Power quality has become an important concern to both electric utilities and end users due to the increased use of non-linear loads in modem power systems over the past decade. Nonlinear loads inject harmonics into the power system and thus may lead to poor power quality and lower power factor. Current and voltage harmonics can adversely affect the operation of sensitive devices. A common remedial solution to reduce the effects of harmonic distortion in a power system is filtering. Passive and active filters are two common types of harmonic filters. An active filter, in general, is a controllable current source that injects current at the same magnitude and opposite phase to that of the harmonic current. For this thesis work, a DSP-controlled active filter to cancel lower order (5th, 7th, 11th, and 13th) harmonics in a three-phase, three-wire power system is designed. The proposed active filter employs a series LC tank tuned to a high frequency, along with a pulse-width modulated (PWM) converter topology. The PWM control of the active filter is implemented in a TMS320F240 DSP. The DSP implementation enhances the performance of the filter in real-time and enables the filter to compensate for varying loads. Additionally, the use of DSP-control reduces the number of components and therefore reduces the cost and improves the reliability of the overall system. The uniqueness of the filter is in its ability to control each harmonic separately. A laboratory prototype of the proposed active filter has been built and tested to verify the performance of the active filter. / Graduation date: 2001

With the increased attention on high efficiency and energy savings, power electronic energy conversion equipment is increasingly incorporated in all levels of the power system. The drawback of such equipment is the generation of nonsinusoidal currents in the power distribution network due to the nonlinear operation. Harmonic currents may distort the line voltages and lead to several unwanted effects including equipment overheating, system failure, interference with communication systems, etc. In response to these concerns, this research presents an active filter for the cancellation of harmonic line current distortion. The active filter used in this research is connected in parallel with the nonlinear load and is designed for a three-phase three-wire industrial power system. The filter consists of a voltage source inverter connected through a coupling inductor to the terminals of the ac-source. The inverter is controlled via a space vector-pulse width modulation (SVPWM) algorithm that is generated using a digital signal processor (DSP). In order to reduce the distortion resulting from the switching nature of the active filter inverter, a switching ripple filter is connected in parallel. The control algorithm of the active filter is based on the rotating reference frame theory. For each harmonic which is to be cancelled, a corresponding synchronous reference frame is generated to extract the harmonic phase and magnitude. With this information, each harmonic current component can be separately controlled and the proposed algorithm can therefore compensate for hardware effects such as measuring delays and component transfer functions. For the extraction of the harmonic components, a finite impulse response filter is used in order to quickly react to changing load currents. An adapting algorithm is implemented to compensate for slowly varying system parameters. Simulations under varying load and transient conditions are performed. The results show nearly perfect cancellation performance for the proposed active filter control algorithm. / Graduation date: 2001

Increasing densities on magnetic data storage devices leads to problems of severe intersymbol interference (ISI), additive noise and non-linearities. Advanced detection strategies for magnetic recording channels fall into two categories: partial response equalization with maximum likelihood decoding and decision feedback equalization. This study focuses on doing an adaptive all-pass forward filter for the decision feedback channel. The decision feedback channel can be equalized by a low-order continuous-time filter, and does not require a transversal filter with high-precision multiplication. This results in considerable savings in both power consumption and chip die area. One problem that has yet to be addressed is how to adaptively set the coefficients of the all-pass filter. This thesis examines the design and performance of an adaptive all-pass filter. The performances in terms of the mean-squared error (MSE) of a first- and second-order all-pass are evaluated. They are compared to a conventional FIR filter design of various lengths. An adaptive algorithm based on the least mean-squared (LMS) error is developed and characterized over a range of storage densities. Since this does not require sampling of the filter input or any states of the forward filter, the system could be realized in continuous-time up to the decision device. Numerical simulations for various data densities and noise variances are done to verify the theoretically expected performance and the adaptation behavior of the all-pass. / Graduation date: 1997

The objective of this thesis is to design a high speed digital FIR filter. The inputs of the system come from a Delta-Sigma modulator. This FIR filter takes 1024 inputs, multiplies them with their coefficients and adds the results. The main design task is to take the input data, which are unweighted single-bit binary numbers at 156MHz, multiply each bit with the corresponding coefficient and add them to get a weighted multi-bit output at 20MHz. / Graduation date: 1997

Due to advances in high-density low-cost VLSI and communication technology, digital filtering and signal processing are being widely used for real-time signal processing applications. Given the filter specification, choosing the best filter structure for a given application is not a trivial task. The choice of a particular filter structure depends on many factors such as sensitivity to finite word-length quantization effects, hardware complexity and power consumption. The objective of this thesis is to examine digital IIR (Infinite Impulse Response) filter structures for the VLSI implementation of narrow-band sharp-transition filters. This thesis examines several different digital IIR filter structures; namely cascade form IIR filter, five different digital lattice filters and lattice wave digital filter structures. For fixed-point implementation, the sensitivity, round-off noise properties and the scaling of these filter structures are described and analyzed. These filter structures are compared with respect to the architectural complexity, the sensitivity to coefficient quantization, the round-off noise due to product quantization and the signal dynamic range. Fixed-point implementation simulations using two's-complement arithmetic are carried out for a number of narrow-band sharp-transition digital low-pass filters. / Graduation date: 1995

With the increased demand for complex digital signal processing systems, real-time signal processing requires higher throughput systems. In the past, the throughput has been increased by increasing the clock rates, but synchronization can become increasingly more difficult. Recently there has been renewed interest in designing asynchronous digital systems. In an asynchronous system, there is no global clock, and all modules communicate through handshaking. In this thesis we demonstrate an implementation of an FIR filter using asynchronous digital circuit techniques. These asynchronous design techniques are used to test whether a practical signal processing filter can be implemented with asynchronous logic. A third-order four-bit filter is developed and simulated with SPICE, comparing favorably with other available technologies in speed and power consumption. Although in practice 8-16 bits are needed, this work is sufficient to demonstrate the feasibility of asynchronous circuits for filtering applications. A chip is laid out in 2 micron CMOS, and testing shows that it has a speed-power product comparable with asynchronous designs fabricated by others. / Graduation date: 1994

Thesis (M.S.)--Ohio State University, 1983. / Includes bibliographical references (leaves 91-93). Available online via OhioLINK's ETD Center

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 152-167). Also available in electronic version.

In a document retrieval system where data is stored and compared with a specific query and then compared with other documents, we need to find the document that is most similar to the query. The most similar document will have the weight higher than other documents. When more than one document are proposed to the user, these documents have to be sorted according to their weights. Once the result is presented to the user by a recommender system, the user may check any document of interest. If there are two different documents' lists, as two proposed results presented by different recommender systems, then, there is a need to find which list is more efficient. To do so, the measuring tool "Search Engine Ranking Efficiency Evaluation Tool [SEREET]" came to existence. This tool assesses the efficiency of each documents list and assigns a numerical value to the list. The value will be closer to 100% if the ranking list efficiency is high which means more relevance documents exist in the list and documents are sorted according to their relevance to the user. The value will be closer to 0% when the ranking list efficiency is poor and all of the presented documents are uninteresting documents to the user. A model to evaluate ranking efficiency is proposed in the dissertation, then it is proved it mathematically. Many mechanisms of search engine have been proposed in order to assess the relevance of a web page. They have focused on keyword frequency, page usage, link analysis and various combinations of them. These methods have been tested and used to provide the user with the most interesting web pages, according to his or her preferences. The collaborative filtering is a new approach, which was developed in this dissertation to retrieve the most interesting documents to the user according to his or her interests. Building a user profile is a very important issue in finding the user interest and categorizes each user in a suitable category. This is a requirement in collaborative filtering implementation. The inference tools such as time spent in a web page, mouse movement, page scrolling, mouse clicks and other tools were investigated. Then the dissertation shows that the most efficient and sufficient tool is the time a user spent on a web page. To eliminate errors, the system introduces a low threshold and high threshold for each user. Once the time spent on a web page breaks this threshold, an error is reported. SEREET tool is one of the contributions to the scientific society, which measures the efficiency of a search engine ranking list. Considerable work were carried, then the conclusion was that the amount of time spent on a web page is the most important factor in determining a user interest of a web page and also it is a sufficient tool which does not require collaborations from other tools such as mouse movements or a page scrolling. The results show that implicit rating is a satisfactory measure and can replace explicit rating. New filtering technique was introduced to design a fully functional recommender system. The linear vector algorithm which was introduced improves the vector space algorithm (VSA) in time complexity and efficiency. The use of machine learning enhances the retrieved list efficiency. Machine learning algorithm uses positive and negative examples for the training, these examples are mandatory to improve the error rate of the system. The result shows that the amount of these examples increases proportionally with the error rate of the system.

Several adaptive filters were investigated to enhance speech auditory brainstem responses (speech ABR). The objective was to shorten the long recording time currently needed by the standard coherent averaging method to obtain acceptable performance, which has limited the clinical adoption of speech ABR. Five algorithms were implemented: Wiener Filter (WF), Steepest Descent (SD), Adaptive Noise Cancellation (ANC) based on Least-Mean-Square error (LMS) and normalized LMS error (nLMS), and a multi-adaptive cascade combination of SD and LMS. The performance of the adaptive filters was assessed on speech ABR data gathered from several subjects and compared with coherent averaging using the overall Signal-to-Noise Ratio (SNR), the local SNR around the fundamental frequency and the first formant, and Mean-Square-Error (MSE) in the time and frequency domains. The adaptive filters could reduce the time needed, by at least one order of magnitude, for obtaining comparable signal quality as that obtained with coherent averaging.