APPLICATION OF DATA FUSION TO FLUID DYNAMIC DATA

In recent years, there have been improvements in the methods of obtaining fluid dynamic data, which has led to the generation of vast amounts of data. Extracting the useful information from large data sets can be a challenging task when investigating data from a single source. However, most experiments use data from multiple sources, such as particle image velocimetry (PIV), pressure sensors, acoustic measurements, and computational fluid dynamics (CFD), to name a few. Knowing the strengths and weaknesses of each measurement technique, one can fuse the data together to improve the understanding of the problem being studied. Concepts from the data fusion community are used to combine fluid dynamic data from the different data sources. The data is fused using techniques commonly used by the fluid dynamics community, such as proper orthogonal decomposition (POD), linear stochastic estimation (LSE), and wavelet analysis. This process can generate large quantities of data and a method of handling all of the data and the techniques in an efficient manner is required. To accomplish this, a framework was developed that is capable of tracking, storing, and, manipulating data. With the framework and techniques, data fusion can be applied. Data fusion is first applied to a synthetic data set to determine the best methods of fusing data. Data fusion was then applied to airfoil data that was obtained from PIV, CFD, and pressure to test the ideas from the synthetic data. With the knowledge gained from applying fusion to the synthetic data and airfoil data, these techniques are ultimately applied to data for a Mach 0.6 jet obtained from large-window PIV (LWPIV), time-resolved PIV (TRPIV), and pressure. Through the fusion of the different data sets, occlusion in the jet data were estimated within 6% error using a new POD based technique called Fused POD. In addition, a technique called Dynamic Gappy POD was created to fuse TRPIV and LWPIV to generate a large-window time-resolved data set. This technique had less error than other standard techniques for accomplishing this such as pressure-based stochastic estimation. The work presented in this document lays the groundwork for future applications of data fusion to fluid dynamic data. With the success of the work in this document, one can begin to apply the ideas from data fusion to other types of fluid dynamic problems, such as bluff bodies, unsteady aerodynamics, and other. These ideas could be used to help improve understanding in the field of fluid dynamics due to the current limitations of obtaining data and the need to better understand flow phenomena

Non-Gaussianity in CMB analysis: bispectrum estimation and foreground subtraction

The focus of this work is the development of statistical and numerical methods forthe study of non-Gaussian and/or anisotropic features in cosmological surveys of themicrowave sky. We focus on two very different types of non-Gaussian (NG) signals. The former is primordial non-Gaussianity (PNG), generated in the very Early Universeduring the inflationary expansion stage. In this case the aim of our study will be that ofexploiting the NG component in order to extract useful cosmological information. The latter is non-Gaussianity generated by astrophysical foreground contamination. In thiscase, the goal is instead that of using non-Gaussianity as a tool to help in removingthese spurious, non-cosmological components (of course foregrounds themselves contain relevant astrophysical information, but the focus in this thesis is on Cosmology, thereforeforegrounds are regarded here only as a contaminant). Considerable efforts have been put so far in the search for deviations from Gaussianity in the CMB anisotropies, that are expected to provide invaluable information aboutthe Inflationary epoch. Inflation is in fact expected to produce an isotropic and nearly-Gaussian fluctuation field. However, a large amount of models also predicts very small,but highly model dependent NG signatures. This is the main reason behind the largeinterest in primordial NG studies. Of course, the pursuit for primordial non-Gaussianity must rely on beyond power spectrum statistics. It turns out that the most important higher order correlator produced by interactions during Inflation is the three pointfunction, or, more precisely, its Fourier space counterpart, called the bispectrum. Toovercome the issue of computing the full bispectrum of the observed field, that would require a prohibitive amount of computational time, the search for PNG features is carriedout by fitting theoretically motivated bispectrum templates to the data. Among those, one can find bispectrum templates with a scale-dependent (SD) bispectrum amplitude. Such templates have actually received little attention so far in the literature, especiallyas long as NG statistical estimation and data analysis are concerned. This is why a significant part of this thesis will be devoted to the development and application of efficientstatistical pipelines for CMB scale-dependent bispectra estimation. We present here theresults of the estimation of several primordial running bispectra obtained from WMAP9 year and Planck data-set. iiiThe second part of this thesis deals instead, as mentioned iin the beginning, withthe component separation problem, i.e. the identification of the different sources thatcontributes to the microwave sky brightness. Foreground emission produces several,potentially large, non-Gaussian signatures that can in principle be used to identify andremove the spurious components from the microwave sky maps. Our focus will be onthe development of a foreground cleaning technique relying on the hypothesis that, ifthe data are represented in a proper basis, the foreground signal is sparse. Sparsenessimplies that the majority of the signal is concentrated in few basis elements, that can be used to fit the corresponding component with a thresholding algorithm. We verifythat the spherical needlet frame has the right properties to disentangle the coherentforeground emission from the isotropic stochastic CMB signal. We will make clear inthe following how sparseness in needlet space is actually in several ways linked to thecoherence, anisotropy and non-Gaussianity of the foreground components.. The mainadvantages of our needlet thresholding technique are that it does not requires multi-frequency information as well as that it can be used in combination with other methods. Therefore it can represent a valuable tool in experiments with limited frequency coverage,as current ground-based CMB surveys

Transients in Power Systems

Power system engineering largely focuses on steady state analysis. The main areas of power system engineering are power flow studies and fault studies - both steady state technologies. But the world is largely transient, and power systems are always subject to time varying and short lived signals. This technical report concerns several important topics in transient analyses of power systems. The leading chapter deals with a new analytical tool-wavelets-for power system transients. Flicker and electric are furnace transients are discussed in Chapters I1 and IV. Chapter 111 deals with transients from shunt capacitor switching. The concluding chapters deal with transformer inrush current and non simultaneous pole closures of circuit breakers. This report was prepared by the students in EE532 at Purdue University. When I first came to Purdue in 1965, Professor El-Abiad was asking for student term projects which were turned into technical reports. I have \u27borrowed\u27 this idea and for many years we have produced technical reports from the power systems courses. The students get practice in writing reports, and the reader is able to get an idea of the coverage of our courses. I think that the students have done a good job on the subject of transients in power systems

Large-scale Analysis and Simulation of Traffic Flow using Markov Models

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps with understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques whose use together to analyze traffic on large road networks has not previously been reported

Proper orthogonal decomposition, dynamic mode decomposition, wavelet and cross wavelet analysis of a sloshing flow

Internal hydrodynamics and its coupling with structural dynamics are non-negligible processes in the design phase of aerospace systems. An improved understanding of the nature of this coupling would allow for greater flexibility in modeling and design of such systems, and could lead eventually to the development of suitable active and/or passive control strategies for enhanced performances. In this manuscript we apply a number of data analysis techniques: proper orthogonal decomposition, dynamic mode decomposition and wavelet transform and their combination to time-resolved images of a liquid sloshing within an enclosure. We use these techniques to identify fluid-dynamic modes in space and time and to verify their coupling with the structural dynamics of vibrating structures. In particular we consider the transient case of a water tank mounted on a free oscillating cantilever. As the acceleration amplitude decays, we observe and quantify the transition from incoherent flow to standing waves. Our results show that the content of the images is very informative and can be used for quantitative analysis. As the main outcome, the hydrodynamic modes are identified using POD and DMD, and related to known features of sloshing flow, such as the frequency of the first symmetric free surface mode. Additionally we perform a comparison of wavelet transforms of POD time coefficients and measured acceleration signals at the tank base. Viewing the latter as the input and the former as the output of the fluid-dynamic system, we are able to correlate the enhanced damping of the cantilever oscillation to the different regimes of the hydrodynamic field

A Survey on Emotion Recognition for Human Robot Interaction

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined

Mathematics of biomedical imaging today—a perspective

Biomedical imaging is a fascinating, rich and dynamic research area, which has huge importance in biomedical research and clinical practice alike. The key technology behind the processing, and automated analysis and quantification of imaging data is mathematics. Starting with the optimisation of the image acquisition and the reconstruction of an image from indirect tomographic measurement data, all the way to the automated segmentation of tumours in medical images and the design of optimal treatment plans based on image biomarkers, mathematics appears in all of these in different flavours. Non-smooth optimisation in the context of sparsity-promoting image priors, partial differential equations for image registration and motion estimation, and deep neural networks for image segmentation, to name just a few. In this article, we present and review mathematical topics that arise within the whole biomedical imaging pipeline, from tomographic measurements to clinical support tools, and highlight some modern topics and open problems. The article is addressed to both biomedical researchers who want to get a taste of where mathematics arises in biomedical imaging as well as mathematicians who are interested in what mathematical challenges biomedical imaging research entails

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition