This thesis aims to utilize data analysis and predictive modeling techniques and apply them in different domains for gaining insights. The topics were chosen keeping the same in mind. Analysis of customer interests is a crucial factor in present marketing trends and hence we worked on twitter data which is a significant part of digital marketing. Neuroscience, especially psychological behavior, is an important research area. We chose eye tracking data based on which we differentiated human concentration while watching controllable (video game) videos and uncontrollable (sports) videos. Currently, cities are using data analysis for becoming smart cities. We worked on the City of Lewisville emergency services data and predicted the vehicle-accident-prone areas for development of precautionary measures in those areas.

Over-the-air programming systems for wireless sensor networks have drawbacks that stem from fundamental limitations in the hardware used in current sensor nodes. Also, advances in technology make it feasible to use capacitors as the sole energy storage mechanism for sensor nodes using energy harvesting, but most current designs require additional electronics. These two considerations led to the design of a new sensor node. A microcontroller was chosen that meets the Popek and Goldberg virtualization requirements. The hardware design for this new sensor node is presented, as well as a preliminary operating system. The prototypes are tested, and demonstrated to be sustainable with a capacitor and solar panel. The issue of capacitor leakage is considered and measured.

In this thesis the general Chebyshev filter synthesis procedure to generate transfer and reflection polynomials and coupling matrices were described. Key concepts such as coupled resonators, non-resonant nodes have been included. This is followed by microwave duplexer synthesis. Next, a technique to design dual band filter has been described including ways to achieve desired return loss and rejection levels at specific bands by manipulating the stopbands and transmission zeros. The concept of dual band filter synthesis has been applied on the synthesis of microwave duplexer to propose a method to synthesize dual band duplexers. Finally a numerical procedure using Cauchy method has been described to estimate the filter and duplexer polynomials from measured responses. The concepts in this thesis can be used to make microwave filters and duplexers more compact, efficient and cost effective.

Modern machine learning techniques focus on extremely deep and multi-pathed networks, resulting in large memory and computational requirements. This thesis explores techniques for designing efficient convolutional networks including pixel shuffling, depthwise convolutions, and various activation fucntions. These techniques are then applied to two image processing domains: single-image super-resolution and image compression. The super-resolution model, TinyPSSR, is one-third the size of the next smallest model in literature while performing similar to or better than other larger models on representative test sets. The efficient deep image compression model is significantly smaller than any other model in literature and performs similarly in both computational cost and reconstruction quality to the JPEG standard.

A modified, single error-correcting, and double error detecting Hamming code, hereafter referred to as modified SEC-DED Hamming code, is proposed in this research. The code requires fewer logic gates to implement than the SEC-DED Hamming code. Also, unlike the popular Hsiao's code, the proposed code can determine the error in the received word from its syndrome location in the parity check matrix. A detailed analysis of the area and power utilization by the encoder and decoder circuits of the modified SEC-DED Hamming code is also discussed. Results demonstrate that this code is an excellent alternative to Hsiao's code as the area and power values are very similar. In addition, the ability to locate the error in the received word from its syndrome is also of particular interest. Primitive polynomials play a crucial role in the hardware realizations for error-correcting codes. This research describes an implementation of a scalable primitive polynomial circuit with coefficients in GF(2). The standard cell area and power values for various degrees of the circuit are analyzed. The physical design of a degree 6 primitive polynomial computation circuit is also provided. In addition to the codes, a background of the already existing SPX GCD computation algorithm is …

In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.

Optical current sensors (OCSs) are initially developed to measure relatively large current over a wide range of frequency band. They are also used as protective devices in the event a fault occurs due to a short circuit, in the power generation and distribution industries. The basic principal used in OCS is the Faraday effect. When a light guiding faraday medium is placed in a magnetic field which is produced by the current flowing in the conductor around the magnetic core, the plane of polarization of the linearly polarized light is rotated. The angle of rotation is proportional to the magnetic field strength, proportionality constant and the interaction length. The proportionality constant is the Verdet constant V (λ, T), which is dependent on both temperature and wavelength of the light. Opto electrical methods are used to measure the angle of rotation of the polarization plane. By measuring the angle the current flowing in the current carrying conductor can be calculated. But the accuracy of the OCS is lost of the angle of rotation of the polarization plane is dependent on the Verdet constant, apart from the magnetic field strength. As temperature increases the Verdet constant decreases, so the angle of rotation …

The focus of this thesis is on developing theories and techniques to extract lossy microwave filter parameters from data. In the literature, the Cauchy methods have been used to extract filters’ characteristic polynomials from measured scattering parameters. These methods are described and some examples are constructed to test their performance. The results suggest that the Cauchy method does not work well when the Q factors representing the loss of filters are not even. Based on some prototype filters and the relationship between Q factors and the loss, we conduct preliminary studies on alternative representations of the characteristic polynomials. The parameters in these new models are extracted using the Levenberg–Marquardt algorithm to accurately estimate characteristic polynomials and the loss information.

This thesis aims to increase photovoltaic (PV) panel power efficiency by employing a cooling system based on water circulation, which represents an improved version of water flow based active cooling systems. Theoretical calculations involved finding the heat produced by the PV panel and the circulation water flow required to remove this heat. A data logger and a cooling system for a test panel of 20W was designed and employed to study the relationship between the PV panel surface temperature and its output power. This logging and cooling system includes an Arduino microcontroller extended with a data logging shield, temperature sensing probes, current sensors, and a DC water pump. Real-time measurements were logged every minute for one or two day periods under various irradiance and air temperature conditions. For these experiments, a load resistance was chosen to operate the test panel at its maximum power point. Results indicate that the cooling system can yield an improvement of 10% in power production. Based on the observations from the test panel experiments, a cooling system was devised for a PV panel array of 640 W equipped with a commercial charge controller. The test data logger was repurposed for this larger system. An identical …

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build …

Modern power generation aims to utilize renewable energy sources such as solar power and wind to supply customers with power. This approach avoids exhaustion of fossil fuels as well as provides clean energy. Microgrids have become popular over the years, as they contain multiple renewable power sources and battery storage systems to supply power to the entities within the network. These microgrids can share power with the main grid or operate islanded from the grid. During an islanded scenario, self-sustainability is crucial to ensure balance between supply and demand within the microgrid. This can be accomplished by a smart microgrid that can monitor system conditions and respond to power imbalance by shedding loads based on priority. Such a method ensures security of the most important loads in the system and manages energy by automatically disconnecting lower priority loads until system conditions have improved. This thesis introduces a prioritized load shedding algorithm for the microgrid at the University of North Texas Discovery Park and highlight how such an energy management algorithm can add reliability to an islanded microgrid.

In the first part of this thesis, we propose a distributed consensus algorithm under multi-layer multi-group structure with communication time delays. It is proven that the consensus will be achieved in both time-varying and fixed communication delays. In the second part, we study the distributed optimization problem with a finite-time mechanism. It is shown that our distributed proportional-integral algorithm can exponentially converge to the unique global minimizer when the gain parameters satisfy the sufficient conditions. Moreover, we equip the proposed algorithm with a decentralized algorithm, which enables an arbitrarily chosen agent to compute the exact global minimizer within a finite number of time steps, using its own states observed over a successive time steps. In the third part, it is shown the implementation of accelerated distributed energy management for microgrids is achieved. The results presented in the thesis are corroborated by simulations or experiments.

The Chief Security Officer Problem (CSO) consists of a CSO, a group of agents trying to communicate with the CSO and a group of eavesdroppers trying to listen to the conversations between the CSO and its agents. Through Lemmas and Theorems, several Information Theoretic questions are answered.

In this thesis, we conduct a preliminary study on the controller design for directional antenna devices carried by drones. The goal of the control system is to ensure the best alignment between two directional antennas so as to enhance the performance of air-to-air communication between the drones. The control system at the current stage relies on the information received from GPS devices. The control system includes two loops: velocity loop and position loop to suppress wind disturbances and to assure the alignment of two directional antennae. The simulation and animation of directional antennae alignment control for two-randomly moving drones was developed using SIMULINK. To facilitate RSSI-based antenna alignment control to be conducted in the future work, a study on initial scanning techniques is also included at the end of this thesis.

Mobility models serve as the foundation for evaluating and designing airborne networks. Due to the significant impact of mobility models on the network performance, mobility models for airborne networks (ANs) must realistically capture the attributes of ANs. In this paper, I develop a comprehensive modeling framework for ANs. The work I have done is concluded as the following three parts. First, I perform a comprehensive and comparative analysis of AN mobility models and evaluate the models based on several metrics: 1) networking performance, 2) ability to capture the mobility attributes of ANs, 3) randomness levels and 4) associated applications. Second, I develop two 3D mobility models and realistic boundary models. The mobility models follow physical laws behind aircraft maneuvering and therefore capture the characteristics of aircraft trajectories. Third, I suggest an estimation procedure to extract parameters in one of the models that I developed from real flight test data. The good match between the estimated trajectories and real flight trajectories also validate the suitability of the model. The mobility models and the estimation procedure lead to the creation of “realistic” simulation and evaluation environment for airborne networks.

Electrocardiography (ECG) signal transmitter is the device used to transmit the electrical signals of the heart to the remote machine. These electrical signals are ECG signals caused due to electrical activities in the heart. ECG signals have very low amplitude and frequency; hence amplification of the signals is needed to strengthen the signal. Conversion of the amplified signal into digital information and transmitting that information without losing any data is the key. This information is further used in monitoring the heart.

Secure multiparty computation refers to the problem where a number of users wish to securely compute a function on their inputs without revealing any unnecessary information. This dissertation focuses on the fundamental limits of secure summation under different constraints. We first focus on the minimal model of secure computation, in which two users each hold an input and wish to securely compute a function of their inputs at the server. We propose a novel scheme base on the algebraic structure of finite field and modulo ring of integers. Then we extend the minimal model of secure computation, in which K users wish to securely compute the sum of their inputs at the server. We prove a folklore result on the limits of communication cost and randomness cost. Then we characterized the optimal communication cost with user dropouts constraint, when some users may lose connection to the server and the server wishes to compute the sum of remaining inputs. Next, we characterize the optimal communication and randomness cost for symmetric groupwise keys and find the feasibility condition for arbitrary groupwise keys. Last, we study the secure summation with user selection, such that the server may select any subset of users to …

PM2.5 particle sensing has been done using surface acoustic wave based sensor for two different frequencies. Due to mass loading and elasticity loading on the sensor's surface, the center frequency of the sensor shifts. The particle concentration can be tracked based on that frequency shift. The fit factor test has been conducted using higher frequency SAW sensor. The consist results has been achieved for particle sensing and fit factor test with SAW based sensor.

This article considers the secure computation problem in a minimal model, where Alice and Bob each holds an input and wish to securely compute a function of their inputs at Carol without revealing any additional information about the inputs. For this minimal secure computation problem, the authors propose a novel coding scheme built from two steps. First, the function to be computed is expanded such that it can be recovered while additional information might be leaked. Second, a randomization step is applied to the expanded function such that the leaked information is protected. The authors implement this expand-and-randomize coding scheme with two algebraic structures—the finite field and the modulo ring of integers, where the expansion step is realized with the addition operation and the randomization step is realized with the multiplication operation over the respective algebraic structures.

Article demonstrating a thermally tunable acoustic beam splitter using a poly(vinyl alcohol) poly(N-isopropylacrylamide) hydrogel (PVA-pNIPAM). The nature of PVA-pNIPAM hydrogel offers exceptional temperature-dependent physical properties due to its phase transition around its lower critical solution temperature. The acoustic impedance of the hydrogel can be tuned below, above, or matched to that of water by changing the environmental temperature. An acoustic wave propagating in water can be split into transmitted and reflected components by the PVA-pNIPAM hydrogel slab on varying its angle of incidence. The intensity ratio between the reflected and the transmitted componence can be adjusted by tuning the temperature of the medium. The acoustic beam can be entirely reflected at a temperature corresponding to the matched impedance between hydrogel and water. The beam-splitting behavior was observed for acoustic waves from both a monochromatic wave and broadband pulse source. In addition, the phase of beam split pulses can be reversed by selecting the hydrogel’s operating temperature.

This thesis presents a new approach to extend and reduce the transmission range in wireless systems. Conditioning is defined as purposeful electromagnetic interference that affects a wireless signal as it propagates through the air. This interference can be used constructively to enhance a signal and increase its energy, or destructively to reduce energy. The constraints and limitations of the technology are described as a system model, and a flow chart is used to describe the circuit process. Remaining theoretical in nature, practical circuit implementations are foregone in the interest of elementary simulations depicting the interactions of modulated signals as they experience phase mismatch. Amplitude modulation and frequency modulation are explored with using both positive and negative conditioning, and conclusions to whether one is more suitable than the other are made.

Among various energy harvesting technologies reverse electrowetting-on-dielectric energy harvesting (REWOD) has been proved to harvest energy from low frequency motion such as many human motion activities (e.g. walking, running, jogging etc.). Voltage rectification and DC-DC boosting of low magnitude AC voltage from REWOD can be used to reliably self-power the wearable sensors. In this work, a commercial component-based rectifier and DC-DC converter is designed and experimentally verified, for further miniaturization standard 180 nm CMOS process is used to design the rectifier and the DC-DC boost converter.This work also includes the MATLAB based model for REWOD energy harvester for various REWOD models. In REWOD energy harvesting, a mechanical input during the motion causes the electrolyte placed in between two dissimilar electrodes to squeeze back and forth thereby periodically changing the effective interfacial area, hence generating alternating current. The alternating current is given to the rectifier design. There is no realistic model that has been developed yet for this technique. Thereby, a MATLAB based REWOD model is developed for the realistic simulation of the REWOD phenomenon. In the work, a comparison of different REWOD models such as planar surface, rough surface and porous models are performed demonstrating the variations in capacitance, current …

This dissertation focuses on the path planning of unmanned aerial vehicle (UAV) swarms under distributed and hybrid control scenarios. It presents two such models and analyzes them both from theory and practice. In the first method, a distributed formation control strategy for UAV swarm based on consensus law is presented. This model makes use of the fundamental concepts of leader-follower structure, social potential functions, and algebraic graph theory to jointly address flocking and de-confliction in the formation control problem. The impact of network topology on formation control is analyzed. It is shown that the degree distribution of the network representing the multi-agent system defines the rate at which formation is attained. Conditions for convergence and stability are derived. In the second method, a hybrid framework for path planning and coverage area by UAV swarms is presented. This strategy significantly improves the current labor-intensive and resource-constraint operations in aquaculture farms. To monitor the farms periodically, an optimized back-and-forth flight path based on the Shamos algorithm is utilized. A trajectory tracking strategy for UAV swarms under uncertain wind conditions is presented.

This thesis focuses on the recording challenges faced in biomedical systems. More specifically, the challenges in neural signal recording are explored. Instead of the typical synchronous ADC system, a level crossing ADC is detailed as it has gained recent interest for low-power biomedical systems. These systems take advantage of the time-sparse nature of the signals found in this application. A 10-bit design is presented to help capture the lower amplitude action potentials (APs) in neural signals. The design also achieves a full-scale bandwidth of 1.2 kHz, an ENOB of 9.81, a power consumption of 13.5 microwatts, operating at a supply voltage of 1.8 V. This design was simulated in Cadence using 180 nm CMOS technology.