Farzad Kamalabadi

The two-year prime mission of the NASA Ionospheric Connection Explorer (ICON) is complete. The baseline operational and scientific objectives have been met and exceeded, as detailed in this report. In October of 2019, ICON was launched into an orbit that provides its instruments the capability to deliver near-continuous measurements of the densest plasma in Earth’s space environment. Through collection of a key set of in-situ and remote sensing measurements that are, by virtue of a detailed mission design, uniquely synergistic, ICON enables completely new investigations of the mechanisms that control the behavior of the ionosphere-thermosphere system under both geomagnetically quiet and active conditions. In a two-year period that included a deep solar minimum, ICON has elucidated a number of remarkable effects in the ionosphere attributable to energetic inputs from the lower and middle atmosphere, and shown how these are transmitted from the edge of space to the peak of plasma density above. The observatory operated in a period of low activity for 2 years and then for a year with increasing solar activity, observing the changing balance of the impacts of lower and upper atmospheric drivers on the ionosphere.

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physical limitations, in this paper, we develop a novel multi-spectral imaging modality that enables higher spatial and spectral resolutions. In the developed computational imaging modality, we exploit a diffractive lens, such as a photon sieve, for both dispersing and focusing the optical field, and achieve measurement diversity by changing the focusing behavior of this lens. Because the focal length of a diffractive lens is wavelength-dependent, each measurement is a superposition of differently blurred spectral components. To reconstruct the individual spectral images from these superimposed and blurred measurements, model-based fast reconstruction algorithms are developed with deep and analytical priors using alternating minimization and unrolling. Finally, the effectiveness and performance of the developed technique is illustrated for an application in astrophysical imaging under various observation scenarios in the extreme ultraviolet (EUV) regime. The results demonstrate that the technique provides not only diffraction-limited high spatial resolution, as enabled by diffractive lenses, but also the capability of resolving close-by spectral sources that would not otherwise be possible with the existing techniques. This work enables high resolution multi-spectral imaging with low cost designs for a variety of applications and spectral regimes.

We propose a computationally efficient estimator for multi-dimensional linear space-invariant system dynamics with periodic boundary conditions that attains low mean squared error from very few temporal steps. By exploiting the inherent redundancy found in many real-world spatiotemporal systems, the estimator performance improves with the dimensionality of the system. This paper provides a detailed analysis of maximum likelihood estimation of the state transition operator in linear space-invariant systems driven by Gaussian noise. The key result of this work is that, by incorporating the space-invariance prior, the mean squared error of a estimator normalized to the number of parameters is upper bounded by N ^-1 M ^-1 + O(N ^-1 M ^-2 ), where N is the number of spatial points, and M is the number of observed timesteps after the initial value.