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.

Poor air quality can greatly affect the public health. Research studies indicate that indoor air can be more polluted than the outdoor air. An indoor air quality monitoring system will help to create an awareness of the quality of air inside which will eventually help in improving it. The objective of this research is to develop a low cost wireless sensor system for indoor air quality monitoring. The major cost reduction of the system is achieved by using low priced sensors. Interface circuits had to be designed to make these sensors more accurate. The system is capable of measuring carbon dioxide, carbon monoxide, ozone, temperature, humidity and volatile organic compounds. The prototype sensor node modules were developed. The sensor nodes were the connected together by Zigbee network. The nodes were developed in such a way that it is compact in size and wireless connection of sensor nodes enable to collect air quality data from multiple locations simultaneously. The collected data was stored in a computer. We employed linear least-square approach for the calibration of each sensor to derive a conversion formula for converting the sensor readings to engineering units. The system was tested with different pollutants and data collected was …

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.

This thesis proposes a design of a rapidly deployable cellular network prototype that provides voice and data communications and it is interoperable with legacy devices and the existing network infrastructure. The prototype is based on software defined radio and makes use of IEEE 802.11 unlicensed wireless radio frequency (RF) band for backhaul link and an open source GSM implementation software. The prototype is also evaluated in environments where there is limited control of the radio frequency landscape, and using Voice Over Internet Protocol (VoIP) performance metrics to measure the quality of service. It is observed that in environments where the IEEE 802.11 band is not heavily utilized, a large number of calls are supported with good quality of service. However, when this band is heavily utilized only a few calls can be supported as the quality of service rapidly degrades with increasing number of calls, which is due to interference. It is concluded that in order to achieve tolerable voice quality, unused licensed spectrum is needed for backhaul communication between base stations.

Memory is an important part of any digital processing system. On-chip SRAM can be found in various levels of the memory hierarchy in a processor and occupies a considerable area of the chip. Leakage is one of the challenges which shrinking of technology has introduced and the leakage of SRAM constitutes a substantial part of the total leakage power of the chip due to its large area and the fact that many of the cells are idle without any access. In this thesis, we introduce asymmetric SRAM cells using stacked transistors which reduce the leakage up to 26% while increasing the delay of the cell by only 1.2% while reducing the read noise margin of the cell by only 15.7%. We also investigate an asymmetric cell configuration in which increases the delay by 33% while reduces the leakage up to 30% and reducing the read noise margin by only 1.2% compared to a regular SRAM cell.

In this thesis, I present my study on the impact of multi-group network structure on the performance of consensus building strategies, and the preliminary mathematical formulation of the problem on improving the performance of the National Airspace system (NAS) through long-term investment. The first part of the thesis is concerned with a structural approach to the consensus building problem in multi-group distributed sensor networks (DSNs) that can be represented by bipartite graph. Direct inference of the convergence behavior of consensus strategies from multi-group DSN structure is one of the contributions of this thesis. The insights gained from the analysis facilitate the design and development of DSNs that meet specific performance criteria. The other part of the thesis is concerned with long-term planning and development of the NAS at a network level, by formulating the planning problem as a resource allocation problem for a flow network. The network-level model viewpoint on NAS planning and development will give insight to the structure of future NAS and will allow evaluation of various paradigms for the planning problem.

Mobile wireless sensor networks are not widely implemented in the real world, even after years of research carried out in this field. One reason is the lack of understanding of the impact that mobility has on network performance. The simulation and emulation of mobile wireless sensor networks is necessary before they are deployed for the real-world applications. This thesis presents a simulation-based study of different mobility models. The total area coverage that depends on the pattern of node movements is observed through simulations. The spatial distribution of node locations is also studied. Various synthetic mobility models available are explored based on their theoretical descriptions. ‘BonnMotion' is used as the network simulator for investigating different mobility scenarios. The results obtained after simulations are imported to MATLAB and the analysis of node movements is done through various plots and inferences from the data. The comparison of mobility models is also discussed based on their spatial node distribution in the simulated scenarios.

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.

In game based learning, motivating the players to learn by providing them a desirable gameplay experience is extremely important. However, it's not an easy task considering the quality of today's commercial non-educational games. Throughout the gameplay, the player should neither get overwhelmed nor under-challenged. The best way to do so is to monitor the player's actions in the game because these actions can tell the reason behind the player's performance. They can also tell about the player's lacking competencies or knowledge. Based on this information, in-game educational interventions in the form of hints can be provided to the player. The success of such games depends on their interactivity, motivational outlook and thus player retention. UNTANGLED is an online mapping game based on crowd-sourcing, developed by Reconfigurable Computing Lab, UNT for the mapping problem of CGRAs. It is also an educational game for teaching the concepts of reconfigurable computing. This thesis performs qualitative comparative analysis on gameplays of low performing players of UNTANGLED. And the implications of this analysis are used to provide recommendations for improving the gameplay experience for these players by guiding them. The recommendations include strategies to reach a high score and a compact solution, hints in the …

Renewable power systems are becoming more affordable and provide better options than fossil-fuel generation, for not only the environment, but a benefit of a reduced cost of operation. Methods to optimize charging batteries from renewable technologies is an important subject for off-grid and micro-grids, and is becoming more relevant for larger installations. Overcharging or undercharging the battery can result in failure and reduction of battery life. The Arduino hybrid MPPT controller takes the advantage of solar and wind energy sources by controlling two systems simultaneously. The ability to manage two systems with one controller is better for an overall production of energy, cost, and manageability, at a minor expense of efficiency. The hybrid MPPT uses two synchronous buck DC-DC converters to control both wind and solar. The hybrid MPPT performed at a maximum of 93.6% efficiency, while the individual controller operated at a maximum 97.1% efficiency when working on the bench test. When designing the controller to manage power production from a larger generator, the inductor size was too large due to the frequency provided by the Arduino. A larger inductor means less allowable current to flow before the inductor becomes over saturated, reducing the efficiency of the controller. Utilizing …

Integrated Circuits (ICs) have a broad range of applications in healthcare, military, consumer electronics etc. The acronym VLSI stands for Very Large Scale Integration and is a process of making ICs by placing millions of transistors on a single chip. Because of advancements in VLSI design technologies, ICs are getting smaller, faster in speed and more efficient, making personal devices handy, and with more features. In this thesis work an interactive framework is designed in which the fundamental concepts of digital logic design and VLSI design such as logic gates, MOS transistors, combinational and sequential logic circuits, and memory are presented in a simple, interactive and user friendly way to create interest in students towards engineering fields, especially Electrical Engineering and Computer Engineering. Most of the concepts are explained in this framework by taking the examples which we see in our daily lives. Some of the critical design concerns such as power and performance are presented in an interactive way to make sure that students can understand these significant concepts in an easy and user friendly way.

In wireless communication systems, one of the most important resources being focused on all the researchers is spectrum. A cognitive radio (CR) system is one of the efficient ways to access the radio spectrum opportunistically, and efficiently use the available underutilized licensed spectrum. Spectrum utilization can be significantly enhanced by developing more applications with adopting CR technology. CR systems are implemented using a radio technology called software-defined radios (SDR). SDR provides a flexible and cost-effective solution to fulfil the requirements of end users. We can see a lot of innovations in Internet of Things (IoT) and increasing number of smart devices. Hence, a CR system application involving an IoT device is studied in this thesis. Opportunistic spectrum access involves two tasks of CR system: spectrum sensing and dynamic spectrum access. The functioning of the CR system is rest upon the spectrum sensing. There are different spectrum sensing techniques used to detect the spectrum holes and a few of them are discussed here in this thesis. The simplest and easiest to implement energy detection spectrum sensing technique is used here to implement the CR system. Dynamic spectrum access involves different models and strategies to access the spectrum. Amongst the available models, …

Allocation of expensive resources, (such as Multiplier) onto the CGRA has been of interest from quite some time. For these architectural solutions to fulfill the designers' requirements, it is of utmost importance that the design offers high performance, low power consumption, and effective area utilization. The allocation problem is studied using the UntangledII gaming environment, which has been developed at the Reconfigurable Computing Lab at UNT to discover the design of custom domain-specific architectures. This thesis explores several case-studies to investigate the arrangement of functional units and interconnects to achieve a low power, high performance, and flexible heterogeneous designs that can fit for a suite of applications. In the later part, several human mapping strategies of top and bottom players to design a custom domain-specific architecture are presented. Some common trends that were examined while analyzing the mapping strategies of the players are also discussed.

Today, there is a growing number of buildings in a neighborhood and business parks that are utilizing renewable energy generation, to reduce their electric bill and carbon footprint. The most current way of implementing a renewable energy generation is to use solar panels or a windmill to generate power; then use a charge controller connected to a battery bank to store power. Once stored, the user can then access a clean source of power from these batteries instead of the main power grid. This type of power structure is utilizing a single module system in respect of one building. As the industry of renewable power generation continues to increase, we start to see a new way of implementing the infrastructure of the power system. Instead of having just individual buildings generating power, storing power, using power, and selling power there is a fifth step that can be added, sharing power. The idea of multiple buildings connected to each other to share power has been named a microgrid by the power community. With this ability to share power in a microgrid system, a catastrophic event which cause shutdowns of power production can be better managed. This paper then discusses the data …

This thesis describes development of low cost indoor air quality (IAQ) monitoring system for research. It describes data collection of various parameters concentration present in indoor air and sends data back to host PC for further processing. Thesis gives detailed information about hardware and software implementation of IAQ monitoring system. Also discussed are building wireless ZigBee network, creating user friendly graphical user interface (GUI) and analysis of obtained results in comparison with professional benchmark system to check system reliability. Throughputs obtained are efficient enough to use system as a reliable IAQ monitor.

Noise-induced hearing loss is a serious problem common to musical environments. Current dosimetry technology is primarily designed for industrial environments and not suited for musical settings. At present, there are no government regulations that apply to the educational music environment as it relates to monitoring and prevention of hearing loss. Also, no system exists than can serve as a proactive tool in observation and reporting of sound exposure levels with the goal of hearing conservation. Newly proposed system takes a software based approach in designing a proactive dosimetry system that can assess the risk of sound noise exposure. It provides real-time feedback trough a graphical user interface that is capable of database storage for further study.

Water scarcity for agriculture is one of the most important challenges to improve food security worldwide. In this thesis we study the potential to develop a low-cost controller for a small scale brackish desalination plant that consists of proven water treatment technologies, reverse osmosis, cation exchange, and nanofiltration to treat groundwater into two final products: drinking water and irrigation water. The plant is powered by a combination of wind and solar power systems. The low-cost controller uses Arduino Mega, and Arduino DUE, which consist of ATmega2560 and Atmel SAM3X8E ARM Cortex-M3 CPU microcontrollers. These are widely used systems characterized for good performance and low cost. However, Arduino also requires drivers and interfaces to allow the control and monitoring of sensors and actuators. The thesis explains the process, as well as the hardware and software implemented.

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.

GNU Radio and OSSIE (Open-Source SCA (Software communication architecture) Implementation-Embedded) are two open source software toolkits for SDR (Software Defined Radio) developments, both of them can be supported by USRP (Universal Software Radio Peripheral). In order to compare the performance of these two toolkits, an FM receiver over GNU Radio and OSSIE are tested in my thesis, test results are showed in Chapter 4 and Chapter 5. Results showed that the FM receiver over GNU Radio has better performance, due to the OSSIE is lack of synchronization between USRP interface and the modulation /demodulation components. Based on this, the SISO (Single Input Single Output) communication system over GNU Radio is designed to transmit and receive sound or image files between two USRP equipped with RFX2400 transceiver at 2.45G frequency. Now, GNU Radio and OSSIE are widely used for academic research, but the future work based on GNU Radio and OSSIE can be designed to support MIMO, sensor network, and real time users etc.

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.

More than 80% of the people in urban regions and about 98% of cities in low and middle income countries have poor air quality according to the World Health Organization. People living in such environment suffer from many disorders like a headache, shortness of breath or even the worst diseases like lung cancer, asthma etc. The main objective of the thesis is to create awareness about the air quality and the factors that are causing air pollution to the people which is really important and provide tools at their convenience to measure and analyze the air quality. Taking real time air quality scenarios, various experiments were made using efficient sensors to study both the indoor and outdoor air quality. These experimental results will eventually help people to understand air quality better. An outdoor air quality data measurement system is developed in this research using Python programming to provide people an opportunity to retrieve and manage the air quality data and get the concentrations of the leading pollutants. The entire designing of the program is made to run with the help of a graphical user interface tool for the user, as user convenience is considered as one of the objectives of …

DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.

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.