This thesis presents a series of neural network classifiers for object detection in both optical and infrared images. The focus of this work is on efficient and accurate solutions. The thesis discusses the evolution of the highly efficient and tiny network Binary Classification Vision Transformer (BC-ViT) and how through thoughtful modifications and improvements, the BC-ViT can be utilized for tasks of increasing complexity. Chapter 2 discusses the creation of BC-ViT and its initial use case for underwater image classification of optical images. The BC-ViT is able to complete its task with an accuracy of 99.29\% while being comprised of a mere 15,981 total trainable parameters. Chapter 3, Waste Multi-Class Vision Transformer (WMC-ViT), introduces the usefulness of mindful algorithm design for the realm of multi-class classification on a mutually exclusive dataset. WMC-ViT shows that the task oriented design strategy allowed for a network to achieve an accuracy score of 94.27\% on a five class problem while still maintaining a tiny parameter count of 35,492. The final chapter demonstrates that by utilizing functional blocks of BC-ViT, a simple and effective target detection algorithm for infrared images can be created. The Edge Infrared Vision Transformer (EIR-ViT) showed admirable results with a high IoU …

The new developments in coding theory research have revolutionized the application of coding to practical systems. Low-Density Parity Check (LDPC) codes form a class of Shannon limit approaching codes opted for digital communication systems that require high reliability. This thesis investigates the underlying relationship between the spectral properties of the parity check matrix and LDPC decoding convergence. The bit error rate of an LDPC code is plotted for the parity check matrix that has different Second Smallest Eigenvalue Modulus (SSEM) of its corresponding Laplacian matrix. It is found that for a given (n,k) LDPC code, large SSEM has better error floor performance than low SSEM. The value of SSEM decreases as the sparseness in a parity-check matrix is increased. It was also found from the simulation that long LDPC codes have better error floor performance than short codes. This thesis outlines an approach to analyze LDPC decoding based on the eigenvalue analysis of the corresponding parity check matrix.

In light of incidents and concerns regarding the vulnerability of the global positioning system (GPS), the main purpose of the thesis is to look at alternative systems for radio guidance and to put up a serious study on such alternatives with receive and transmit antenna. There is also the need to design such antennas with multiple frequencies to offer robustness in the unlikely event that such adversarial attacks on the GPS happen. The basis on which such alternative antennas are designed is a slotted microstrip. The characteristics of the slot or slots on the microstrip are analyzed by mapping their exact locations on the patch and then noting the resultant center frequencies, the return losses, and the bandwidth. The activities associated with this also focus on the design, fabrication, validation, and characterization of one or more slotted antennas prototypes. The measurement of the antenna prototypes does confirm several frequencies that coexist to see applications, in aeronautical radionavigation, fixed-mobile radionavigation, and radiolocation. The antennas could also feature in a wide-area augmentation system (WAAS), satellite ground link system (SGLS) as well as in surveillance and precision approach radars. Some variations of the antenna are deployed in the areas of law enforcement, surveillance, …

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising in developing high performance low-power portable applications. In this research, we crowdsource a mapping problem using gamification to harnass human intelligence. A scientific puzzle game, Untangled, was developed to solve a mapping problem by encapsulating architectural characteristics. The primary motive of this research is to draw insights from the mapping solutions of players who possess innate abilities like decision-making, creative problem-solving, recognizing patterns, and learning from experience. In this dissertation, an extensive analysis was conducted to investigate how players' computational skills help to solve an open-ended problem with different constraints. From this analysis, we discovered a few common strategies among players, and subsequently, a library of dictionaries containing identified patterns from players' solutions was developed. The findings help to propose a better version of the game that incorporates these techniques recognized from the experience of players. In the future, an updated version of the game that can be developed may help low-performance players to provide better solutions for a mapping problem. Eventually, these solutions may help to develop efficient mapping algorithms, In addition, this research can be an exemplar for future researchers who want to crowdsource such electrical engineering problems and this approach can …

In this work, we explored one material from the broad family of 2D semiconductors, namely WSe2 to serve as an enabler for advanced, low-power, high-performance nanoelectronics and optoelectronic devices. A 2D WSe2 based field-effect-transistor (FET) was designed and fabricated using electron-beam lithography, that revealed an ultra-high mobility of ~ 625 cm2/V-s, with tunable charge transport behavior in the WSe2 channel, making it a promising candidate for high speed Si-based complimentary-metal-oxide-semiconductor (CMOS) technology. Furthermore, optoelectronic properties in 2D WSe2 based photodetectors and 2D WSe2/2D MoS2 based p-n junction diodes were also analyzed, where the photoresponsivity R and external quantum efficiency were exceptional. The monolayer WSe2 based photodetector, fabricated with Al metal contacts, showed a high R ~502 AW-1 under white light illumination. The EQE was also found to vary from 2.74×101 % - 4.02×103 % within the 400 nm -1100 nm spectral range of the tunable laser source. The interfacial metal-2D WSe2 junction characteristics, which promotes the use of such devices for end-use optoelectronics and quantum scale systems, were also studied and the interfacial stated density Dit in Al/2D WSe2 junction was computed to be the lowest reported to date ~ 3.45×1012 cm-2 eV-1. We also examined the large exciton binding …

This dissertation is particularly focused on a novel approach of a wirelessly powered neuromodulation system for chronic patients. The inductively coupled transmitter (TX) and receiver (RX) coils are designed through optimization to achieve maximum efficiency. A power management unit (PMU) consisting of a voltage rectifier, voltage regulator along with a stimulation circuitry is also designed to provide pulse stimulation to genetically modified neurons. For continuous health monitoring purposes, the response from the brain due to stimulation needs to be recorded and transmitted wirelessly outside the brain for analysis. A low-power high-data duty-cycled impulse-radio ultra-wideband (IR-UWB) transmitter is designed and implemented using the standard CMOS process. Another focus of this dissertation is the design of a reverse electrowetting-on-dielectric (REWOD) based energy harvesting circuit for wearable sensor applications which is capable of generating a very low-frequency signal from motion activity such a walking, running, jogging, etc. A commercial off-the-shelf (COTS) based and on-chip based energy harvesting circuit is designed for very low-frequency signals. The experimental results show promising progress towards the advancement in the wirelessly powered neuromodulation system and building the self-powered wearable sensor.

This thesis creates a new method for the ethical splitting of data as well as improvements to neural network architectures to increase performance. Ethical dataset splitting should be based on statistics from the data, this prevents artificial manipulation of the data that helps or hurts the performance of a network. This bias introduced to the dataset can also be present by using the popular method of randomly splitting data into datasets. To remove bias from dataset splitting, the splits of a dataset must be based on statistics from the data. Improving neural network architectures to increase performance is very important for a wide range of applications, especially for classification of heartbeats. Every improvement matters, especially when the application means that any errors could put the life of a person in danger. These advancements being applied to heartbeat classification have exciting implications for saving thousands of lives and billions of dollars. The presented methods can also be expanded to a wide variety of applications and adapted to different types of data as increasing performance and splitting up datasets is important in all fields of machine learning.

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 …

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.

Occupancy monitoring is an important research problem with a broad range of applications in security, surveillance, and resource management in smart building environments. As a result, it has immediate solutions to solving some of society's most pressing issues. For example, HVAC and lighting systems in the US consume approximately 45-50% of the total energy a building uses. Smart buildings can reduce wasted energy by incorporating networkable occupancy sensors to obtain real-time occupancy data for the facilities. Therefore, occupancy monitoring systems can enable significant cost savings and carbon reduction. In addition, workplaces have quickly adapted and implemented COVID-19 safety measures by preventing overcrowding using real-time information on people density. While there are many sensors, RGB cameras have proven to be the most accurate. However, cameras create privacy concerns. Hence, our research aims to design an efficient occupancy monitoring system with minimal privacy invasion. We conducted a systematic study on sensor characterization using various low-resolution infrared sensors and proposed a unified processing algorithms pipeline for occupancy estimation. This research also investigates low-resolution thermal imaging sensors with a chessboard reading pattern, focusing on algorithm design issues and proposing solutions when detecting moving objects. Our proposed approach achieves about 99% accuracy in occupancy estimation, …

Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.

Low Earth orbit (LEO) satellite systems have been proposed as a resource for combating the challenges in 5G network coverage and expanding connectivity to a global realm. This research focuses on the current architecture of LEO satellite constellations, with an emphasis on satellite coverage, visibility patterns and coordination schemes. Key-elements of integrating LEO satellites into the eMBB component of 5G are presented and a breakdown of potential link channel characteristics and physical layer performance metrics are described. The produced information allows for a justified analysis on the conceptualized integration.

This thesis covers applications of machine learning to the fields of remote sensing and environmental monitoring. First, a generalized background on the concepts, tools, and methods used throughout the remainder of the research project are introduced. Chapter 3 covers the implementation of artificial neural networks to improve low-cost particulate matter sensing networks using collocated high-quality sensors with varying dataset parameters. In Chapter 4, an attention-enhanced LSTM-Convolutional neural network is presented to reconstruct satellite-based aerosol optical depth data lost to atmospheric interference. Chapter 5 applies attention mechanisms and convolutional neural networks to the reconstruction and upsampling of satellite-based land surface temperature maps. Chapter 6 presents a model employing geospatial techniques and machine learning methods with a combination of ground-based and remote sensing data to produce a daily ultra-high resolution 30 meter mapping of the PM2.5 concentration across Denton County, Texas.

The following thesis proposes solutions to problems in two main areas of focus, computer vision and machine learning. Chapter 2 utilizes traditional computer vision methods implemented in a novel manner to successfully identify overlays contained in broadcast footage. The remaining chapters explore machine learning algorithms and apply them in various manners to big data, multi-channel image data, and ECG data. L1 and L2 principal component analysis (PCA) algorithms are implemented and tested against each other in Python, providing a metric for future implementations. Selected algorithms from this set are then applied in conjunction with other methods to solve three distinct problems. The first problem is that of big data error detection, where PCA is effectively paired with statistical signal processing methods to create a weighted controlled algorithm. Problem 2 is an implementation of image fusion built to detect and remove noise from multispectral satellite imagery, that performs at a high level. The final problem examines ECG medical data classification. PCA is integrated into a neural network solution that achieves a small performance degradation while requiring less then 20% of the full data size.

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 thesis targets at providing an algebraic method to indicate network behaviors. Furthermore, for a signed-average consensus problem of the system behaviors, event-triggering signed-average algorithms are designed to reduce the communication overheads. In Chapter 1, the background is introduced, and the problem is formulated. In Chapter 2, notations and basics of graph theory are presented. It is known that the terminal value of the system state is determined by the initial state, left eigenvector and right eigenvector associated with zero eigenvalue of the Laplacian matrix. Since there is no mathematical expression of right eigenvector, in Chapter 3, mathematical expression of right eigenvector is given. In Chapter 4, algebraic trait for structurally balanced property of a node is proposed. In Chapter 5, a method for characterization of collective behaviors under directed signed networks is developed. In Chapter 6, dynamic event-triggering signed-average algorithms are proposed and proved for the purpose of relieving the communication burden between agents. Chapter 7 summarizes the thesis and gives future directions.

The syndrome source coding for lossless data compression with side information based on fixed-length linear block codes is the main emphasis of this work. We demonstrate that the source entropy rate can be achieved for syndrome source coding with side information when the sources are correlated. Next, we examine employing LDPC codes to apply the channel and syndrome concepts in order to satisfy the Slepian Wolf limit. Our findings indicate that irregular codes perform significantly better when the compression ratio is larger. Additionally, we looked at how well different applications performed when running on two different mobile networks. We have tested those applications which are used in our day-to-day life. Our main focus is to make wireless communication much easier. We know that nowadays data is increasing which led to increase in the transfer of data. There are a lot of errors while doing so like channel error, bit error rate, jitter, etc. To overcome such kind of problems compression and decompression should be done effectively without any complexity to achieve a high performance ratio.

Broadly defined, group testing is the study of finding defective items in a large set. In the medical infection setting, that implies classifying each member of a population as infected or uninfected, while minimizing the total number of tests.

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 …

Although wireless sensor networks have been successfully used for environmental monitoring, one of the major challenges that this technology has been facing is supplying continuous and reliable electrical power during long-term field deployment. Batteries require repetitive visits to the deployment site to replace them once discharged; admittedly, they can be recharged from solar panels, but this only works in open areas where solar radiation is unrestricted. This dissertation introduces a novel approach to design and implement a reliable efficient solar energy harvester to continuously, and autonomously, provide power to wireless sensor nodes for long-term applications. The system uses supercapacitors charged by a solar panel and is designed to reduce power consumption to very low levels. Field tests were conducted for more than a year of continuous operation and under a variety of conditions, including areas under dense foliage. The resulting long-term field data demonstrates the feasibility and sustainability of the harvester system for challenging applications. In addition, we analyzed solar radiation data and supercapacitor charging behavior and showed that the harvester system can operate battery free, running on the power provided by supercapacitors. A battery is included only for backup in case the supercapacitor storage fails. The proposed approach provides …

A wearable body temperature sensor would allow for early detection of fever or infection, as well as frequent and accurate hassle-free recording. This thesis explores the design of a body-temperature-sensing device inkjet-printed on a flexible substrate. All structures were first modeled by first-principles, theoretical calculations, and then simulated in HFSS. A variety of planar square inductor geometries were studied before selecting an optimal design. The designs were fabricated using multiple techniques and compared to the simulation results. It was determined that inductance must be carefully measured and documented to ensure good functionality. The same is true for parallel-plate and interdigitated capacitors. While inductance remains relatively constant with temperature, the capacitance of the device with a temperature-sensitive dielectric layer will result in a shift in the resonant frequency as environmental or ambient temperature changes. This resonant frequency can be wirelessly detected, with no battery required for the sensing device, from which the temperature can be deduced. From this work, the optimized version of the design comprises of conductive silver in with a temperature-sensitive graphene oxide layer, intended for inkjet-printing on flexible polyimide substrates. Graphene oxide demonstrates a high dielectric permittivity with good sensing capabilities and high accuracy. This work pushes the …

This dissertation is devoted to the development of novel devices for optoelectronic and photovoltaic applications using the promise of inkjet printing with two-dimensional (2D) materials. A systematic approach toward the characterization of the liquid exfoliated 2D inks comprising of graphene, molybdenum disulfide (MoS2), tungsten diselenide (WSe2), and 2D perovskites is discussed at depth. In the first study, the biocompatibility of 2D materials -- graphene and MoS2 -- that were drop cast onto flexible PET and polyimide substrates using mouse embryonic fibroblast (STO) and human esophageal fibroblast (HEF) cell lines, was explored. The polyimide samples for both STO and HEF showed high biocompatibility with a cell survival rate of up to ~ 98% and a confluence rate of 70-98%. An inkjet printed, biocompatible, heterostructure photodetector was constructed using inks of photo-active MoS2 and electrically conducting graphene, which facilitated charge collection of the photocarriers. The importance of such devices stems from their potential utility in age-related-macular degeneration (AMD), which is a condition where the photosensitive retinal tissue degrades with aging, eventually compromising vision. The biocompatible inkjet printed 2D heterojunction devices were photoresponsive to broadband incoming radiation in the visible regime, and the photocurrent scaled proportionally with the incident light intensity, exhibiting a …

The main objective of this thesis is to propose a UAV (also called as drones) location estimation system based on LoRaWAN using received signal strength indicator in a GPS denied environment. The drones are finding new applications in areas such as surveillance, search, rescue missions, package delivery, and precision agriculture. Nearly all applications require the localization of UAV during flight. Localization is the method of determining a UAVs physical position using a real or virtual coordinate system. This thesis proposes a LoRaWAN-based UAV location method and presents experimental findings from a prototype. The thesis mainly consists of two different sections: one is the distance estimation and the other is the location estimation. First, the distance is estimated based on the mean RSSI values which are recorded at the ground stations using the path loss model. Later using the slant distance estimation technique, the path loss model parameters L and C are estimated whose values are unknown at the beginning. These values completely depend on the environment. Finally, the trilateration system architecture is employed to find the 3-D location of the UAV.