<p>This research presents thermal image analysis for Fault Detection and Diagnosis (FDD) of Photovoltaic (PV) Systems. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Aerial Thermography (AT). Thermal image analysis conducted in this research will contribute to utilizing thermography and UAVs for PV inspection by providing a more accurate and cost-efficient diagnosis of PV faults. In this research, PV module inspection was achieved through two steps: (i) PV monitoring and (ii) PV Fault Detection and Diagnosis (FDD). In the PV monitoring stage, PV cells were monitored by aerial thermography. In this stage, the thermal data was acquired for the next step. In the PV FDD stage, hot spot phenomenon and the condition of the PV modules were detected and measured. The proposed research will help with the problems of the modern PV inspection and, eventually, contribute to the performance of PV power generation.</p>

This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.

Thermal imaging is a non-invasive and marker-free approach for intraoperative measurements of small temperature variations. In this work, we demonstrate the abilities of active dynamic thermal imaging for analysis of tissue perfusion state in case of cerebral ischemia. For this purpose, a NaCl irrigation is applied to the exposed cortex during hemicraniectomy. The cortical temperature changes are measured by a thermal imaging system and the thermal signal is recognized by a novel machine learning framework. Subsequent tissue heating is then approximated by a double exponential function to estimate tissue temperature decay constants. These constants allow us to characterize tissue with respect to its dynamic thermal properties. Using a Gaussian mixture model we show the correlation of these estimated parameters with infarct demarcations of post-operative CT. This novel scheme yields a standardized representation of cortical thermodynamic properties and might guide further research regarding specific intraoperative diagnostics.

No description available.

The purpose of this bachelor’s thesis was to create a functioning turret that can recognize heat sources and automatically shoot water at them using a thermal camera and water pump. The main areas to research was the cameras capability regarding fire detection and also the turret’s efficacy in putting out fires using this detection. The reason for building FIRESAFE is to understand how technology can be implemented to improve existing fire extinguishers in a way that enables fires to be put out at an earlier stage while minimizing water damage to property. The turret is controlled using two servo motors which rotate the structure in 2-axis and a IR-camera which locates heat sources. A Raspberry Pi was used to control the software side of the project, a ultrasonic sensor for distance measurement and a basic washer pump for a car was used for the water cannon. The prototype was able to extinguish candles at a distance of 1.5m with an accuracy of 50% on the first attempt. It had an average extinguishing time of 20 seconds. / Syftet med denna kandidatuppsats var att skapa ett fungerande torn som kan lokalisera värmekällor och automatiskt släcka dessa med hjälp av en värmekamera och en vattenpump. De huvudsakliga områdena att undersöka var kamerans förmåga att upptäck eld samt tornets förmåga att släcka bränder med hjälp av eldens position. Anledningen till att bygga FIRESAFE är att förstå hur teknologi kan implementeras för att förbättra befintliga brandsläckare så att bränder kan släckas i ett tidigare stadium och minimera vattenskador på egendom. Tornet styrs med hjälp av två servo motorer som roterar strukturen i 2-axlar och värmekameran används för att upptäcka värmekällor. En Raspberry Pi användes för att styra mjukvarudelen av projektet, en ultraljudssensor för avståndsbedömning och en grundläggande spolarvätskepump från en bil användes för vattenkanonen. Prototypen kunde bekämpa ett ljus på 1.5m med en träffsäkerhet på 50% på först försöket. Det hade en genomsnittlig släckningstid på 20 sekunder.

Continuous welding is one of the prominent techniques used in producing seamless piping used in many applications such as the mining and the oil and gas industries. Weld defects cause significant loss of time and money in the piping production industry. Therefore, there is a need for effective online weld defects detection systems. A laser-based weld defects detection (LBWDD) system has been developed by the industrial partner. However, the current LBWDD system can only detect some geometrically based weld defects, but not material inhomogeneity such as voids, impurities, inclusions, etc. The main objective of this study is to assess the predictability of a thermal imaging-based weld defects detection system (TIBWDD) using an IR camera that can be integrated with the current LBWDD system. The aim of the integrated detection system is to be able to detect a wider range of weld defects. A test rig has been designed and used to carry out a set of emissivity (ε) calculation experiments considering three different materials – Aluminum 5154 (Al), Stainless Steel 304L (SS), and Low Carbon Steel A131 (LCS) with two surface finishes 0.25 μm (FM) and 2.5 μm (RM), which are relevant to pipe welding operations. Al showed least change in ε varying from 0.162 to 0.172 for FM samples and from 0.225 to 0.250 for RM samples from 50°C to 550°C. LCS showed highest change in ε varying from 0.257 – 0.918 for FM samples and from 0.292 to 0.948 for RM samples. SS showed a consistent increase in ε for both FM and RM samples. Experimental and numerical analysis have been carried out mimicking two sets of possible weld defects investigating defect size, Dh, and distance between effect and sample surface, δ. Results showed that the δ based defects that are located within 3 mm can be detected by the IR camera. Defects with Dh = 1. 5 mm can be detected by the IR camera with and without glass wool. Laser welding simulations using 2D and 3D Gaussian heat source models have been carried out to assess the predictability of a set of possible weld defects. The heat source models have been validated using experimental data. Three sets of defects were considered representing material-based inhomogeneity, step and inclined misalignment defects. For material-based inhomogeneity in thin plates all defects located at 1.25 mm from the surface are found detectable as ΔT (temperature difference obtained on surface) > ΔTmin (detectability limit of TIBWDD system). For inhomogeneity defects in thick plates, except defects of 2.5 mm in square size all other defects were found detectable as ΔT > ΔTmin. All step misalignment defects were detected for thin and thick plates. In the case of inclined misalignment defects, for thin plates, the misalignment error in the thin plate had to be at least 0.275 mm to be detected. In the case of thick plates, the misalignment error had be at least 0.375 mm to be detected. Overall, results of the present study confirm that thermal imaging can be successfully used in detecting material-based and geometry-based weld defects. / Thesis / Master of Applied Science (MASc)

Stem rot of peanut (Arachis hypogaea L.), caused by a soilborne fungus Athelia rolfsii (Curzi) C. C. Tu and Kimbr. (anamorph: Sclerotium rolfsii Sacc.), is one of the most important diseases in peanut production worldwide. Though new varieties with increased partial resistance to this disease have been developed, there is still a need to utilize fungicides for disease control during the growing season. Fungicides with activity against A. rolfsii are available, and several new products have been recently registered for control of stem rot in peanut. However, fungicides are most effective when applied before or during the early stages of infection. Current scouting methods can detect disease once signs or symptoms are present, but to optimize the timing of fungicide applications and protect crop yield, a method for early detection of soilborne diseases is needed. Previous studies have utilized optical sensors combined with machine learning analysis for the early detection of plant diseases, but these studies mainly focused on foliar diseases. Few studies have applied these technologies for the early detection of soilborne diseases in field crops, including peanut. Thus, the overall goal of this research was to integrate sensor technologies, modern data analytic tools, and properties of standard and newly registered fungicides to develop improved management strategies for stem rot control in peanuts. The specific objectives of this work were to 1) characterize the spectral and thermal responses of peanut to infection with A. rolfsii under controlled conditions, 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate the standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. For Objective 1, spectral reflectance and leaf temperature of peanut plants were measured by spectral and thermal sensors in controlled greenhouse experiments. Differences in sensor-based responses between A. rolfsii-infected and non-infected plants were detected 0 to 1 day after observation of foliar disease symptoms. In addition, spectral responses of peanut to the infection of A. rolfsii were more pronounced and consistent than thermal changes as the disease progressed. Objective 2 aimed to identify specific signatures of stem rot from reflectance data collected in Objective 1 utilizing a machine learning approach. Wavelengths around 505, 690, and 884 nm were repeatedly selected by different methods. The top 10 wavelengths identified by the recursive feature selection methods performed as well as all bands for the classification of healthy peanut plants and plants at different stages of disease development. Whereas the first two objectives focused on disease detection, Objective 3 focused on disease control and compared the properties of different fungicides that are labeled for stem rot control in peanut using a laboratory bioassay of detached peanut tissues. All of the foliar-applied fungicides evaluated provided inhibition of A. rolfsii for up to two weeks on plant tissues that received a direct application. Succinate dehydrogenase inhibitors provided less basipetal protection of stem tissues than quinone outside inhibitor or demethylation inhibitor fungicides. Overall, results of this research provide a foundation for developing sensor/drone-based methods that use disease-specific spectral indices for scouting in the field and for making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases. / Doctor of Philosophy / Plant diseases are a major constraint to crop production worldwide. Developing effective and economical management strategies for these diseases, including selection of proper fungicide chemistries and making timely fungicide application, is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Optical sensors combined with machine learning analysis are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of this research was to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies and different fungicide chemistries that can be utilized for the management of soilborne plant diseases. The specific objectives of this work were to 1) characterize the temporal progress of spectral and thermal responses of peanut to infection and colonization with Athelia rolfsii, the causal agent of peanut stem rot 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. Results of this work demonstrate that spectral reflectance measurements are able to distinguish between diseased and healthy plants more consistently than thermal measurements. Several wavelengths were identified using machine learning approaches that can accurately differentiate between peanut plants with symptoms of stem rot and non-symptomatic plants. In addition, a new method was developed to select the top-ranked, non-redundant wavelengths with a custom distance. These selected wavelengths performed better than using all wavelengths, providing a basis for designing low-cost optical filters to specifically detect this disease. In the laboratory bioassay evaluation of fungicides, all of the foliar-applied fungicides provided inhibition of A. rolfsii for up to two weeks on leaf tissues that received a direct application. Percent inhibition of A. rolfsii decreased over time, and the activity of all fungicides decreased at a similar rate. Overall, the findings of this research provide a foundation for developing sensor-based methods for disease scouting and making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases.

Jährlich erkranken in Deutschland etwa 7.800 Menschen neu an einem Tumor des zentralen Nervensystems, wobei das Glioblastom (GBM) die häufigste und aggressivste Form darstellt. Der aktuelle Therapieansatz für GBM und andere Gliome besteht hauptsächlich in der chirurgischen Resektion, wobei die vollständige Entfernung des Tumorgewebes der wichtigste Prädiktor für das progressionsfreie Überleben ist. Gleichzeitig müssen Patientensicherheit und der Erhalt überlebenswichtiger Hirnfunktionen gewährleistet werden. Daher kommen in der modernen Neurochirurgie umfangreiche diagnostische und operationsunterstützende Technologien zum Einsatz. Zu den Standards der neurochirurgischen Bildgebung zählen konventionelle Schnittbildverfahren wie Magnetresonanztomografie (MRT) und Computertomografie (CT). Diese Verfahren haben jedoch Einschränkungen, insbesondere durch intrakranielle Massenverschiebungen während der Operation. Moderne optische Bildgebungsverfahren gewinnen daher an Bedeutung, da sie flexibel, einfach anzuwenden und nichtinvasiv sind. Diese Arbeit untersucht den Einsatz der intraoperativen thermografischen Bildgebung (ITI), die auf der Detektion von Wärmestrahlung im Infrarot-Bereich basiert. ITI ermöglicht die Ermittlung der Temperatur des exponierten Hirngewebes und eignet sich besonders für die Untersuchung zerebraler Pathologien, die mit der Hirnperfusion zusammenhängen. Die ersten Studien zur ITI zeigten deren prinzipielle Anwendbarkeit und untersuchten die thermischen Signale hinsichtlich ihrer zeitlichen und räumlichen Eigenschaften. Zwei vielversprechende Einsatzmöglichkeiten der ITI in der Neurochirurgie sind die Visualisierung der Durchblutung (Perfusion) und die Kartierung kortikaler, funktionell aktivierter Areale. Allerdings gibt es verschiedene Faktoren, die die Detektion von thermischen Signalen beeinflussen. Eine Anpassung und Optimierung der Untersuchungsmethoden und Auswertealgorithmen wurde bisher nicht durchgeführt. Die zentrale Hypothese dieser Arbeit ist, dass durch die Optimierung der Auswertung und Methodik sowie die Einordnung der ITI in das Feld klinisch etablierter und apparativ vergleichbarer intraoperativer Bildgebungsmethoden die Relevanz der ITI für die klinische Anwendung gesteigert werden kann. Es wird vermutet, dass eine optimierte ITI, die gut in den klinischen Workflow integriert werden kann, eine verbesserte Visualisierung der Durchblutung und eine präzisere Detektion funktioneller Areale ermöglicht. Das übergeordnete Ziel besteht in der Erhöhung der Patientensicherheit durch eine verbesserte intraoperative Gewebecharakterisierung, um Schäden an funktionell intaktem Hirngewebe zu vermeiden und postoperative Defizite zu reduzieren. Es wurden verschiedene Optimierungsstrategien für die ITI-Methodik entwickelt und angewendet, um diese Hypothese zu testen. Dazu gehörten die Anpassung des Bildgebungssystems an den multimodalen intraoperativen Einsatz sowie die Berücksichtigung verschiedener Einflussfaktoren im Operationssaal. Potenzielle Störquellen, wie Bewegungsartefakte durch Herzschlag und Atmung sowie thermische Einflüsse des Bildgebungssystems und der Umgebung, wurden analysiert. Das führte zur Entwicklung eines standardisierten Workflows und spezifischer Algorithmen zur Bildverarbeitung und -analyse für die jeweilige ITI-Anwendung, um trotz kleiner Signalamplituden die Qualität der thermografischen Daten zu verbessern und die Detektion relevanter physiologischer Signale zu ermöglichen. Die Validierung erfolgte durch zwei Patientenstudien, inklusive des Vergleichs der ITI-Ergebnisse mit etablierten Bildgebungsmethoden wie der fluoreszenzbasierten Indocyaningrün-Videoangiografie (ICG-VA) für die Visualisierung der Durchblutung und der Intraoperativen Optischen Bildgebung (IOI) für die Kartierung kortikaler, funktionell aktivierter Areale. Darüber hinaus wurde in einer dritten Patientenstudie das Potenzial der ITI für eine weitere Anwendung in der Epilepsiechirurgie untersucht. Die in der ersten Studie gewonnenen Erkenntnisse markieren einen signifikanten Fortschritt in der Anwendung der ITI zur Erfassung und Überwachung der zerebralen Perfusion während neurochirurgischer Eingriffe. Durch die Einführung eines semiparametrischen Regressionsmodells zur Analyse der ITI-Daten wurde das Signal-Rausch-Verhältnis verbessert und das verwendete thermografische Kontrastmittel präziser detektiert. Die Analyse von 12 Patienten mit unterschiedlichen Pathologien zeigte, dass ITI sowohl physiologische Durchblutungsmuster als auch Perfusionsstörungen effektiv darstellen kann. Ein Vergleich mit der ICG-VA bestätigte die Effektivität der ITI, wobei die ITI früher und sensibler auf Minderdurchblutung reagierte, jedoch eine niedrigere räumliche Auflösung aufweist. In der zweiten Studie wurden ITI und IOI verglichen, um ihre Fähigkeit zur Detektion des primären somatosensorischen Kortex zu bewerten. Beide Methoden konnten zuverlässige Aktivitätskarten nach Stimulation des Nervus medianus erstellen. Bei Patienten ohne sensorische Beeinträchtigungen identifizierten beide Techniken die Aktivierung im S1-Areal korrekt. Die ITI führte jedoch bei einigen Patienten mit kortikalen Tumoranteilen zu falsch-positiven Ergebnissen, während die IOI solche Effekte nicht zeigte. Die dritte Studie zur ITI bei Epilepsiechirurgie lieferte wichtige Erkenntnisse über die Identifikation und Lokalisierung epileptogener Zonen im Gehirn. Die ITI ermöglichte eine präzise Darstellung der von Epilepsie betroffenen Bereiche durch spezifische thermische Frequenzmuster, die mit epileptischer Aktivität assoziiert sind. Die Anwendung der ITI in der neurochirurgischen Bildgebung bietet bedeutende Vorteile, insbesondere durch die Möglichkeit der nicht-invasiven, kontaktlosen und mehrfach wiederholbaren Überwachung der zerebralen Perfusion und der Detektion funktioneller Regionen. Sie zeigte eine schnellere und sensiblere Reaktion auf Durchblutungsstörungen als die ICG-VA, obwohl sie eine geringere räumliche Auflösung aufweist. Die Kombination von ITI und ICG-VA eröffnet neue Möglichkeiten für Forschung und Therapieplanung. Die Studienergebnisse unterstreichen das Potenzial der ITI für die intraoperative Überwachung und die Identifikation eloquenter Hirnareale. Weitere Untersuchungen sind notwendig, um die Auswirkungen von Tumoren auf die ITI-Signale und deren Interpretation besser zu verstehen. ITI hat sich auch in der Epilepsiechirurgie als nützlich erwiesen, indem sie spezifische thermische Muster aufdeckt, die mit epileptischer Aktivität verbunden sind. Dies könnte neue Ansätze in der Diagnose und Behandlung von Epilepsie ermöglichen. Die optimierte ITI-Methodik verbesserte das Signal-Rausch-Verhältnis und ermöglichte eine präzisere Detektion thermischer Signale. Zudem wurde die ITI erfolgreich mit etablierten Methoden wie ICG-VA und IOI verglichen und zeigte dabei ihre Stärken in der schnelleren und sensibleren Erkennung von Durchblutungsstörungen sowie in der zuverlässigen Identifikation funktioneller Hirnareale. Diese Verbesserungen unterstützen die Integration der ITI in die klinische Praxis und unterstreichen ihr Potenzial, die Patientensicherheit und die Qualität neurochirurgischer Eingriffe zu erhöhen. Zukünftige Entwicklungen in der Kameratechnologie und fortschrittlichere Algorithmen zur Bildverarbeitung sowie die Kombination mit anderen Bildgebungsverfahren werden entscheidend sein, um die Grenzen der ITI zu überwinden und ihre Anwendungsbereiche zu erweitern. / Each year in Germany, approximately 7,800 people are newly diagnosed with a tumor of the central nervous system, with glioblastoma (GBM) being the most common and aggressive form. The current therapeutic approach for GBM and other gliomas mainly involves surgical resection, where the complete removal of tumor tissue is the most crucial predictor for progression-free survival. At the same time, patient safety and the preservation of vital brain functions must be ensured. Therefore, extensive diagnostic and surgical support technologies are used in modern neurosurgery. Standard neuroimaging techniques include conventional imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). However, these methods have limitations, particularly due to intracranial mass shifts during surgery. Modern optical imaging methods are gaining importance as they are flexible, easy to use, and non-invasive. This work investigates the use of intraoperative thermal imaging (ITI), which is based on the detection of thermal radiation in the infrared range. ITI allows for the determination of the temperature of exposed brain tissue and is particularly suitable for examining cerebral pathologies associated with brain perfusion. Initial studies on ITI demonstrated its principal applicability and investigated the thermal signals in terms of their temporal and spatial characteristics. Two promising applications of ITI in neurosurgery are the visualization of blood flow (perfusion) and the mapping of cortically, functionally activated areas. However, various factors affect the detection of thermal signals. Adaptation and optimization of the examination methods and evaluation algorithms have not yet been carried out. The central hypothesis of this thesis is that by optimizing the evaluation and methodology, as well as integrating ITI into the field of clinically established and comparably intraoperative imaging methods, the relevance of ITI for clinical application can be increased. It is hypothesized that an optimized ITI, well-integrated into the clinical workflow, enables improved visualization of blood flow and more precise detection of functional areas. The comprehensive goal is to enhance patient safety through better intraoperative tissue characterization, to avoid damage to functionally intact brain tissue and reduce postoperative deficits. To test this hypothesis, various optimization strategies for the ITI methodology were developed and applied. These included adapting the imaging system for multimodal intraoperative use and considering various influencing factors in the operating room. Potential sources of interference, such as movement artifacts due to heartbeat and breathing, as well as thermal influences from the imaging system and the environment, were analyzed. A standardized workflow for ITI application was developed, and specific algorithms for image processing and analysis were created for each ITI application to improve the quality of thermographic data and enable the detection of relevant physiological signals despite small signal amplitudes. Validation was performed through two patient studies, including a comparison of ITI results with established imaging methods such as fluorescence-based indocyanine green video angiography (ICG-VA) for blood flow visualization and intraoperative optical imaging (IOI) for mapping cortically, functionally activated areas. Additionally, a third patient study investigated the potential of ITI for further application in epilepsy surgery. The findings from the first study mark a significant advancement in the application of ITI for the detection and monitoring of cerebral perfusion during neurosurgical procedures. By introducing a semiparametric regression model for analyzing ITI data, the signal-to-noise ratio was improved, and the thermographic contrast agent was detected more precisely. The analysis of 12 patients with different pathologies showed that ITI can effectively depict both physiological blood flow patterns and perfusion disturbances. A comparison with ICG-VA confirmed the effectiveness of ITI, with ITI responding earlier and more sensitively to reduced blood flow, although it shows a lower spatial resolution. In the second study, ITI and IOI were compared to evaluate their ability to detect the primary somatosensory cortex. Both methods were able to create reliable activity maps following stimulation of the Nervus medianus. In patients without preoperative sensory impairments, both techniques correctly identified activation in the S1 area. However, ITI produced false-positive results in some patients with cortical tumor involvement, while IOI did not show such effects. The third study on ITI in epilepsy surgery provided important insights into the identification and localization of epileptogenic zones in the brain. ITI enabled precise depiction of areas affected by epilepsy through specific thermal frequency patterns associated with epileptic activity. The application of ITI in neurosurgical imaging offers significant advantages, particularly through the possibility of non-invasive, contactless, and repeatedly monitor cerebral perfusion and detect functional areas. It showed a faster and more sensitive response to blood flow disturbances than ICG-VA, although having a lower spatial resolution. The combination of ITI and ICG-VA opens new possibilities for research and therapy planning. The study results underscore the potential of ITI for intraoperative monitoring and the identification of eloquent brain areas. Further investigations are necessary to better understand the impact of tumors on ITI signals and their interpretation. ITI has also proven useful in epilepsy surgery by uncovering specific thermal patterns associated with epileptic activity, potentially enabling new approaches in the diagnosis and treatment of epilepsy. The optimized ITI methodology improved the signal-to-noise ratio and allowed for more precise detection of thermal signals. Additionally, ITI was successfully compared with established methods such as ICG-VA and IOI, demonstrating its strengths in earlier and more sensitive detection of blood flow disturbances and reliable identification of functional brain areas. These improvements support the integration of ITI into clinical practice and highlight its potential to enhance patient safety and the quality of neurosurgical procedures. Future developments in camera technology, advanced image processing algorithms, and the combination with other imaging techniques will be crucial to overcome the limitations of ITI and expand its applications.

Approved for public release; distribution in unlimited. / Night Vision Devices (NVD) using Image Intensification (II) technology are among the most important sensors used by ground troops and aviators in night operations for modern combat. With the intensified images from these devices, soldiers can see an enemy's movement better and further in darkness. This thesis explores different test methods in evaluating the performances and sensitivities of several NVDs for future image fusion studies. Specification data such as sensitivity, resolution (Modulation Transfer Function) and pixel size are obtained. Comparative analyses of the collected results are made to characterize the performances of the different NVDs. A new method using MATLAB programming to objectively analyze digitized images for characterization of II based NVDs is proposed. This test method can also be extended to the evaluation of Thermal Imaging (TI) systems for comparative analysis with II NVDs. In addition, the feasibility of testing NVDs using both II and TI technologies, with common operating conditions and target boards is discussed. Finally, the potential of using these digitized images for image fusion studies is verified with the test and evaluation results. / Republic of Singapore

The electric Joule heating of solid materials through direct current excitation can be used to generate a temperature profile throughout a powdermetallic (P/M) compact. When recording the surface temperature distribution with an infrared (IR) camera important information regarding the integrity of the sample can be gained. This research will concentrate on the formulation of a mathematical model capable of predicting the temperature distribution and heat flow behavior in P/M parts and its relations to the supplied current, injection method, geometric shape as well as the thermo-physical properties. This theoretical model will subsequently be employed as a tool to aid in the actual measurements of infrared signatures over the sample surface and their correlation with the detection of surface and subsurface flaws. In this work we will develop the theoretical background of IR testing of green-state and sintered P/M compacts in terms of stating the governing equations and boundary conditions, followed by devising analytical and numerical solutions. Our main emphasis is placed on modeling various flaw sizes and orientations in an effort to determine flaw resolution limits as a function of minimally detectable temperature distributions. Preliminary measurements with controlled and industrial samples have shown that this IR testing methodology can successfully be employed to test both green-state and sintered P/M compacts.