Modeling Kepler Eclipsing Binaries: Homogeneous Inference of Orbital & Stellar Properties

We report on the properties of eclipsing binaries from the Kepler mission with a newly developed photometric modeling code, which uses the light curve, spectral energy distribution of each binary, and stellar evolution models to infer stellar masses without the need for radial velocity measurements. We present solutions and posteriors to orbital and stellar parameters for 728 systems, forming the largest homogeneous catalogue of full Kepler binary parameter estimates to date. Using comparisons to published radial velocity measurements, we demonstrate that the inferred properties (e.g., masses) are reliable for well-detached main-sequence binaries, which make up the majority of our sample. The fidelity of our inferred parameters degrades for a subset of systems not well described by input isochrones, such as short-period binaries that have undergone interactions, or binaries with post-main sequence components. Additionally, we identify 35 new systems which show evidence of eclipse timing variations, perhaps from apsidal motion due to binary tides or tertiary companions. We plan to subsequently use these models to search for and constrain the presence of circumbinary planets in Kepler eclipsing binary systems.Comment: 36 pages, 16 figures; accepted 2019 July 30 to MNRA

Effects of discrete wavelet compression on automated mammographic shape recognition

At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images

Мікрохвильові діелектричні структури з мікромеханічним перелаштуванням частотних і фазових характеристик

У дисертації розв'язана проблема розробки основ створення діелектричних керуючих мікрохвильових пристроїв з мікромеханічним способом перелаштування, що надає можливість застосувати п'єзоелектричні або електрострикційні рушії для електромеханічної перелаштування характеристик. На відміну від електричних, магнітних і оптичних методів перелаштування, мікромеханічний метод відрізняється збереженням високої добротності систем, більш широким діапазоном перелаштування, а використання діелектричних матеріалів не має фундаментальних обмежень робочої частоті аж до інфрачервоного діапазону. На основі виявлених закономірностей вироблені критерії забезпечення мінімальних втрат та найбільшого перелаштування мікрохвильових характеристик за рахунок мінімально можливих переміщень металевих або діелектричних частин пристроїв. Теоретично і експериментально досліджені мікромеханічні перелаштовувані фазообертачі на основі частково-заповненого діелектриком хвилеводу, мікросмужкової і копланарної ліній передач, резонансні елементи на основі смужкових ліній, відкритих і екранованих діелектричних резонаторів, розроблено їх електродинамічні та схемотехнічні моделі. Розроблено нові та удосконалено існуючі методи вимірювання електрофізичних параметрів діелектричних матеріалів і плівок

Eigensystem realization algorithm user's guide forVAX/VMS computers: Version 931216

The eigensystem realization algorithm (ERA) is a multiple-input, multiple-output, time domain technique for structural modal identification and minimum-order system realization. Modal identification is the process of calculating structural eigenvalues and eigenvectors (natural vibration frequencies, damping, mode shapes, and modal masses) from experimental data. System realization is the process of constructing state-space dynamic models for modern control design. This user's guide documents VAX/VMS-based FORTRAN software developed by the author since 1984 in conjunction with many applications. It consists of a main ERA program and 66 pre- and post-processors. The software provides complete modal identification capabilities and most system realization capabilities

Detection of frequency and intensity changes using synthetic vowels and other sounds

Formant frequency transition detection thresholds In synthetic vowels were Investigated for their dependence on formant number, vowel type and duration of frequency transition. With transitions In final position It was found that F2 Is easier to detect than FI for all tested vowels when thresholds are expressed In terms of critical bands. The proximity of neighbouring formants appeared to affect the thresholds; lower values were obtained for formants that were positioned In excess of a critical bandwidth from neighbouring formants. All thresholds exhibited a decreasing tendency with Increasing transition duration. An excitation pattern model was used to compare thresholds from each of the experimental conditions. This was effective In normalising the data and confirming a masking hypothesis as an explanation for threshold differences. Thresholds were also obtained for sinewave stimuli. These, as one would expect, proved to be superior to those obtained for formants (on average by 4.5:1} though the ratio was dependent on frequency. Comparisons were made between frequency transitions for pure tones and DL data, a well-documented area of psychoacoustics. The data show a similar relationship, although discrimination thresholds were consistently lower than transition thresholds. Frequency transition detection thresholds In Initial position were Investigated for their alleged Inferiority compared with final position transitions. This was confirmed universally by experiments on both formant and sinewave transitions. For the vowel stimuli Inconsistencies with a masking model were found. This suggested the possibility of a speech mode of perception that operated predominantly when speech cues were more powerful. Finally, intensity transition detection thresholds (and difference limens) were obtained for synthetic vowel stimuli. Experimental conditions were analogous to those used for frequency transition detection. Similar functions were obtained, and vowel type proved to be unimportant. However, transitions of increasing intensity proved to be easier to detect than those of decreasing intensity

The Comet Halley archive: Summary volume

The contents are as follows: The Organizational History of the International Halley Watch; Operations of the International Halley Watch from a Lead Center Perspective; The Steering Group; Astrometry Network; Infrared Studies Network; Large-Scale Phenomena Network; Meteor Studies Network; Near-Nucleus Studies Network; Photometry and Polarimetry Network; Radio Science Network; Spectroscopy and Spectrophotometry Network; Amateur Observation Network; Use of the CD-ROM Archive; The 1986 Passage of Comet Halley; and Recent Observations of Comet Halley

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Effects of Channel Mismatches on Beamforming and Signal Detection

Tuner gain measurements of a multichannel receiver are reported. A linear regression model is used to characterize the gain, as a function of channel number, tuner set-on frequency, and intermediate frequency. Residual errors of this model are characterized by a t distribution. Very strong autocorrelation of tuner gain at various frequencies is noted. Tuner performance from one channel to the next is diverse; several defects at specific frequencies are noted. The Wilcoxon signed rank test is used to test normality of tuner gain among devices; normality is rejected. Antenna directivity and phase pattern measurements are also reported. An antenna element pattern is presented, along with residual errors. An array pattern model is constructed using steering vectors. Simulated gain and phase mismatches are used to predict their effects on antenna beamforming and signal detection

X-ray images and microtomography using scattering and phase contrast

Orientador: Carlos Manuel Giles Antunez de MayoloTese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: O escopo desta tese foi o estudo, implementação, caracterização e aplicação das técnicas de imagens de raios X por contraste de fase e contraste de espalhamento. Este trabalho inicialmente descreve a formação das imagens por contraste de fase pelo método da propagação. Apresenta simulações de primeiros princípios para esta técnica comparando estes com a literatura. Em seguida reporta o desenvolvimento da instrumentação para uma estação experimental de microtomografias de raios X bem como os métodos de processamento de dados para reconstruções tomográficas. Microtomografias com alta resolução foram obtidas e são apresentadas com o intuito de caracterizar a instrumentação e suas aplicações. Essa técnica de instrumentação foi aplicada, em particular, no estudo da histomorfometria óssea em ratos Sprague-Dawley com o objetivo de quantificar os efeitos da dieta alimentar na estrutura óssea trabecular e compacta. Argumenta-se que ocorre uma ligeira tendência a diferenciação nesses tecidos em função da dieta alimentar. Além disso, neste trabalho foi implementado, caracterizado e aplicado a técnica de imagens harmônicas por espalhamento em amostras biológicas, detecção de fraturas e espalhamento anisotrópico. Também são descritos os esforços na melhoria da qualidade sinal-ruído dessa técnicaAbstract: The main scope of this work was the study, implementation, characterization and application of x-ray phase contrast and scattering constrast imaging techniques. First we describe the production of phase constrast images and early attempts to simulate phase contrast phenomena following the known literature. We also report efforts toward development of an experimental x-ray microtomography station as well as imaging processing techniques for tomography reconstruction. We present results on high resolution x-ray microtomography. This technique was applied in the study of Sprague-Dawley bone morphometric properties aiming quantification of high-fat diet effects on compact and trabecular bone structuture. It is argued about a tendency to differentiation on these tissues according to the diet. We also implemented, characterized and applied harmonic scattering imaging in biological samples, fracture detection and anisotropic scattering describing efforts to enhance signal to noise ratio in this techniqueDoutoradoFísicaDoutor em Ciências142800/2010-4CNP