The effect of time delay on nonlinear oscillators is an important problem in the study of dynamical systems. The dynamics of an erbium-doped fiber ring laser with an extra loop providing time-delayed feedback is studied experimentally by measuring the intensity of the laser. The delay time for the feedback is varied from $\sim 0.3$ to $\sim 900$ times the cavity round-trip time, over four orders of magnitude, by changing the length of fiber in the delay line. Depending on the delay, we observe either regular oscillations or complex dynamics. The size of the fluctuations increases for delays long compared with the round-trip time of the laser cavity. The complexity of the fluctuations is quantified by creating spatiotemporal representations of the time series and performing a Karhunen-Loève decomposition. The complexity increases with increasing delay time. The experiment is extended by mutually coupling two fiber ring lasers together. The delay time for the mutual coupling is varied from $\sim 0.2$ to $\sim 600$ times the cavity round-trip time, over four orders of magnitude again. In this case the fluctuations are generally larger than the single laser case. The complexity of the dynamics for the mutually coupled system is less at short delays and larger at long delays when compared to the uncoupled case. The width of the optical spectra of the coupled lasers also narrows.

This paper discusses computational modeling and experimental demonstration of a Fresnel zone light field spectral imaging system. This type of system couples an axial dispersion binary diffractive optic with a light field detector design providing a snapshot spectral imaging capability. The computational model was validated experimentally and provides excellent predictions of potential system capabilities. Additionally, the experimentally demonstrated prototype was able to digitally refocus monochromatic images by wavelength across greater than a 100 nm bandwidth. Through simulation, the demonstrated system was approximated to have a full range from $∼400$ to 800 nm at close to a 15-nm spectral sampling interval. We also demonstrated experimentally the capability of resolving between and processing two different spectral signatures in a single snapshot. The type of system demonstrated here offers substantial new capability as an optically simple, snapshot spectral imager. The experimental proof of concept and computational model set the stage for further development of these types of systems.

We investigate the complexity of the dynamics of two mutually coupled systems with internal delays and vary the coupling delay over 4 orders of magnitude. Karhunen-Loève decomposition of spatiotemporal representations of fiber laser intensity data is performed to examine the eigenvalue spectrum and significant orthogonal modes. We compute the Shannon information from the eigenvalue spectra to quantify the dynamical complexity. A reduction in complexity occurs for short coupling delays while a logarithmic growth is observed as the coupling delay is increased.

High-frequency skywave propagation can be heavily impacted by D- and E-region dynamics requiring accurate global measurements to optimize performance. A standard measurement technique is to use ionosondes, but they are unable to measure below 1 MHz and are only available at a limited number of land-locked sites around the globe. In contrast, the Global Navigation Satellite System radio occultation (GNSS-RO) bottom-up method is a new approach specifically designed to generate electron density profiles in the D- and E- region ionosphere. It takes advantage of satellite constellations that currently provide over 20,000 daily measurements and global coverage. In this paper, GNSS-RO profiles were compared against ionosonde profiles at four sites covering a wide latitudinal range, and FIRI modeled profiles corresponding to the same latitude and local solar time. This comparison was completed using daytime profiles when sporadic-E ($E_s$) was not present. The average GNSS-RO profile is found to be a few kilometers higher in altitude than the ionosonde profiles at the minimum frequency, fmin. When the ionosonde profiles are shifted so that the altitudes match at fmin, they are in good agreement up to the E-region peak altitude, hmE. Below fmin, the GNSS-RO profile is in good agreement with the FIRI profile, indicating that the profiles can measure the D- to E- transition region. The frequency of the E-region peak, foE, showed general agreement between the GNSS-RO and ionosonde measurements; however, the hmE agreement was weaker and the GNSS-RO profiles tend to have an hmE in a narrow altitude range for all profiles. Virtual heights were simulated for the GNSS-RO profiles using a numerical ray tracer for direct comparison with ionosonde observations, which showed agreement for many of the virtual heights near fmin, but also indicated a positive bias in the GNSS-RO virtual heights that may be due to low foE or elevated hmE estimates. For a quiet ionosphere, the shifted GNSS-RO electron density profiles were a good match for both measured ionosonde profiles and modeled FIRI profiles and the method is capable of providing global coverage of the D- and E-regions. Future work will require more data for seasonal and morning–afternoon comparisons as well as comparisons for the disturbed ionosphere when the sporadic-E layer is present.

The nuclear fuel cycle forms the basis for producing special nuclear materials used in nuclear weapons via a series of interdependent industrial operations. These industrial operations each produce characteristic emanations that can be gathered to ascertain signatures of facility operations. Machine learning and deep learning techniques were applied to time series magnetic field sensor data collected at the High Flux Isotope Reactor (HFIR) to assess the feasibility of determining the ON/OFF operational state of the reactor. When data collected by the sensor near the cooling fans, position 9, are transformed to the frequency domain, it was found that both machine and deep learning methods were able to classify the operational state of the reactor with a balanced accuracy of over 90%. This result suggests that the utilized methods show promise for application as techniques to verify declared activities involving nuclear reactors. Additional effort is recommended to develop models and architectures that will more fully capitalize on the data’s temporal nature by incorporating the magnetic field’s time dependence to improve the model’s robustness and classification performance.