UNT Theses and Dissertations - 147 Matching Results

Search Results

Results: 1 - 50 next
A Preliminary Controller Design for Drone Carried Directional Communication System
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.
Development of a Cost Effective Wireless Sensor System for Indoor Air Quality Monitoring Applications
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 compared with a professional grade monitoring system for analyzing its performance. The results indicated that the data from our system matched quite well with the professional grade monitoring system.
Neural Network Classifiers for Object Detection in Optical and Infrared Images
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 of 79\% coupled with a massive reduction in parameters compared to current state of the art solutions at roughly 120,000. This thesis ends with summary of Chapters 2, 3, and 4 that further discusses the implications this design strategy offers.
The Role of Eigenvalues of Parity Check Matrix in Low-Density Parity Check Codes
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.
Practical Evaluation of a Software Defined Cellular Network
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.
Low Leakage Asymmetric Stacked Sram Cell
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.
Consensus Building in Sensor Networks and Long Term Planning for the National Airspace System
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.
A Study of Mobility Models based on Spatial Node Distribution and Area Coverage
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.
Localization of UAVs Using Computer Vision in a GPS-Denied Environment
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.
Improving the Gameplay Experience and Guiding Bottom Players in an Interactive Mapping Game
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 form of preset patterns and a clustering based approach.
Arduino Based Hybrid MPPT Controller for Wind and Solar
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 a different microcontroller like the PIC16C63A produces a much faster frequency, which will reduce the inductor size needed and allow more current before over saturation.
An Interactive Framework for Teaching Fundamentals of Digital Logic Design and VLSI Design
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.
A Cognitive Radio Application through Opportunistic Spectrum Access
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, an interweave model is more challenging and is used in this thesis. Interweave model needs effective spectrum sensing before accessing the spectrum opportunistically. The system designed and simulated in this thesis is capable of transmitting an output from an IoT device using USRP and GNU radio through accessing the radio spectrum opportunistically.
Investigation of the Effect of Functional Units/Connectivity Arrangement on Energy Consumption of Reconfigurable Architectures Using an Interactive Design Framework
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.
Resilience of Microgrid during Catastrophic Events
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 from simulations and a built physical model of a resilient microgrid utilizing these principles.
Development of Wireless Sensor Network System for Indoor Air Quality Monitoring
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.
A Real-Time Electronic Sound Analysis System with Graphical User Interface
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.
An Arduino Based Control System for a Brackish Water Desalination Plant
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.
Machine Learning Improvements for Data Partitioning and Classification Applied to Cardiac Arrhythmia Signals
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.
Communication System over Gnu Radio and OSSIE
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.
Efficient Convolutional Neural Networks for Image Processing Applications
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.
Measurement and Analysis of Indoor Air Quality Conditions
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 the thesis. A graphical user interface is made for the user convenience to visualize graphically the data from the database. The designed system is tested and used for the measurement and analysis of the outdoor air quality. This data will be available in the database so it can be used for analyzing the air quality data for several days or months or years. Using the GrayWolf system and the designed outdoor air quality data measurement system, both the indoor and outdoor air quality was measured to analyze and correlate.
Implementations of Fuzzy Adaptive Dynamic Programming Controls on DC to DC Converters
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.
Emotion Recognition Using EEG Signals
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.
Analysis of the Integration of LEO Satellite Constellations into 5G Networks
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.
The Art and Science of Data Analysis
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.
Applications of Machine Learning for Remote Sensing and Environmental Monitoring
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.
Smart Microgrid Energy Management Using a Wireless Sensor Network
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.
Parameter Estimation Using Consensus Building Strategies with Application to Sensor Networks
Sensor network plays a significant role in determining the performance of network inference tasks. A wireless sensor network with a large number of sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in WSN is developing an efficient protocol which has a significant impact on the convergence of the network. Parameter estimation is one of the most important applications of sensor network. In order to model such large and complex networks for estimation, efficient strategies and algorithms which take less time to converge are being developed. To deal with this challenge, an approach of having multilayer network structure to estimate parameter and reach convergence in less time is estimated by comparing it with known gossip distributed algorithm. Approached Multicast multilayer algorithm on a network structure of Gaussian mixture model with two components to estimate parameters were compared and simulated with gossip algorithm. Both the algorithms were compared based on the number of iterations the algorithms took to reach convergence by using Expectation Maximization Algorithm.Finally a series of theoretical and practical results that explicitly showed that Multicast works better than gossip in large and complex networks for estimation in consensus building strategies.
Development and Application of Novel Computer Vision and Machine Learning Techniques
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 and Fit Factor Test of a Respirator with SAW-Based Sensor
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.
A Low-cost Wireless Sensor Network System Using Raspberry Pi and Arduino for Environmental Monitoring Applications
Sensors are used to convert physical quantity into numerical data. Various types of sensors can be coupled together to make a single node. A distributed array of these nodes can be deployed to collect environmental data by using appropriate sensors. Application of low powered short range radio transceivers as a communication medium between spatially distributed sensor nodes is known as wireless sensor network. In this thesis I build such a network by using Arduino, Raspberry Pi and XBee. My goal was to accomplish a prototype system so that the collected data can be stored and managed both from local and remote locations. The system was targeted for both indoor and outdoor environment. As a part of the development a controlling application was developed to manage the sensor nodes, wireless transmission, to collect and store data using a database management service. Raspberry Pi was used as base station and webserver. Few web based application was developed for configuring the network, real time monitoring, and database management. Whole system functions as a single entity. The use of open source hardware and software made it possible to keep the cost of the system low. The successful development of the system can be considered as a prototype which needs to be expanded for large scale environmental monitoring applications.
Development of Indium Oxide Nanowires as Efficient Gas Sensors
Crystalline indium oxide nanowires were synthesized following optimization of growth parameters. Oxygen vacancies were found to impact the optical and electronic properties of the as-grown nanowires. Photoluminescence measurements showed a strong U.V emission peak at 3.18 eV and defect peaks in the visible region at 2.85 eV, 2.66 eV and 2.5 eV. The defect peaks are attributed to neutral and charged states of oxygen vacancies. Post-growth annealing in oxygen environment and passivation with sulphur are shown to be effective in reducing the intensity of the defect induced emission. The as-grown nanowires connected in an FET type of configuration shows n-type conductivity. A single indium oxide nanowire with ohmic contacts was found to be sensitive to gas molecules adsorbed on its surface.
Distributed Source Coding with LDPC Codes: Algorithms and Applications
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.
A Feasibility Study of Cellular Communication and Control of Unmanned Aerial Vehicles
Consumer drones have used both standards such as Wi-Fi as well as proprietary communication protocols, such as DJI's OcuSync. While these methods are well suited to certain flying scenarios, they are limited in range to around 4.3 miles. Government and military unmanned aerial vehicles (UAVs) controlled through satellites allow for a global reach in a low-latency environment. To address the range issue of commercial UAVs, this thesis investigates using standardized cellular technologies for command and control of UAV systems. The thesis is divided into five chapters: Chapter 1 is the introduction to the thesis. Chapter 2 describes the equipment used as well as the test setup. This includes the drone used, the cellular module used, the microcontroller used, and a description of the software written to collect the data. Chapter 3 describes the data collection goals, as well as locations in the sky that were flown in order to gather experimental data. Finally, the results are presented in Chapter 4, which draws limited correlation between the collected data and flight readiness Chapter 5 wraps up the thesis with a conclusion and future areas for research are also presented.
Group Testing: A Practical Approach
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.
Design and Implementation of Communication Platform for Autonomous Decentralized Systems
This thesis deals with the decentralized autonomous system, in which individual nodes acting like peers, communicate and participate in collaborative tasks and decision making processes. An experimental test-bed is created using four Garcia robots. The robots act like peers and interact with each other using user datagram protocol (UDP) messages. Each robot continuously monitors for messages coming from other robots and respond accordingly. Each robot broadcasts its location to all the other robots within its vicinity. Robots do not have built-in global positioning system (GPS). So, an indoor localization method based on signal strength is developed to estimate robot's position. The signal strength that the robot gets from the nearby wireless access points is used to calculate the robot's position. Trilateration and fingerprint are some of the indoor localization methods used for this purpose. The communication functionality of the decentralized system has been tested and verified in the autonomous systems laboratory.
Practical Robust MIMO OFDM Communication System for High-Speed Mobile Communication
This thesis presents the design of a communication system (PRCS) which improves on all aspects of the current state of the art 4G communication system Long Term Evolution (LTE) including peak to average power ratio (PAPR), data reliability, spectral efficiency and complexity using the most recent state of the art research in the field combined with novel implementations. This research is relevant and important to the field of electrical and communication engineering because it provides benefits to consumers in the form of more reliable data with higher speeds as well as a reduced burden on hardware original equipment manufacturers (OEMs). The results presented herein show up to a 3 dB reduction in PAPR, less than 10-5 bit errors at 7.5 dB signal to noise ratio (SNR) using 4QAM, up to 3 times increased throughput in the uplink mode and 10 times reduced channel coding complexity.
Airbourne WiFi Networks Through Directional Antenna: An Experimental Study
In situations where information infrastructure is destroyed or not available, on-demand information infrastructure is pivotal for the success of rescue missions. In this paper, a drone-carried on demand information infrastructure for long-distance WiFi transmission system is developed. It can be used in the areas including emergency response, public event, and battlefield. The WiFi network can be connected to the Internet to extend WiFi access to areas where WiFi and other Internet infrastructures are not available. In order to establish a local area network to propagate WIFI service, directional antennas and wireless routers are used to create it. Due to unstable working condition on the flying drones, a precise heading turning stage is designed to maintain the two directional antennas facing to each other. Even if external interferences change the heading of the drones, the stages will automatically rotate back to where it should be to offset the bias. Also, to maintain the same flying altitude, a ground controller is designed to measure the height of the drones so that the directional antennas can communicate to each other successfully. To verify the design of the whole system, quite a few field experiments were performed. Experiments results indicates the design is reliable, viable and successful. Especially at disaster areas, it’ll help people a lot.
Electrical Equivalent Modeling of the Reverse Electrowetting-on-Dielectric (REWOD) Based Transducer along with Highly Efficient Energy Harvesting Circuit Design towards Self-Powered Motion Sensor
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 and voltage.
Integrating environmental data acquisition and low cost Wi-Fi data communication.
This thesis describes environmental data collection and transmission from the field to a server using Wi-Fi. Also discussed are components, radio wave propagation, received power calculations, and throughput tests. Measured receive power resulted close to calculated and simulated values. Throughput tests resulted satisfactory. The thesis provides detailed systematic procedures for Wi-Fi radio link setup and techniques to optimize the quality of a radio link.
The Effect of Mobility on Wireless Sensor Networks
Wireless sensor networks (WSNs) have gained attention in recent years with the proliferation of the micro-electro-mechanical systems, which has led to the development of smart sensors. Smart sensors has brought WSNs under the spotlight and has created numerous different areas of research such as; energy consumption, convergence, network structures, deployment methods, time delay, and communication protocols. Convergence rates associated with information propagations of the networks will be questioned in this thesis. Mobility is an expensive process in terms of the associated energy costs. In a sensor network, mobility has significant overhead in terms of closing old connections and creating new connections as mobile sensor nodes move from one location to another. Despite these drawbacks, mobility helps a sensor network reach an agreement more quickly. Adding few mobile nodes to an otherwise static network will significantly improve the network’s ability to reach consensus. This paper shows the effect of the mobility on convergence rate of the wireless sensor networks, through Eigenvalue analysis, modeling and simulation.
Development and Analysis of a Mobile Node Tracking Antenna Control System
A wireless communication system allows two parties to exchange information over long distances. The antenna is the component of a wireless communication system that allows information to be converted into electromagnetic radiation that propagates through the air. A system using an antenna with a highly directional beam pattern allows for high power transmission and reception of data. For a directional antenna to serve its purpose, it must be accurately pointed at the object it is communicating with. To communicate with a mobile node, knowledge of the mobile node's position must be gained so the directional antenna can be regularly pointed toward the moving target. The Global Positioning System (GPS) provides an accurate source of three-dimensional position information for the mobile node. This thesis develops an antenna control station that uses GPS information to track a mobile node and point a directional antenna toward the mobile node. Analysis of the subsystems used and integrated system test results are provided to assess the viability of the antenna control station.
Spectrum Analysis and Prediction Using Long Short Term Memory Neural Networks and Cognitive Radios
One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.
Design of a Wearable Flexible Resonant Body Temperature Sensor with Inkjet-Printing
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 state-of-the-art in applying these novel materials and techniques to enable flexible body temperature sensors for future biomedical applications.
Quantile Regression Deep Q-Networks for Multi-Agent System Control
Training autonomous agents that are capable of performing their assigned job without fail is the ultimate goal of deep reinforcement learning. This thesis introduces a dueling Quantile Regression Deep Q-network, where the network learns the state value quantile function and advantage quantile function separately. With this network architecture the agent is able to learn to control simulated robots in the Gazebo simulator. Carefully crafted reward functions and state spaces must be designed for the agent to learn in complex non-stationary environments. When trained for only 100,000 timesteps, the agent is able reach asymptotic performance in environments with moving and stationary obstacles using only the data from the inertial measurement unit, LIDAR, and positional information. Through the use of transfer learning, the agents are also capable of formation control and flocking patterns. The performance of agents with frozen networks is improved through advice giving in Deep Q-networks by use of normalized Q-values and majority voting.
Dual-Band Quarter Wavelength and Half Wavelength Microstrip Transmission Line Design
The thesis represents the design for dual-band quarter wavelength and half wavelength microstrip transmission line. Chapter 2 proposed the design of a novel dual-band asymmetric pi-shaped short-circuited quarter wavelength microstrip transmission line working at frequencies 1GHz and 1.55 GHz for 50Ω transmission line and at frequencies 1GHz and 1.43GHz for 60Ω transmission line. Chapter 3 proposed the design of a novel dual-band quarter wavelength microstrip transmission line with asymmetrically allocated open stubs and short-circuited stubs working at frequencies 1GHz and 1.32GHz. Chapter 4 proposed the design of dual-band pi-shaped open stub half wavelength microstrip transmission line working at frequencies 1GHz and 2.07GHz. Numerical simulations are performed both in HyperLynx 3D EM and in circuit simulator ADS for all of the proposed designs to measure the return loss (S11) and insertion loss (S12) in dB and phase response for S12 in degree.
Teaching Fundamentals of Digital Logic Design and VLSI Design Using Computational Textiles
This thesis presents teaching fundamentals of digital logic design and VLSI design for freshmen and even for high school students using e-textiles. This easily grabs attention of students as it is creative and interesting. Using e-textiles to project these concepts would be easily understood by students at young age. This involves stitching electronic circuits on a fabric using basic components like LEDs, push buttons and so on. The functioning of these circuits is programmed in Lilypad Arduino. By using this method, students get exposed to basic electronic concepts at early stage which eventually develops interest towards engineering field.
Moteino-Based Wireless Data Transfer for Environmental Monitoring
Data acquisition through wireless sensor networks (WSNs) has enormous potential for scalable, distributed, real-time observations of monitored environmental parameters. Despite increasing versatility and functionalities, one critical factor that affects the operation of WSNs is limited power. WSN sensor nodes are usually battery powered, and therefore the long-term operation of the WSN greatly depends on battery capacity and the node's power consumption rate. This thesis focuses on WSN node design to reduce power consumption in order to achieve sustainable power supply. For this purpose, this thesis proposes a Moteino-based WSN node and an energy efficient duty cycle that reduces current consumption in standby mode using an enhanced watchdog timer. The nodes perform radio communication at 915 MHz, for short intervals (180ms) every 10 minutes, and consume 6.8 mA at -14dBm. For testing, the WSN node monitored a low-power combined air temperature, relative humidity, and barometric pressure sensor, together with a typical soil moisture sensor that consumes more power. Laboratory tests indicated average current consumption of ~30µA using these short radio transmission intervals. After transmission tests, field deployment of a star-configured network of nine of these nodes and one gateway node provides a long-term platform for testing under rigorous conditions. A webserver running on a Raspberry Pi connected serially to the gateway node provides real-time access to this WSN.
Estimation of Drone Location Using Received Signal Strength Indicator
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.
Back to Top of Screen