Matching Items (226)
Description
In this dissertation a new wideband circular HIS is proposed. The circular periodicity made it possible to illuminate the surface with a cylindrical TEMz wave and; a novel technique is utilized to make it wideband. Two models are developed to analyze the
reflection characteristics of the proposed HIS.
The circularly symmetric high impedance surface is used as a ground plane for the design of a low-profile loop and spiral radiating elements. It is shown that a HIS with circular periodicity provides a wider operational bandwidth for curvilinear radiating elements such, such as loops and spirals, compared to canonical rectangular HISs.
It is also observed that, with the aid of a circular HIS ground plane the gain of a loop and a spiral increases compared to when a perfect magnetic conductor (PMC) or rectangular HIS is used as a ground plane. The circular HIS was fabricated and the loop and spiral elements were placed individually in close proximity to it.
Also, due to the growing demand for low-radar signature (RCS) antennas for advanced airborne vehicles, curved and flexible HIS ground planes, which meet both the aerodynamic and low RCS requirements, have recently become popular candidates within the antenna and microwave technology. This encouraged us, to propose a spherical HIS where a 2-D curvature is introduced to the previously designed flat HIS.
The major problem associated with spherical HIS is the impact of the curvature on its reflection properties. After characterization of the flat circular HIS, which is addressed in the first part of this dissertation, a spherical curvature is introduced to the flat circular HIS and its impact on the reflection properties was examined when it was illuminated with the same cylindrical TEMz wave. The same technique, as for the flat HIS ground plane, is utilized to make the spherical HIS wideband. A loop and spiral element were placed in the vicinity of the curved HIS and their performanceswere investigated. The HISs were also fabricated and measurements were conducted to verify the simulations. An excellent agreement was observed.
reflection characteristics of the proposed HIS.
The circularly symmetric high impedance surface is used as a ground plane for the design of a low-profile loop and spiral radiating elements. It is shown that a HIS with circular periodicity provides a wider operational bandwidth for curvilinear radiating elements such, such as loops and spirals, compared to canonical rectangular HISs.
It is also observed that, with the aid of a circular HIS ground plane the gain of a loop and a spiral increases compared to when a perfect magnetic conductor (PMC) or rectangular HIS is used as a ground plane. The circular HIS was fabricated and the loop and spiral elements were placed individually in close proximity to it.
Also, due to the growing demand for low-radar signature (RCS) antennas for advanced airborne vehicles, curved and flexible HIS ground planes, which meet both the aerodynamic and low RCS requirements, have recently become popular candidates within the antenna and microwave technology. This encouraged us, to propose a spherical HIS where a 2-D curvature is introduced to the previously designed flat HIS.
The major problem associated with spherical HIS is the impact of the curvature on its reflection properties. After characterization of the flat circular HIS, which is addressed in the first part of this dissertation, a spherical curvature is introduced to the flat circular HIS and its impact on the reflection properties was examined when it was illuminated with the same cylindrical TEMz wave. The same technique, as for the flat HIS ground plane, is utilized to make the spherical HIS wideband. A loop and spiral element were placed in the vicinity of the curved HIS and their performanceswere investigated. The HISs were also fabricated and measurements were conducted to verify the simulations. An excellent agreement was observed.
ContributorsAmiri, Mikal Askarian (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T (Committee member) / Bakkaloglu, Bertan (Committee member) / Trichopoulos, Georgios C (Committee member) / Arizona State University (Publisher)
Created2018
Description
A Microbial fuel cell (MFC) is a bio-inspired carbon-neutral, renewable electrochemical converter to extract electricity from catabolic reaction of micro-organisms. It is a promising technology capable of directly converting the abundant biomass on the planet into electricity and potentially alleviate the emerging global warming and energy crisis. The current and power density of MFCs are low compared with conventional energy conversion techniques. Since its debut in 2002, many studies have been performed by adopting a variety of new configurations and structures to improve the power density. The reported maximum areal and volumetric power densities range from 19 mW/m2 to 1.57 W/m2 and from 6.3 W/m3 to 392 W/m3, respectively, which are still low compared with conventional energy conversion techniques. In this dissertation, the impact of scaling effect on the performance of MFCs are investigated, and it is found that by scaling down the characteristic length of MFCs, the surface area to volume ratio increases and the current and power density improves. As a result, a miniaturized MFC fabricated by Micro-Electro-Mechanical System(MEMS) technology with gold anode is presented in this dissertation, which demonstrate a high power density of 3300 W/m3. The performance of the MEMS MFC is further improved by adopting anodes with higher surface area to volume ratio, such as carbon nanotube (CNT) and graphene based anodes, and the maximum power density is further improved to a record high power density of 11220 W/m3. A novel supercapacitor by regulating the respiration of the bacteria is also presented, and a high power density of 531.2 A/m2 (1,060,000 A/m3) and 197.5 W/m2 (395,000 W/m3), respectively, are marked, which are one to two orders of magnitude higher than any previously reported microbial electrochemical techniques.
ContributorsRen, Hao (Author) / Chae, Junseok (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Phillips, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the $D_p$-divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the $D_p$-divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.
ContributorsWisler, Alan (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Liss, Julie (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017
Description
High-efficiency DC-DC converters make up one of the important blocks of state-of-the-art power supplies. The trend toward high level of transistor integration has caused load current demands to grow significantly. Supplying high output current and minimizing output current ripple has been a driving force behind the evolution of Multi-phase topologies. Ability to supply large output current with improved efficiency, reduction in the size of filter components, improved transient response make multi-phase topologies a preferred choice for low voltage-high current applications.
Current sensing capability inside a system is much sought after for applications which include Peak-current mode control, Current limiting, Overload protection. Current sensing is extremely important for current sharing in Multi-phase topologies. Existing approaches such as Series resistor, SenseFET, inductor DCR based current sensing are simple but their drawbacks such low efficiency, low accuracy, limited bandwidth demand a novel current sensing scheme.
This research presents a systematic design procedure of a 5V - 1.8V, 8A 4-Phase Buck regulator with a novel current sensing scheme based on replication of the inductor current. The proposed solution consists of detailed system modeling in PLECS which includes modification of the peak current mode model to accommodate the new current sensing element, derivation of power-stage and Plant transfer functions, Controller design. The proposed model has been verified through PLECS simulations and compared with a transistor-level implementation of the system. The time-domain parameters such as overshoot and settling-time simulated through transistor-level
implementation is in close agreement with the results obtained from the PLECS model.
Current sensing capability inside a system is much sought after for applications which include Peak-current mode control, Current limiting, Overload protection. Current sensing is extremely important for current sharing in Multi-phase topologies. Existing approaches such as Series resistor, SenseFET, inductor DCR based current sensing are simple but their drawbacks such low efficiency, low accuracy, limited bandwidth demand a novel current sensing scheme.
This research presents a systematic design procedure of a 5V - 1.8V, 8A 4-Phase Buck regulator with a novel current sensing scheme based on replication of the inductor current. The proposed solution consists of detailed system modeling in PLECS which includes modification of the peak current mode model to accommodate the new current sensing element, derivation of power-stage and Plant transfer functions, Controller design. The proposed model has been verified through PLECS simulations and compared with a transistor-level implementation of the system. The time-domain parameters such as overshoot and settling-time simulated through transistor-level
implementation is in close agreement with the results obtained from the PLECS model.
ContributorsBurli, Venkatesh (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2017
Description
The increasing demand for clean energy solutions requires more than just expansion, but also improvements in the efficiency of renewable sources, such as solar. This requires analytics for each panel regarding voltage, current, temperature, and irradiance. This project involves the development of machine learning algorithms along with a data logger for the purpose of photovoltaic (PV) monitoring and control. Machine learning is used for fault classification. Once a fault is detected, the system can change its reconfiguration to minimize the power losses. Accuracy in the fault detection was demonstrated to be at a level over 90% and topology reconfiguration showed to increase power output by as much as 5%.
ContributorsNavas, John (Author) / Spanias, Andreas (Thesis director) / Rao, Sunil (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where
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.
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.
ContributorsZhang, Sai (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017