Thesis (Electrical Engineer and M.S. in Electrical Engineering)--Naval Postgraduate School, Dec. 2004. / Thesis Advisor(s): Monique P. Fargues. Includes bibliographical references (p. 107-109). Also available online.

Thesis (M.S. in Applied Physics)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Richard M. Harkins, Gamani Karunasiri. Includes bibliographical references (p. 77-78). Also available online.

Thesis (M.S.)--Florida State University, 2006. / Advisor: Xiaolei Zou, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed June 13, 2006). Document formatted into pages; containsxi, 65 pages. Includes bibliographical references.

Thesis (Ph.D.)--Ohio University, March, 2002. / Title from PDF t.p.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xi, 117 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 115-117).

Thesis (Msc Food Sc (Food Science))--University of Stellenbosch, 2009. / The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between whole maize kernels of varying degrees of hardness and fungal infected and non-infected kernels have been investigated. Near infrared hyperspectral images of whole maize kernels of varying degrees of hardness were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm as well as a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) on absorbance images was used to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between floury and glassy endosperm along principal component (PC) three. Interpreting the PC loading line plots important absorbance peaks responsible for the variation were 1215, 1395 and 1450 nm, associated with starch and moisture for both MatrixNIR images (12 and 24 kernels). The loading line plots for the sisuChema (24 kernels) illustrated peaks of importance at the aforementioned wavelengths as well as 1695, 1900 and 1940 nm, also associated with starch and moisture. Partial least squares-discriminant analysis (PLS-DA) was applied as a means to predict whether the different endosperm types observed, were glassy or floury. For the MatrixNIR image (12 kernels), the PLS-DA model exhibited a classification rate of up to 99% for the discrimination of both floury and glassy endosperm. The PLS-DA model for the second MatrixNIR image (24 kernels) yielded a classification rate of 82% for the discrimination of glassy and 73% for floury endosperm. The sisuChema image (24 kernels) yielded a classification rate of 95% for the discrimination of floury and 92% for glassy endosperm. The fungal infected and sound whole maize kernels were imaged using the same instruments. Background, bad pixels and shading were removed by applying PCA on absorbance images. On the cleaned images, PCA could be used effectively to find the infected regions, pedicle as well as non-infected regions. A distinct difference between infected and sound kernels was illustrated along PC1. Interpreting the PC loading line plots showed important absorbance peaks responsible for the variation and predominantly associated with starch and moisture: 1215, 1450, 1480, 1690, 1940 and 2136 nm for both MatrixNIR images (15 and 21 kernels). The MatrixNIR image (15 kernels) exhibited a PLS-DA classification rate of up to 96.1% for the discrimination of infected kernels and the sisuChema had a classification rate of 99% for the same region of interest. The The iv sisuChema image (21-kernels) had a classification rate for infected kernels of 97.6% without pre-processing, 97.7% with multiplicative scatter correction (MSC) and 97.4% with standard normal variate (SNV). Near infrared hyperspectral imaging is a promising technique, capable of distinguishing between maize kernels of varying hardness and between fungal infected and sound kernels. While there are still limitations with hardware and software, these results provide the platform which would greatly assist with the determination of maize kernel hardness in breeding programmes without having to destroy the kernel. Further, NIR hyperspectral imaging could serve as an objective, rapid tool for identification of fungal infected kernels.

The feasibility of Fourier transform infrared (FTIR)-imaging spectroscopy as a tool to discriminate samples from patients suffering from benign prostatic hyperplasia (BPH) and prostate cancer (CaP) samples in blood serum was investigated. Prostate cancer is known to be an age related disease, with the risk of developing the disease dramatically increasing in men past forty years old. Currently the PSA blood test is notoriously unreliable and is non specific for CaP thus leading to overtreatment of the disease. It is important therefore to develop diagnostic method that is non-invasive, reliable, and specific for CaP.In order to achieve the objective of establishing a robust protocol, which could be applied to a clinical study, obtaining optimal sample preparation for the FTIR analysis of serum smears, had to be achieved. A protocol was developed to prepare the serum samples prior to their FTIR analysis. First, the samples were centrifuged with ultrafiltration devices of different sizes to obtain several fractions which were then smeared to obtain thin films of serum. The spectra from the larger (>100 kDa components) and medium (containing the 10–100 kDa components) fractions were utilised for both a pilot and a clinical study, while the spectra from the smaller fractions (containing the 3–10 and <3 kDa components) were affected by fringing and could therefore not be used. A major novelty of this project involved the application of FTIR-imaging to the analysis of serum smears. The use of the Focal Plane Array detector system enabled the collection of a spectral image containing 16,384 spectra, on which a Quality Testing and pre-processing techniques were applied to select the “good spectra” and reject the spectra that failed the Quality Test. Several types of substrates were assessed to determine the most appropriate for the analysis of the smears and it was established that the spectra obtained from the serum smeared on CaF2 windows gave the most reproducible results. 5 BPH and 5 CaP samples were analysed for the pilot study following the developed protocol. While no clear separation was observed in the Principal Component Analysis (PCA) plots between the BPH and the cancerous samples, a trend emerged throughout the results, with the CaP samples clustering together and the BPH samples scattered around them. A larger clinical study was conducted with 60 BPH samples and 60 CaP samples. PCA was applied on the “good spectra” and while the over 100 kDa fraction did not show a clear separation between the two types of samples, the 10–100 kDa fraction showed a distinct classification between the BPH and CaP samples. An artificial neural network was then applied to create a model using patients from the database used for the PCA analysis to determine whether the discrimination between the two types of samples could be increased or highlight different classification trends. For the >100 kDa fraction, the sensitivity value was calculated to be 97.8% and the specificity value was calculated to be 44.3% while the sensitivity and the specificity value for the 10 to 100 kDa fraction were calculated to be 78.9% and 60% respectively. A complementary study using mass spectrometry was carried out on healthy and diseased samples to identify the components contained within the different fractions and determine whether they could be correlated with the components identified from the spectral features of the FTIR data. While no quantitative information was obtained from this study, the components found in the different fractions were identified, confirming the results of the FTIR studies.

During the DARPA SCENICC program, J. Ford, et al., demonstrated that CFBs provide a compelling route to compact, wide angle imagers. Monocentric lenses readily provide diffraction-limited images over wide field but onto a hemispherically curved image surface. They demonstrated visible CFBs can be tapered, cut and polished to relay curved images to flat sensors. We have shown that this provides a volumetric imaging efficiency a hundredfold larger than bulk optics can produce; a hundred times the resolution in the same volume or a hundred times less volume for the same resolution. Ford's work leveraged commercial fiber bundles available for the visible spectrum based on silica. We have developed hybrid fiber bundles using step-index confinement between chalcogenide glass cores and polymer cladding with high index-contrast. The high contrast is necessary to provide tight confinement to the high-index As-Se core with minimal crosstalk between closely spaced cores. Tight confinement also minimizes absorption losses in the PEI polymer cladding. The high contrast of this system also provides a large NA to optimize coupling into the CFB from fast lenses. We introduce disorder into the core radius as a mechanism to further decouple adjacent cores, reduce crosstalk and increase fill-factor. We present coupled-mode theory, modal crosstalk superposition, and finite-element modelling to quantify coupling losses and crosstalk as a function of geometry and disorder. We fabricated preforms, drew small fiber bundles and characterized optical properties of the bundles to aid scale-up to megapixel MWIR CFBs.

No description available.

No description available.