In this dissertation, a hybrid volume and surface integral equation is used to solve scattering problems. It is implemented with RWG basis on the surface and the edge basis in the volume. Numerical results shows the correctness of the hybrid VSIE in inhomogeneous medium. The MLFMM method is also implemented for the new VSIEs. Further more, a synthetic apature radar imaging method is used in a 2D microwave imaging for complex objects. With the mono-static and bi-static interpolation scheme, a 2D FFT is applied for the imaging with the data simulated with VSIE method. Then we apply a background cancelling scheme to improve the imaging quality for the targets in interest. Numerical results shows the feasibility of applying the background canceling into wider applications.

This dissertation presents and analyzes two new algorithms for sparse direct solution methods based on the use of local-global solution (LOGOS) modes. One of the new algorithms is a rigorous error control strategy for LOGOS-based matrix factorizations that utilize overlapped, localizing modes (OL-LOGOS) on a shifted grid. The use of OL-LOGOS modes is critical to obtaining asymptotically efficient factorizations from LOGOS-based methods. Unfortunately, the approach also introduces a non-orthogonal basis function structure. This can cause errors to accumulate across levels of a multilevel implementation, which has previously posed a barrier to rigorous error control for the OL-LOGOS factorization method. This limitation is overcome, and it is shown that it is possible to efficiently decouple the fundamentally non-orthogonal factorization subspaces in a manner that prevents multilevel error propagation. This renders the OL-LOGOS factorization error controllable in a relative RMS sense. The impact of the new, error-controlled OL-LOGOS factorization algorithm on computational resource utilization is discussed and several numerical examples are presented to illustrate the performance of the improved algorithm relative to previously reported results. The second algorithmic development considered is the development of efficient out-of-core (OOC) versions of the OL-LOGOS factorization algorithm that allow associated software tools to take advantage of additional resources for memory management. The proposed OOC algorithm incorporates a memory page definition that is tailored to match the flow of the OL-LOGOS factorization procedure. Efficiency of the function of the part is evaluated using a quantitative approach, because the tested massive storage device performances do not follow analytical results. The performance latency and the memory usage of the resulting OOC tools are compared with in-core performance results. Both the new error control algorithm and the OOC method have been incorporated into previously existing software tools, and the dissertation presents results for real-world simulation problems.

A theoretical model is developed for calculating the sensitivity of resistive sensors based on aligned multiwall carbon nanotubes (MWCNT) embedded in the pores of alumina membranes. Aligned MWCNTs offer more surface area as each CNT acts as a landing site for detecting gas species. The MWCNTs behave as a p-type semiconducting layer; when the bus bar contacts are placed at either end of the top surface the resistance between the contacts responds to oxidizing (resistance decreases) and reducing gases (resistance increases). The model presented in this thesis aims to understand the device resistance dependence upon the MWCNT resistance, and the sensitivity dependence upon the device structure and design. The model was utilized for enhancing the sensitivity of MWCNT sensors for ammonia (30% sensitivity) and nitrogen dioxide (40% sensitivity) gases. Experimental results from sensitivity measurements are compared with theoretical predictions.

In decades, the tremendous development of integrated circuits industry could be mostly attributed to SiO2, since its satisfactory properties as a gate dielectric candidate. The effectivity of SiO2 has been challenged since dielectric layer was scaled down below 3nm, when the gate leakage current of SiO2 became unacceptable. Institution to silicon-based CMOS techniques were proposed, but they have their own limitations. Nowadays, materials with high dielectric constants are mainstream gate dielectric materials in industry, but a SiO2 interfacial layer is still necessary to avoid gap between gate dielectric layer and Si substrate, and to minimize interface trap charges. In this thesis work, by applying lateral heating process on Si wafer with thermally grown ultrathin SiO2, the gate leakage current density could be reduced by 3-5 order of magnitude. MOS capacitors were fabricated, and electrical properties were tested with semiconductor parameter analyzer and LCR meter. The underlying mechanism of this appealing phenomenon was explored. Since unacceptable gate leakage current is one of the main reasons which prevent the scaling trend in semiconductor industry, this technology brings a possibility to post-pone the end of scaling trend, and pave a way for extensive application in industry. A new method for fabrication of MOS capacitors metal gate has been developed, and lift-off process has been replaced by wet etching process. This method provides better contact between dielectric layer and metal gate, meanwhile much easier operation.

The development of nanoscale patterns has a vast variety of applications going from biology to solid state devices. In this research we present a new direct patterning technique in which laser photoreduction of silver from a liquid is controlled by a scanning atomic force microscope tip. While pursuing the formation of patterns using the plasmonic field enhancement of an electromagnetic wave incident on a metallic Atomic Force Microscope (AFM) tip, our group discovered that contrary to expectations, the tip suppresses, rather than enhances, deposition on the underlying substrate, and this suppression persists in the absence of the tip. Experiments presented here exclude three potential mechanisms: purely mechanical material removal, depletion of the silver precursor, and preferential photoreduction on existing deposits. An example of a nano-scaled pattern was generated to show the possibilities of this work. These results represent a first step toward direct, negative tone, tip-based patterning of functional materials.

Designing hardware to output pixels for light field displays or multi-projector systems is challenging owing to the memory bandwidth and speed of the application. A new technique of hardware that implements ‗anywhere pixel routing‘ was designed earlier at the University of Kentucky. This technique uses hardware to route pixels from input to output based upon a Look up Table (LUT). The initial design suffered from high memory latency due to random accesses to the DDR SDRAM input buffer. This thesis presents a cache design that alleviates the memory latency issue by reducing the number of random SDRAM accesses. The cache is implemented in the block RAM of a field programmable gate array (FPGA). A number of simulations are conducted to find an efficient cache. It is found that the cache takes only a few kilobits, about 7% of the block RAM and on an average speeds up the memory accesses by 20-30%.

Smart grid technologies are starting to be the future of electric power systems. These systems are giving the utilities detailed information about their systems in real time. One of the most challenging things of implementing smart grid applications is employing the communications into the systems. Understanding the available communications can help ease the transition to these smart grid applications. Many of the utility personnel are spending too much time trying to figure out which communication is better for their application or applications. So this thesis presents the different communication types available with discussing the different attributes in which these communication types are going to offer to the utility. Then these communication types are looked such that utilities can quickly understand how to approach the difficult task of obtaining the information from the different smart grid applications by the use of different communication options.

Accurate methods for glottal feature extraction include the use of high-speed video imaging (HSVI). There have been previous attempts to extract these features with the acoustic recording. However, none of these methods compare their results with an objective method, such as HSVI. This thesis tests these acoustic methods against a large diverse population of 46 subjects. Two previously studied acoustic methods, as well as one introduced in this thesis, were compared against two video methods, area and displacement for open quotient (OQ) estimation. The area comparison proved to be somewhat ambiguous and challenging due to thresholding effects. The displacement comparison, which is based on glottal edge tracking, proved to be a more robust comparison method than the area. The first acoustic methods OQ estimate had a relatively small average error of 8.90% and the second method had a relatively large average error of -59.05% compared to the displacement OQ. The newly proposed method had a relatively small error of -13.75% when compared to the displacements OQ. There was some success even though there was relatively high error with the acoustic methods, however, they may be utilized to augment the features collected by HSVI for a more accurate glottal feature estimation.

Binary Sensor Networks are widely used in target tracking and target parameter estimation. It is more computationally and financially efficient than surveillance camera systems. According to the sensing area, binary sensors are divided into disk shaped sensors and line segmented sensors. Different mathematical methods of target trajectory estimation and characterization are applied. In this thesis, we present a mathematical model of target tracking including parameter estimation (size, intrusion velocity, trajectory, etc.) with line segmented sensor networks. Software simulation and hardware experiments are built based on the model. And we further analyze how the quantization noise affects the results.

Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.