ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: engineering
- Creators: Tepedelenlioğlu, Cihan
The gain and loss factor of conical horns are revisited in this dissertation based on
spherical and quadratic aperture phase distributions. The gain is compared with published classical data in an attempt to confirm their validity and accuracy and to determine whether they were derived based on spherical or quadratic aperture phase distributions. In this work, it is demonstrated that the gain of a conical horn antenna obtained by using a spherical phase distribution is in close agreement with published classical data. Moreover, more accurate expressions for the loss factor, to account for amplitude and phase tapers over the horn aperture, are derived. New formulas for the design of optimum gain conical horns, based on the more accurate spherical aperture phase distribution, are derived.
To better understand the impact of edge diffractions on aperture antenna performance, an extensive investigation of the edge diffractions impact is undertaken in this dissertation for commercial aperture antennas. The impact of finite uncoated and coated PEC ground plane edge diffractions on the amplitude patterns in the principal planes of circular apertures is intensively examined. Similarly, aperture edge diffractions of aperture antennas without ground planes are examined. Computational results obtained by the analytical model are compared with experimental and HFSS-simulated results for all cases studied. In addition, the impact of the ground plane size, coating thickness, and relative permittivity of the dielectric layer on the radiation amplitude in the back region has been examined.
This investigation indicates that the edge diffractions do impact the main forward lobe pattern, especially in the E plane. Their most significant contribution appears in far side and back lobes. This work demonstrates that the finite edge contributors must be considered to obtain more accurate amplitude patterns of aperture antennas.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.