31 citations · 57 across the 6 of their papers we have counts for
6 papers
Unifying Common Signal Analyses with Instantaneous Time-Frequency Atoms
Steven Sandoval, Phillip L. De Leon
In previous work, we presented a general framework for instantaneous time-frequency analysis but did not provide any specific details of how to compute a particular instantaneous s…
Estimation of motion blur kernel parameters using regression convolutional neural networks
Luis G. Varela, Laura E. Boucheron, Steven Sandoval +2
Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the…
Deep Learning Estimation of Modified Zernike Coefficients and Recovery of Point Spread Functions in Turbulence
Abu Bucker Siddik, Steven Sandoval, David Voelz +2
Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a co…
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
Abu Bucker Siddik, Dawson Drake, Thomas Wilkinson +3
Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in th…
Theory of the Hilbert Spectrum
Steven Sandoval, Phillip L. De Leon
This paper is a contribution to the old problem of representing a signal in the coordinates of time and frequency. As the starting point, we abandon Gabor's complex extension and r…
Latent Signal Analysis and the Analytic Signal
Steven Sandoval, Phillip L. De Leon
In this paper we present the latent signal analysis problem as a recasting of the complex extension problem. Almost universally, the approach has been to use the Hilbert Transform…