Ionospheric Turbulence Near the Upper Hybrid Layer: Theory and Experiment

The thesis presents experimental results, simulations, and theory on turbulence excited in magnetized plasmas near the ionosphere’s upper hybrid layer. The results include: The first experimental observations of super small striations (SSS) excited by the High-Frequency Auroral Research Project (HAARP) The first detection of high-frequency (HF) waves from the HAARP transmitter over a distance of 16x10^3 km The first simulations indicating that upper hybrid (UH) turbulence excites electron Bernstein waves associated with all nearby gyroharmonics Simulation results that indicate that the resulting bulk electron heating near the upper hybrid (UH) resonance is caused primarily by electron Bernstein waves parametrically excited near the first gyroharmonic. On the experimental side we present two sets of experiments performed at the HAARP heating facility in Alaska. In the first set of experiments, we present the first detection of super-small (cm scale) striations (SSS) at the HAARP facility. We detected density structures smaller than 30 cm for the first time through a combination of satellite and ground based measurements. In the second set of experiments, we present the results of a novel diagnostic implemented by the Ukrainian Antarctic Station (UAS) in Verdansky. The technique allowed the detection of the HAARP signal at a distance of nearly 16 Mm, and established that the HAARP signal was injected into the ionospheric waveguide by direct scattering off of dekameter-scale density structures induced by the heater. On the theoretical side, we present results of Vlasov simulations near the upper hybrid layer. These results are consistent with the bulk heating required by previous work on the theory of the formation of descending artificial ionospheric layers (DIALs), and with the new observations of DIALs at HAARP’s upgraded effective radiated power (ERP). The simulations that frequency sweeps, and demonstrate that the heating changes from a bulk heating between gyroharmonics, to a tail acceleration as the pump frequency is swept through the fourth gyroharmonic. These simulations are in good agreement with experiments. We also incorporate test particle simulations that isolate the effects of specific wave modes on heating, and we find important contributions from both electron Bernstein waves and upper hybrid waves, the former of which have not yet been detected by experiments, and have not been previously explored as a driver of heating. In presenting these results, we analyzed data from HAARP diagnostics and assisted in planning the second round of experiments. We integrated the data into a picture of experiments that demonstrated the detection of SSS, hysteresis effects in simulated electromagnetic emission (SEE) features, and the direct scattering of the HF pump into the ionospheric waveguide. We performed simulations and analyzed simulation data to build the understanding of collisionless heating near the upper hybrid layer, and we used these simulations to show that bulk electron heating at the upper hybrid layer is possible, which is required by current theories of DAIL formation. We wrote a test particle simulation to isolate the effects of electron Bernstein waves and upper hybrid layers on collisionless heating, and integrated this code to work with both the output of Vlasov simulations and the input for simulations of DAIL formation.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.