In this thesis, a simple on-chip automatic frequency tuning circuit is presented. The tuning circuit is improved from voltage-controlled filter (VCF) frequency tuning circuit. We use a single time constant (STC) circuit to substitute the voltage-controlled filter. The STC circuit can produce a controllable delay time clock. The tuning circuit uses the constant delay time to tune the frequency of the filter. The design of a STC circuit is easy. Because the circuit is simple, the tuning circuit has less chip area and less power consumption. The circuit has been fabricated with 0.35£gm CMOS technology. It operates with supply voltages ¡Ó1.5 V. The filter operates at a 3-dB frequency of 10MHz. In simulation, the frequency tuning circuit has a 3-dB frequency tuning error of less than 12% and the power consumption less than 9.05mW over a range of supply voltages (¡Ó10%), operating temperatures (-20¢J to 70¢J) and five models of SPICE model.

Developing 3D application for low-performance embedded system often contains some limitations as hardware specifications (e.g. memory and processing efficiency). Existing OpenGL ES emulators are designed to provide the development environment for programmers, but these emulators often are lack of cross-platform performance tuning analysis for embedded systems and are only suitable for a designated hardware. In this thesis, we present an OpenGL ES emulator with performance tuning for developing 3D application of embedded systems without conforming to a specific hardware. It can further help programmers to emulate 3D application on PC for different development platforms.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on December 22, 2009). Thesis advisor: Yuyi Lin. Includes bibliographical references.

Thesis (M.A.)--York University, 2000. Graduate Programme in Music. / Typescript. Includes bibliographical references (leaves 108-111). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ59201.

Autonomic Computing is an approach to designing systems that are capable of self-management. Fundamental to the autonomic ideal is a software's awareness of and ability to tune parameters that affect metrics like performance and security. Traditionally, these parameters are tuned by human experts with extensive knowledge of parameter names and effects---existing software was not designed to be self-tuning. Efforts to automate the isolation and tuning of parameters have yielded encouraging results. However, the parameters are identified manually. This thesis proposes the adaptation of reverse engineering techniques for automating the recovery of software tuning parameters. Tuning parameters from several industrially relevant applications are studied for patterns of use. These patterns are used to classify the parameters into a taxonomy, and to develop a metamodel of the source code elements and relationships needed to express them. An extractor is then built to obtain instances of the relationships from source code. The relationships are represented as graphs, which are manipulated and queried for instances of tuning parameter patterns. The recovery is implemented as a tool for finding tuning parameters in applications. Experimental results show that the approach is effective at recovering documented tuning parameters, as well as other undocumented ones. The results also indicate that the tuning parameter patterns are not specific to a particular application, or application domain. / Thesis (Master, Computing) -- Queen's University, 2008-06-28 19:36:43.291

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Prompted by the desire to increase the industrial applicability range of self-tuning control, the objective of this work has been to extend the standard linear self-tuning framework to facilitate the design of self-tuning controllers for bilinear systems. Bilinear systems form a well structured class of non-linear systems within which linear systems coexist as a special subclass. They are, therefore, appropriate for modelling a wider range of processes and plant than the restrictive, yet convenient, linear model structures since such models are valid both within the linear subregion and beyond. In addition to extending the self-tuning framework for bilinear systems another significant contribution of the Thesis is the introduction of a cautious least squares estimation procedure which also enhances the existing linear self-tuning schemes.

Over the last decade, considerable effort has been devoted to the implementation and analysis of self-tuning controllers on systems which are assumed to be represented exactly by linear dynamical models. In this thesis we examine the robustness of the self-tuning controller, when applied to systems consisting of a nominal linear plant which may have linear or nonlinear perturbations. Robust stability is the primary criterion and most of the results are for the Clarke-Gawthrop version of the self-tuning controller. Conditions are derived for the robust stability of the adaptively controlled system in terms of the design choices available to the engineer setting up the self-tuning controller. These are strong stability results in that they are in terms of both 1₂ and 1_∞ stability. The results are shown to be applicable to the general delay case and in the presence of non-zero mean disturbances. Preliminary results are also obtained for the robust stability of the explicit self-tuning controller.

The MapReduce programming model has become widely adopted for large scale analytics on big data. MapReduce systems such as Hadoop have many tuning parameters, many of which have a significant impact on performance. The map and reduce functions that make up a MapReduce job are developed using arbitrary programming constructs, which makes them black-box in nature and prevents users from making good parameter tuning decisions for a submitted MapReduce job. Some research projects, such as the Starfish system, aim to provide automatic tuning decisions for input MapReduce jobs. Starfish and similar systems rely on an execution profile of a MapReduce job being tuned, and this profile is assumed to come from a previous execution of the same job. Managing these execution profiles has not been previously studied. This thesis presents PStorM, a profile store that organizes the collected profiling information in a scalable and extensible data model, and a profile matcher that accurately picks the relevant profiling information even for previously unseen MapReduce jobs. PStorM is currently integrated with the Starfish system, providing the necessary profiles that Starfish needs to tune a job. The thesis presents results that demonstrate the accuracy and efficiency of profile matching. The results also show that the profiles returned by PStorM lead to Starfish tuning decisions that are as good as the decisions made by profiles collected from a previous run of the job.

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