Dissertations / Theses on the topic 'Department of Electrical Engineering and Computer Engineering'

This dissertation concerns the fabrication, analysis, and simulation of computer generated geometric phase holograms (CGHs). The current knowledge of CGHs is advanced to enable the creation of new sophisticated optical elements with unique characteristics. These elements enable new technologies related to displays, astronomy, sensing, beam-steering, beam-shaping, and more.

First, a novel direct-write system for CGH creation is presented. A mathematical description of the system is developed which allows the result of a given scan pattern to be predicted. The accuracy of the model is validated with various scan patterns, then a high-quality direct-write polarization grating and q-plate are fabricated for the first time.

With a system capable of creating CGHs, the most common and useful CGHs are explored in depth: the polarization grating, the geometric phase lens, and the Fourier geometric phase hologram. For each element, the possible scan patterns and parameters and their effect on the resulting element's quality are studied. Ultimately, the optimal scan patterns and parameters are found, then best-quality elements of each type are created and characterized.

Finally, a new tool for simulating periodic CGHs is developed. This begins with the derivation of the algorithm, which is based on the finite-difference time-domain (FDTD) method. Next tool's capabilities are verified by simulating many test structures and comparing the results to known solutions. The tool is used to simulate, for the first time, a CGH multiple beam splitter and a GPL array.

For generations, humans dreamed about the ability to communicate and interact with machines through thought alone or to create devices that can peer into a person’s mind and thoughts. Researchers have developed new technologies to create brain computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles. The objective of the first part of this thesis is to develop a new BCI based on electroencephalography (EEG) to move a computer cursor over a short training period in real time. The work motivations of this part are to increase: speed and accuracy, as in BCI settings, subject has a few seconds to make a selection with a relatively high accuracy.

Recently, improvements have been developed to make EEG more accurate by increasing the spatial resolution. One such improvement is the application of the surface Laplacian to the EEG, the second spatial derivative. Tripolar concentric ring electrodes (TCREs) automatically perform the Laplacian on the surface potentials and provide better spatial selectivity and signal-to-noise ratio than conventional EEG that is recorded with conventional disc electrodes. Another important feature using TCRE is the capability to record the EEG and the TCRE EEG (tEEG) signals concurrently from the same location on the scalp for the same electrical activity coming from the brain. In this part we also demonstrate that tEEG signals can enable users to control a computer cursor rapidly in different directions with significantly higher accuracy during their first session of training for 1D and 2D cursor control.

Output tracking control of non-minimum phase systems is a highly challenging problem encountered in many practical engineering applications. Classical inversion techniques provide exact output tracking but lead to internal instability, whereas modern inversion methods provide stable asymptotic tracking but produce large transient errors. Both methods provide an approximation of feedback control, which leads to non robust systems, very sensitive to noise, considerable tracking errors and a significant singularity problem. Aiming at the problem of system inversion to the true system, the objective of the second part of this thesis is to develop a new method based on true inversion for minimum phase system and approximate inversion for non-minimum phase systems. The proposed algorithm is automatic and has minimal computational complexities which make it suitable for real-time control.

The process to develop the proposed algorithm is partitioned into (1) minimum phase feedforward inverse filter, and (2) non-minimum phase inversion. In a minimum phase inversion, we consider the design of a feedforward controller to invert the response of a feedback loop that has stable zero locations. The complete control system consists of a feedforward controller cascaded with a closed-loop system. The outputs of the resulting inverse filter are delayed versions of the corresponding reference input signals, and delays are given by the vector relative degree of the closed-loop.

The latest advances in technology have facilitated the emergence of Wireless Sensor Networks (WSNs) as a promising technology. Because of their wide range of applications in industry, environmental monitoring, military and civilian domains, WSNs have become one of the most popular topics for research and development. The sensor nodes are low in cost and are simple in architecture. Wireless sensor networks are being employed in security-critical applications. However, their inherent characteristics make them prone to various security outbreaks that can negatively affect data collection. The current project presents an active detection-based routing scheme for WSNs, which can quickly create numerous detection routes as well as obtain nodal trust and thereby improve data security. Simulation results show that this scheme can detect Black Hole attacks, protect against them, and conserve energy, thus improving the network lifetime.

Over the last several decades, there has been consistent growth in the research and development of multilevel voltage-source inverter-based adjustable speed motor drives (ASDs) as a result of low cost, high reliability power semiconductors. The three-level neutral-point-clamped (NPC) ASD is a popular multilevel inverter used in low and medium voltage applications because of its ability to produce lower levels of total harmonic distortion (THD) and withstand higher voltages while preserving the rated output power compared to two-level ASDs.

As with other voltage-source inverters, three-level NPC ASDs produce common-mode voltage (CMV) that can cause motor shaft voltages, bearing currents, and excess voltage stresses on motor windings, resulting in the deterioration of motor bearings and insulation. Furthermore, the CMV and resultant currents can generate electromagnetic interference that can hinder the operation of sensitive control electronics. In this thesis, three carrier-based, three-level pulse-width-modulation (PWM) strategies were investigated to examine the levels of CMV, common-mode current, and dv/dt produced by the three-level NPC ASD. Additionally, the effects that each PWM strategy has on the THD in the output waveforms, as well as the total switching and conduction losses were analyzed through software simulation programs using a resistive-inductive load over a range of modulation indices. The first of the three methods, in-phase disposition sub-harmonic PWM (PD-SPWM), was verified experimentally using a laboratory-scale, 7.5 kVA three-level NPC ASD prototype.

It was determined that PD-SPWM produced the highest CMV amplitude of one-third the dc bus voltage, but the lowest values of differential-mode dv/dt, THD, and drive losses. The second strategy, phase-opposition (PO)-SPWM, reduced the CMV amplitude to one-sixth the dc bus voltage, at the cost of higher THD and drive losses and a doubling of the differential-mode dv/dt. The final strategy, zero common-mode (ZCM)-SPWM, was modified (MZCM-SPWM) to accommodate IGBT dead-time by delaying the output voltage transitions based on the polarity of the output currents and the direction of the commanded voltage transitions. The MZCM-SPWM method nearly eliminated all CMV pulses while maintaining comparable levels of THD, but produced twice the switching losses compared to PD- and PO- SPWM, and twice the differential-mode dv/dt compared to PD-SPWM.

Tunable diode laser absorption tomography (TDLAT) has emerged as a popular nonintrusive technique for simultaneous sensing of gas concentration and temperature. The gas concentration and temperature is computed by making light absorbance measurements using tunable diode lasers. Major challenges of TDLAT imaging include a highly nonlinear measurement process and availability of only a few light absorbance measurements. Therefore, TDLAT imaging of concentration and temperature is an ill-posed, nonlinear inverse problem. Conventional approaches to TDLAT primarily consist of making restrictive assumptions about the gas flow to simplify the problem.

In this thesis, we study the problem of reconstruction of TDLAT measurements into images representing 2D flow fields. We first propose a novel model-based iterative reconstruction (MBIR) framework for TDLAT imaging. To do this, we formulate a nonlinear measurement model for TDLAT that incorporates the physics of light absorbance through the gaseous media. In model based inference, apart from the measurement model, there also exists a model for the unknown signals to be reconstructed, called the prior model. We develop a non-Gaussian prior model based on a Gaussian mixture distribution that can be trained using a sparse training set. We set up an optimization problem using maximum a posteriori (MAP) estimation. In order to speed up the computation of the reconstruction algorithm, we propose a multigrid algorithm along with a majorization minimization framework to solve this optimization problem.

The inclusion of prior models can introduce bias in the reconstructions which is part of the well known bias variance trade off. This is particularly problematic if the training data used to tune the parameters of the prior model is not sufficient and representative. So, for the scenarios where there is limited training data available for training the prior model, we propose a novel hybrid Gaussian prior model by combining a conventional Gaussian distribution with a Gaussian Markov random field. We combine the two distributions using a mixing parameter γ ∈ [0, 1]. The hybrid prior produces reconstructions without overfitting the sparse training set.

Finally, we propose a systematic framework to indicate inaccuracies in the posterior distribution/model. This is extremely important when there is no ground truth available for the reconstructions. Inaccuracies in models can reflect in the form of errors in the reconstruction which can be hard to identify due to the lack of availability of the ground truth. We analyze the residual error between the absorbance measurements and the predicted absorbance values to identify unlikely patterns in the error. The existence of non-random structures or unexpected dynamic range of the residual error is an indication of possible modeling errors that may result in an inaccurate posterior distribution. Inaccuracy in the posterior distribution can arise either due to an inaccurate forward model, or an inaccurate prior model typically caused by insufficient or poor quality training data that is not representative of the true prior distribution. We look for inaccuracy in the posterior distribution by developing a metric based on hypothesis testing theory, and we demonstrate that we can detect when the posterior distribution is inaccurate by using the methods we propose.

A genetic algorithm from literature is adapted to design piezoelectric sonar sensors using results from physics simulations to optimize for a uniform voltage response over a wide range of frequencies. The adapted genetic algorithm produces valid sensor designs, and algorithm improvements are proposed. The best case general methods for bathymetric mapping with a sonar sensor is determined by sweeping various point selection algorithms, interpolation algorithms, and algorithm parameters. A set of methods are proposed based on how many sample points are used and what error metric is preferred. Additional algorithms are suggested for future improvements.

Supervisory Control and Data Acquisition (SCADA) systems monitor and control industrial control systems in many industrials and economic sectors which are considered critical infrastructure. In the past, most SCADA systems were isolated from all other networks, but recently connections to corporate enterprise networks and the Internet have increased. Security concerns have risen from this new found connectivity. This thesis makes one primary contribution to researchers and industry. Two datasets have been introduced to support intrusion detection system research for SCADA systems. The datasets include network traffic captured on a gas pipeline SCADA system in Mississippi State University’s SCADA lab. IDS researchers lack a common framework to train and test proposed algorithms. This leads to an inability to properly compare IDS presented in literature and limits research progress. The datasets created for this thesis are available to be used to aid researchers in assessing the performance of SCADA IDS systems.

Wireless devices in both office and home are playing a significant role in today’s communication system. The most known communication gadgets include cellular telephone and wireless local area network (WLAN) peripherals. Wireless connectivity products main target is to provide data access at any time but at the lowest possible data rate. In the last few years, the demand for higher data is increasing because the wireless devices became more popular. The fast development in wireless technology in recent years would initiate a new era of communication networks enabling data access everywhere at higher rates. The most challenging components at UWB PHY level are analog to digital converter and RF front ends since these circuit components need to perform in a broad range of frequency spectrum while consuming very little power and with little area overhead. Traditional design methodology practiced in the era of narrow band can’t meet the challenges of broadband system, thus new circuit topologies and design methodologies are needed. In this dissertation, a new radio frequency chain and low power analog-to-digital converters are proposed. The proposed converters process analog signals in a high range of frequencies with lower power consumption. The experimental results show significant improvement in the timing, throughput, and energy performances with a slight overhead in the circuit area.

The current project presents the development of R-Eye, a face detection and tracking system implemented as an embedded device based on the Arduino microcontroller. The system is programmed in Python using the Viola-Jones algorithm for image processing. Several experiments designed to measure and compare the performance of the system under various conditions show that the system performs well when used with an integrated camera, reaching a 93% face recognition accuracy for a clear face. The accuracy is lower when detecting a face with accessories, such as a pair of eyeglasses (80%), or when a low-resolution low-quality camera is used. Experimental results also show that the system is capable of detecting and tracking a face within a frame containing multiple faces.

This thesis explores techniques by which the accuracy of gas demand forecasts can be improved during extreme cold events. Extreme cold events in natural gas demand data are associated with large forecast error, which represents high business risk to gas distribution utilities.

This work begins by showing patterns associated with extreme cold events observed in natural gas demand data. We present a temporal pattern identification algorithm that identifies extreme cold events in the data. Using a combination of phase space reconstruction and a nearest neighbor classifier, we identify events with dynamics similar to those of an observed extreme event. Results obtained show that our identification algorithm (RPS-kNN) is able to successfully identify extreme cold events in natural gas demand data.

Upon identifying the extreme cold events in the data, we attempt to learn the residuals of the gas demand forecast estimated by a base-line model during extreme cold events. The base-line model overforecasts days before and underforecasts days after the coldest day in an extreme cold event due to an unusual response in gas demand to extreme low temperatures. We present an adjustment model architecture that learns the pattern of the forecast residuals and predicts future values of the residuals. The forecasted residuals are used to adjust the initial base model’s estimate to derive a new estimate of the daily gas demand. Results show that the adjustment model only improves the forecast in some instances.

Next, we present another technique to improve the accuracy of gas demand forecast during extreme cold events. We begin by introducing the Prior Day Weather Sensitivity (PDWS), an indicator that quantifies the impact of prior day temperature on daily gas demand. By investigating the complex relationship between prior day temperature and daily gas demand, we derived a PDWS function that suggests PDWS varies by temperature and temperature changes. We show that by accounting for this PDWS function in a gas demand model, we obtain a gas model with better predictive power. We present results that show improved accuracy for most unusual day types.

With a constantly changing technological landscape, the Engineering world is continually tasked with justifying the optimality of accepted standards and practices. The recent development of inexpensive simple microprocessors and linux computers has the potential to replace various methodologies for low energy, low-rate information transfer and control such as environmental monitoring, smart houses, smart lighting and others.

In the area of wireless sensor networks, a design standard is developing incorporating Xbee series 2 as a wireless bridge between Arduino or Raspberry Pi sensor and data aggregate nodes. In this thesis I construct an Xbee series 2 ZigBee wireless star topology network with an Arduino as a ZigBee End Device and Raspberry Pi as the ZigBee network coordinator.

The End Device uses an Arduino Uno v3 for local signal processing on a Parallax PMB-648 GPS and DS18B20 temperature sensor for periodic signal transmission via Xbee series 2. Xbee uses API mode 2 with escaping for package formation and transmission and is connected to the Arduino via the hardware serial port.

The Coordinator node consists of an Xbee Series 2 with Coordinator firmware communicating via the Raspberry Pi GPIO serial input ports. The Raspberry Pi uses specialized Python libraries to parse incoming API statements from active end devices.

The Raspberry Pi doubles as an internet gateway to an SQLite database run on a Ruby on Rails web application framework. The Raspberry Pi uses the Python requests library to transmit received End Device sensor measurements to the cloud server as URL parameters. The Ruby on Rails framework uses a Model View Controller architecture to pass data as URL parameters to an SQLite database, as well as display End Device sensor data on an interactive user interface upon a browser request. The user interface uses Gmaps4Rails to render an interactive map consisting of the GPS markers of reporting End Devices and their corresponding temperature measurements. The cloud server functions as a shared database linking multiple complete wireless sensor networks together under a single web app.

By testing End Device node lifetimes with various data transmission frequencies, an experimental relationship between Arduino/Xbee sleep duration and End Device lifetime is found. Using direct current measurements and information on the End Device hardware, a theoretical relationship between battery charge and End Device charge consumption during runtime is used to generate experimental equations relating End Device average current consumption during different phases in End Device lifetime. Multiple regression analysis is performed to derive an experimental value for the average current consumption of the End Device during all phases of operation, resulting in an experimental relationship between End Device average current and data transmission frequency. The above relationship was able to predict the average current for all End Device trials to within 5% error. (Abstract shortened by ProQuest.)

This thesis report has introduced the UWB Indoor Localization System. In the beginning, this thesis report has explained the Indoor Localization System and presented existing techniques (such as Wi-Fi and Bluetooth) to construct an Indoor Localization System. Then, this thesis report has discussed the Ultra Wideband Radio fundamentals to analyze its construction and operating mechanism. During the transmission, the UWB signals will pass an additive white Gaussian noise channel with multipath effects, which cause errors in the values of bits. This thesis report has studied different solutions (such as Modulation Methods and Rake Receiver) to improve the bit error rate in different situations (such as Multipath-free AWGN channel). Next, this thesis report utilizes the UWB Radio fundamentals to show and compare different positioning algorithms (such as TOA and AOA). This thesis report focuses on TOA algorithm. For TOA algorithm, this thesis report has analyzed the IEEE UWB standards and the UWB Radio fundamentals to present and compare different types of receivers. Finally, this thesis report has studied algorithms (such as WLS) to solve non-linear equations to find the position of a mobile station with NLOS effects. In this thesis report, an algorithm (removing excess delay) has been used to mitigate NLOS effects with the simulation based on IEEE 802.15.4a channels. The simulation results are shown in chapter 12, and the average positioning error is around 7 cm.

In a three-dimensional world, for perception of the objects around us, we not only wish to classify them, but also know where these objects are. The task of object detection combines both classification and localization. In addition to predicting the object category, we also predict where the object is from sensor data. As it is not known ahead of time how many objects that we have interest in are in the sensor data and where are they, the output size of object detection may change, which makes the object detection problem difficult.

In this dissertation, I focus on the task of object detection, and use fusion to improve the detection accuracy and robustness. To be more specific, I propose a method to calculate measure of conflict. This method does not need external knowledge about the credibility of each source. Instead, it uses the information from the sources themselves to help assess the credibility of each source. I apply the proposed measure of conflict to fuse independent sources of tracking information from various stereo cameras. Besides, I propose a computational intelligence system for more accurate object detection in real--time. The proposed system uses online image augmentation before the detection stage during testing and fuses the detection results after. The fusion method is computationally intelligent based on the dynamic analysis of agreement among inputs. Comparing with other fusion operations such as average, median and non-maxima suppression, the proposed methods produces more accurate results in real-time. I also propose a multi--sensor fusion system, which incorporates advantages and mitigate disadvantages of each type of sensor (LiDAR and camera). Generally, camera can provide more texture and color information, but it cannot work in low visibility. On the other hand, LiDAR can provide accurate point positions and work at night or in moderate fog or rain. The proposed system uses the advantages of both camera and LiDAR and mitigate their disadvantages. The results show that comparing with LiDAR or camera detection alone, the fused result can extend the detection range up to 40 meters with increased detection accuracy and robustness.