Effective Reconfigurable Antenna Designs to Enhance Performance and Enable Wireless Powering

With the increase in traffic on the internet, there is a greater demand for wireless mobile and ubiquitous applications. These applications need antennas that are not only broadband, but can also work in different frequency spectrums. Even though there is a greater demand for such applications, it is still imperative to conserve power. Thus, there is a need to design multi-broadband antennas that do not use a lot of power. Reconfigurable antennas can work in different frequency spectrums as well as conserve power. The current designs of reconfigurable antennas work only in one band. There is a need to design reconfigurable antennas that work in different frequency spectrums. In this current era of high power consumption there is also a greater demand for wireless powering. This dissertation explores ideal designs of reconfigurable antennas that can improve performance and enable wireless powering. This dissertation also presents lab results of the multi-broadband reconfigurable antenna that was created. A detailed mathematical analyses, as well as extensive simulation results are also presented. The novel reconfigurable antenna designs can be extended to Multiple Input Multiple Output (MIMO) environments and military applications.

Development of distributed generation infrastructure for microgrid connectivity to operational power systems

Distributed Generation (DG) from alternate sources and smart grid technologies represent good solutions for the increase in energy demands. Employment of these DG assets requires solutions for the new technical challenges that are accompanied by the integration and interconnection into operational power systems. A DG infrastructure comprised of alternate energy sources in addition to conventional sources, is developed as a test bed. The test bed is operated by synchronizing, wind, photovoltaic, fuel cell, micro generator and energy storage assets, in addition to standard AC generators. Connectivity of these DG assets is tested for viability and for their operational characteristics. The control and communication layers for dynamic operations are developed to improve the connectivity of alternates to the power system. A real time application for the operation of alternate sources in microgrids is developed. Multi agent approach is utilized to improve stability and sequences of actions for black start are implemented. Experiments for control and stability issues related to dynamic operation under load conditions have been conducted and verified.

Physical dynamic simulation of shipboard power system components in a distributed computational environment

Shipboard power systems have different characteristics than the utility power systems. In the Shipboard power system it is crucial that the systems and equipment work at their peak performance levels. One of the most demanding aspects for simulations of the Shipboard Power Systems is to connect the device under test to a real-time simulated dynamic equivalent and in an environment with actual hardware in the Loop (HIL). The real time simulations can be achieved by using multi-distributed modeling concept, in which the global system model is distributed over several processors through a communication link. The advantage of this approach is that it permits the gradual change from pure simulation to actual application. In order to perform system studies in such an environment physical phase variable models of different components of the shipboard power system were developed using operational parameters obtained from finite element (FE) analysis. These models were developed for two types of studies low and high frequency studies. Low frequency studies are used to examine the shipboard power systems behavior under load switching, and faults. High-frequency studies were used to predict abnormal conditions due to overvoltage, and components harmonic behavior. Different experiments were conducted to validate the developed models. The Simulation and experiment results show excellent agreement. The shipboard power systems components behavior under internal faults was investigated using FE analysis. This developed technique is very curial in the Shipboard power systems faults detection due to the lack of comprehensive fault test databases. A wavelet based methodology for feature extraction of the shipboard power systems current signals was developed for harmonic and fault diagnosis studies. This modeling methodology can be utilized to evaluate and predicate the NPS components future behavior in the design stage which will reduce the development cycles, cut overall cost, prevent failures, and test each subsystem exhaustively before integrating it into the system.

On-screen pre-deblurring of digital images using the wavefront aberration function of the human eye to improve computer access for the visually impaired

Traditional Optics has provided ways to compensate some common visual limitations (up to second order visual impairments) through spectacles or contact lenses. Recent developments in wavefront science make it possible to obtain an accurate model of the Point Spread Function (PSF) of the human eye. Through what is known as the "Wavefront Aberration Function" of the human eye, exact knowledge of the optical aberration of the human eye is possible, allowing a mathematical model of the PSF to be obtained. This model could be used to pre-compensate (inverse-filter) the images displayed on computer screens in order to counter the distortion in the user's eye. This project takes advantage of the fact that the wavefront aberration function, commonly expressed as a Zernike polynomial, can be generated from the ophthalmic prescription used to fit spectacles to a person. This allows the pre-compensation, or onscreen deblurring, to be done for various visual impairments, up to second order (commonly known as myopia, hyperopia, or astigmatism). The technique proposed towards that goal and results obtained using a lens, for which the PSF is known, that is introduced into the visual path of subjects without visual impairment will be presented. In addition to substituting the effect of spectacles or contact lenses in correcting the loworder visual limitations of the viewer, the significance of this approach is that it has the potential to address higher-order abnormalities in the eye, currently not correctable by simple means.

Waveguide microgripper for identification, sensing and manipulation

A Waveguide Microgripper utilizes flexible optical waveguides as gripping arms, which provide the physical means for grasping a microobject, while simultaneously enabling light to be delivered and collected. This unique capability allows extensive optical characterization of the structure being held such as transmission, reflection or fluorescence. One of the simplest capabilities of the waveguide microgripper is to be able to detect the presence of a microobject between the microgripper facets by monitoring the transmitted intensity of light coupled through the facets. The intensity of coupled light is expected to drop when there is an object obstructing the path of light. The optical sensing and characterization function of the microgripper is a strong function of the optical power incident on the structure of interest. Hence it is important to understand the factors affecting the power distribution across the facet. The microgripper is also capable of detecting the fluorescence. This capability of microgripper is expected to have applications in medical, bio-medical and related fields.

Analysis and comparison of network performance with different network parameters

The purpose of this study was to analyze the network performance by observing the effect of varying network size and data link rate on one of the most commonly found network configurations. Computer networks have been growing explosively. Networking is used in every aspect of business, including advertising, production, shipping, planning, billing, and accounting. Communication takes place through networks that form the basis of transfer of information. The number and type of components may vary from network to network depending on several factors such as requirement and actual physical placement of the networks. There is no fixed size of the networks and they can be very small consisting of say five to six nodes or very large consisting of over two thousand nodes. The varying network sizes make it very important to study the network performance so as to be able to predict the functioning and the suitability of the network. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected significantly due to the increase in the size of the network and that there exists a correlation between the various parameters and the size of the network. These variations are not only dependent on the magnitude of the change in the actual physical area of the network but also on the data link rate used to connect the various components of the network.

Elastic modulus study of gold thin film for use as an actuated membrane in a superconductive radio frequency (RF) MicroElectroMechanical (MEM) switch

The objective of this research was to find Young's elastic modulus for thin gold films at room and cryogenic temperatures based on the flexional model which has not been previously attempted. Electrical Sonnet simulations and numerical methods using Abacus for the mechanical responses were employed for this purpose. A RF MEM shunt switch was designed and a fabrication process developed in house. The switch is composed of a superconducting YBa2Cu3O7 coplanar waveguide structure with an Au bridge membrane suspended above an area of the center conductor covered with BaTiO3 dielectric. The Au membrane is actuated by the electrostatic attractive force acting between the transmission line and the membrane when voltage is applied. The value of the actuation force will greatly depend on the switch pull-down voltage and on the geometry and mechanical properties of the bridge material. Results show that the elastic modulus for Au thin film can be 484 times higher at cryogenic temperature than it is at room temperature.

Analysis of the lateral diffuse metal-oxide semiconductor (LDMOS) power amplifier for cellular base stations

The primary purpose of this thesis was to present a theoretical large-signal analysis to study the power gain and efficiency of a microwave power amplifier for LS-band communications using software simulation. Power gain, efficiency, reliability, and stability are important characteristics in the power amplifier design process. These characteristics affect advance wireless systems, which require low-cost device amplification without sacrificing system performance. Large-signal modeling and input and output matching components are used for this thesis. Motorola's Electro Thermal LDMOS model is a new transistor model that includes self-heating affects and is capable of small-large signal simulations. It allows for most of the design considerations to be on stability, power gain, bandwidth, and DC requirements. The matching technique allows for the gain to be maximized at a specific target frequency. Calculations and simulations for the microwave power amplifier design were performed using Matlab and Microwave Office respectively. Microwave Office is the simulation software used in this thesis. The study demonstrated that Motorola's Electro Thermal LDMOS transistor in microwave power amplifier design process is a viable solution for common-source amplifier applications in high power base stations. The MET-LDMOS met the stability requirements for the specified frequency range without a stability-improvement model. The power gain of the amplifier circuit was improved through proper microwave matching design using input/output-matching techniques. The gain and efficiency of the amplifier improve approximately 4dB and 7.27% respectively. The gain value is roughly .89 dB higher than the maximum gain specified by the MRF21010 data sheet specifications. This work can lead to efficient modeling and development of high power LDMOS transistor implementations in commercial and industry applications.

Optimal kernel development for real-time communications

The purpose of this research is to develop an optimal kernel which would be used in a real-time engineering and communications system. Since the application is a real-time system, relevant real-time issues are studied in conjunction with kernel related issues. The emphasis of the research is the development of a kernel which would not only adhere to the criteria of a real-time environment, namely determinism and performance, but also provide the flexibility and portability associated with non-real-time environments. The essence of the research is to study how the features found in non-real-time systems could be applied to the real-time system in order to generate an optimal kernel which would provide flexibility and architecture independence while maintaining the performance needed by most of the engineering applications. Traditionally, development of real-time kernels has been done using assembly language. By utilizing the powerful constructs of the C language, a real-time kernel was developed which addressed the goals of flexibility and portability while still meeting the real-time criteria. The implementation of the kernel is carried out using the powerful 68010/20/30/40 microprocessor based systems.

Cascade artificial neural networks technique for solving ellipsometry problems

Ellipsometry is a well known optical technique used for the characterization of reflective surfaces in study and films between two media. It is based on measuring the change in the state of polarization that occurs as a beam of polarized light is reflected from or transmitted through the film. Measuring this change can be used to calculate parameters of a single layer film such as the thickness and the refractive index. However, extracting these parameters of interest requires significant numerical processing due to the noninvertible equations. Typically, this is done using least squares solving methods which are slow and adversely affected by local minima in the solvable surface. This thesis describes the development and implementation of a new technique using only Artificial Neural Networks (ANN) to calculate thin film parameters. The new method offers a speed in the orders of magnitude faster than preceding methods and convergence to local minima is completely eliminated.

Determining polarization dependent diffraction coefficients from building edges using FDTD method

Electromagnetic waves in suburban environment encounter multiple obstructions that shadow the signal. These waves are scattered and random in polarization. They take multiple paths that add as vectors at the portable device. Buildings have vertical and horizontal edges. Diffraction from edges has polarization dependent characteristics. In practical case, a signal transmitted from a vertically polarized high antenna will result in a significant fraction of total power in the horizontal polarization at the street level. Signal reception can be improved whenever there is a probability of receiving the signal in at least two independent ways or branches. The Finite-Difference Time-Domain (FDTD) method was applied to obtain the two and three-dimensional dyadic diffraction coefficients (soft and hard) of right-angle perfect electric conductor (PEC) wedges illuminated by a plane wave. The FDTD results were in good agreement with the asymptotic solutions obtained using Uniform Theory of Diffraction (UTD). Further, a material wedge replaced the PEC wedge and the dyadic diffraction coefficient for the same was obtained.

User Effects on Wearable Conformal SCMR System

The performance of a compact, wearable Conformal Strongly Coupled Magnetic Resonance (CSCMR) system is studied when the antenna is in the air and is worn on a user’s arm. The wireless powering system consists of the receiver and load elements designed on a printed circuit board that is attached to a polyester fabric band. The wearable antenna achieves high efficiency, has a small volume, and can be easily printed on substrates. Although the user effect on mobile terminal antennas has been studied in detail, absorption losses in wearable antennas have not been widely investigated. Our results show that efficiency of the antenna in free space is 70% and on a user’s arm is 50%. Human tissue in the close proximity of our wearable Conformal SCMR caused a decrease in radiated efficiency and total efficiency. This undesired degradation in antenna efficiency might be attributed to body loss and absorption losses. Our findings can be used as a reference for future studies on wearable devices and their applications, such as health and sports monitoring.

Testing of Interposer-Based 2.5D Integrated Circuits

The unprecedented and relentless growth in the electronics industry is feeding the demand for integrated circuits (ICs) with increasing functionality and performance at minimum cost and power consumption. As predicted by Moore's law, ICs are being aggressively scaled to meet this demand. While the continuous scaling of process technology is reducing gate delays, the performance of ICs is being increasingly dominated by interconnect delays. In an effort to improve submicrometer interconnect performance, to increase packing density, and to reduce chip area and power consumption, the semiconductor industry is focusing on three-dimensional (3D) integration. However, volume production and commercial exploitation of 3D integration are not feasible yet due to significant technical hurdles.

At the present time, interposer-based 2.5D integration is emerging as a precursor to stacked 3D integration. All the dies and the interposer in a 2.5D IC must be adequately tested for product qualification. However, since the structure of 2.5D ICs is different from the traditional 2D ICs, new challenges have emerged: (1) pre-bond interposer testing, (2) lack of test access, (3) limited ability for at-speed testing, (4) high density I/O ports and interconnects, (5) reduced number of test pins, and (6) high power consumption. This research targets the above challenges and effective solutions have been developed to test both dies and the interposer.

The dissertation first introduces the basic concepts of 3D ICs and 2.5D ICs. Prior work on testing of 2.5D ICs is studied. An efficient method is presented to locate defects in a passive interposer before stacking. The proposed test architecture uses e-fuses that can be programmed to connect or disconnect functional paths inside the interposer. The concept of a die footprint is utilized for interconnect testing, and the overall assembly and test flow is described. Moreover, the concept of weighted critical area is defined and utilized to reduce test time. In order to fully determine the location of each e-fuse and the order of functional interconnects in a test path, we also present a test-path design algorithm. The proposed algorithm can generate all test paths for interconnect testing.

In order to test for opens, shorts, and interconnect delay defects in the interposer, a test architecture is proposed that is fully compatible with the IEEE 1149.1 standard and relies on an enhancement of the standard test access port (TAP) controller. To reduce test cost, a test-path design and scheduling technique is also presented that minimizes a composite cost function based on test time and the design-for-test (DfT) overhead in terms of additional through silicon vias (TSVs) and micro-bumps needed for test access. The locations of the dies on the interposer are taken into consideration in order to determine the order of dies in a test path.

To address the scenario of high density of I/O ports and interconnects, an efficient built-in self-test (BIST) technique is presented that targets the dies and the interposer interconnects. The proposed BIST architecture can be enabled by the standard TAP controller in the IEEE 1149.1 standard. The area overhead introduced by this BIST architecture is negligible; it includes two simple BIST controllers, a linear-feedback-shift-register (LFSR), a multiple-input-signature-register (MISR), and some extensions to the boundary-scan cells in the dies on the interposer. With these extensions, all boundary-scan cells can be used for self-configuration and self-diagnosis during interconnect testing. To reduce the overall test cost, a test scheduling and optimization technique under power constraints is described.

In order to accomplish testing with a small number test pins, the dissertation presents two efficient ExTest scheduling strategies that implements interconnect testing between tiles inside an system on chip (SoC) die on the interposer while satisfying the practical constraint that the number of required test pins cannot exceed the number of available pins at the chip level. The tiles in the SoC are divided into groups based on the manner in which they are interconnected. In order to minimize the test time, two optimization solutions are introduced. The first solution minimizes the number of input test pins, and the second solution minimizes the number output test pins. In addition, two subgroup configuration methods are further proposed to generate subgroups inside each test group.

Finally, the dissertation presents a programmable method for shift-clock stagger assignment to reduce power supply noise during SoC die testing in 2.5D ICs. An SoC die in the 2.5D IC is typically composed of several blocks and two neighboring blocks that share the same power rails should not be toggled at the same time during shift. Therefore, the proposed programmable method does not assign the same stagger value to neighboring blocks. The positions of all blocks are first analyzed and the shared boundary length between blocks is then calculated. Based on the position relationships between the blocks, a mathematical model is presented to derive optimal result for small-to-medium sized problems. For larger designs, a heuristic algorithm is proposed and evaluated.

In summary, the dissertation targets important design and optimization problems related to testing of interposer-based 2.5D ICs. The proposed research has led to theoretical insights, experiment results, and a set of test and design-for-test methods to make testing effective and feasible from a cost perspective.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Delay-Sensitive Service Request Scheduling for Cloud Computing

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.