Massey Documents by Type

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Image processing and DSP technology applied to remote activity monitoring : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Technology at Massey University
(Massey University, 2000) Body, Nick
This thesis describes the development of image processing algorithms to achieve industrial remote activity monitoring. The design of such algorithms is constrained by the requirement that they must be implemented in real-time on a cost effective digital signal processor (DSP). A review of the literature revealed that two viable alternatives were available. These are JPEG, a well established lossy block-based image compression algorithm and methods using wavelets. A suitable family of wavelets was identified as the biorthogonal-7.9, the coefficients indicating the number of taps in the quadrature mirror filters at the heart of the transform. Data compression is then achieved through the construction of zerotrees followed by sequential baseline coding. The two candidate algorithms were then systematically compared for recovered image quality and implementation cost at high compression ratios in the range of 16:1 to 64:1. On this basis the wavelet approach was selected and its implementation on a DSP studied. The architectural features of the Motorola 56303 DSP are presented and analysed. It is shown that the various components required for the wavelet based algorithm can be efficiently mapped onto the DSP architecture. Motion detection and image watermarking algorithms were designed and co-operatively implemented with the compression algorithm. A new method of watermarking highly compressed images was developed and this algorithm has been named the Image Authentication Watermark. A new way of representing optimal Huffman code tables has been developed to enable Huffman entropy coding to perform competitively with the more complex arithmetic coding. A product of this research is a smart digital camera that has been integrated into an automated video surveillance system now in industrial production.
A resampling theory for non-bandlimited signals and its applications : a thesis presented for the partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Wellington, New Zealand
(Massey University, 2008) Huang, Beilei
Currently, digital signal processing systems typically assume that the signals are bandlimited. This is due to our knowledge based on the uniform sampling theorem for bandlimited signals which was established over 50 years ago by the works of Whittaker, Kotel'nikov and Shannon. However, in practice the digital signals are mostly of finite length. This kind of signals are not strictly bandlimited. Furthermore, advances in electronics have led to the use of very wide bandwidth signals and systems, such as Ultra-Wide Band (UWB) communication systems with signal bandwidths of several giga-hertz. This kind of signals can effectively be viewed as having infinite bandwidth. Thus there is a need to extend existing theory and techniques for signals of finite bandwidths to that for non-bandlimited signals. Two recent approaches to a more general sampling theory for non-bandlimited signals have been published. One is for signals with finite rate of innovation. The other introduced the concept of consistent sampling. It views sampling and reconstruction as projections of signals onto subspaces spanned by the sampling (acquisition) and reconstruction (synthesis) functions. Consistent sampling is achieved if the same discrete signal is obtained when the reconstructed continuous signal is sampled. However, it has been shown that when this generalized theory is applied to the de-interlacing of video signals, incorrect results are obtained. This is because de-interlacing is essentially a resampling problem rather than a sampling problem because both the input and output are discrete. While the theory for the resampling for bandlimited signals is well established, the problem of resampling without bandlimited constraints is largely unexplored. The aim of this thesis is to develop a resampling theory for non-bandlimited discrete signals and explore some of its potential applications. The first major contribution is the the theory and techniques for designing an optimal resampling system for signals in the general Hilbert Space when noise is not present. The system is optimal in the sense that the input of the system can always be obtained from the output. The theory is based on the concept of consistent resampling which means that the same continuous signal will be obtained when either the original or the resampled discrete signal is presented to the reconstruction filter. While comparing the input and output of a sampling/reconstruction system is relatively simple since both are continuous signals, comparing the discrete input and output of a resampling system is not. The second major contribution of this thesis is the proposal of a metric that allows us to evaluate the performance of a resampling system. The performance is analyzed in the Fourier domain as well. This performance metric also provides a way by which different resampling algorithms can be compared effectively. It therefore facilitates the process of choosing proper resampling schemes for a particular purpose. Unfortunately consistent resampling cannot always be achieved if noise is present in the signal or the system. Based on the performance metric proposed, the third major contribution of this thesis is the development of procedures for designing resampling systems in the presence of noise which is optimal in the mean squared error (MSE) sense. Both discrete and continuous noise are considered. The problem is formulated as a semi-definite program which can be solved effciently by existing techniques. The usefulness and correctness of the consistent resampling theory is demonstrated by its application to the video de-interlacing problem, image processing, the demodulation of ultra-wideband communication signals and mobile channel detection. The results show that the proposed resampling system has many advantages over existing approaches, including lower computational and time complexities, more accurate prediction of system performances, as well as robustness against noise.
Improved Memoryless RNS Forward Converter Based on the Periodicity of Residues
(Massey University., 2006-02-01) Premkumar, A. B.; Ang, E. L.; Lai, Edmund M-K.
The residue number system (RNS) is suitable for DSP architectures because of its ability to perform fast carry-free arithmetic. However, this advantage is over-shadowed by the complexity involved in the conversion of numbers between binary and RNS representations. Although the reverse conversion (RNS to binary) is more complex, the forward transformation is not simple either. Most forward converters make use of look-up tables (memory). Recently, a memoryless forward converter architecture for arbitrary moduli sets was proposed by Premkumar in 2002. In this paper, we present an extension to that architecture which results in 44% less hardware for parallel conversion and achieves 43% improvement in speed for serial conversions. It makes use of the periodicity properties of residues obtained using modular exponentiation.
Low-power and High-speed Implementation of Pulse Shaping Filters in Software Defined Radio Receivers
(Massey University., 2006-07-01) Vinod, A. P.; Lai, Edmund M-K.
No abstract available
FIR Filter Implementation by Efficient Sharing of Horizontal and Vertical Common Sub-expressions
(Massey University., 2003-01-23) Vinod, A. P.; Lai, Edmund M-K.; Premkumar, A. B.; Lau, C. T.
No abstract available
On the Implementation of Efficient Channel Filters for Wideband Receivers by Optimizing Common Subexpression Elimination Methods
(Massey University., 2005-02-01) Vinod, A. P.; Lai, Edmund M-K.
No abstract available
Design and Implementation of an RNS-based 2D DWT Processor
(Massey University., 2004-02-01) Liu, Y.; Lai, Edmund M-K.
No abstract available

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