Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices

Living Well in Early Childhood Childcare Settings: Evaluating the Impact of a Community-Based Early Childhood Obesity Prevention Project

Faculty Mentor: Dr. Meghan Slinin

Hand-Eye Dominance and Depth Perception Effects in Performance on a Basic Laparoscopic Skills Set

Depth perception defects appear to compromise a novice's ability to perform basic laparoscopic skills. These basic skills can be improved with laparoscopic simulator training

The CCAT 25m diameter submillimeter-wave telescope

CCAT will be a 25 m diameter telescope operating in the 2 to 0.2 mm wavelength range. It will be located at an altitude of 5600 m on Cerro Chajnantor in Northern Chile. The telescope will be equipped with wide-field, multi-color cameras for surveys and multi-object spectrometers for spectroscopic follow up. Several innovations have been developed to meet the <0.5 arcsec pointing error and 10 µm surface error requirements while keeping within the modest budget appropriate for radio telescopes

Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications

Advanced Optical Technologies in Food Quality and Waste Management

Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements

Disclosure: Contemporary Drawing Interpreted, Emphasized, and Revealed

Show card for exhibition Disclosure. November 4 - 15, 2002.https://digitalcommons.udallas.edu/disclosure/1000/thumbnail.jp

Influence of genomic variations on glanders serodiagnostic antigens using integrative genomic and transcriptomic approaches

Glanders is a highly contagious and life-threatening zoonotic disease caused by Burkholderia mallei (B. mallei). Without an effective vaccine or treatment, early diagnosis has been regarded as the most effective method to prevent glanders transmission. Currently, the diagnosis of glanders is heavily reliant on serological tests. However, given that markedly different host immune responses can be elicited by genetically different strains of the same bacterial species, infection by B. mallei, whose genome is unstable and plastic, may result in various immune responses. This variability can make the serodiagnosis of glanders challenging. Therefore, there is a need for a comprehensive understanding and assessment of how B. mallei genomic variations impact the appropriateness of specific target antigens for glanders serodiagnosis. In this study, we investigated how genomic variations in the B. mallei genome affect gene content (gene presence/absence) and expression, with a special focus on antigens used or potentially used in serodiagnosis. In all the genome sequences of B. mallei isolates available in NCBI’s RefSeq database (accessed in July 2023) and in-house sequenced samples, extensive small and large variations were observed when compared to the type strain ATCC 23344. Further pan-genome analysis of those assemblies revealed variations of gene content among all available genomes of B. mallei. Specifically, differences in gene content ranging from 31 to 715 genes with an average of 334 gene presence-absence variations were found in strains with complete or chromosome-level genome assemblies, using the ATCC 23344 strain as a reference. The affected genes included some encoded proteins used as serodiagnostic antigens, which were lost due mainly to structural variations. Additionally, a transcriptomic analysis was performed using the type strain ATCC 23344 and strain Zagreb which has been widely utilized to produce glanders antigens. In total, 388 significant differentially expressed genes were identified between these two strains, including genes related to bacterial pathogenesis and virulence, some of which were associated with genomic variations, particularly structural variations. To our knowledge, this is the first comprehensive study to uncover the impacts of genetic variations of B. mallei on its gene content and expression. These differences would have significant impacts on host innate and adaptive immunity, including antibody production, during infection. This study provides novel insights into B. mallei genetic variants, knowledge which will help to improve glanders serodiagnosis

Monitoring SARS-CoV-2 in Wastewater during New York City\u27s Second Wave of COVID-19: Sewershed-Level Trends and relationships to Publicly Available Clinical Testing Data

New York City\u27s wastewater monitoring program tracked trends in sewershed-level SARS-CoV-2 loads starting in the fall of 2020, just before the start of the city\u27s second wave of the COVID-19 outbreak. During a five-month study period, from November 8, 2020 to April 11, 2021, viral loads in influent wastewater from each of New York City\u27s 14 wastewater treatment plants were measured and compared to new laboratory-confirmed COVID-19 cases for the populations in each corresponding sewershed, estimated from publicly available clinical testing data. We found significant positive correlations between viral loads in wastewater and new COVID-19 cases. The strength of the correlations varied depending on the sewershed, with Spearman\u27s rank correlation coefficients ranging between 0.38 and 0.81 (mean = 0.55). Based on a linear regression analysis of a combined data set for New York City, we found that a 1 log10 change in the SARS-CoV-2 viral load in wastewater corresponded to a 0.6 log10 change in the number of new laboratory-confirmed COVID-19 cases per day in a sewershed. An estimated minimum detectable case rate between 2–8 cases per day/100 000 people was associated with the method limit of detection in wastewater. This work offers a preliminary assessment of the relationship between wastewater monitoring data and clinical testing data in New York City. While routine monitoring and method optimization continue, information on the development of New York City\u27s wastewater monitoring program may provide insights for similar wastewater-based epidemiology efforts in the future

Projected future changes in tropical cyclones using the CMIP6 HighResMIP multimodel ensemble

Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere‐only and coupled simulations run over the period 1950–2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050