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博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Forensic fiber evidence plays an important role in many criminal investigations. Nondestructive techniques that preserve the physical integrity of the fibers for further court examination are highly valuable in forensic science. Non-destructive techniques that can either discriminate between similar fibers or match a known to a questioned fiber - and still preserve the physical integrity of the fibers for further court examination - are highly valuable in forensic science. When fibers cannot be discriminated by non-destructive tests, the next reasonable step is to extract the questioned and known fibers for dye analysis with a more selective technique such as high-performance liquid chromatography (HPLC) and/or gas chromatography-mass spectrometry (GC-MS). The common denominator among chromatographic techniques is to primarily focus on the dyes used to color the fibers and do not investigate other potential discriminating components present on the fiber. Differentiating among commercial dyes with very similar chromatographic behaviors and almost identical absorption spectra and/or fragmentation patterns is a challenging task. This dissertation explores a different aspect of fiber analysis as it focuses on the total fluorescence emission of fibers. In addition to the contribution of the textile dye (or dyes) to the fluorescence spectrum of the fiber, we investigate the contribution of intrinsic fluorescence impurities – i.e. impurities imbedded into the fibers during fabrication of garments - as a reproducible source of fiber comparison. Differentiation of visually indistinguishable fibers is achieved by comparing excitation-emission matrices (EEMs) recorded from single textile fibers with the aid of a commercial spectrofluorimeter coupled to an epi-fluorescence microscope. Statistical data comparison was carried out via principal component analysis. An application of iv this statistical approach is demonstrated using challenging dyes with similarities both in twodimensional absorbance spectra and in three dimensional EEM data. High accuracy of fiber identification was observed in all the cases and no false positive identifications were observed at 99% confidence levels.

Performing non-destructive testing (NDT) on materials is a helpful tool for maintenance and quality control because the materials are not destroyed or disturbed; ultrasound imaging is one type of NDT. Ultrasound imaging of heterogeneous materials contains many echoes from the material itself. These echoes come from changes in the acoustic impedance, i.e. changes in the relation between the density and the sound speed of the material. However, these echoes will show speckle characteristics in images, making it hard to detect any defects in the imaged material. In this work, a method of suppressing this speckle noise is proposed. The proposed method is a 2D Wiener filter, which with the help of an image of the healthy material models changes in the material when a new image is taken later. The filter models the changes of the speckle noise between images of a defected- and healty material and then supresses the speckle from the image with defects. The filter works well on the artificial images used in this work but have yet to be tested on actual data. A version of a weighted moving average filter was also looked into, but this filter did not produce usable results.

As the nation’s infrastructure continues to age, there is a growing need for methods to safely inspect critical structures, often during operation. The failure of post-tensioned anchor rods in Tainter style flood gates presented an immediate need for new inspection capabilities for U.S. Army Corps of Engineers (USACE) managed flood control gates. In response to this need, the Sensor Integration Branch (SIB) of The U.S. Army Engineer Research and Develop Center (ERDC) developed the capability to non-destructively test (NDT) both greased and grouted cylindrical steel anchor rods using higher order guided wave ultrasonic testing. Understanding the results requires a knowledge of both guided waves and digital signal processing in order to identify the possibility of a defect. In order to both facilitate rapid defect identification and expanding ease-of-use of the equipment, the research in this thesis uses a combination of the discrete wavelet transform (DWT) and gradient boosting machine learning to build a model capable of identifying the dispersive defect responses in the rods.

In Phase I of this study, the purpose was to compare mechanical and physical wood properties from current wood supplies to those from previous studies (Newlin and Wilson 1917, Markwardt and Wilson 1935, wood handbook 2010). The results indicate that minor changes have occurred in the hardwood species values from the previous two studies with a few exceptions. Differences, where they occurred, could be explained by the growth locations of each sample. Differences between pine values occurred for MOE and MOR, an increase in MOE and a decrease in MOR. The objective of Phase II was to correlate the non-destructive and destructive testing methods. The non-destructive test by longitudinal vibration wave can be used to predict the static modulus of elasticity since it is strongly correlated with the destructive static bending test for all the three methods used (A-Grader, FFT and Smart-Thumper).

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