Scanning Acoustic Microscopy in Materials Characterization

The scanning acoustic microscopy is a powerful tool for subsurface imaging and therefore fault detection in coated parts. In this paper several methods are established to reveal the imaging of hidden structures. First efforts were made to find out the information depth due to the various distances between lens and surface of the object. By means of a specially developed test specimen it was possible to estimate the penetration depth for monitoring structural details. The indepth analysis of layered composites is considered by the determination of the V(z)-characteristics. Furthermore the gain of image processing by means of Fourier transformed patterns and simultaneous filtering is shown by a typical example

Optical Flow in a Smart Sensor Based on Hybrid Analog-Digital Architecture

The purpose of this study is to develop a motion sensor (delivering optical flow estimations) using a platform that includes the sensor itself, focal plane processing resources, and co-processing resources on a general purpose embedded processor. All this is implemented on a single device as a SoC (System-on-a-Chip). Optical flow is the 2-D projection into the camera plane of the 3-D motion information presented at the world scenario. This motion representation is widespread well-known and applied in the science community to solve a wide variety of problems. Most applications based on motion estimation require work in real-time; hence, this restriction must be taken into account. In this paper, we show an efficient approach to estimate the motion velocity vectors with an architecture based on a focal plane processor combined on-chip with a 32 bits NIOS II processor. Our approach relies on the simplification of the original optical flow model and its efficient implementation in a platform that combines an analog (focal-plane) and digital (NIOS II) processor. The system is fully functional and is organized in different stages where the early processing (focal plane) stage is mainly focus to pre-process the input image stream to reduce the computational cost in the post-processing (NIOS II) stage. We present the employed co-design techniques and analyze this novel architecture. We evaluate the system’s performance and accuracy with respect to the different proposed approaches described in the literature. We also discuss the advantages of the proposed approach as well as the degree of efficiency which can be obtained from the focal plane processing capabilities of the system. The final outcome is a low cost smart sensor for optical flow computation with real-time performance and reduced power consumption that can be used for very diverse application domains

Theory and applications of multi-dimensional stationary stochastic processes

The theory of stationary stochastic processes in several dimensions has been investigated to provide a general model which may be applied to various problems which involve unknown functions of several variables. In particular, when values of the function are known only at a finite set of points, treating the unknown function as a realisation of a stationary stochastic process leads to an interpolating function which reproduces the values exactly at the given points. With suitable choice of auto-correlation for the model, the interpolating function may also he shown to be continuous in all its derivatives everywhere. A few parameters only need to be found for the interpolator, and these may be estimated from the given data. One problem tackled using such an interpolator is that of automatic contouring of functions of two variables from arbitrarily scattered data points. A "two-stage" model was developed, which incorporates a long-range "trend" component as well as a shorter-range "residual" term. This leads to a contouring algorithm which gives good results with difficult data. The second area of application is that of optimisation, particularly of objective functions which are expensive to compute. Since the interpolator gives an estimate of the derivatives with little work, it is simple to optimise it using conventional techniques, and to re-evaluate the true function at the apparent optimum point. An iterative algorithm along these lines gives good results with test functions, especially with fuactions of more than two variables. A program has been developed whicj incorporates both the optimisation and contouring applications into a single peckage. Finally, the theory of excursions of a stationary process above a fixed level has been applied to the problem of modelling the occurrence of oilfields, with special reference to their spatial distribution and tendency to cluster. An intuitively reasonable model with few parameters has been developed and applied to North Sea data, with interesting results

Effects of Mean Shear and Scalar Initial Length Scale on Three-Scalar Mixing in Turbulent Coaxial Jets

The effects of the velocity and length scale ratios of the annular flow to the center jet on three-scalar mixing in turbulent coaxial jets are investigated. In this flow a center jet and an annular flow, consisting of acetone-doped air and ethylene respec-tively, are mixed with the co-flow air. Simultaneous planar laser-induced fluorescence and Rayleigh scattering are employed to measure the mass fractions of the acetone-doped air and ethylene. The velocity ratio alters the relative mean shear rates in the mixing layers between the center jet and the annular flow and between the annular flow and the co-flow, modifying the scalar fields through mean-flow advection, turbu-lent transport, and small-scale mixing. The length scale ratio determines the degree of separation between the center jet and the co-flow. The results show that while varying the velocity ratio can alter the mixing characteristics qualitatively, varying the annulus width only has quantitative effects. Increasing the velocity ratio and the annulus width always delays the evolution of the scalar fields. The evolution of the mean scalar profiles are dominated by the mean-flow advection, while the shape of the joint probability density function (JPDF) is largely determined by the turbulent transport and molecular diffusion. The JPDF for the higher velocity ratio cases is bimodal at some locations while it is unimodal for the lower velocity ratio cases. The diffusion velocity streamlines in scalar space representing the conditional diffusion generally converge quickly to a manifold along which they continue at a lower rate. The curvature of the manifold is significantly larger for the higher velocity ratio cases. Predicting the mixing path along the manifold as well as its dependence on the velocity and length scale ratios presents a challenging test for mixing models. The three-scalar subgrid-scale (SGS) mixing in the context of large eddy simu-lation and its dependence on the velocity and length scale ratios are also investigated. The analysis reveals two SGS mixing regimes depending on the SGS variance value of the center jet scalar. For small SGS variance the scalars are well mixed with uni-modal filtered joint density function (FJDF) and the three-scalar mixing configuration is lost. For large SGS variance, the scalars are highly segregated with bimodal FJDFs at radial locations near the peak of the mean SGS variance of the center jet scalar. Two competing factors, the SGS variance and the scalar length scale, are important for the bimodal FJDF. For the higher velocity ratio cases, the peak value of the SGS variance is higher, thereby resulting in stronger bimodality. For the lower velocity ratio cases, the wider mean SGS variance profiles and the smaller scalar length scale cause bimodal FJDFs over a wider range of physical locations. The diffusion stream-lines first converge to a manifold and continue on it toward a stagnation point. The curvature of the diffusion manifold is larger for the larger velocity ratio cases. The manifold provides a SGS mixing path for the center jet scalar and the co-flow air, and thus the three-scalar mixing configuration characteristics is maintained for the large SGS variance. The SGS mixing characteristics observed present a challenging test for SGS mixing models. The scalar dissipation rate structures have similarities to those of mixture fraction and temperature in turbulent nonpremixed/partially pre-mixed flames. The results in the present work, therefore, also provide a basis for investigating multiscalar SGS mixing in turbulent reactive flows

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Guidance, navigation and control system for autonomous proximity operations and docking of spacecraft

This study develops an integrated guidance, navigation and control system for use in autonomous proximity operations and docking of spacecraft. A new approach strategy is proposed based on a modified system developed for use with the International Space Station. It is composed of three V-bar hops in the closing transfer phase, two periods of stationkeeping and a straight line V-bar approach to the docking port. Guidance, navigation and control functions are independently designed and are then integrated in the form of linear Gaussian-type control. The translational maneuvers are determined through the integration of the state-dependent Riccati equation control formulated using the nonlinear relative motion dynamics with the weight matrices adjusted at the steady state condition. The reference state is provided by a guidance function, and the relative navigation is performed using a rendezvous laser vision system and a vision sensor system, where a sensor mode change is made along the approach in order to provide effective navigation. The rotational maneuvers are determined through a linear quadratic Gaussian-type control using star trackers and gyros, and a vision sensor. The attitude estimation mode change is made from absolute estimation to relative attitude estimation during the stationkeeping phase inside the approach corridor. The rotational controller provides the precise attitude control using weight matrices adjusted at the steady state condition, including the uncertainty of the moment of inertia and external disturbance torques. A six degree-of-freedom simulation demonstrates that the newly developed GNC system successfully autonomously performs proximity operations and meets the conditions for entering the final docking phase --Abstract, page iii

Statistical Analysis of Random Symmetric Positive Definite Matrices Via Eigen-Decomposition

The work in this dissertation is motivated by applications in the analysis of imaging data, with an emphasis on diffusion tensor imaging (DTI), a modality of MRI used to non-invasively map the structure of the brain in living subjects. In the DTI model, the local movement of water molecules within a small region of the brain is summarized by a 3-by-3 symmetric positive-definite (SPD) matrix, called a diffusion tensor. Diffusion tensors can be uniquely associated with three-dimensional ellipsoids which, when plotted, provide an image of the brain. We are interested in analyzing diffusion tensor data on the eigen-decomposition space because the eigenvalues and eigenvectors of a diffusion tensor describe the shape and orientation of its corresponding ellipsoid, respectively. One of the major contributions of this dissertation is the creation of the first statistical estimation framework for SPD matrices using the eigen-decomposition-based scaling-rotation (SR) geometric framework from Jung et al (2015). In chapter 3, we define the set of sample scaling-rotation means of a sample of SPD matrices, propose a procedure for approximating the sample SR mean set, provide conditions under which this procedure will provide a unique solution, and provide conditions guaranteeing consistency and a Central Limit Theorem for the sample SR mean set. Our procedure for approximating the sample SR mean can also be extended to compute a weighted SR mean, which can be useful for smoothing DTI data or interpolation to improve image resolution. In chapter 4, we present moment-based hypothesis tests concerning the eigenvalue multiplicity pattern of the mean of a sample of diffusion tensors which can be used to classify the mean as one of four possible shapes: isotropic, prolate, oblate, or triaxial. The derivations of these test procedures lead to the creation of consistent estimators of the eigenvalues of the mean diffusion tensor. In the final chapter, we present a mixture distribution framework which can be used to model the variability of SPD matrices on the eigen-decomposition space, and an accompanying likelihood based estimation procedure which can be used for estimation of parameters of interest or inference via likelihood ratio tests

Restoration methods for biomedical images in confocal microscopy

Diese Arbeit stellt neue Loesungen zum Problem Bildrestauration im biomedizinischen Bereich vor. Das Konfokal-Mikroskop ist eine verhaeltnismaessig neue Bildungstechnik, die als Standardwerkzeug in biomedizinischen Studien eingesetzt wird. Diese Technik dient zum Sammeln einer Reihe von 2D Bildern der einzelnen Abschnitte innerhalb eines Probestuecks, um eine 3D Darstellung des Gegenstandes zu erzeugen. Trotz seiner verbesserten Belichtungseigenschaften unterliegen die beobachteten Bilder Stoerungen augrund der begrenzten Groesse der Punktantwort (PSF) und das Poisson-Rauschens. Bildrestaurationstechniken versuchen diese Stoerungen herauszurechnen und das Originalbild zu rekonstruieren. Diese Doktorarbeit beginnt mit der Beschreibung des Konfokal-Mikroskops und den Quellen von Artefakten. Dann werden die vorhandenen Bildwiederherstellungsmethoden vorgestellt und verglichen. Die Arbeit ist in drei Teile gegliedert: Im ersten Teil wird eine neue begrenzte blinde Dekonvolutionsmethode eingefuehrt. Durch eine passende Re-Parametrisierung wird dabei a priori Wissen eingebaut. Fuer die PSF wird ein parametrisches Modell, mit einem begrenzten Satz von Basisunktionen benutzt, um Nicht-Negativitaet, zirkulaaere Symmetrie und Limitierung der Frequenzbandbreite sicher zu stellen. Fuer das Bild stellt die quadratische Re-Parametrisierung die Nicht-Negativitaet sicher. Die Entfaltungsmethode wird anhand von simulierten und realen Konfokal-Mikroskopie Daten ausgewertet. Der Vergleich mit einem nicht-parametrisierten Algorithmus zeigt, dass die vorgeschlagene Methode verbesserte Leistung und schnellere Konvergenz erreicht. Im zweiten Teil der Arbeit wird eine neue Methode eingefuehrt, die versucht die anisotrope tiefabhaengige Unschaerfe zu beheben. Wenn roehrenfoermige Gegenstaende -wie Neuronen- abgebildet werden, sind die aufgenommenen Bilder degradiert und die Extraktion der genauen Morphologie der Neuronen wird erschwert. Es wird eine neue Methode vorgeschlagen, mit der sich die PSF ohne irgendein Vorwissen ueber das Belichtungssystem aus dem augenommenen Bild schaetzen laesst. Diese Methode, die auf der Schaetzung des urspruenglichen Gegenstandes basiert ist fuer Faelle verwendbar, in denen der abgebildete Gegenstand eine bekannte Geometrie hat. Mit der vorgeschlagenen Dekonvolutionsmethode werden geometrische Verzerrungen beseitigt und die wiederhergestellten Bilder sind fuer weitere Analysen besser verwendbar. Im dritten Teil wird eine neue Methode zur adaptiven Regularisierung vorgeschlagen. Diese vorgeschlagene Technik passt ihr Verhalten abhaengig von den lokalen Intensitaetsgradient im Bild an. Die neue Technik wird getestet und mit der ''total variation'' und der Tikhonov Regularisierungtechnik verglichen. Die Experimente zeigen, dass mit dem adaptiven Verahren, die Qualitaet der rekonstruierten Bilder verbessert wird.This thesis introduces new solutions to the problem of image restoration in biomedical fields. The confocal microscope is a relatively new imaging technique that is emerging as a standard tool in biomedical studies. This technique is capable of collecting a series of 2D images of single sections inside a specimen to form a 3D image of the object. Despite of its improved imaging properties, the observed images are blurred due to the finite size of the the point spread function and corrupted by Poisson noise due to the counting nature of image detection. Image restoration techniques aim at reversing the degradation and recovering an estimate of the true image. This thesis starts with the description of the confocal microscope and the sources of degradation. Then, the existing image restoration methods are studied and compared. The work done in this thesis is divided into three parts: In the first part, a new constrained blind deconvolution method is introduced. Re-parameterization is used to strictly enforce prior knowledge. A parametric model based on a set of constrained basis functions is used for the PSF to ensure non-negativity, circular symmetry, and band-limitedness. For the image, quadratic re-parameterization ensures non-negativity. The deconvolution method is evaluated on both simulated and real confocal microscopy data sets. The comparison with non-parameteric algorithms shows that the proposed method exhibits improved performance and faster convergence. In the second part, a new method to correct the anisotropic, depth-variant blur is introduced. When objects of tubular-like structure, like neurons, are imaged, the acquired images are degraded and the extraction of accurate morphology of neurons is hampered. A new method to estimate the PSF from the acquired image, without any prior knowledge about the imaging system, is proposed. This method which is based on the estimation of the original object and is suitable for cases in which, the object being imaged has a known geometry. Using the proposed restoration method, geometric distortions are eliminated and the restored images are more suitable for further analysis. In the third part, a new method for adaptive regularization is proposed. The proposed technique adapts its behavior depending on the local activities in the image, as reflected in the magnitude of the intensity gradient. The new technique is tested and compared to both the total variation and the Tikhonov regularization techniques. Experiments show that, using the adaptive technique, the quality of the restored images is improved