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The increased use of infrared imaging as a flow visualization technique and as a validation technique for computational fluid dynamics (CFD) codes has led to an in-depth study of infrared band models. The ability to create fast and accurate images of airframe and plume infrared emissions often depends on the complexity of the band model. An infrared band model code has been created based largely on the band model published in NASA SP-3080, Handbook of Infrared Radiation from Combustion Gases. Improvements to the NASA SP-3080 model using the N I RA T AM data files have been made. The model and its theoretical basis are thoroughly described. Results are presented and are compared with results from the band models contained in SCORPIO and LOIR. / Master of Science

An important consideration in the design of military aircraft is observability, or how visible an aircraft is to hostile weapons. One area of great importance to overall observability is an aircraft’s infrared signature, particularly the infrared emissions from the exhaust nozzle and plume. This creates the need for accurate modeling of the infrared signatures from these sources as a design aid or for comparison of candidate designs. To that end, a parametric model has been developed based on the General Electric F110-GE-129 jet engine. The basis of the model is a highly flexible Monte-Carlo ray-trace formulation which is capable of simulating real surface behavior, such as specular reflections, and allows for variation of input parameters such as temperature, surface properties, and geometry. For given input parameters, the model predicts the overall infrared signature due to surface radiation from the exhaust nozzle and interior components. It also indicates the relative contribution of each interior surface to the overall signature and predicts the image that would be seen using infrared imaging equipment. The basic principles of the simulation method and the theory behind the application are discussed. Results are presented, primarily in graphical format, and recommendations are made for further work. / Master of Science

The focus of this research is to numerically predict an infrared image of a jet engine exhaust plume, given field variables such as temperature, pressure, and exhaust plume constituents as a function of spatial position within the plume, and to compare this predicted image directly with measured data. This work is motivated by the need to validate Computational Fluid Dynamic (CFD) codes through infrared imaging. The technique of reducing the three-dimensional field variable domain to a two-dimensional infrared image invokes the use of an inverse Monte-Carlo ray trace algorithm and an infrared band model for exhaust gases. This dissertation describes an experiment in which the above-mentioned field variables were carefully measured. Results from this experiment, namely tables of measured temperature and pressure data, as well as measured infrared images, are given. The inverse Monte-Carlo ray trace technique is described. Finally, experimentally obtained infrared images are directly compared to infrared images predicted from the measured field variables. / Ph. D.

Phenotypic profiling systematically maps and analyzes observable traits (phenotypes) exhibited in cells, tissues, organisms or systems in response to various conditions, including chemical, genetic and disease perturbations. This approach seeks to comprehensively understand the functional consequences of perturbations on biological systems, thereby informing diverse research areas such as drug discovery, disease modeling, functional genomics and systems biology. Corresponding techniques should capture high-dimensional features to distinguish phenotypes affected by different conditions. Current methods mainly include fluorescence imaging, mass spectrometry and omics technologies, coupled with computational analysis, to quantify diverse features such as morphology, metabolism and gene expression in response to perturbations. Yet, they face challenges of high costs, complicated operations and strong batch effects. Vibrational imaging offers an alternative for phenotypic profiling, providing a sensitive, cost-effective and easily operated approach to capture the biochemical fingerprint of phenotypes. Among vibrational imaging techniques, infrared (IR) imaging has further advantages of high throughput, fast imaging speed and full spectrum coverage compared with Raman imaging. However, current biomedical applications of IR imaging mainly concentrate on "digital disease pathology", which uses label-free IR imaging with machine learning for tissue pathology classification and disease diagnosis. The thesis contributes as the first comprehensive study of using IR imaging for phenotypic profiling, focusing on three key areas. First, IR-active vibrational probes are systematically designed to enhance metabolic specificity, thereby enriching measured features and improving sensitivity and specificity for phenotype discrimination. Second, experimental workflows are established for phenotypic profiling using IR imaging across biological samples at various levels, including cellular, tissue and organ, in response to drug and disease perturbations. Lastly, complete data analysis pipelines are developed, including data preprocessing, statistical analysis and machine learning methods, with additional algorithmic developments for analyzing and mapping phenotypes. Chapter 1 lays the groundwork for IR imaging by delving into the theory of IR spectroscopy theory and the instrumentation of IR imaging, establishing a foundation for subsequent studies. Chapter 2 discusses the principles of popular machine learning methods applied in IR imaging, including supervised learning, unsupervised learning and deep learning, providing the algorithmic backbone for later chapters. Additionally, it provides an overview of existing biomedical applications using label-free IR imaging combined with machine learning, facilitating a deeper understanding of the current research landscape and the focal points of IR imaging for traditional biomedical studies. Chapter 3-5 focus on applying IR imaging coupled with machine learning for novel application of phenotypic profiling. Chapter 3 explores the design and development of IR-active vibrational probes for IR imaging. Three types of vibrational probes, including azide, 13C-based probes and deuterium-based probes are introduced to study dynamic metabolic activities of protein, lipids and carbohydrates in cells, small organisms and mice for the first time. The developed probes largely improve the metabolic specificity of IR imaging, enhancing the sensitivity of IR imaging towards different phenotypes. Chapter 4 studies the combination of IR imaging, heavy water labeling and unsupervised learning for tissue metabolic profiling, which provides a novel method to map metabolic tissue atlas in complex mammalian systems. In particular, cell type-, tissue- and organ-specific metabolic profiles are identified with spatial information in situ. In addition, this method further captures metabolic changes during brain development and characterized intratumor metabolic heterogeneity of glioblastoma, showing great promise for disease modeling. Chapter 5 developed Vibrational Painting (VIBRANT), a method using IR imaging, multiplexed vibrational probes and supervised learning for cellular phenotypic profiling of drug perturbations. Three IR-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. Supervised learning is used to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with novel mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic drug screening.

Approved for public release; distribution in unlimited. / In recent years there has been an increased interest in effective individual control and enhanced security measures, and face recognition schemes play an important role in this increasing market. In the past, most face recognition research studies have been conducted with visible imaging data. Only recently have IR imaging face recognition studies been reported for wide use applications, as uncooled IR imaging technology has improved to the point where the resolution of these much cheaper cameras closely approaches that of cooled counterparts. This study is part of an on-going research conducted at the Naval Postgraduate School which investigates the feasibility of applying a low cost uncooled IR camera for face recognition applications. This specific study investigates whether nonlinear kernel-based classifiers may improve overall classification rates over those obtained with linear classification schemes. The study is applied to a 50 subject IR database developed in house with a low resolution uncooled IR camera. Results show best overall mean classification performances around 98.55% which represents a 5% performance improvement over the best linear classifier results obtained previously on the same database. This study also considers several metrics to evaluate the impacts variations in various user-specified parameters have on the resulting classification performances. These results show that a low-cost, low-resolution IR camera combined with an efficient classifier can play an effective role in security related applications. / Captain, Hellenic Air Force

Buildings and public infrastructures are crucial to our societies in that they provide habitations, workplaces, commodities and services indispensible to our daily life. As vital parts of facility management, operations and maintenance (O&M) ensure a facility to continuously function as intended, which take up the longest time in a facility’s life cycle and demand great expense. Therefore, computers and information technology have been actively adopted to automate traditional maintenance methods and processes, making O&M faster and more reliable. Augmented reality (AR) offers a new approach towards human-computer interaction through directly displaying information related to real objects that people are currently perceiving. People’s sensory perceptions are enhanced (augmented) with information of interest naturally without deliberately turning to computers. Hence, AR has been proved to be able to further improve O&M task performance. The research motif of this thesis is user evaluations of AR applications in the context of facility maintenance. The studies look into invisible target designation tasks assisted by developed AR tools in both indoor and outdoor scenarios. The focus is to examine user task performance, which is influenced by both AR system performance and human perceptive, cognitive and motoric factors. Target designation tasks for facility maintenance entail a visualization-interaction dilemma. Two AR systems built upon consumer-level hand-held devices using an off-the-shelf AR software development toolkit are evaluated indoors with two disparate solutions to the dilemma – remote laser pointing and the third person perspective (TPP). In the study with remote laser pointing, the parallax effect associated with AR “X-ray vision” visualization is also an emphasis. A third hand-held AR system developed in this thesis overlays infrared information on façade video, which is evaluated outdoors. Since in an outdoor environment marker-based tracking is less desirable, an infrared/visible image registration method is developed and adopted by the system to align infrared information correctly with the façade in the video. This system relies on the TPP to overcome the aforementioned dilemma.

Sleep is a behavioral condition fraught with mystery. Its definition—either a suite of diagnostic behavioral characters, electrophysiological signatures, or a combination of the two—varies in the literature and lacks an over-arching purpose. In spite of these vagaries, sleep supports a large and dynamic research community studying the mechanisms, ontogeny, possible functions and, to a lesser degree, its evolution across vertebrates and in a small number of invertebrates. Sleep has been described and examined in many social organisms, including eusocial honey bees (Apis mellifera), but the role of sleep within societies has rarely been addressed in non-human animals. I investigated uniquely social aspects of sleep within honey bees by asking basic questions relating to who sleeps, when and where individuals sleep, the flexibility of sleep, and why sleep is important within colonies of insects. First, I investigated caste-dependent sleep patterns in honey bees and report that younger workers (cell cleaners and nurse bees) exhibit arrhythmic and brief sleep bouts primarily while inside comb cells, while older workers (food storers and foragers) display periodic, longer sleep bouts primarily outside of cells. Next, I mapped sleep using remote thermal sensing across colonies of honey bees after introducing newly eclosed workers to experimental colonies and following them through periods of their adult lives. Bees tended to sleep outside of cells closer to the edge of the hive than when asleep inside cells or awake, and exhibited caste-dependent thermal patterns, both temporally and spatially. Wishing to test the flexibility of sleep, I trained foragers to a feeder and made a food resource available early in the morning or late in the afternoon. The bees were forced to shift their foraging schedule, which consequently also shifted their sleep schedule. Finally, I sleep-deprived a subset of foragers within a colony by employing a magnetic “insominator” to test for changes in their signaling precision. Sleep-deprived foragers exhibited reduced precision when encoding direction information to food sources in their waggle dances. These studies reveal patterns and one possible purpose of sleep in the context of a society. / text

Thesis (M.A.Sc.)--Naval Postgraduate School, 2001 / Includes bibliographical references (p. 163-166). Also available in electronic format via the Defense Technical Information Center website.