Advanced Methods for Efficient Digital Signal Processing and Matrix-Based Computations
Date
2018-04-12
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Abstract
Modern engineering and scientific problems demand a great amount of data processing power. The type of data that needs to be processed varies from application to application. Image processing, genome matching, physics phenomena simulation, and cryptography are a few examples of processing-power demanding applications. In a wide range of those computationally intensive applications, the arithmetic complexity plays an important role, having direct impact on the implementation performance. In this thesis, we present several methods that are novel contributions of the author to some computationally intensive problems. The introduced methods reduce the overall computing time or other relevant hardware’ and software implementation metrics by decreasing the arithmetic complexity associated with each task. Verified results are shown with peer-reviewed journal papers in reputable journals. In particular, problems on signal processing, eigenvalue computation, and matrix inversion for radar image classification are considered.
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Keywords
Fast Algorithms, Digital Signal Processing, Matrix Computation
Citation
Coelho, D. F. G. (2018). Advanced Methods for Efficient Digital Signal Processing and Matrix-Based Computations (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31793