Wang, Tianming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 59-62). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Like many other multimedia applications, image compression involves a significant amount of data processing for coding images. Sophisticated general-purpose processors with parallel architectures and advanced cache systems can be dedicated to enhancing performance for serial multimedia applications through parallelization. This thesis describes parallelization of the JasPer reference software for the JPEG-2000 image compression standard and presents results from simulation, and from hardware execution on a multicore processor where speedups of more than 2 are obtained with 4 processors. Results from execution and cache behavior analysis are presented to establish the expected speedup and to further characterize JasPer execution. The JasPer encoding process has been analyzed on a single processor for both simulated and hardware execution in order to obtain more insights into application behavior. On recent hardware platforms, the significant contributors to the total execution time have been identified through profiling. The granularity of parallelism for parallelizable loops have been analyzed for execution on real hardware. Cache behavior and memory access pattern have been studied closely for the simulated execution. To facilitate parallelization, selected parallelizable loops have been transformed in order to assist the partitioning of loop iterations for parallel execution and to increase workload granularity and reduce synchronization overhead. These modifications include loop index and body transformation, and loop fusion. A memory access pattern tracking feature has also been introduced for serial and parallel execution of a program in simulation. This feature tracks the number of memory accesses in a particular data region during a particular interval of time in order to gain additional insights into execution behavior. The multithreaded execution of the parallelized JasPer encoder presents a relatively balanced workload which indicates a reasonable efficiency for parallel execution. The generated images have been compared against their original images by using analytical tools to ensure the image quality and to verify correctness. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-10-29 00:10:43.437

No description available.

No description available.

Thesis (M. App. Sc.)--University of Adelaide, Dept. of Applied Mathematics, 1993? / Includes bibliographical references (leaves 92-93).

Thesis supervisor: Palle Jorgensen. Includes bibliographic references (p. 85-87).

Thesis (M.S. in electrical engineering)--Washington State University, May 2010. / Title from PDF title page (viewed on July 23, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 55-57).

Thesis (M.S.)--University of Wisconsin--Madison, 1984. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 75-77).

<p>The design and analyses of Space-Time Block Codes (STBC) for both single antenna and two-antenna distributed relay channels are considered in this thesis. Due to the fact that the equivalent channel gains for two-phase relay channels are the product of two channel coefficients, many current STBC designs for Multiple Input Multiple Output (MIMO) channels cannot be implemented to distributed relay channels efficiently. The direct application of Orthogonal Space-Time Block Coding (OSTBC) for MIMO systems to distributed cooperative relay networks makes the equivalent channel matrix for maximum likelihood (ML) detection lose its orthogonality. Hence, a new design that makes the channel matrix be \textit{orthogonally distributed} (OD) for a suboptimal symbol-by-symbol detector (SBSD) is proposed in this thesis. With ODSTBC, an asymptotic symbol error probability (SEP) formula with SBSD is derived, showing the optimal diversity gain function for single antenna distributed relay channels $\frac{\ln^N\rho}{\rho^N}$ is achieved. In addition, two ODSTBC designs for the distributed relay networks are presented, which interestingly renders that SBSD is equivalent to the ML detector. The ODSTBC enjoys both optimal diversity function and low detection complexity. However, the symbol rate of ODSTBC is relatively low in order to maintain the orthogonal conditions. To address this problem, another Alamouti Based Toeplitz Space-Time Block Code (ABTSTBC) for two-antenna distributed relay channels is proposed. Both the code structure and the equivalent channel matrix has a block Toeplitz structure, whose blocks are the addition and product of two Alamouti matrices, respectively. With the linear SBSD, the optimal diversity function $\frac{\ln^N\rho}{\rho^{2N}}$ is achieved. At the same time, the advantages of low computational complexity and high symbol rate are maintained. Numerical results verify the diversity analyses and indicate competitive error performance to currently available distributed STBC designs with much lower complexity.</p> / Master of Applied Science (MASc)

The combination of the well-known Hilbert spectral analysis (HAS) and the recently developed Empirical Mode Decomposition (EMD) designated as the Hilbert-Huang Transform (HHT) by Huang in 1998, represents a paradigm shift of data analysis methodology. The HHT is designed specifically for analyzing nonlinear and nonstationary data. The key part of HHT is EMD with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMFs). For two dimension, bidimensional IMFs (BIMFs) is decomposed by use of bidimensional EMD (BEMD). However, the HHT has some limitations in signal processing and image processing. This thesis addresses the problems of using HHT for signal and image processing. To reduce end effect in EMD, we propose a boundary extend method for EMD. A linear prediction based method combined with boundary extrema points information is employed to extend the signal, which reduces the end effect in EMD sifting process. It is a simple and effective method. In the EMD decomposition, interpolation method is another key point to get ideal components. The envelope mean in EMD is computed from the upper and lower envelopes by cubic spline interpolation, which has overshooting problem and is time-consuming. Based on the linear interpolation (straight line) method, we propose using the extrema points information to get the mean envelope, which is Extrema Mean Empirical Mode Decomposition (EMEMD). The mean envelope taken by EMEMD is smoother than EMD and the undershooting and overshooting problems in cubic spline are reduced compared with EMD. EMEMD also reduces the computation complex. Experimental results show the IMFs of EMEMD present more and clearer time-frequency information than EMD. Hilbert spectral of EMEMD is also clearer and more meaningful than EMD. Furthermore, based on the procedure of EMEMD method, a fast method to detect the frequency change location information of the piecewise stationary signal is also proposed, which is Extrema Points Empirical Mode Decomposition (EPEMD). Later, two applications based on the improved EMD/BEMD methods are proposed. One application is texture classification in image processing. A saddle points added BEMD is developed to supply multi-scale components (BIMFs) and Riesz transform is used to get the frequency domain characters of these BIMFs. Based on local descriptor Local Binary Pattern (LBP), two new features (based on BIMFs and based on Monogenic-BIMFs signals) are developed. In these new multi-scale components and frequency domain components, the LBP descriptor can achieve better performance than in original image. Experimental results show the texture images recognition rate based on our methods are better than other texture features methods. Another application is signal forecasting in one dimensional time series. EMEMD combined with Local Linear Wavelet Neural Network (LLWNN) for signal forecasting is proposed. The architecture is a decomposition-trend detection-forecasting-ensemble methodology. The EMEMD based decomposition forecasting method decomposed the time series into its basic components, and more accurate forecasts are obtained. In short, the main contributions of this thesis are summarized as following: 1. A boundary extension method is developed for one dimensional EMD. This extension method is based on linear prediction and end points adjusting. This extension method can reduce the end effect in EMD. 2. A saddle points added BEMD is developed to analysis and classify the texture images. This new BEMD detected more high oscillation in BIMFs and contributed for texture analysis. 3. A new texture analysis and classification method is proposed, which is based on BEMD (no/with saddle points), LBP and Riesz transform. The texture features based on BIMFs and BIMFs’ frequency domain 2D monogenic phase are developed. The performances and comparisons on the Brodatz, KTH-TIPS2a, CURet and Outex databases are reported. 4. An improved EMD method, EMEMD, is proposed to overcome the shortcoming in interpolation. EMEMD can provide more meaningful IMFs and it is also a fast decomposition method. The decomposition result and analysis in simulation temperature signal compare with Fourier transform, Wavelet transform are reported. 5. A forecasting methodology based on EMEMD and LLWNN is proposed. The architecture is a decomposition-trend detection-forecasting-ensemble methodology. The predicted results of Hong Kong Hang Seng Index and Global Land-Ocean Temperature Index are reported.