Electromagnetic Wave Scattering at Biperiodic Surfaces: Variational Formulation, Boundary Integral Equations and High Order Solvers

In this thesis we consider time-harmonic electromagnetic wave scattering at impenetrable biperiodic surfaces in a homogeneous medium. Besides their rigorous analysis in biperiodic Sobolev spaces, which aims at answering the questions about existence and uniqueness of solutions, we will derive a high order solver for its numerical approximation - a collocation method based on trigonometric polynomials

Medienkritik im angloamerikanischen Gegenwartsroman : dargestellt an Romanen von John Irving, Douglas Coupland und Ian McEwan ; mit einer Auswahlbibliographie

Diese Arbeit stellt drei Romane englischsprachiger Schriftsteller vor. Im Einzelnen sind das »Die vierte Hand« von John Irving, »Miss Wyoming« von Douglas Coupland und »Amsterdam« von Ian McEwan. Der New Journalism kann für diese Bücher in gewisser Hinsicht als Vorläufer angesehen werden, weshalb das erste Kapitel einen kurzen Überblick über dessen Entwicklung gibt. Am Anfang stellen die Kapitel 2 bis 4 die Biographien der Autoren mit ihren bisherigen Veröffentlichungen dar, gefolgt von eingehenden Inhaltsangaben der ausgewählten Bücher, Untersuchungen von Struktur und Stil sowie Charakterisierungen der Hauptfiguren. Im Anschluss werden die zentralen Themen der Romane, besonders die darin enthaltenen medienkritischen Aspekte, und Rezensionen von Zeitungen beziehungsweise Zeitschriften analysiert. Der Hauptteil schließt mit einem strukturellen und inhaltlichen Vergleich im fünften Kapitel und mit einem zusammenfassenden Ausblick im sechsten Kapitel ab. Als letztes wird eine Auswahlbibliographie zum Thema Medienkritik angeboten

dacl10k: Benchmark for Semantic Bridge Damage Segmentation

Reliably identifying reinforced concrete defects (RCDs)plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.Comment: 23 pages, 6 figure

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Raman spectra of biological samples often exhibit variations originating from changes of spectrometers, measurement conditions, and cultivation conditions. Such unwanted variations make a classification extremely challenging, especially if they are more significant compared with the differences between groups to be separated. A classifier is prone to such unwanted variations (ie, intragroup variations) and can fail to learn the patterns that can help separate different groups (ie, intergroup differences). This often leads to a poor generalization performance and a degraded transferability of the trained model. A natural solution is to separate the intragroup variations from the intergroup differences and build the classifier based on merely the latter information, for example, by a well-designed feature extraction. This forms the idea of this contribution. Herein, we modified two commonly applied feature extraction approaches, principal component analysis (PCA) and partial least squares (PLS), in order to extract merely the features representing the intergroup differences. Both of the methods were verified with two Raman spectral datasets measured from bacterial cultures and colon tissues of mice, respectively. In comparison to ordinary PCA and PLS, the modified PCA was able to improve the prediction on the testing data that bears significant difference to the training data, while the modified PLS could help avoid overfitting and lead to a more stable classification. © 2019 The Authors. Journal of Chemometrics published by John Wiley & Sons Lt

Using the Blue/Green Emission in Fluorescent Nuclear Track Detectors for Ion Beam Therapy Research

For studies on the mechanisms of proton and ion radiotherapy it is necessary to have a detector able to quantify local energy deposition on nano- to micrometer scales. Fluorescent nuclear track detectors (FNTDs) based on biocompatible doped alumina single crystals fulfill these criteria. However, the concentration of fluorescent color centers can vary from detector to detector and even within the same sample. This can hamper severely the application of FNTDs. This work's purpose was therefore to investigate the relations between (usually used) red/near IR and (hitherto unused) blue/green fluorescence and corresponding color center concentrations and their influence on the detector sensitivity as well as to assess the feasibility of employing the blue-greenish fluorescence (blue channel) for quantification and normalization. The study was mainly done on a set of 20 differently colored, hand-selected unirradiated FNTDs representing the span of coloration found during the crystal growth process. We found out that is feasible to read out the blue channel consistently with our standard equipment. The blue/green fluorescence before irradiation (and after until 10 Gy) is a good measure for the total color center concentration. Both the sensitivity and the background (before irradiation) were found to correlate significantly with the blue signal after irradiation, allowing for normalization of intersample variability. We did not find saturation of blue signal for doses up to 10 kGy which might open the use of FNTD for high dose measurements. New laser dependent color center depletion was discovered with yet unclear implications for dosimetry. We also found that simple optical absorption measurements for coloration do not replace blue channel measurements

A Collocation Method for Integral Equations with Super-Algebraic Convergence Rate

We consider biperiodic integral equations of the second kind with weakly singular kernels such as they arise in boundary integral equation methods. The equations are solved numerically using a collocation scheme based on trigonometric polynomials. The weak singularity is removed by a local change to polar coordinates. The resulting operators have smooth kernels and are discretized using the tensor product composite trapezodial rule. We prove stability and convergence of the scheme under suitable parameter choices, achieving algebraic convergence of any order under appropriate regularity assumptions. The method can be applied to typical boundary value problems such as potential and scattering problems both for bounded obstacles and for periodic surfaces. We present numerical results demonstrating that the expected convergence rates can be observed in practice

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay