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 …

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 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, …

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.

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 …

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.

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.

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.

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 …

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.

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, …

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 …

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.

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.

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 …

The major advantages of radio frequency identification (RFID) technology over barcodes are that the RFID-tagged objects do not require to be in line-of-sight with the reader for their identification and multiple objects can be read simultaneously. But when multiple objects are read simultaneously there is always a problem of collision which reduces the efficiency of the system. This thesis presents a comprehensive study of the dynamic framed slotted ALOHA (DFSA)-based anti-collision multi-tag identification algorithms for passive RFID system. Performance of various DFSA algorithms is compared through extensive simulation results. In addition, a number of simple performance improvement techniques have also been investigated in this thesis, including improved estimation techniques for the number of tags in each read cycle and a low-complexity heuristic stopping criterion that can be easily implemented in the practical system.