Skip to main content

Scalable Parallel Optimization of Digital Signal Processing in the Fourier Domain

Kapinchev, Konstantin (2017) Scalable Parallel Optimization of Digital Signal Processing in the Fourier Domain. Doctor of Philosophy (PhD) thesis, University of Kent. (KAR id:61075)

Language: English
Download (2MB) Preview


The aim of the research presented in this thesis is to study different approaches to the parallel optimization of digital signal processing algorithms and optical coherence tomography methods. The parallel approaches are based on multithreading for multi-core and many-core architectures. The thesis follows the process of designing and implementing the parallel algorithms and programs and their integration into optical coherence tomography systems. Evaluations of the performance and the scalability of the proposed parallel solutions are presented. The digital signal processing considered in this thesis is divided into two groups. The first one includes generally employed algorithms operating with digital signals in Fourier domain. Those include forward and inverse Fourier transform, cross-correlation, convolution and others. The second group involves optical coherence tomography methods, which incorporate the aforementioned algorithms. These methods are used to generate cross-sectional, en-face and confocal images. Identifying the optimal parallel approaches to these methods allows improvements in the generated imagery in terms of performance and content. The proposed parallel accelerations lead to the generation of comprehensive imagery in real-time. Providing detailed visual information in real-time improves the utilization of the optical coherence tomography systems, especially in areas such as ophthalmology.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Barnes, Frederick
Uncontrolled keywords: Parallel Computing, GPU Computing, Digital Signal Processing, Optical Coherence Tomography
Divisions: Faculties > Sciences > School of Computing
Depositing User: Users 1 not found.
Date Deposited: 28 Mar 2017 17:00 UTC
Last Modified: 29 May 2019 18:52 UTC
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):


Downloads per month over past year