A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item HIERARCHICAL MAPPING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS ON PARALLEL PLATFORMS(2014) Wang, Lai-Huei; Bhattacharyya, Shuvra S.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Dataflow models are widely used for expressing the functionality of digital signal processing (DSP) applications due to their useful features, such as providing formal mechanisms for description of application functionality, imposing minimal data-dependency constraints in specifications, and exposing task and data level parallelism effectively. Due to the increased complexity of dynamics in modern DSP applications, dataflow-based design methodologies require significant enhancements in modeling and scheduling techniques to provide for efficient and flexible handling of dynamic behavior. To address this problem, in this thesis, we propose an innovative framework for mode- and dynamic-parameter-based modeling and scheduling. We apply, in a systematically integrated way, the structured mode-based dataflow modeling capability of dynamic behavior together with the features of dynamic parameter reconfiguration and quasi-static scheduling. Moreover, in our proposed framework, we present a new design method called parameterized multidimensional design hierarchy mapping (PMDHM), which is targeted to the flexible, multi-level reconfigurability, and intensive real-time processing requirements of emerging dynamic DSP systems. The proposed approach allows designers to systematically represent and transform multi-level specifications of signal processing applications from a common, dataflow-based application-level model. In addition, we propose a new technique for mapping optimization that helps designers derive efficient, platform-specific parameters for application-to-architecture mapping. These parameters help to maximize system performance on state-of-the-art parallel platforms for embedded signal processing. To further enhance the scalability of our design representations and implementation techniques, we present a formal method for analysis and mapping of parameterized DSP flowgraph structures, called topological patterns, into efficient implementations. The approach handles an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for implementation. We demonstrate our methods with case studies in the fields of wireless communication and computer vision. Experimental results from these case studies show that our approaches can be used to derive optimized implementations on parallel platforms, and enhance trade-off analysis during design space exploration. Furthermore, their basis in formal modeling and analysis techniques promotes the applicability of our proposed approaches to diverse signal processing applications and architectures.Item INTEGRATION AND CHARACTERIZATION OF TOBACCO MOSAIC VIRUS BASED NANOSTRUCTURED MATERIALS IN THREE-DIMENSIONAL MICROBATTERY ARCHITECTURES(2012) Gerasopoulos, Konstantinos; Ghodssi, Reza; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The realization of next-generation portable electronics, medical implants and miniaturized, autonomous microsystems is directly linked with the development of compact and efficient power sources and energy storage devices with high energy and power density. As the components of these devices are continuously scaled down in size, there is a growing demand for decreasing the size of their power supply as well, while maintaining performance comparable to larger assemblies. This dissertation presents a novel approach for the development of microbattery electrodes that is based on integrating both micro and nano structured components for the formation of hierarchical electrodes. These electrodes combine both high energy density (enabled by the high surface area and mass loading) with high power density (due to the small thickness of the active battery materials). The key building block technologies in this work are the bottom-up self-assembly and metallization of a biological template and the top-down microfabrication processes enabled by Microelectromechanical Systems (MEMS) technology. The biotemplate used is the Tobacco mosaic virus (TMV), a rod-like particle that can be genetically modified to express functional groups with enhanced metal binding properties. In this project, this feature is combined with standard microfabrication techniques for the synthesis of nanostructured energy-related materials as well as their hierarchical patterning in device architectures. Specifically, synthesis of anode (TiO2) and cathode (V2O5) materials for Li-ion batteries in a core/shell configuration is presented, where the TMV biomineralization is combined with atomic layer deposition of the active material. These nanostructured electrodes demonstrate high energy storage capacities, high rate capabilities and superior performance to electrodes with planar geometries. In addition, a toolbox of biofabrication processes for the defined patterning of virus-templated structures has been developed. Finally, the nanocomposite electrodes are integrated with three-dimensional micropillars to form hierarchical electrodes that maintain the high rate performance capabilities of nanomaterials while exhibiting an increase in energy density compared to nanostructures alone. This is in accordance with the increase in surface area added by the microstructures. Investigation of capacity scaling for varying active material thickness reveals underlying limitations in nanostructured electrodes and highlights the importance of this method in controlling both energy and power density with structural hierarchy. These results present a paradigm-shifting technology for the fabrication of next-generation microbatteries for MEMS and microsystems applications.