Matching Items (40)

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Comparison of MIMD and SIMT Parallel Iterative Solvers for Laplace's Equation

Description

A comparison of the performance of CUDA versus OpenMP for Jacobi, Gauss-Seidel, and S.O.R. iterative methods for Laplace's Equation with Dirichlet boundary conditions is presented. Both the number of cores

A comparison of the performance of CUDA versus OpenMP for Jacobi, Gauss-Seidel, and S.O.R. iterative methods for Laplace's Equation with Dirichlet boundary conditions is presented. Both the number of cores and the grid size were varied for the OpenMP program, while the grid size was varied for the CUDA program. CUDA outperforms the 8-core OpenMP program with the Jacobi and Gauss-Seidel schemes for all grid sizes, and is competitive with S.O.R for all grid sizes examined.

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Created

Date Created
  • 2013-05

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Edge Detection from Non-Uniform Fourier Data via a Modified Method of Convolutional Gridding

Description

The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI).

The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI). Edge detection can be important as a goal in and of itself in the identification of tissue boundaries such as those defining the locations of tumors. It can also be an invaluable tool in the amelioration of the negative effects of the Gibbs phenomenon on reconstructions of functions with discontinuities or images in multi-dimensions with internal edges. In this thesis we develop a novel method for recovering edges from non-uniform Fourier data by adapting the "convolutional gridding" method of function reconstruction. We analyze the behavior of the method in one dimension and then extend it to two dimensions on several examples.

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Created

Date Created
  • 2013-05

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FOURFUN: A new system for automatic computations using Fourier expansions

Description

Using object-oriented programming in MATLAB, a collection of functions, named Fourfun, has been created to allow quick and accurate approximations of periodic functions with Fourier expansions. To increase efficiency and

Using object-oriented programming in MATLAB, a collection of functions, named Fourfun, has been created to allow quick and accurate approximations of periodic functions with Fourier expansions. To increase efficiency and reduce the number of computations of the Fourier transform, Fourfun automatically determines the number of nodes necessary for representations that are accurate to close to machine precision. Common MATLAB functions have been overloaded to keep the syntax of the Fourfun class as consistent as possible with the general MATLAB syntax. We show that the system can be used to efficiently solve several differential equations. Comparisons with Chebfun, a similar system based on Chebyshev polynomial approximations, are provided.

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Date Created
  • 2014-05

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Edge Informed Fourier Reconstruction from Non-Uniform Spectral Data

Description

The reconstruction of piecewise smooth functions from non-uniform Fourier data arises in sensing applications such as magnetic resonance imaging (MRI). This thesis presents a new polynomial based resampling method (PRM)

The reconstruction of piecewise smooth functions from non-uniform Fourier data arises in sensing applications such as magnetic resonance imaging (MRI). This thesis presents a new polynomial based resampling method (PRM) for 1-dimensional problems which uses edge information to recover the Fourier transform at its integer coefficients, thereby enabling the use of the inverse fast Fourier transform algorithm. By minimizing the error of the PRM approximation at the sampled Fourier modes, the PRM can also be used to improve on initial edge location estimates. Numerical examples show that using the PRM to improve on initial edge location estimates and then taking of the PRM approximation of the integer frequency Fourier coefficients is a viable way to reconstruct the underlying function in one dimension. In particular, the PRM is shown to converge more quickly and to be more robust than current resampling techniques used in MRI, and is particularly amenable to highly irregular sampling patterns.

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Created

Date Created
  • 2013-05

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An l1 Regularization Algorithm for Reconstructing Piecewise Smooth Functions from Fourier Data Using Wavelet Projection

Description

Imaging technologies such as Magnetic Resonance Imaging (MRI) and Synthetic Aperture Radar (SAR) collect Fourier data and then process the data to form images. Because images are piecewise smooth, the

Imaging technologies such as Magnetic Resonance Imaging (MRI) and Synthetic Aperture Radar (SAR) collect Fourier data and then process the data to form images. Because images are piecewise smooth, the Fourier partial sum (i.e. direct inversion of the Fourier data) yields a poor approximation, with spurious oscillations forming at the interior edges of the image and reduced accuracy overall. This is the well known Gibbs phenomenon and many attempts have been made to rectify its effects. Previous algorithms exploited the sparsity of edges in the underlying image as a constraint with which to optimize for a solution with reduced spurious oscillations. While the sparsity enforcing algorithms are fairly effective, they are sensitive to several issues, including undersampling and noise. Because of the piecewise nature of the underlying image, we theorize that projecting the solution onto the wavelet basis would increase the overall accuracy. Thus in this investigation we develop an algorithm that continues to exploit the sparsity of edges in the underlying image while also seeking to represent the solution using the wavelet rather than Fourier basis. Our method successfully decreases the effect of the Gibbs phenomenon and provides a good approximation for the underlying image. The primary advantages of our method is its robustness to undersampling and perturbations in the optimization parameters.

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Date Created
  • 2015-12

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Computations on Parameterized Surfaces with Chebfun2

Description

Chebfun is a collection of algorithms and an open-source software system in object-oriented Matlab that extends familiar powerful methods of numerical computation involving numbers to continuous or piecewise-continuous functions. The

Chebfun is a collection of algorithms and an open-source software system in object-oriented Matlab that extends familiar powerful methods of numerical computation involving numbers to continuous or piecewise-continuous functions. The success of this strategy is based on the mathematical fact that smooth functions can be represented very efficiently by polynomial interpolation at Chebyshev points or by trigonometric interpolation at equispaced points for periodic functions. More recently, the system has been extended to handle bivariate functions and vector fields. These two new classes of objects are called Chebfun2 and Chebfun2v, respectively. We will show that Chebfun2 and Chebfun2v, and can be used to accurately and efficiently perform various computations on parametric surfaces in two or three dimensions, including path trajectories and mean and Gaussian curvatures. More advanced surface computations such as mean curvature flows are also explored. This is also the first work to use the newly implemented trigonometric representation, namely Trigfun, for computations on surfaces.

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Created

Date Created
  • 2016-05

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Two Approaches to MRI Reconstruction: Gaussian Radial Basis Functions and Single Shot Parse

Description

Physical limitations of Magnetic Resonance Imaging (MRI) introduce different errors in the image reconstruction process. The discretization and truncation of data under discrete Fourier transform causes oscillations near jump discontinuities,

Physical limitations of Magnetic Resonance Imaging (MRI) introduce different errors in the image reconstruction process. The discretization and truncation of data under discrete Fourier transform causes oscillations near jump discontinuities, a phenomenon known as the Gibbs effect. Using Gaussian-based approximations rather than the discrete Fourier transform to reconstruct images serves to diminish the Gibbs effect slightly, especially when coupled with filtering. Additionally, a simplifying assumption is made that, during signal collection, the amount of transverse magnetization decay at a point does not depend on that point's position in space. Though this methodology significantly reduces operational run-time, it nonetheless introduces geometric error, which can be mitigated using Single-Shot (SS) Parse.

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Created

Date Created
  • 2015-05

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Two-Dimensional Stratified Cavity Flow Under Harmonic Forcing

Description

We study an idealized model of a wind-driven ocean, namely a 2-D lid-driven cavity with a linear temperature gradient along the side walls and constant hot and cold temperatures on

We study an idealized model of a wind-driven ocean, namely a 2-D lid-driven cavity with a linear temperature gradient along the side walls and constant hot and cold temperatures on the top and bottom boundaries respectively. In particular, we determine numerically the response on flow field and temperature stratification associated with the velocity of the lid driven by harmonic forcing using the Navier-Stokes equations with Boussinesq approximation in an attempt to gain an understanding of how variations of external forces (such as the wind over the ocean) transfer energy to a system by exciting internal modes through resonances. The time variation of the forcing, accounting for turbulence at the boundary is critical for allowing penetration of energy waves through the stratified medium in which the angles of the internal waves depend on these perturbation frequencies. Determining the results of the interaction of two 45 degree angle wave beams at the center of the cavity is of particular interest.

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Created

Date Created
  • 2015-05

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Design and Analysis of Algorithmic Trading Automation

Description

With the coming advances of computational power, algorithmic trading has become one of the primary strategies to trading on the stock market. To understand why and how these strategies have

With the coming advances of computational power, algorithmic trading has become one of the primary strategies to trading on the stock market. To understand why and how these strategies have been effective, this project has taken a look at the complete process of creating tools and applications to analyze and predict stock prices in order to perform low-frequency trading. The project is composed of three main components. The first component is integrating several public resources to acquire and process financial trading data and store it in order to complete the other components. Alpha Vantage API, a free open source application, provides an accurate and comprehensive dataset of features for each stock ticker requested. The second component is researching, prototyping, and implementing various trading algorithms in code. We began by focusing on the Mean Reversion algorithm as a proof of concept algorithm to develop meaningful trading strategies and identify patterns within our datasets. To augment our market prediction power (“alpha”), we implemented a Long Short-Term Memory recurrent neural network. Neural Networks are an incredibly effective but often complex tool used frequently in data science when traditional methods are found lacking. Following the implementation, the last component is to optimize, analyze, compare, and contrast all of the algorithms and identify key features to conclude the overall effectiveness of each algorithm. We were able to identify conclusively which aspects of each algorithm provided better alpha and create an entire pipeline to automate this process for live trading implementation. An additional reason for automation is to provide an educational framework such that any who may be interested in quantitative finance in the future can leverage this project to gain further insight.

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Created

Date Created
  • 2019-05

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Computations on Spherical Domains

Description

The main goal of this project is to study approximations of functions on circular and spherical domains using the cubed sphere discretization. On each subdomain, the function is approximated by

The main goal of this project is to study approximations of functions on circular and spherical domains using the cubed sphere discretization. On each subdomain, the function is approximated by windowed Fourier expansions. Of particular interest is the dependence of accuracy on the different choices of windows and the size of the overlapping regions. We use Matlab to manipulate each of the variables involved in these computations as well as the overall error, thus enabling us to decide which specific values produce the most accurate results. This work is motivated by problems arising in atmospheric research.

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Created

Date Created
  • 2018-05