rates of $>$200 kHz are feasible with further optimization of the transfer rate between the data-acquisition system and data-analysis system.