This thesis dissertation mainly compares and investigates laboratory study of different implementation methodologies of applied control systems and how they can be adopted in industrial, as well as commercial, automation applications. Namely the research paper aims to assess or evaluate eventual feedback control loops' performance and robustness over multiple conventional or state-of-the-art technologies in the field of applied industrial automation and instrumentation by implementing a laboratory case study setup: the ball on beam system. Hence, the paper tries to close the gap between industry and academia by: first, conducting a historical study and background information of main evolutional and technological eras in the field of industrial process control automation and instrumentation. Then, some related basic theoretical as well as practical concepts are reviewed in Chapter 2 of the report before displaying the detailed design. After that, the next Chapter, analyses the ball on beam control system problem as the case studied in the context of this research through reviewing previous literature, modeling and simulation. The following Chapter details the proposed design and implementation of the ball on beam case study as if it is under the introduced distributed industrial automation architecture. Finally, Chapter 5 concludes this work by listing several …

A wireless sensor network (WSN) is a spatially distributed network used to monitor the physical and environmental conditions such as temperature, pressure, sound, humidity, heat, etc. WSNs can be modeled using different simulation frameworks like OMNeT++, Prowler, Atarraya, PiccSIM, Network Simulator, etc. In this research, Simulink framework was used to model WSN system. The complete WSN consisting of transmitting nodes, communication channel, and receiver nodes are built in the Simulink framework. Orthogonal frequency division multiplexing technique was used to transmit the information. The implemented wireless sensor system behavior is studied using temperature as the measurement parameter at different values of signal to noise ratio. The plots of bit error rate versus signal to noise ratio and frame error rate versus signal to noise ratio are generated in the Simulink framework. It is easy to study the effect of different physical layer parameters on the performance of wireless sensor networks by implementing WSN in the Simulink framework.

This Master thesis proposes a human-computer interface for individual with limited hand movements that incorporate the use of facial gesture as a means of communication. The system recognizes faces and extracts facial gestures to map them into Morse code that would be translated in English in real time. The system is implemented on a MACBOOK computer using Python software, OpenCV library, and Dlib library. The system is tested by 6 students. Five of the testers were not familiar with Morse code. They performed the experiments in an average of 90 seconds. One of the tester was familiar with Morse code and performed the experiment in 53 seconds. It is concluded that errors occurred due to variations in features of the testers, lighting conditions, and unfamiliarity with the system. Implementing an auto correction and auto prediction system will decrease typing time considerably and make the system more robust.

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.

This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition and a feedback unit called head pose estimation is implemented to validate the correctness of predicted gestures. This entire system was developed using Python programming language and other supporting libraries like OpenCV, Tensor flow and Dlib to perform various image processing and machine learning tasks. This entire application can be deployed as a web application using Flask to make it operating system independent.

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 …

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.

Through this research, engineering students and conservation biologists constructed an off-grid video system for observing western burrowing owls in El Paso, Texas. The burrowing owl has a declining population and their range decreasing, driving scientists' interest to see inside the den for observing critical nesting behavior. Texas Parks and Wildlife Department (TPWD) biologists wanted videos from inside the dark, isolated hillside owl burrows. This research yielded a replicable multi-camera prototype, empowering others to explore applications of engineering and wildlife monitoring. The remote station used an off-the-shelf video recording system, solar panels, charge controller, and lead acid batteries. Four local K-12 science educators participated in system testing at Lake Ray Roberts State Park through the Research Experiences for Teachers (RET, NSF #1132585) program, as well as four undergraduate engineering students as senior design research.

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

The goal of the proposed device and software architecture is to apply the functionality of mixed reality (MR) in order to make a virtual environment that is more accessible to the visually-impaired. We propose a glove-based system for MR that will use finger and hand movement tracking along with tactile feedback so that the visually-impaired can interact with and obtain a more detailed sense of virtual objects and potentially even virtual environments. The software architecture makes current MR frameworks more accessible by augmenting the existing software and extensive 3D model libraries with both the interfacing of the glove-based system and the audibly navigable user interface (UI) of a virtual environment we have developed. We implemented a circuit with finger flexion/extension tracking for all 5 fingers of a single hand and variable vibration intensities for the vibromotors on all 5 fingertips of a single hand. The virtual environment can be hosted on a Windows 10 application. The virtual hand and its fingers can be moved with the system's input and the virtual fingertips touching the virtual objects trigger vibration motors (vibromotors) to vibrate while the virtual objects are being touched. A rudimentary implementation of picking up and moving virtual objects inside …

ZigBee is a substandard of IEEE 802.15 family that is specially designed to take care of factors such as power, data rate and area that primarily affect network performance. This has controlling and monitoring capability, which finds potential applications in different sectors. ZigBee allows the concept of hybrid networks and mobility. A comprehensive analysis of ZigBee networks was carried out by constructing and simulating the networks to evaluate the performance in terms of throughput, delay, network load, and packets dropped. This research is aimed at evaluating the effect of network topology on the system performance. A careful review of simulation platforms brought the conclusion of using OPNET Modeler which has the required frame work. Different network topologies of simple and hybrid were built and simulated. Throughout the simulations, the best-case scenarios were drawn to the conclusion by the graphical analysis of parameters of evaluation. Mobile networks were constructed and simulated to investigate the effect of mobility on communication.

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.

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 …

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

This thesis offers the design and improvement of a 2 GHz to 20 GHz low noise amplifier (LNA) utilizing pHEMT technology. The pHEMT technology allows the LNA to generate a boosted signal at a lower noise figure (NF) while consuming less power and achieving smooth overall gain. The design achieves an overall gain (S21) of ≥ 10 dB with an NF ≤ 2 dB while consuming ≤ 30 mA of power while using commercial off-the-shelf (COTS) components.

CGRAs (coarse grained reconfigurable array architectures) try to fill the gap between FPGAs and ASICs. Over three decades, the research towards CGRA design has produced number of architectures. Each of these designs lie at different points on a line drawn between FPGAs and ASICs, depending on the tradeoffs and design choices made during the design of architectures. Thus, design space exploration (DSE) takes a very important role in the circuit design process. In this work I propose the design space exploration of CGRAs can be done quickly and efficiently through crowd-sourcing and a game driven approach based on an interactive mapping game UNTANGLED and a design environment called SmartBricks. Both UNTANGLED and SmartBricks have been developed by our research team at Reconfigurable Computing Lab, UNT. I present the results of design space exploration of domain-specific reconfigurable architectures and compare the results comparing stripe vs mesh style, heterogeneous vs homogeneous. I also compare the results obtained from different interconnection topologies in mesh. These results show that this approach offers quick DSE for designers and also provides low power architectures for a suite of benchmarks. All results were obtained using standard cell ASICs with 90 nm process.

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

The survival rate for a suddent cardiac arrest (SCA) is incredibly low, with less than one in ten surviving; most SCAs occur outside of a hospital setting. There is a need to develop an effective and efficient system that can sense, communicate and remediate potential SCA situations on a near real-time basis. This research presents a novel Zeolite-PDMS-based optically unobtrusive flexible dry electrodes for biosignal acquisition from various subjects while at rest and in motion. Two zeolite crystals (4A and 13X) are used to fabricate the electrodes. Three different sizes and two different filler concentrations are compared to identify the better performing electrode suited for electrocardiogram (ECG) data acquisition. A low-power, low-noise amplifier with chopper modulation is designed and implemented using the standard 180nm CMOS process. A commercial off-the-shelf (COTS) based wireless system is designed for transmitting ECG signals. Further, this dissertation provides a framework for Machine Learning Classification algorithms on large, open-source Arrhythmia and SCA datasets. Supervised models with features as the input data and deep learning models with raw ECG as input are compared using different methods. The machine learning tool classifies the datasets within a few minutes, saving time and effort for the physicians. The experimental results …

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

One of the challenges of utilizing higher frequencies in the RF spectrum, for any number of applications, is the hardware constraints of analog-to-digital converters (ADCs). Since mid-20th century, we have accepted the Nyquist-Shannon Sampling Theorem in that we need to sample a signal at twice the max frequency component in order to reconstruct it. Compressive Sampling (CS) offers a possible solution of sampling sub-Nyquist and reconstructing using convex programming techniques. There has been significant advancements in CS research and development (more notably since 2004), but still nothing to the advantage of everyday use. Not for lack of theoretical use and mathematical proof, but because of no implementation work. There has been little work on hardware in finding the realistic constraints of a working CS system used for digital signal process (DSP). Any parameters used in a system is usually assumed based on stochastic models, but not optimized towards a specific application. This thesis aims to address a minimal viable platform to implement compressive sensing if applied to a wireless sensor network (WSN), as well as address certain parameters of CS theory to be modified depending on the application.

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, …

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.

In this thesis, I present my work on the modeling and control of a motor system using the Lego EV3 robot. The overall goal is to apply introductory systems and controls engineering techniques for estimation and design to a real-world system. First I detail the setup of materials used in this research: the hardware used was the Lego EV3 robot; the software used was the Student 2014 version of Simulink; a wireless network was used to communicate between them using a Netgear WNA1100 wifi dongle. Next I explain the approaches used to model the robot’s motor system: from a description of the basic system components, to data collection through experimentation with a proportionally controlled feedback loop, to parameter estimation (through time-domain specification relationships, Matlab’s curve-fitting toolbox, and a formal least-squares parameter estimation), to the discovery of the effects of frictional disturbance and saturation, and finally to the selection and verification of the final model through comparisons of simulated step responses of the estimated models to the actual time response of the motor system. Next I explore three different types of controllers for use within the motor system: a proportional controller, a lead compensator, and a PID controller. I catalogue the …

In this dissertation a computationally efficient cognitive multiple-input multiple-output (MIMO) orthogonal frequency division duplexing (OFDM) detector is designed to decode perfect space-time coded signals which are able maximize the diversity and multiplexing properties of a rich fading MIMO channel. The adaptive nature of the cognitive detector allows a MIMO OFDM communication system to better meet to needs of future wireless communication networks which require both high reliability and low run-time complexity depending on the propagation environment. The cognitive detector in conjunction with perfect space-time coding is able to achieve up to a 2 dB bit-error rate (BER) improvement at low signal-to-noise ratio (SNR) while also achieving comparable runtime complexity in high SNR scenarios.