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1. Diagnostic Test Generation that Addresses Diagnostic Holes

   Pomeranz, I (January 2018, IEEE transactions on computer-aided design of integrated circuits and systems)

   A diagnostic test generation procedure targets fault pairs in a set of target faults with the goal of distinguishing all the fault pairs. When a fault pair cannot be distinguished, it prevents the diagnostic test set from providing information about the faults, and consequently, about defects whose diagnosis would have benefited from a diagnostic test for the indistinguishable fault pair. This is referred to in this paper as a diagnostic hole. The paper observes that it is possible to address diagnostic holes by targeting different but related fault pairs, possibly from a different fault model. As an example, the paper considers the case where diagnostic test generation is carried out for single stuck-at faults, and related bridging faults are used for addressing diagnostic holes. Considering fault detection, an undetectable single stuck-at fault implies that certain related bridging faults are undetectable. The paper observes that, even if a pair of single stuck-at faults is indistinguishable, a related pair of bridging faults may be distinguishable. Based on this observation, diagnostic tests for pairs of bridging faults are added to a diagnostic test set when the related single stuck-at faults are indistinguishable. Experimental results of defect diagnosis for defects that do not involve bridging faults demonstrate the importance of eliminating diagnostic holes. 

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2. Experimental Study on Digital Image Correlation for Deep Learning-Based Damage Diagnostic

   https://doi.org/10.1007/978-3-030-12115-0_28  

   Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N. (May 2019, Proceedings of the Society for Experimental Mechanics)

   Large quantities of data which contain detailed condition information over an extended period of time should be utilized to prioritize infrastructure repairs. As the temporal and spatial resolution of monitoring data drastically increase by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. In the previous study of authors, a real-time deep learning platform was developed to solve damage detection and localization challenge. The network was trained by using simulated structural connection mimicking the real test object with a variety of loading cases, damage scenarios, and measurement noise levels for successful and robust diagnosis of damage. In this study, the proposed damage diagnosis platform is validated by using temporally and spatially dense data collected by Digital Image Correlation (DIC) from the specimen. Laboratory testing of the specimen with induced damage condition is performed to evaluate the performance and efficiency of damage detection and localization approach. 

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3. Uncertainty quantification in digital image correlation for experimental evaluation of deep learning based damage diagnostic

   https://doi.org/10.1080/15732479.2020.1815224  

   Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N. (September 2020, Structure and Infrastructure Engineering)

   null (Ed.)
4. Diagnostic Applications of Lasers

   Fried, Nathaniel; Seeber, Fred; MacGregor, Tom (January 2021, Diagnostic Applications of Lasers)

   Panayiotou, Chrysanthos; Pedrotti, Leno (Ed.)

   About the LASER-TEC Laser and Fiber Optics Educational Series This series was created for use in engineering technology programs such as electronics, photonics, laser-electro-optics, and related programs. This series of publications has three goals in mind:1) to create educational materials for areas of laser electro-optics technology in which no materials exist 2) work with industry to use, adapt and enhance available industry-created material, 3) make these materials available to technicians at no cost to them making education in these areas more accessible to everyone. The Laser and Fiber Optics Educational Series is available for free online at www.laser-tec.org. About Diagnostic Applications of Lasers Diagnostic Applications of Lasers was created to provide a fundamental background for technicians on the theory of optical imaging, microscopy, and spectroscopy, as well as other major imaging modalities in medicine, (e.g. ultrasound, nuclear imaging, x-rays, and computed tomography). There are numerous examples of successful optical diagnostic technologies, such as the pulsed oximeter for the measurement of blood oxygenation levels and optical coherence tomography for diagnosis of ophthalmic diseases. Promising emerging fields are also briefly covered, such as super-resolution microscopy and photoacoustic tomography. 

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5. Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images

   https://doi.org/10.1117/12.2611807  

   Deshpande, Rucha; Anastasio, Mark A.; Brooks, Frank J. (April 2022, SPIE 12032, Medical Imaging 2022: Image Processing, 120321X (4 April 2022))

   Išgum, Ivana; Colliot, Olivier (Ed.)