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

In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.

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.

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 …

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.

Sight is essential in our daily tasks. Compensatory senses have been used for centuries by visually impaired individuals to navigate independently. The help of technology can minimize some challenges for visually impaired individuals. Assistive navigation technologies facilitate the pathfinding and tracing in indoor scenarios. Different modules are added to assistive navigation technologies to warn about the obstacles not only on the ground but about hanging objects. In this work, we attempt to explore new methods to assist visually impaired individuals in navigating independently in an indoor scenario. We employed a location estimation algorithm based on the fingerprinting method to estimate the initial location of the user. We mitigate the error of estimation with particle filter. The shortest path has been calculated with an A* algorithm. To provide the user with an accident-free experiment, we employed an obstacle avoidance algorithm capable of warning the users about the potential hazards. Finally, to provide an effective means of communication with the user, we employed text-to-speech and speech recognition algorithms. The main contribution of this work is to glue these modules together efficiently and affordably.

This thesis focuses on the recording challenges faced in biomedical systems. More specifically, the challenges in neural signal recording are explored. Instead of the typical synchronous ADC system, a level crossing ADC is detailed as it has gained recent interest for low-power biomedical systems. These systems take advantage of the time-sparse nature of the signals found in this application. A 10-bit design is presented to help capture the lower amplitude action potentials (APs) in neural signals. The design also achieves a full-scale bandwidth of 1.2 kHz, an ENOB of 9.81, a power consumption of 13.5 microwatts, operating at a supply voltage of 1.8 V. This design was simulated in Cadence using 180 nm CMOS technology.

A bidirectional two-hop relay network with decode-and-forward strategy is implemented using GNU Radio (software) and several USRPs (hardware) on Ubuntu (operating system). The relay communication system is comprised of three nodes; Base Station A, Base Station B, and Relay Station (the intermediate node). During the first time slot, Base Station A and Base Station B will each transmit data, e.g., a JPEG file, to Relay Station using DBPSK modulation and FDMA. For the final time slot, Relay Station will perform a bitwise XOR of the data, and transmit the XORed data to Base Station A and Base Station B, where the received data is decoded by performing another XOR operation with the original data.

Sensors have been an integral part of our life since a long time. Traditionally, the transmit information to a data collection center through a physical wire. However, with the introduction of Bluetooth Low Energy (BLE) communication protocol, more research is being done into the field of wireless sensor networks (WSN). BLE was introduced to target low power applications. The CC2650 Launchpad designed by Texas Instruments (TI) can lead to a bulky final product. The aim was to design hardware for the CC2650 micro-controller with the aim of making it more compact for use in WSNs. A top-down approach was used wherein the available product is studied to identify the redundant and reverse engineer it to design a new product. A 2 layer printed circuit board (PCB) was designed which resulted in a 64 percent decrease in size compared to the Launchpad. Also, experiments were performed to test the proof of concept.

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.

Channel diversity techniques are effective ways to combat channel fading and noise in communication systems. In this thesis, I compare the performance of source and channel diversity techniques on fading and symmetric continuous channels. My experiments suggest that when SNR is low, channel diversity performs better, and when SNR is high, source diversity shows better performance than channel diversity.

Low-density parity check (LDPC) codes are very popular among error correction codes because of their high-performance capacity. Numerous investigations have been carried out to analyze the performance and simplify the implementation of LDPC codes. Relatively slow convergence of iterative decoding algorithm affects the performance of LDPC codes. Faster convergence can be achieved by reducing the number of iterations during the decoding process. In this thesis, a new approach for faster convergence is suggested by choosing a systematic parity check matrix that yields lowest Second Smallest Eigenvalue Modulus (SSEM) of its corresponding Laplacian matrix. MATLAB simulations are used to study the impact of eigenvalues on the number of iterations of the LDPC decoder. It is found that for a given (n, k) LDPC code, a parity check matrix with lowest SSEM converges quickly as compared to the parity check matrix with high SSEM. In other words, a densely connected graph that represents the parity check matrix takes more iterations to converge than a sparsely connected graph.

This work presents a novel technique for data compression based on multi-residue number systems. The basic theorem is that an under-determined system of congruences could be solved to accomplish data compression for a signal satisfying continuity of its information content and bounded in peak-to -peak amplitude by the product of relatively prime moduli,. This thesis investigates this property and presents quantitative results along with MATLAB codes. Chapter 1 is introductory in nature and Chapter 2 deals in more detail with the basic theorem. Chapter 3 explicitly mentions the assumptions made and chapter 4 shows alternative solutions to the Chinese remainder theorem. Chapter 5 explains the experiments in detail whose results are mentioned in chapter 6. Chapter 7 concludes with a summary and suggestions for future work.

Since its invention, phased array has been extensively applied in both military and civil areas. The applications include target detecting and tracking, space probe communication, broadcasting, human-machine interfaces, and remote sensing. Although the phased array applications show a broad range of potential market, there are some limitations of phased array's development: high cost, complex structure, narrow bandwidth, and high power consumption. Therefore, novel ideas are needed to reduce these constraints. In this thesis, several new approaches about the design and application of phased array are presents. First, the principle of phased array and fundamental design equations are introduced. Second, a new application of phased array antenna for radar respiration measurement is presented. By integrating a 4×4 Butler matrix with four-element antenna array, there will be four distinct main beams in radiation pattern. This new approach can improve the measurement accuracy and realize a high detecting rate. Third, a compact phased array antenna system based on dual-band operations is introduced. Dual-band function can make N-antenna system obtain 2N unique radiation beams (N is an integer) and achieve a significant size reduction compared to the conventional single-band system. To verify the design concept, a four-element phased array antenna working at 5GHz and …

The thesis implements a vision-based leader-follower tracking algorithm on a ground robot system. One camera is the only sensor installed the leader-follower system and is mounted on the follower. One sphere is the only feature installed on the leader. The camera identifies the sphere in the openCV Library and calculates the relative position between the follower and leader using the area and position of the sphere in the camera frame. A P controller for the follower and a P controller for the camera heading are built. The vision-based leader-follower tracking algorithm is verified according to the simulation and implementation.

The thesis deals with the design and implementation of broadband and narrowband antennas and their applications in practical environment. In this thesis, a new concept for designing the UWB antenna is proposed based on the CRLH metamaterials and this UWB antenna covers a frequency range from 2.45 GHz to 11.6 GHz. Based on the design of the UWB antenna, another antenna is developed that can cover a very wide bandwidth i.e from 0.66 GHz to 120 GHz. This antenna can not only be used for UWB applications but also for other communication systems working below the UWB spectrum such as GSM, GPS, PCS and Bluetooth. The proposed antenna covering the bandwidth from 0.66 GHz to 120 GHz is by far the largest bandwidth antenna developed based on metamaterials. Wide band antennas are not preferred for sensing purpose as it is difficult to differentiate the received signals. A multiband antenna which can be used as a strain sensor for structural health monitoring is proposed. The idea is to correlate the strain applied along the length or width with the multiple resonant frequencies. This gives the advantage of detecting the strain applied along any direction (either length or width), thus increasing the …

In retrospect we can see that from the last century, wireless electronic technology has been in a rapid state of development. With the popularity of wireless communication, the power amplifier demand is rising. In general, magnitude, maximum noise figure, minimum noise figure, efficiency, and output power are important indicators of the amplifier. The IC industry is exploring how to reduce the additional cost and improve the high-frequency performance. Therefore, designing a strong adaptability and high cost performance of the PA has become a priority. As these technologies advance, the power amplifiers need to have better integration, lower cost, and lower power dissipation. Also, some special requirements are being asked in some areas, such as multi-mode and multi-band. In general, people have to use several power amplifiers parallel to frame a multifunction chip. Each of them working at different frequencies of interest has to have separate matching network, design, and area; also, the diversity amplifier prices will increase with the number of amplifiers, and its cost is also changed. In this thesis, because Class E power amplifier has lower power dissipation, 100% ideal efficiency, simple circuit structure, and strong applicability, the Class E is used as power amplifier in main stage. …

Piezoelectricity crystal is used in different area in industry, such as downhole oil, gas industry, and ballistics. The piezoelectricity crystals are able to create electric fields due to mechanical deformation called the direct piezoelectric effect, or create mechanical deformation due to the effect of electric field called the indirect piezoelectric effect. In this thesis, piezoelectricity effect is the core part. There are 4 parts in the frequency output pressure transducer: two crystal oscillators, phase-locked loop (PLL), mixer, frequency counter. Crystal oscillator is used to activate the piezoelectricity crystal which is made from quartz. The resonance frequency of the piezoelectricity crystal will be increased with the higher pressure applied. The signal of the resonance frequency will be transmitted to the PLL. The function of the PLL is detect the frequency change in the input signal and makes the output of the PLL has the same frequency and same phase with the input signal. The output of the PLL will be transmitted to a Mixer. The mixer has two inputs and one output. One input signal is from the pressure crystal oscillator and another one is from the reference crystal oscillator. The frequency difference of the two signal will transmitted to the …

Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as a neural signal, electrocardiogram (ECG), and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increases, data volume is increased resulting in impermissible transmitting power. This thesis work presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. This thesis work also includes the (reverse electrowetting-on-dielectric) REWOD based energy harvesting model for self-powered bio-sensor which utilizes the electrical energy generated through the process of conversion of mechanical energy to electrical energy. This REWOD based energy harvesting model can be a good alternative to battery usage, particularly for the bio-wearable applications. The comparative analysis of the results generated for voltage, current and capacitance of the rough surface model is compared to that of results of planar surface REWOD.

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.

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.

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.

A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.

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.