Qualitative and quantitative properties of solutions of ordinary differential equations
- Authors: Ogundare, Babatunde Sunday
- Date: 2009
- Subjects: Differential equations , Lyapunov functions , Chebyshev polynomials , Algorithms
- Language: English
- Type: Thesis , Doctoral , PhD (Applied Mathematics)
- Identifier: vital:11588 , http://hdl.handle.net/10353/244 , Differential equations , Lyapunov functions , Chebyshev polynomials , Algorithms
- Description: This thesis is concerned with the qualitative and quantitative properties of solutions of certain classes of ordinary di erential equations (ODEs); in particular linear boundary value problems of second order ODE's and non-linear ODEs of order at most four. The Lyapunov's second method of special functions called Lyapunov functions are employed extensively in this thesis. We construct suitable complete Lyapunov functions to discuss the qualitative properties of solutions to certain classes of non-linear ordinary di erential equations considered. Though there is no unique way of constructing Lyapunov functions, We adopt Cartwright's method to construct complete Lyapunov functions that are required in this thesis. Su cient conditions were established to discuss the qualitative properties such as boundedness, convergence, periodicity and stability of the classes of equations of our focus. Another aspect of this thesis is on the quantitative properties of solutions. New scheme based on interpolation and collocation is derived for solving initial value problem of ODEs. This scheme is derived from the general method of deriving the spline functions. Also by exploiting the Trigonometric identity property of the Chebyshev polynomials, We develop a new scheme for approximating the solutions of two-point boundary value problems. These schemes are user-friendly, easy to develop algorithm (computer program) and execute. They compare favorably with known standard methods used in solving the classes of problems they were derived for
- Full Text:
- Date Issued: 2009
- Authors: Ogundare, Babatunde Sunday
- Date: 2009
- Subjects: Differential equations , Lyapunov functions , Chebyshev polynomials , Algorithms
- Language: English
- Type: Thesis , Doctoral , PhD (Applied Mathematics)
- Identifier: vital:11588 , http://hdl.handle.net/10353/244 , Differential equations , Lyapunov functions , Chebyshev polynomials , Algorithms
- Description: This thesis is concerned with the qualitative and quantitative properties of solutions of certain classes of ordinary di erential equations (ODEs); in particular linear boundary value problems of second order ODE's and non-linear ODEs of order at most four. The Lyapunov's second method of special functions called Lyapunov functions are employed extensively in this thesis. We construct suitable complete Lyapunov functions to discuss the qualitative properties of solutions to certain classes of non-linear ordinary di erential equations considered. Though there is no unique way of constructing Lyapunov functions, We adopt Cartwright's method to construct complete Lyapunov functions that are required in this thesis. Su cient conditions were established to discuss the qualitative properties such as boundedness, convergence, periodicity and stability of the classes of equations of our focus. Another aspect of this thesis is on the quantitative properties of solutions. New scheme based on interpolation and collocation is derived for solving initial value problem of ODEs. This scheme is derived from the general method of deriving the spline functions. Also by exploiting the Trigonometric identity property of the Chebyshev polynomials, We develop a new scheme for approximating the solutions of two-point boundary value problems. These schemes are user-friendly, easy to develop algorithm (computer program) and execute. They compare favorably with known standard methods used in solving the classes of problems they were derived for
- Full Text:
- Date Issued: 2009
Clustering algorithms and their effect on edge preservation in image compression
- Authors: Ndebele, Nothando Elizabeth
- Date: 2009
- Subjects: Image compression , Vector analysis , Cluster analysis , Cluster anaylsis -- Data processing , Algorithms
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5576 , http://hdl.handle.net/10962/d1008210 , Image compression , Vector analysis , Cluster analysis , Cluster anaylsis -- Data processing , Algorithms
- Description: Image compression aims to reduce the amount of data that is stored or transmitted for images. One technique that may be used to this end is vector quantization. Vectors may be used to represent images. Vector quantization reduces the number of vectors required for an image by representing a cluster of similar vectors by one typical vector that is part of a set of vectors referred to as the code book. For compression, for each image vector, only the closest codebook vector is stored or transmitted. For reconstruction, the image vectors are again replaced by the the closest codebook vectors. Hence vector quantization is a lossy compression technique and the quality of the reconstructed image depends strongly on the quality of the codebook. The design of the codebook is therefore an important part of the process. In this thesis we examine three clustering algorithms which can be used for codebook design in image compression: c-means (CM), fuzzy c-means (FCM) and learning vector quantization (LVQ). We give a description of these algorithms and their application to codebook design. Edges are an important part of the visual information contained in an image. It is essential therefore to use codebooks which allow an accurate representation of the edges. One of the shortcomings of using vector quantization is poor edge representation. We therefore carry out experiments using these algorithms to compare their edge preserving qualities. We also investigate the combination of these algorithms with classified vector quantization (CVQ) and the replication method (RM). Both these methods have been suggested as methods for improving edge representation. We use a cross validation approach to estimate the mean squared error to measure the performance of each of the algorithms and the edge preserving methods. The results reflect that the edges are less accurately represented than the non - edge areas when using CM, FCM and LVQ. The advantage of using CVQ is that the time taken for code book design is reduced particularly for CM and FCM. RM is found to be effective where the codebook is trained using a set that has larger proportions of edges than the test set.
- Full Text:
- Date Issued: 2009
- Authors: Ndebele, Nothando Elizabeth
- Date: 2009
- Subjects: Image compression , Vector analysis , Cluster analysis , Cluster anaylsis -- Data processing , Algorithms
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5576 , http://hdl.handle.net/10962/d1008210 , Image compression , Vector analysis , Cluster analysis , Cluster anaylsis -- Data processing , Algorithms
- Description: Image compression aims to reduce the amount of data that is stored or transmitted for images. One technique that may be used to this end is vector quantization. Vectors may be used to represent images. Vector quantization reduces the number of vectors required for an image by representing a cluster of similar vectors by one typical vector that is part of a set of vectors referred to as the code book. For compression, for each image vector, only the closest codebook vector is stored or transmitted. For reconstruction, the image vectors are again replaced by the the closest codebook vectors. Hence vector quantization is a lossy compression technique and the quality of the reconstructed image depends strongly on the quality of the codebook. The design of the codebook is therefore an important part of the process. In this thesis we examine three clustering algorithms which can be used for codebook design in image compression: c-means (CM), fuzzy c-means (FCM) and learning vector quantization (LVQ). We give a description of these algorithms and their application to codebook design. Edges are an important part of the visual information contained in an image. It is essential therefore to use codebooks which allow an accurate representation of the edges. One of the shortcomings of using vector quantization is poor edge representation. We therefore carry out experiments using these algorithms to compare their edge preserving qualities. We also investigate the combination of these algorithms with classified vector quantization (CVQ) and the replication method (RM). Both these methods have been suggested as methods for improving edge representation. We use a cross validation approach to estimate the mean squared error to measure the performance of each of the algorithms and the edge preserving methods. The results reflect that the edges are less accurately represented than the non - edge areas when using CM, FCM and LVQ. The advantage of using CVQ is that the time taken for code book design is reduced particularly for CM and FCM. RM is found to be effective where the codebook is trained using a set that has larger proportions of edges than the test set.
- Full Text:
- Date Issued: 2009
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