A Method to Compute Three Dimensional Magnetospheric Equilibria with Dipole Tilt and its Application in Estimating Magnetic Flux Tube Volume

In this thesis we describe a new version of a magneto-friction model, which was developed for computing the magnetospheric equilibrium that includes an arbitrary Earth's dipole tilt and interplanetary magnetic field. We also describe the algorithms of this new friction code that trace magnetic field lines, locate the neutral sheet, and identify the magnetopause In addition, we present a generalized theory for calculating magnetic flux tube volume in the magnetotail, in an attempt to generalize the Wolf [2006] empirical formula, and describe a method for estimating flux tube volume from measurements at geosynchronous orbit. This new method has been tested against various equilibrated magnetospheres generated by the new friction code. Although still incomplete, the method exhibits promising features, and is to be completed in the future

Content adaptive sparse illumination for Fourier ptychography

Fourier Ptychography (FP) is a recently proposed technique for large field of view and high resolution imaging. Specifically, FP captures a set of low resolution images under angularly varying illuminations and stitches them together in Fourier domain. One of FP's main disadvantages is its long capturing process due to the requisite large number of incident illumination angles. In this letter, utilizing the sparsity of natural images in Fourier domain, we propose a highly efficient method termed as AFP, which applies content adaptive sparse illumination for Fourier ptychography by capturing the most informative parts of the scene's spatial spectrum. We validate the effectiveness and efficiency of the reported framework with both simulations and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP by around 30%-60%

Motion-corrected Fourier ptychography

Fourier ptychography (FP) is a recently proposed computational imaging technique for high space-bandwidth product imaging. In real setups such as endoscope and transmission electron microscope, the common sample motion largely degrades the FP reconstruction and limits its practicability. In this paper, we propose a novel FP reconstruction method to efficiently correct for unknown sample motion. Specifically, we adaptively update the sample's Fourier spectrum from low spatial-frequency regions towards high spatial-frequency ones, with an additional motion recovery and phase-offset compensation procedure for each sub-spectrum. Benefiting from the phase retrieval redundancy theory, the required large overlap between adjacent sub-spectra offers an accurate guide for successful motion recovery. Experimental results on both simulated data and real captured data show that the proposed method can correct for unknown sample motion with its standard deviation being up to 10% of the field-of-view scale. We have released our source code for non-commercial use, and it may find wide applications in related FP platforms such as endoscopy and transmission electron microscopy

Source of the low-altitude hiss in the ionosphere

We analyze the propagation properties of low-altitude hiss emission in the ionosphere observed by DEMETER (Detection of Electromagnetic Emissions Transmitted from Earthquake Regions). There exist two types of low-altitude hiss: type I emission at high latitude is characterized by vertically downward propagation and broadband spectra, while type II emission at low latitude is featured with equatorward propagation and a narrower frequency band above ∼fcH+. Our ray tracing simulation demonstrates that both types of the low-altitude hiss at different latitude are connected and they originate from plasmaspheric hiss and in part chorus emission. Type I emission represents magnetospheric whistler emission that accesses the ionosphere. Equatorward propagation associated with type II emission is a consequence of wave trapping mechanisms in the ionosphere. Two different wave trapping mechanisms are identified to explain the equatorial propagation of Type II emission; one is associated with the proximity of wave frequency and local proton cyclotron frequency, while the other occurs near the ionospheric density peak

Effects Of Magnetic Drift Shell Splitting On Electron Diffusion In The Radiation Belts

Drift shell splitting in the presence of pitch angle scattering breaks all three adiabatic invariants of radiation belt electron motion and produces new diffusion terms that fully populate the diffusion tensor in the Fokker-Planck equation. The Radbelt Electron Model (REM) solves such a Fokker-Planck equation and is used to investigate the phase space density sources. Our simulation results and theoretical arguments suggest that drift shell splitting changes the phase space location of the source to smaller L shells, which typically reduces outer zone phase space density enhancements, and this reduction has a limit corresponding to two-dimensional local diffusion on a curved surface in the phase space

Large-scale single-photon imaging

Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods