In this work, we demonstrate an InGaAsP multiple quantum well tunable laser diode that amalgamates two gain sections with different bandgap energies. This is achieved using selective area intermixing of the multiple quantum wells, and impurity-free vacancy induced disordering. When different current combination is injected into each section, that leads to a laser wavelength peak whose position depends on the relative magnitudes of the two injected currents. The laser wavelength can be fine-tuned from 1538 nm to 1578 nm with relatively constant output power. The free spectral range FSR of the tunable laser found to be 0.25 nm. This tunable laser was launched into an optical surface plasmon resonance sensor head to provide an input light source for the SPR sensor. Using the tunable laser diode, we have demonstrated an optical surface plasmon resonance sensor head that is based on an inverted rib dielectric waveguide, in which the resonance wavelength of the surface plasmon excited at the gold metal-dielectric interface depends on the refractive index of the liquid in contact with it. The inverted-rib waveguide of the SPR sensor head is made of a layer of SU-8 polymer with a refractive index of 1.568. While the lower cladding layer consists of silicon oxynitride (SiOxNy) with a refractive index of 1.526. The top surface is coated with 20 nm of chromium followed by a 50 nm thick layer of gold or with 4 nm of titanium followed by a 25 nm thick layer of gold. The SPR sensor head was designed, to allow monitoring of analyte media with a refractive index, ranging from 1.43 to the 1.52. Using a set of reference liquids representing the analyte medium, the sensitivity of the SPR sensor was measured using the fabricated tunable laser, an optical spectrum analyzer, and a photodiode. It was found that with various calibrated sample liquids in contact with the gold metal, a sharp resonance dip in the transmission spectrum occurred, and its position shifted to a shorter wavelength when the refractive index of the sample liquids was increased. The average sensitivity of the SPR sensor devices was determined to be S = 334 nm/RIU.

Electrostatic Discharge (ESD) caused failures are major reliability issues in IC industry. Device modeling for ESD conditions is necessary to evaluate ESD robustness in simulation. Although SPICE model is accurate and efficient for circuit simulations in most cases, devices under ESD conditions operate in abnormal status. SPICE model cannot cover the device operating region beyond normal operation. Thermal failure is one of the main reasons to cause device failure under ESD conditions. A compact model is developed to predict thermal failure with circuit simulators. Instead of considering the detailed failure mechanisms, a failure temperature is introduced to indicate device failure. The developed model is implemented by a multiple-stage thermal network. P-N junction is the fundamental structure for ESD protection devices. An enhanced diode model is proposed and is used to simulate the device behaviors for ESD events. The model includes all physical effects for ESD conditions, which are voltage overshoot, self-heating effect, velocity saturation and thermal failure. The proposed model not only can fit the I-V and transient characteristics, but also can predict failure for different pulses. Safe Operating Area (SOA) is an important factor to evaluate the LDMOS performance. The transient SOA boundary is considered as power-defined. By placing the failure monitor under certain conditions, the developed modeling methodology can predict the boundary of transient SOA for any short pulse stress conditions. No matter failure happens before or after snapback phenomenon. Weibull distribution is popular to evaluate the dielectric lifetime for CVS. By using the transformative version of power law, the pulsing stresses are converted into CVS, and TDDB under ESD conditions for SiN MIMCAPs is analyzed. The thickness dependency and area independency of capacitor breakdown voltage is observed, which can be explained by the constant ?E model instead of conventional percolation model.

Reconfigurable reflectarrays are a class of antennas that combine the advantages of traditional parabolic antennas and phased array antennas. Chapter 1 discusses the basic operational theory of reflectarrays and their design. A review of previous research and the current status is also presented. Furthermore the inherent advantages and disadvantages of the reflectarray topography are presented. In chapter 2, a BST-integrated reflectarray operating at Ka band is presented. Due to the monolithic integration of the tuning element, this design is then extended to V band where a novel interdigital gap configuration is utilized. Finally to overcome loss and phase limitations of the single resonant design, a BST-integrated, dual-resonance unit cell operating at Ka band is designed. While the losses are still high, a 360° phase range is demonstrated. In chapter 3, the operational theory of dual-resonant array elements is introduced utilizing Q theory. An equivalent circuit is developed and used to demonstrate design tradeoffs. Using this theory the design procedure of a varactor tuned dual-resonant unit cell operating at X-band is presented. Detailed analysis of the design is performed by full-wave simulations and verified via measurements. In chapter 4, the array performance of the dual-resonance unit cell is analyzed. The effects of varying angles of incidence on the array element are studied using Floquet simulations. The beam scanning, cross-polarization and bandwidth performance of a 7 x 7 element reflectarray is analyzed using full-wave simulations and verified via measurements. In chapter 5 a loss analysis of the dual-resonant reflectarray element is performed. Major sources of loss are identified utilizing full-wave simulations before an equivalent circuit is utilized to optimize the loss performance while maintaining a full phase range and improved bandwidth performance. Finally the dual-resonance unit cell is modified to support two linear polarizations. Overall, the operational and design theory of dual resonant reflectarray unit cells using Q theory is developed. A valuable equivalent circuit is developed and used to aid in array element design as well as optimize the loss and bandwidth performance. The proposed theoretical models provide valuable physical insight through the use of Q theory to greatly aid in reflectarray design.

Simultaneous Transmit and Receive (STAR) communication is being developed as a means of improving spectral efficiency in wireless communication systems. If the obstacle of self-interference can be sufficiently overcome, it is possible to double the spectral efficiency of an equivalent time or frequency division duplexed system. Spread spectrum techniques can reduce self-interference by using orthogonal or pseudo-orthogonal codes to encode the transmit signal and decode the receive signal. Hardware correlator filters are developed for use with STAR radio systems using orthogonal frequency coded (OFC) surface acoustic wave (SAW) devices. OFC is a type of spread spectrum communication that can be implemented using SAW transducers to create a correlator filter, also known as a matched filter. OFC allows code division multiple access and processing gain, similar to other spread spectrum techniques, but is more well-suited to low loss inline SAW design due to the use of multiple orthogonal carriers. The development of low loss fixed code OFC SAW correlator filters is documented, including design criteria and multiple approaches that progressively reduce insertion loss. Using the results from progressive designs and experiments, a pair of correlator filters with matched codes are presented with approximately 6 dB insertion loss at 950 MHz. A second development focusing on OFC SAW correlator filters with programmable codes using RF switches is also described. The programmable correlators use a fixed OFC code with programmable binary phase shift keying (BPSK), and demonstrate positive results. The programmable correlators presented require less than 1 mW of DC power.

DC-DC power converters are widely used in renewable energy-based power generation systems due to the constant demand of high-power density and high-power conversion efficiency. DC-DC converters can be classified into non-isolated and isolated topologies. For non-isolated topologies, they are typically derived from buck, boost, buck-boost or forth order (such as Cuk, Sepic and Zeta) converters and they usually have relatively higher conversion efficiency than isolated topologies. However, with the applications where the isolation is required, either these topologies should be modified, or alternative topologies are needed. Among various isolated DC-DC converters, the LLC resonant converter is an attractive selection due to its soft switching, isolation, wide gain range, high reliability, high power density and high conversion efficiency. In low power applications, such as battery chargers and solar microinverters, increasing the switching frequency can reduce the size of passive components and reduce the current ripple and root-mean-square (RMS) current, resulting in higher power density and lower conduction loss. However, switching losses, gate driving loss and electromagnetic interference (EMI) may increase as a consequence of higher switching frequency. Therefore, switching frequency modulation, components optimization and soft switching techniques have been proposed to overcome these issues and achieve a tradeoff to reach the maximum conversion efficiency. This dissertation can be divided into two categories: the first part is focusing on the well-known non-isolated bidirectional cascaded-buck-boost converter, and the second part is concentrating on the isolated dual-input single resonant tank LLC converter. Several optimization approaches have been presented to improve the efficiency, power density and reliability of the power converters. In the first part, an adaptive switching frequency modulation technique has been proposed based on the precise loss model in this dissertation to increase the efficiency of the cascaded-buck-boost converter. In adaptive switching frequency modulation technique, the optimal switching frequency for the cascaded-buck-boost converter is adaptively selected to achieve the minimum total power loss. In addition, due to the major power losses coming from the inductor, a new low profile nanocrystalline inductor filled with copper foil has been designed to significantly reduce the core loss and winding loss. To further improve the efficiency of the cascaded-buck-boost converter, the adaptive switching frequency modulation technique has been applied on the converter with designed nanocrystalline inductor, in which the peak efficiency of the converter can break the 99% bottleneck. In the second part, a novel dual-input DC-DC converter is developed according to the LLC resonant topology. This design concept minimizes the circuit components by allowing single resonant tank to interface with multiple input sources. Based on different applications, the circuit configuration for the dual-input LLC converter will be a little different. In order to improve the efficiency of the dual-input LLC converter, the semi-active rectifiers have been used on the transformer secondary side to replace the low-side bridge diodes. In this case, higher magnetizing inductance can be selected while maintaining the same voltage gain. Besides, a burst-mode control strategy has been proposed to improve the light load and very light load efficiency of the dual- input LLC converter. This control strategy is able to be readily implemented on any power converter since it can be achieved directly through firmware and no circuit modification is needed in implementation of this strategy.

Bacterial presence in low-moisture foods such as flour, cereals, baby formula, and spices, have become a concern due to sanitizing challenges. The food industry currently focuses on wet food sanitation as opposed to low-moisture foods because of bacteria’s inability to reproduce in low water activity media. Traditionally, food processing RF heating pasteurizes in mass quantities while an equivalent consumer device does not exist the market today. A consumer product would help eliminate food waste by providing an easy way to sanitize food and extend shelf life. The Portable Food Pasteurization (PFP) project is an interdisciplinary project involving the Electrical Engineering, Biology, and Food Science departments to develop an RF heating consumer device to pasteurize low-moisture foods. A prototype device was designed but construction was delayed because of the COVID-19 pandemic. We are continuing this project by replacing the previously designed MOSFET inverter with a class C amplifier due to parts availability and performance. The food chamber is redesigned by Jonathan Souza to incorporate parallel plate electrodes for more uniform heating without risk of burning. Tim Erwin improved the flyback converter with a snubber and discharge circuit. Tradeoff analysis is performed on various system components to define a configuration for future development.

<p>Virtually all satellite repeaters use a traveling-wave tube (TWT) as their main power amplifier. Because on-board power is a limited commodity, it is highly desirable that the TWT be operated as efficiently as possible, namely in or near saturation where it is highly nonlinear. These nonlinear effects manifest themselves as an amplitude compression (AM/AM conversion) effect and an amplitude dependent phase modulation (AM/PM conversion) effect. In this thesis a number of investigations have been made in relation to the TWT nonlinearities and their effect on the performance of communication systems.</p> <p>A novel quadrature model of the TWT has been developed. This model is most useful in that it is analytic and requires the choice of only four parameters to obtain an excellent fit to the TWT characteristics.</p> <p>An optimal bandpass nonlinear transfer characteristic that maximizes its output signal to interference power ratio has also been derived. By making use of this optimal transfer characteristic and the quadrature model of the tube, a computer-aided design procedure has been described for obtaining a predistortion compensation network for the TWT. This network consists of a simple arrangement of attenuators and power-law devices and has been shown, by computer simulation, to yield about 1 dB improvement in system performance for the case when only a single carrier is present in the TWT.</p> <p>In the case when a single sample detection and majority logic decision circuit is assumed at the receiver, it has been possible to derive analytical expression for the probability of error of M-ary CPSK signals transmitted through a piecewise-linear envelope limiting repeater. An infinite series expression for the bit error rate of binary CPSK transmission through an actual TWT channel has also been derived.</p> <p>A performance analysis of a correlation receiver with a linear integrate and dump circuit has been carried out for the case of binary CPSK transmission through a bandpass nonlinearity exhibiting AM/PM conversion.</p> <p>For the case of purely amplitude-limiting channels, an optimal (maximum-likelihood) receiver structure and its approximate performance has also been investigated.</p> / Thesis / Doctor of Philosophy (PhD)

<p>The optimal linear block transform for coding images is well known to be the Karhunen-Loève transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. A new approach to data representation, a mixture of principal components (MPC), is developed in this thesis. It combines advantages of both principal components analysis and vector quantization and is therefore well suited to the problem of compressing images. The author proposes a number of new transform coding methods which optimally adapt to such local differences based on neural network methods using the MPC representation. The new networks are modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by an optimal classifier. The performance of the resulting adaptive networks is shown to be superior to that of the optimal nonadaptive linear transformation, both in terms of rate-distortion and computational complexity. When applied to the problem of compressing digital chest radiographs, compression ratios of between 30:1 and 40:1 are possible without any significant loss in image quality. In addition, the quality of the images were consistently judged to be as good as or better than the KLT at equivalent compression ratios.</p> <p>The new networks can also be used as segmentors with the resulting segmentation being independent of variations in illumination. In addition, the organization of the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex.</p> / Thesis / Doctor of Philosophy (PhD)

A conventional wireless sensor networks (WSN), consisting of sensors powered by nonrechargeable batteries, has a strictly limited lifetime. Energy harvesting (EH) from the environment is a promising solution to address the energy constraint problem in conventional WSNs, and to render these networks to self-sustainable networks with perpetual lifetimes. In EH-powered WSNs, where sensors are capable of harvesting and storing energy, power control is necessary to balance the rates of energy harvesting and energy consumption for data transmission. In addition, wireless communication channels change randomly in time due to fading. These together prompt the need for developing new power control strategies for an EH-enabled transmitter that can best exploit and adapt to the random energy arrivals and time-varying fading channels. We consider parallel structure EH-powered WSNs tasked with solving a binary distributed detection problem. Sensors process locally their observations, adapt their transmission according to the battery and fading channel states, and transmit their data symbols to the fusion center (FC) over orthogonal fading channels. We study adaptive transmission schemes that optimize detection performance metrics at the FC, subject to certain battery and transmit power constraints. In the first part, modeling the random energy arrival as a Poisson process, we propose a novel transmit power control strategy that is parameterized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals and we find the jointly optimal quantization thresholds and the scale factors such that detection metric at the FC is maximized. We have assumed that the battery operates at the steady-state and the energy arrival and channel models are independent and identically distributed across transmission blocks. In the second part, we assume the battery is not at the steady-state and both the channel and the energy arrival are modeled as homogeneous finite-state Markov chains. Therefore, the power control optimization problem at hand becomes a multistage stochastic optimization problem and can be solved via the Markov decision process (MDP) framework. This is the first work that develops MDP-based channel-dependent power control policy for distributed detection in EH-powered WSNs.

We all hope for the best but sometimes, one must plan for ways of dealing with the worst-case scenarios, especially in a network with adversaries. This dissertation illustrates a detailed description of distributed optimization algorithms over a network of agents, in which some agents are adversarial. The model considered is such that adversarial agents act to subvert the objective of the network. The algorithms presented in this dissertation are solved via gradient-based distributed optimization algorithm and the effects of the adversarial agents on the convergence of the algorithm to the optimal solution are characterized. The analyses presented establish conditions under which the adversarial agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents. The adversarial agents act by using up network bandwidth, forcing the communication of the non-adversarial agents to be constrained. A distributed gradient-based optimization algorithm is explored in which the non-adversarial agents exchange quantized information with one another using fixed and adaptive quantization scheme. Additionally, convergence of the solution to a neighborhood of the optimal solution is proved in the communication-constrained environment amidst the presence of adversarial agents.