ADVANCED THOMSON SCATTERING DIAGNOSTICS FOR VARIOUS APPLICATIONS

Controlled nuclear fusion has been pursued as an ideal form of renewable energy for decades and the study of fusion plasma is fueling an increased demand for diagnostic capability. Furthermore, with the increasing applications of plasma in industry and medicine, it has become essential to characterize plasma dynamics and properties. Laser Thomson scattering diagnostics are considered to be the most reliable plasma diagnostic approaches for measuring electron temperature and electron density, the two most important parameters of a plasma. Four advanced Thomson scattering systems are discussed in this work to respectively address four different limitations or difficulties commonly encountered in conventional Thomson scattering based plasma diagnostic scenarios. The background of this study is discussed in Chapter One. Chapter Two discusses the solution for lifting the limitation of spatial resolution of Thomson scattering diagnostics. A multi-point Thomson scattering system has been implemented for an electrothermal arc source to test its diagnostic capability. A high-speed Thomson scattering system is demonstrated in Chapter Three as a solution to the low temporal resolution in the conventional setup. This chapter presents the development of a high-repetition-rate Thomson scattering system to greatly increase the temporal resolution of measurements while maintaining a high rate of data acquisition. Chapter Four identifies a challenge in low-temperature plasma, especially in a weakly ionized gas discharge, that the probing laser of a Thomson scattering system could also induce rotational Raman scattering. A new approach presented in this chapter bypasses the necessity of making the estimation of gas temperature and seek to resolve this problem directly with a forward scattering approach. Chapter Five demonstrates a preliminary study on a compressed sensing-based enhanced data acquisition technique for future planar laser-based 2D Thomson scattering diagnostic. The work presented in this chapter demonstrates a compressed single-shot hyperspectral imaging system. And lastly, Chapter Six summarizes all works in the four tasks and discusses unaddressed problems, potential upgrades and future works

A Novel Method for Spectral Similarity Measure by Fusing Shape and Amplitude Features

Spectral similarity measure is the basis of spectral information extraction. The description of spectral features is the key to spectral similarity measure. To express the spectral shape and amplitude features reasonably, this paper presents the definition of shape and amplitude feature vector, constructs the shape feature distance vector and amplitude feature distance vector, proposes the spectral similarity measure by fusing shape and amplitude features (SAF), and discloses the relationship of fusing SAF with Euclidean distance and spectral information divergence. Different measures were tested on the basis of United States Geological Survey (USGS) mineral_beckman_430. Generally, measures by integrating SAF achieve the highest accuracy, followed by measures based on shape features and measures based on amplitude features. In measures by integrating SAF, fusing SAF shows the highest accuracy. Fusing SAF expresses the measured results with the inner product of shape and amplitude feature distance vectors, which integrate spectral shape and amplitude features well. Fusing SAF is superior to other similarity measures that integrate SAF, such as spectral similarity scale, spectral pan-similarity measure, and normalized spectral similarity score(NS3 )

Evaluating Post-fire Vegetation Recovery in Canadian Mixed Prairie Using Remote Sensing Approaches

This study investigated a wildfire occurred in April 2013 at Grasslands National Park, aiming to quantify vegetation's post-fire recovery with both field and remote sensing approaches. Biophysical parameters and hyperspectral reflectances were collected through field surveys conducted one year prior to the fire as well as five continuous years post-fire at growing seasons. These data were processed into burned and unburned samples followed by significance test to reveal biophysical differences across samples. Results indicated an overall recovery of the grassland within 4-5 years, with different vegetation forms recovering at various post-fire growing seasons. Green grass was the most resilient component that fully recovered one year post-fire, followed by forbs at two years post-fire, with shrubs and soil organic crust taking longer than four years to recover compared to the adjacent unburned communities. Hyperspectral dataset was used to establish the utility of remote sensing approaches in grasslands fire-study. Results suggested the potential of satellite remote sensing data in such application. Furthermore, Landsat dataset were processed and significance test was repeated to further prove the sensitivity of Landsat product (especially NDVI) in distinguishing burned and unburned samples, as well as good agreement with conclusions established from field data analysis. Finally, major driving factors were analyzed with ANOVA and results indicated the significant role of meteorological variables and topography in vegetation's post-fire recovery. Findings from this research contribute to a better understanding of fire's effect on the under-studied Canadian northern mixed prairie. Also, the successful validation of RS based approaches can provide as the theoretical basis for potential future RS applications in modelling grassland post-fire recovery in the mixed prairie

Applied Geochemistry with Case Studies on Geological Formations, Exploration Techniques and Environmental Issues

Geochemistry has become an essential subject to understand our origins and face the challenges that humanity will meet in the near future. This book presents several studies that have geochemistry as their central theme, from the description of different geological formations, through its use for the characterization of contaminated sites and their possible impact on ecosystems and human health, as well as the importance of geochemical techniques as a complement to other current scientific disciplines. Through the different chapters, the reader will be able to approach the world of geochemistry in several of its subfields (e.g. environmental, isotope, or biogeochemistry) and learn through practical cases

NON-LINEAR REGULARIZATION FOR IMAGING THROUGH TURBID MEDIA

Ph.DDOCTOR OF PHILOSOPH

Multiscale Analysis for Characterization of Remotely Sensed Images.

In this study we addressed fundamental characteristics of image analysis in remote sensing, enumerated unavoidable problems in spectral analysis, and highlighted the spatial structure and features that increase information amount and measurement accuracy. We addressed the relationship between scale and spatial structure and the difficulties in characterizing them in complex remotely sensed images. We suggested that it is necessary to employ multiscale analysis techniques for analyzing and extracting information from remotely sensed images. We developed a multiscale characterization software system based on an existing software called ICAMS (Image Characterization And Modeling System), and applied the system to various test data sets including both simulated and real remote sensing data in order to evaluate the performance of these methods. In particular, we analyzed the fractal and wavelet methods. For the fractal methods, the results from using a set of simulated surfaces suggested that the triangular prism surface area method was the best technique for estimating the fractal dimension of remote sensing images. Through examining Landsat TM images of four different land covers, we found that fractal dimension and energy signatures derived from wavelets can measure some interesting aspects of the spatial content of remote sensing data, such as spatial complexity, spatial frequency, and textural orientation. Forest areas displayed the highest fractal dimension values, followed by coastal, urban, and agriculture respectively. However, fractal dimension by itself is insufficient for accurate classification of TM images. Wavelet analysis is more accurate for characterizing spatial structures. A longer wavelet was shown to be more accurate in the representation and discrimination of land-cover classes than a similar function of shorter length, and the combination of energy signatures from multiple decomposition levels and multispectral bands led to better characterization results than a single resolution and single band decomposition. Significant improvements in classification accuracy were achieved by using fractal dimensions in conjunction with the energy signature. This study has shown that multiscale analysis techniques are very useful to complement spectral classification techniques to extract information from remotely sensed images

Homodyne spin noise spectroscopy and noise spectroscopy of a single quantum dot

The steady-state fluctuations of a spin system are closely interlinked with its dynamics in linear response to external perturbations. Spin noise spectroscopy exploits this link to extract parameters characterizing the dynamics without needing an intricate spin polarization scheme. In samples with an accessible optical resonance, the spin fluctuations are imprinted onto a transmitted linearly polarized quasi-resonant probe laser beam according to the optical selection rules, making an all-optical observation of spin dynamics possible. The beam’s detuning and intensity determine whether the system is probed at thermal equilibrium or under optical driving. The technique is uniquely applicable for studying single quantum dots, where a charge carrier’s spin and occupancy dynamics can be observed simultaneously. This thesis presents a step-by-step derivation of the shape and statistical properties of experimental spectra and highlights the experimental limitations faced by the technique at very low probe intensities through uncorrelated broadband technical noise contributions. Optical homodyne amplification is evaluated in a proof-of-principle experiment to determine whether this limitation can be overcome at low frequencies < 5 MHz. Unlike previous attempts, the presented proof-of-principle experiment demonstrates that shot-noise limited spin noise measurements are possible in low-frequency ranges down to ≳ 100 kHz. For even lower frequencies, the suppression of laser intensity noise by the limited common-mode rejection of conventional balanced detectors is found to be the limiting contribution. In the second part of the thesis, optical spin noise spectroscopy is used to conduct a long-term study of spin and occupancy dynamics of an individual hole spin confined in an (In,Ga)As quantum dot with high radial symmetry in the high magnetic fields regime. For magnetic fields ≳ 250 mT, the splitting of the Zeeman branches with an effective g-factor of 2.159(2) exceeds the quantum dot’s trion resonance’s homogeneous line width of 6.3(2) μeV, revealing a rich spectral structure of spin and occupancy dynamics. This structure reveals a so far neglected contribution of an internal photoeffect to the charge dynamics between the quantum dot and its environment. Previously developed theoretical modeling is extended to incorporate the photoeffect and successfully achieves excellent qualitative correspondence with experimental spectra for almost all detuning ranges. The photoeffect shuffles the charge from and into the quantum dot with two distinct rates. Within the model, the previously required Auger process is unnecessary to describe the experimental data. The rates of discharging and recharging the quantum dot are determined to be on the order of 12(7) kHz·μm²·nW⁻¹ and 6(2) kHz·μm²·nW⁻¹, respectively. For magnetic fields < 500 mT, very long T1 hole spin relaxation times ≫ 1 ms are observed, while above 500 mT, T1 falls to 5(2) μs at 2.5 T, qualitatively confirming the theoretical prediction of a single-phonon mediated relaxation process. Furthermore, the electron spin relaxation time T1 in the trion state shows no pronounced dependence on magnetic fields above 500 mT and stays at a constant value of 101(2) ns. The saturation intensity of the transition also does not depend on the magnetic field and stays at a constant value of 4.8(7) nW·μm⁻²

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Specialized data analysis of SSME and advanced propulsion system vibration measurements

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques