Master of Science / Department of Electrical and Computer Engineering / William B. Kuhn / Inductors have been produced in LTCC in a unique manner that increases the cross-sectional area of the conductor. The method uses metal-filled trenches and cavities in the tape to create conductors which are as thick as an entire layer of tape. This geometry helps to compensate for high-frequency non-idealities such as skin effect, current crowding, and proximity effect. An array of test structures has been fabricated. The measured results achieved inductors with Qs of 60 and suggest that Qs up to 100 are possible. Accurate measurements of such values require careful consideration of error sources and are discussed. A potential application of the inductors is presented in a two-pole filter, which has been modeled and fabricated. Lastly, a list of conclusions which would be helpful for future work on this subject is presented.

Master of Science / Department of Electrical and Computer Engineering / William B. Kuhn / With the increasing number of satellites and space debris in all orbits the need for individual satellites to be able to autonomously detect and determine methods to navigate around them is increasing. Even with continued input and control from a ground station, the ability for a satellite to act to save itself from obstacles not visible from ground stations, or if communications were temporarily lost could be key to saving millions of dollars in hardware as well as improving overall performance and operational lifetimes.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Anil Pahwa, Sanjoy Das / Occurrence of outages in overhead distribution systems is a significant factor in determining distribution system reliability. Analysis of animal-related outages has practical value since animals cause a large number of outages in overhead distribution systems. This dissertation presents several different methods to investigate the impact of weather and time of the year on the animal-related outage rate. The animal-related outages from year 1998 to year 2007 for different cities in Kansas are provided by Westar Energy. From examinations of the historical data, two factors which influence the animal-related outages, the month type and the number of fair weather days are taken as inputs along with historical outage data for prediction models. Poisson regression model, neural network model, wavelet based neural network model and Bayesian model combined with Monte Carlo simulations are applied to the weekly data of different cites. Even though Poisson regression models, Bayesian models and neural network models are able to recognize the changing pattern of outage rates under different weather conditions, they are limited in their ability to follow the high peaks in the time series of weekly animal-related outages. The introduction of wavelet transform techniques overcomes this problem. Simulation results indicate that the wavelet based neural network models are able to capture the pattern of fast fluctuations in the weekly outages of different cities in Kansas of various sizes. A hyperpermutation method inspired by artificial immune system algorithm is used to solve the overtraining problem in the application of neural networks. Finally, Monte Carlo simulations based on conditional probability tables from Bayesian models are used to find out the confidence intervals of the predictions. We aggregate the weekly data and carry out the analysis on a monthly and yearly basis too. Simulation results indicate that the models are able to capture the pattern as at least 90% of the observed values are within the upper limits of 95% confidence in the predictions for weekly, monthly and yearly animal-related outages of different cities in Kansas. The results obtained from Monte Carlo simulations are compared with the wavelet based neural network model to indentify years with more than expected level of outages.

Master of Science / Department of Electrical and Computer Engineering / William B. Kuhn / Excitement about using ultra-wideband (UWB) technology for networking has grown considerably over the last few years. UWB has several strengths, including high data rates, security, and robustness in multipath environments. Despite these benefits, UWB has been scrutinized for its potential to interfere with narrowband technologies like the Global Positioning System (GPS). Until recently, much of the literature about UWB and GPS compatibility has been published on the basis of theoretical analysis alone. We have investigated the compatibility of UWB and GPS signals using theoretical analysis as well as laboratory measurements with a consumer GPS receiver and a WiMedia UWB device. We conclude from our tests that the UWB device does emit interference in the GPS L1 band, but the interference is low enough that careful antenna and chipset placement could allow UWB and GPS radios to coexist in a single product. Also, we have evaluated several UWB antennas to determine their fitness for use in a handheld electronic product. We find that the antennas’ gain pattern and return loss do not have a significant effect upon the data throughput of the UWB system. Thus, we infer that the indoor environment is highly dispersive and that the UWB system is able to exploit multipath propagation. Furthermore, we have created a link budget to estimate the distances over which a WiMedia UWB system should be capable of operating. In the lab, the maximum distances over which the UWB device actually operated were about half of what we expected. This suggests that the path loss exponent and standard deviation of fading could be higher than we expected or that the implementation loss of the UWB chipset is quite high. Currently the market potential of UWB is uncertain. If UWB is embraced by the consumer electronics industry as the wireless platform of choice for Certified Wireless USB and high-speed Bluetooth technology, it could become a ubiquitous networking feature for electronic products such as phones, laptops, cameras, media players, and portable navigation devices. In this thesis, we strive to provide information that would be useful when undertaking a GPS and UWB radio integration project.

Master of Science / Department of Electrical and Computer Engineering / James E. DeVault / Working with the subject of sensorless motor control requires an understanding of several topical areas; this report presents an understanding that was gained during this research work. The fundamentals of electric motors (particularly brushless motors) are developed from first principles and the basic models are discussed. The theory of sinusoidal synchronous motors is reviewed (phasor analysis of the single phase equivalent circuit). The concept of a complex space vector is introduced and developed using a working knowledge of the sinusoidal synchronous motor. This leads to the presentation of the space vector model of the permanent magnet synchronous motor, in both the stationary and rotor reference frames. An overview of the operation of three-phase voltage source inverters is given, followed by an explanation of space vector modulation and its relationship to regular sinusoidal pulse width modulation. Torque control of the permanent magnet synchronous machine is reviewed in several reference frames and then rotor-flux-field-oriented-control is explained. Finally, some schemes for sensorless operation are discussed.

Master of Science / Department of Electrical and Computer Engineering / Steven Warren / The greenhouse in Throckmorton Hall at Kansas State University (KSU) has a temperature and humidity monitoring system. The system updates its measurements every thirty minutes online, and air temperature is controlled by an automated system. Each room has one temperature and humidity sensor box, which provides a suitable reference but is insufficient for more detailed plant research. To provide a distribution of temperature and humidity, a sensing system should be composed of a collection of sensors that gather data simultaneously. The new multi-point greenhouse monitoring system presented here can be helpful for plant research on a low budget. The demonstration system uses 27 sensor boxes in a 3x3x3 sensor grid (nine sensors at the same height and three different heights). Each sensor box contains temperature, humidity and light sensors that record data once per minute. MATLAB plots of these data indicate that temperature varied between 20 and 25 °C at night. Daytime temperatures are increased by sunlight, and rise to a maximum around noon. Sun-lit areas have higher temperatures than shaded areas, and during cloudy days all areas were almost the same temperature. Relative humidity is inversely related to temperature changes; when the temperature is stable, humidity is also stable. Humidity drops at noon because of increasing temperature and rises again at night. When researchers water the plants, humidity increases immediately. Greenhouse light intensity depends on the room design and the angle of the sunlight. Direct sunlight makes an obvious difference in shaded areas, and cloudy days promote even light distribution. Lighting at night time diffuses well at lower heights.

Master of Science / Department of Electrical and Computer Engineering / Caterina M. Scoglio / Forecasting of network traffic plays a very important role in many domains such as congestion control, adaptive applications, network management and traffic engineering. Characterizing the traffic and modeling are necessary for efficient functioning of the network. A good traffic model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD), self-similarity, and heavy-tailed distribution. Because of the persistent dependence, modeling LRD time series is a challenging task. In this thesis, we propose a non-linear time series model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) of order p and q, with innovation process generalized to the class of heavy-tailed distributions. The GARCH model is an extension of the AutoRegressive Conditional Heteroskedasticity (ARCH) model, has been used in many financial data analysis. Our model is fitted on a real data from the Abilene Network which is a high-performance Internet-2 backbone network connecting research institutions with 10Gbps bandwidth links. The analysis is done on 24 hours data of three different links aggregated every 5 minutes. The orders are selected based on the minimum modified Akaike Information Criterion (AICC) using Introduction to Time Series Modeling (ITSM) tool. For our model the best minimum order was found to be (1,1). The goodness of fit is evaluated based on the Q-Q (t-distributed) plot and the ACF plot of the residuals and our results confirm the goodness of fit of our model. The forecast analysis is done using a simple one-step prediction. The first 24 hrs of the data set are used as the training part to model the traffic; the next 24 hrs are used for performing the forecast and the comparison. The actual traffic data and the predicted traffic data is compared to evaluate the performance of the model. Based on the prediction error the performance metrics are evaluated. A comparative study of GARCH model with other existing models is performed and our results confirms the simplicity and the better performance of our model. The complexity of the model is measured based on the number of parameters to be estimated. From this study, the GARCH model is found to have the ability to forecast aggregated traffic but further investigation need to be conducted on a less aggregated traffic. Based on the forecast model developed from the GARCH model, we also intend to develop a dynamic bandwidth allocation algorithm as a future work.

Master of Science / Department of Electrical and Computer Engineering / William B. Kuhn / In microchip circuitry, the tiny size of inductors creates low Q values, limiting a bandpass filter’s ability to have narrow bandwidths at RF frequencies. To counter this problem and also compensate for losses, Q-enhancement can be implemented to facilitate narrower bandwidths and boost gain. With Q-enhancement, temperature sensitivity of the circuitry causes the filter parameters to drift over time, making it necessary to adjust the filter periodically in order to keep the filter centered at the desired frequency. With the proper additional on-chip circuits used with a microprocessor, a tuning algorithm makes it possible to automatically tune the filter in-situ. The algorithm is based on increasing Q-enhancement until the filter begins to oscillate, reading the frequency of oscillation, adjusting to the desired frequency, and then decreasing Q-enhancement until the filter no longer oscillates. A 500MHz single-pole differential filter was designed with an on-chip amplitude detector and frequency prescaler to facilitate tuning. The filter was made adjustable across frequency with banks of binary weighted switchable capacitors. Q-enhancement adjustment was achieved via banks of cross-coupled FETs, also binary weighted. The circuit was fabricated in 0.5μm silicon-on-sapphire technology. The finished filter chip was controlled with a PIC microprocessor which had been programmed in C with the tuning algorithm. With the tuning algorithm in place, the filter was successfully able to align itself to within ±1MHz of the desired 500MHz center frequency. Q-enhancement levels were also able to self-adjust to maintain a desired bandwidth. An improved design based around an off-chip coupled-resonator two-pole filter has also been designed. This filter includes adjustable coupling capacitance between the two poles, which also must be tuned. A new method of tuning is proposed for such applications. The properties of a two-pole filter cause it to oscillate at two frequencies with Q-enhancement. A modified amplitude detector is capable of reading the beat frequency which results from the two oscillations, a value which relates directly to and allows tuning of the bandwidth of the filter.

Master of Science / Department of Electrical and Computer Engineering / D. V. Satish Chandra / Biometric technologies are the foundation of personal identification systems. A biometric system recognizes an individual based on some characteristics or processes. Characteristics used for recognition include features measured from face, fingerprints, hand geometry, handwriting, iris, retina, vein, signature and voice. Among the various techniques, iris recognition is regarded as the most reliable and accurate biometric recognition system. However, the technology of iris coding is still at an early stage. Iris recognition system consists of a segmentation system that localizes the iris region in an eye image and isolates eyelids, eyelashes. Segmentation is achieved using circular Hough transform for localizing the iris and pupil regions, linear Hough transform for localizing the eyelids and thresholding for detecting eyelashes. The segmented iris region is normalized to a rectangular block with fixed polar dimensions using Daugman’s rubber sheet model. The work presented in this report involves extraction of iris templates using the algorithms developed by Daugman. Features are then extracted from these templates using wavelet transform to perform the recognition task. Method of extracting features using cumulative sums is also investigated. Iris codes are generated for each cell by computing cumulative sums which describe variations in the grey values of iris. For determining the performance of the proposed iris recognition systems, CASIA database and UBRIS.v1 database of digitized grayscale eye images are used. K-nearest neighbor and Hamming distance classifiers are used to determine the similarity between the iris templates. The performance of the proposed methods is evaluated and compared.

Master of Science / Department of Electrical and Computer Engineering / Christopher L. Lewis / This thesis presents a novel approach for detecting robust and scale invariant interest points in images. The detector accurately and efficiently approximates the Laplacian of Gaussian using an optimal set of weighted box filters that take advantage of integral images to reduce computations. When combined with state-of-the art descriptors for matching, the algorithm performs better than leading feature tracking algorithms including SIFT and SURF in terms of speed and accuracy.