Zhang, Qian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 97-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically.

Employing a combination of biochemistry and chemometrics, the field of metabolomics has the potential to reveal some very significant insights into biological pathways related to drugs and diseases. This thesis explores this field in its depths; specifically focusing on Nuclear Magnetic Resonance (NMR) based methods. The thesis begins with an exploration of the quantum level relationships of molecules, and how these coupling patterns evolve into an NMR spectrum. The thesis will describe the development of a simplified spin simulation algorithm to predict NMR spin coupling patterns that are computed in fractions of a second and to build mathematically relevant basis functions. Later in the thesis, the issue of baseline distortions of real NMR experimental data is addressed by the development of an automated baseline correction algorithm. Data reduction techniques are further analyzed to understand the importance of the quality of the data used in advanced chemometric methods. For analysis of the data, the use of simple univariate techniques applied to NMR spectra of urine is explored to determine statistically significant biomarkers between disease states in asthma. More advanced statistics in the way of multivariate models, namely Partial Least Squares – Discriminant Analysis (PLS-DA), were used to build predictive models of Streptococcus pneumoniae pneumonia from NMR spectra of urine. Potential characteristics of the data that may invalidate assumptions required in our models were accounted for, such as ensuring the statistical normality of the S. pneumoniae pneumonia data by using log transformations. After the analysis, focus was given to the use of unique visualization techniques to further explore the complex relationships that exist between samples and variables, and relationships between variables. As will be made evident, this thesis deals with the basic physics of an NMR signal to building highly sophisticated models to help understand the NMR spectra from complex mixtures. All of these notions are important in the objective to garner the most information provided through an NMR experiment, as such to aid in the discovery of biochemical knowledge. / Process Control

As ocean ecosystems continue to deteriorate in the face of human induced pressures, marine management professionals are increasingly being urged to predict the impacts of various activities on ocean ecosystems. Many ecosystem interactions are still not adequately understood, so managers often turn to scientists to provide data and analysis on impacts resulting from specific actions. One important physical ocean process in need of more empirical data is microscale turbulence. Because it is responsible for mixing across isopycnal surfaces in stratified waters, turbulence is important in many physical, chemical and biological processes in the ocean. An elementary description of turbulence and mixing is presented along with a summary of the role of turbulence in marine ecosystems. In order to be of use to scientists, turbulence must be measured over large areas of the ocean. This paper presents a discussion of techniques for measuring turbulence. Measurements of turbulence are specialized and costly. A new microstructure data acqusition system was developed to acquire microstructure data eight times faster than present methods allow. The design details of the high-speed microstructure data acquisition system called MicroSoar are presented along with some preliminary data obtained from its deployment on actual cruises. / Graduation date: 1998

In the context of forest products, a cutting order is a list of dimension parts along with demanded quantities. The cutting-order problem is to minimize the total cost of filling the cutting order from a given lumber grade (or grades). Lumber of a given grade is supplied to the production line in a random sequence, and each board is cut in a way that maximizes the total value of dimension parts produced, based on a value (or price) specified for each dimension part. Hence, the problem boils down to specifying suitable dimension-part prices for each board to be cut. The method we propose is adapted from Gilmore and Gomory's linear programming approach to the cutting stock problem. The main differences are the use of a random sample to construct the linear program and the use of prices rather than cutting patterns to specify a solution. The primary result of this thesis is that the expected cost of filling an order under the proposed method is approximately equal to the minimum possible expected cost, in the sense that the ratio (expected cost divided by the minimum expected cost) approaches one as the size of the order (e.g., in board feet) and the size of the random sample grow large. A secondary result is a lower bound on the minimum possible expected cost. The actual minimum is usually impractical to calculate, but the lower bound can be used in computer simulations to provide an absolute standard against which to compare costs. It applies only to independent sequences, whereas the convergence property above applies to a large class of dependent sequences, called alpha-mixing sequences. Experimental results (in the form of computer simulations) suggest that the proposed method is capable of attaining nearly minimal expected costs in moderately large orders. The main drawbacks are that the method is computationally expensive and of questionable value in smaller orders. / Graduation date: 2001

Thesis (M.S.)--University of Florida, 2000. / Title from first page of PDF file. Document formatted into pages; contains ix, 41 p.; also contains graphics. Vita. Includes bibliographical references (p. 39-40).

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations.

Frequency estimation has been studied for a large number of years. One reason for this is that the problem is easy to understand, but difficult to solve. Another reason, for sure, is the large number of applications that involve frequency estimation, e.g radar using frequency modulated continuous wave (FMCW) techniques where the distance to the target is embedded in the frequency, resonance sensor systems where the output signal is given as the frequency displacement from a nominal frequency, radio frequency identification systems (RFID) where frequency modulation is used in the communication link, etc. The requirement on the frequency estimator varies with the application and typical issues include: accuracy, precision or (bias) processing speed or complexity, and ability to handle multiple signals. A lot of solutions to different problems in this area has been proposed, but still several open questions remain. The first part of this thesis addresses the problem of frequency estimation using low complexity algorithms. One way of achieving such an algorithm is to employ a coarse quantization on the input signal. In this thesis, a 1-bit quantizer is considered which enables the use of low complexity algorithms. Frequency estimation using look-up tables is studied and the properties of such an estimator are presented. By analyzing the look-up tables using the Hadamard transform a novel type of lowcomplexity frequency estimators is proposed. They use operations such as binary multiplication and addition of precalculated constants. This fact makes them suitable in applications where low complexity and high speed are major issues. A hardware demonstrator using the table look-up technique is designed and a prototype is analysed by real measurements. Today, the interest of using digital signal processing instead of analog processing is almost absolute. For example, in testing analog-to-digital converters an important part is to fit a sinewave to the recorded data, as well as to calculate the parameters that in least-squares sense result in the best fit. In this thesis, the sinewave fitting method included in the IEEE Standard 1057 is studied in some detail. Asymptotic Cramér-Rao bounds for three- and four model parameters are derived under the Gaussian assumption. Further, the sinewave fitting properties of the algorithm are analyzed by the parsimony principle. A novel model order selection criterion is proposed for waveform fitting methods in the case of a linear signal model. A generalization of this criterion is made to include the non-linear sinewave fitting application. For multiple sinewave fitting applications two iterative algorithms are proposed. The first method is a combination of the standardized sinewave fit algorithm and the expectation maximization algorithm. The second algorithm is an extension of a single sinewave model to a multiple sinewave model employing the standardized sinewave fitting algorithm. Both algorithms are analysed by numerical means and are shown to accurately resolve multiple sinewaves and produce efficient estimates. Initialization issues of such algorithms are included to some extent. / QC 20100830

Traditional signal processing systems, based on linear modeling principles, face a stifling pressure to meet present-day demands caused by the deluge of data generated, transmitted and processed across the globe. Fortunately, recent advances have resulted in the emergence of more sophisticated, nonlinear signal models. Such nonlinear models have inspired fundamental changes in which information processing systems are designed and analyzed. For example, the sparse signal model serves as the basis for Compressive Sensing (CS), an exciting new framework for signal acquisition. In this thesis, we advocate a geometry-based approach for nonlinear modeling of signal ensembles. We make the guiding assumption that the signal class of interest forms a nonlinear low-dimensional manifold belonging to the high-dimensional signal space. A host of traditional data models can be essentially interpreted as specific instances of such manifolds. Therefore, our proposed geometric approach provides a common framework that can unify, analyze, and significantly extend the scope of nonlinear models for information acquisition and processing. We demonstrate that the geometric approach enables new algorithms and analysis for a number of signal processing applications. Our specific contributions include: (i) new convex formulations and algorithms for the design of linear systems for data acquisition, compression, and classification; (ii) a general algorithm for reconstruction, deconvolution, and denoising of signals, images, and matrix-valued data; (iii) efficient methods for inference from a small number of linear signal samples, without ever resorting to reconstruction; and, (iv) new signal and image representations for robust modeling and processing of large-scale data ensembles.

Abstract The goal of this investigation is based on the Taiwan seafood industry development and its critical issues. In 2004, I have personally established a joint venture between a Japanese company-HANWA and our company-LUXE in developing a market for Saury fish,eel an Tailapia. 1.Using long-term relationship and trust between companies to establish a business relationship. 2.Both agreeing to use the new brand in market and good quality .The total sale for 2004 on Saury fish reached 720 tons,in our corperation. 3.Establishing a set price for the raw materials, processing rate,processing expenses, and exchange rate are the main factors of setting the price of the product itself. Therefore, obtaining the highest profit between the two companies. 4.Utilizing the Taiwanese seafood products¡¦ advantage, we focus on the most profitable and suitable production of goods based on the needs of nearby countries, and their influence on the market in all aspects.