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.
Displaying 1 - 10 of 12
Filtering by
- All Subjects: Electrical Engineering
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Spectral congestion is quickly becoming a problem for the telecommunications sector. In order to alleviate spectral congestion and achieve electromagnetic radio frequency (RF) convergence, communications and radar systems are increasingly encouraged to share bandwidth. In direct opposition to the traditional spectrum sharing approach between radar and communications systems of complete isolation (temporal, spectral or spatial), both systems can be jointly co-designed from the ground up to maximize their joint performance for mutual benefit. In order to properly characterize and understand cooperative spectrum sharing between radar and communications systems, the fundamental limits on performance of a cooperative radar-communications system are investigated. To facilitate this investigation, performance metrics are chosen in this dissertation that allow radar and communications to be compared on the same scale. To that effect, information is chosen as the performance metric and an information theoretic radar performance metric compatible with the communications data rate, the radar estimation rate, is developed. The estimation rate measures the amount of information learned by illuminating a target. With the development of the estimation rate, standard multi-user communications performance bounds are extended with joint radar-communications users to produce bounds on the performance of a joint radar-communications system. System performance for variations of the standard spectrum sharing problem defined in this dissertation are investigated, and inner bounds on performance are extended to account for the effect of continuous radar waveform optimization, multiple radar targets, clutter, phase noise, and radar detection. A detailed interpretation of the estimation rate and a brief discussion on how to use these performance bounds to select an optimal operating point and achieve RF convergence are provided.
ContributorsChiriyath, Alex Rajan (Author) / Bliss, Daniel W (Thesis advisor) / Cochran, Douglas (Committee member) / Kosut, Oliver (Committee member) / Richmond, Christ D (Committee member) / Arizona State University (Publisher)
Created2018
Description
The explosive growth of data generated from different services has opened a new vein of research commonly called ``big data.'' The sheer volume of the information in this data has yielded new applications in a wide range of fields, but the difficulties inherent in processing the enormous amount of data, as well as the rate at which it is generated, also give rise to significant challenges. In particular, processing, modeling, and understanding the structure of online social networks is computationally difficult due to these challenges. The goal of this study is twofold: first to present a new networked data processing framework to model this social structure, and second to highlight the wireless networking gains possible by using this social structure.
The first part of the dissertation considers a new method for modeling social networks via probabilistic graphical models. Specifically, this new method employs the t-cherry junction tree, a recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable probabilistic model. There are a number of advantages in this approach: 1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; 2) constructing a t-cherry junction tree can be largely parallelized; and 3) inference can be performed using distributed computation. To improve the quality of approximation, an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch, is developed. this approach is applied to Twitter data containing 100,000 nodes to study the problem of recommending connections to new users.
Next, the t-cherry junction tree framework is extended by considering the impact of estimating the distributions involved from a training data set. Understanding this impact is vital to real-world applications as distributions are not known perfectly, but rather generated from training data. First, the fidelity of the t-cherry junction tree approximation due to this estimation is quantified. Then the scaling behavior, in terms of the size of the t-cherry junction tree, is approximated to show that higher-order t-cherry junction trees, which with perfect information are higher fidelity approximations, may actually result in decreased fidelity due to the difficulties in accurately estimating higher-dimensional distributions. Finally, this part concludes by demonstrating these findings by considering a distributed detection situation in which the sensors' measurements are correlated.
Having developed a framework to model social structure in online social networks, the study then highlights two approaches for utilizing this social network data in existing wireless communication networks. The first approach is a novel application: using social networks to enhance device-to-device wireless communication. It is well known that wireless communication can be significantly improved by utilizing relays to aid in transmission. Rather than deploying dedicated relays, a system is designed in which users can relay traffic for other users if there is a shared social trust between them, e.g., they are ``friends'' on Facebook, and for users that do not share social trust, implements a coalitional game framework to motivate users to relay traffic for each other. This framework guarantees that all users improve their throughput via relaying while ensuring that each user will function as a relay only if there is a social trust relationship or, if there is no social trust, a cycle of reciprocity is established in which a set of users will agree to relay for each other. This new system shows significant throughput gain in simulated networks that utilize real-world social network traces.
The second application of social structure to wireless communication is an approach to reduce the congestion in cellular networks during peak times. This is achieved by two means: preloading and offloading. Preloading refers to the process of using social network data to predict user demand and serve some users early, before the cellular network traffic peaks. Offloading allows users that have already obtained a copy of the content to opportunistically serve other users using device-to-device communication, thus eliminating the need for some cellular traffic. These two methods work especially well in tandem, as preloading creates a base of users that can serve later users via offloading. These two processes can greatly reduce the peak cellular traffic under ideal conditions, and in a more realistic situation, the impact of uncertainty in human mobility and the social network structure is analyzed. Even with the randomness inherent in these processes, both preloading and offloading offer substantial improvement. Finally, potential difficulties in preloading multiple pieces of content simultaneously are highlighted, and a heuristic method to solve these challenges is developed.
The first part of the dissertation considers a new method for modeling social networks via probabilistic graphical models. Specifically, this new method employs the t-cherry junction tree, a recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable probabilistic model. There are a number of advantages in this approach: 1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; 2) constructing a t-cherry junction tree can be largely parallelized; and 3) inference can be performed using distributed computation. To improve the quality of approximation, an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch, is developed. this approach is applied to Twitter data containing 100,000 nodes to study the problem of recommending connections to new users.
Next, the t-cherry junction tree framework is extended by considering the impact of estimating the distributions involved from a training data set. Understanding this impact is vital to real-world applications as distributions are not known perfectly, but rather generated from training data. First, the fidelity of the t-cherry junction tree approximation due to this estimation is quantified. Then the scaling behavior, in terms of the size of the t-cherry junction tree, is approximated to show that higher-order t-cherry junction trees, which with perfect information are higher fidelity approximations, may actually result in decreased fidelity due to the difficulties in accurately estimating higher-dimensional distributions. Finally, this part concludes by demonstrating these findings by considering a distributed detection situation in which the sensors' measurements are correlated.
Having developed a framework to model social structure in online social networks, the study then highlights two approaches for utilizing this social network data in existing wireless communication networks. The first approach is a novel application: using social networks to enhance device-to-device wireless communication. It is well known that wireless communication can be significantly improved by utilizing relays to aid in transmission. Rather than deploying dedicated relays, a system is designed in which users can relay traffic for other users if there is a shared social trust between them, e.g., they are ``friends'' on Facebook, and for users that do not share social trust, implements a coalitional game framework to motivate users to relay traffic for each other. This framework guarantees that all users improve their throughput via relaying while ensuring that each user will function as a relay only if there is a social trust relationship or, if there is no social trust, a cycle of reciprocity is established in which a set of users will agree to relay for each other. This new system shows significant throughput gain in simulated networks that utilize real-world social network traces.
The second application of social structure to wireless communication is an approach to reduce the congestion in cellular networks during peak times. This is achieved by two means: preloading and offloading. Preloading refers to the process of using social network data to predict user demand and serve some users early, before the cellular network traffic peaks. Offloading allows users that have already obtained a copy of the content to opportunistically serve other users using device-to-device communication, thus eliminating the need for some cellular traffic. These two methods work especially well in tandem, as preloading creates a base of users that can serve later users via offloading. These two processes can greatly reduce the peak cellular traffic under ideal conditions, and in a more realistic situation, the impact of uncertainty in human mobility and the social network structure is analyzed. Even with the randomness inherent in these processes, both preloading and offloading offer substantial improvement. Finally, potential difficulties in preloading multiple pieces of content simultaneously are highlighted, and a heuristic method to solve these challenges is developed.
ContributorsProulx, Brian (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
A principal goal of this dissertation is to study wireless network design and optimization with the focus on two perspectives: 1) socially-aware mobile networking and computing; 2) security and privacy in wireless networking. Under this common theme, this dissertation can be broadly organized into three parts.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
ContributorsGong, Xiaowen (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Cochran, Douglas (Committee member) / Runger, George C. (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Resource allocation in communication networks aims to assign various resources such as power, bandwidth and load in a fair and economic fashion so that the networks can be better utilized and shared by the communicating entities. The design of efficient resource-allocation algorithms is, however, becoming more and more challenging due to the precipitously increasing scale of the networks. This thesis strives to understand how to design such low-complexity algorithms with performance guarantees.
In the first part, the link scheduling problem in wireless ad hoc networks is considered. The scheduler is charge of finding a set of wireless data links to activate at each time slot with the considerations of wireless interference, traffic dynamics, network topology and quality-of-service (QoS) requirements. Two different yet essential scenarios are investigated: the first one is when each packet has a specific deadline after which it will be discarded; the second is when each packet traverses the network in multiple hops instead of leaving the network after a one-hop transmission. In both scenarios the links need to be carefully scheduled to avoid starvation of users and congestion on links. One greedy algorithm is analyzed in each of the two scenarios and performance guarantees in terms of throughput of the networks are derived.
In the second part, the load-balancing problem in parallel computing is studied. Tasks arrive in batches and the duty of the load balancer is to place the tasks on the machines such that minimum queueing delay is incurred. Due to the huge size of modern data centers, sampling the status of all machines may result in significant overhead. Consequently, an algorithm based on limited queue information at the machines is examined and its asymptotic delay performance is characterized and it is shown that the proposed algorithm achieves the same delay with remarkably less sampling overhead compared to the well-known power-of-two-choices algorithm.
Two messages of the thesis are the following: greedy algorithms can work well in a stochastic setting; the fluid model can be useful in "derandomizing" the system and reveal the nature of the algorithm.
In the first part, the link scheduling problem in wireless ad hoc networks is considered. The scheduler is charge of finding a set of wireless data links to activate at each time slot with the considerations of wireless interference, traffic dynamics, network topology and quality-of-service (QoS) requirements. Two different yet essential scenarios are investigated: the first one is when each packet has a specific deadline after which it will be discarded; the second is when each packet traverses the network in multiple hops instead of leaving the network after a one-hop transmission. In both scenarios the links need to be carefully scheduled to avoid starvation of users and congestion on links. One greedy algorithm is analyzed in each of the two scenarios and performance guarantees in terms of throughput of the networks are derived.
In the second part, the load-balancing problem in parallel computing is studied. Tasks arrive in batches and the duty of the load balancer is to place the tasks on the machines such that minimum queueing delay is incurred. Due to the huge size of modern data centers, sampling the status of all machines may result in significant overhead. Consequently, an algorithm based on limited queue information at the machines is examined and its asymptotic delay performance is characterized and it is shown that the proposed algorithm achieves the same delay with remarkably less sampling overhead compared to the well-known power-of-two-choices algorithm.
Two messages of the thesis are the following: greedy algorithms can work well in a stochastic setting; the fluid model can be useful in "derandomizing" the system and reveal the nature of the algorithm.
ContributorsKang, Xiaohan (Author) / Ying, Lei (Thesis advisor) / Cochran, Douglas (Committee member) / Dai, Jim (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2015
Description
There is an ever-growing need for broadband conformal antennas to not only reduce the number of antennas utilized to cover a broad range of frequencies (VHF-UHF) but also to reduce visual and RF signatures associated with communication systems. In many applications antennas needs to be very close to low-impedance mediums or embedded inside low-impedance mediums. However, for conventional metal and dielectric antennas to operate efficiently in such environments either a very narrow bandwidth must be tolerated, or enough loss added to expand the bandwidth, or they must be placed one quarter of a wavelength above the conducting surface. The latter is not always possible since in the HF through low UHF bands, critical to Military and Security functions, this quarter-wavelength requirement would result in impractically large antennas.
Despite an error based on a false assumption in the 1950’s, which had severely underestimated the efficiency of magneto-dielectric antennas, recently demonstrated magnetic-antennas have been shown to exhibit extraordinary efficiency in conformal applications. Whereas conventional metal-and-dielectric antennas carrying radiating electric currents suffer a significant disadvantage when placed conformal to the conducting surface of a platform, because they induce opposing image currents in the surface, magnetic-antennas carrying magnetic radiating currents have no such limitation. Their magnetic currents produce co-linear image currents in electrically conducting surfaces.
However, the permeable antennas built to date have not yet attained the wide bandwidth expected because the magnetic-flux-channels carrying the wave have not been designed to guide the wave near the speed of light at all frequencies. Instead, they tend to lose the wave by a leaky fast-wave mechanism at low frequencies or they over-bind a slow-wave at high frequencies. In this dissertation, we have studied magnetic antennas in detail and presented the design approach and apparatus required to implement a flux-channel carrying the magnetic current wave near the speed of light over a very broad frequency range which also makes the design of a frequency independent antenna (spiral) possible. We will learn how to construct extremely thin conformal antennas, frequency-independent permeable antennas, and even micron-sized antennas that can be embedded inside the brain without damaging the tissue.
Despite an error based on a false assumption in the 1950’s, which had severely underestimated the efficiency of magneto-dielectric antennas, recently demonstrated magnetic-antennas have been shown to exhibit extraordinary efficiency in conformal applications. Whereas conventional metal-and-dielectric antennas carrying radiating electric currents suffer a significant disadvantage when placed conformal to the conducting surface of a platform, because they induce opposing image currents in the surface, magnetic-antennas carrying magnetic radiating currents have no such limitation. Their magnetic currents produce co-linear image currents in electrically conducting surfaces.
However, the permeable antennas built to date have not yet attained the wide bandwidth expected because the magnetic-flux-channels carrying the wave have not been designed to guide the wave near the speed of light at all frequencies. Instead, they tend to lose the wave by a leaky fast-wave mechanism at low frequencies or they over-bind a slow-wave at high frequencies. In this dissertation, we have studied magnetic antennas in detail and presented the design approach and apparatus required to implement a flux-channel carrying the magnetic current wave near the speed of light over a very broad frequency range which also makes the design of a frequency independent antenna (spiral) possible. We will learn how to construct extremely thin conformal antennas, frequency-independent permeable antennas, and even micron-sized antennas that can be embedded inside the brain without damaging the tissue.
ContributorsYousefi, Tara (Author) / Diaz, Rodolfo E (Thesis advisor) / Cochran, Douglas (Committee member) / Goodnick, Stephen (Committee member) / Pan, George (Committee member) / Arizona State University (Publisher)
Created2017
Description
Electric field imaging allows for a low cost, compact, non-invasive, non-ionizing alternative to other methods of imaging. It has many promising industrial applications including security, safely imaging power lines at construction sites, finding sources of electromagnetic interference, geo-prospecting, and medical imaging. The work presented in this dissertation concerns low frequency electric field imaging: the physics, hardware, and various methods of achieving it.
Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables.
The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference.
An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method.
The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.
Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables.
The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference.
An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method.
The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.
ContributorsChung, Hugh Emanuel (Author) / Allee, David R. (Thesis advisor) / Cochran, Douglas (Committee member) / Aberle, James T (Committee member) / Phillips, Stephen M (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation builds a clear understanding of the role of information in wireless networks, and devises adaptive strategies to optimize the overall performance. The meaning of information ranges from channel
etwork states to the structure of the signal itself. Under the common thread of characterizing the role of information, this dissertation investigates opportunistic scheduling, relaying and multicast in wireless networks. To assess the role of channel state information, the problem of opportunistic distributed opportunistic scheduling (DOS) with incomplete information is considered for ad-hoc networks in which many links contend for the same channel using random access. The objective is to maximize the system throughput. In practice, link state information is noisy, and may result in throughput degradation. Therefore, refining the state information by additional probing can improve the throughput, but at the cost of further probing. Capitalizing on optimal stopping theory, the optimal scheduling policy is shown to be threshold-based and is characterized by either one or two thresholds, depending on network settings. To understand the benefits of side information in cooperative relaying scenarios, a basic model is explored for two-hop transmissions of two information flows which interfere with each other. While the first hop is a classical interference channel, the second hop can be treated as an interference channel with transmitter side information. Various cooperative relaying strategies are developed to enhance the achievable rate. In another context, a simple sensor network is considered, where a sensor node acts as a relay, and aids fusion center in detecting an event. Two relaying schemes are considered: analog relaying and digital relaying. Sufficient conditions are provided for the optimality of analog relaying over digital relaying in this network. To illustrate the role of information about the signal structure in joint source-channel coding, multicast of compressible signals over lossy channels is studied. The focus is on the network outage from the perspective of signal distortion across all receivers. Based on extreme value theory, the network outage is characterized in terms of key parameters. A new method using subblock network coding is devised, which prioritizes resource allocation based on the signal information structure.
etwork states to the structure of the signal itself. Under the common thread of characterizing the role of information, this dissertation investigates opportunistic scheduling, relaying and multicast in wireless networks. To assess the role of channel state information, the problem of opportunistic distributed opportunistic scheduling (DOS) with incomplete information is considered for ad-hoc networks in which many links contend for the same channel using random access. The objective is to maximize the system throughput. In practice, link state information is noisy, and may result in throughput degradation. Therefore, refining the state information by additional probing can improve the throughput, but at the cost of further probing. Capitalizing on optimal stopping theory, the optimal scheduling policy is shown to be threshold-based and is characterized by either one or two thresholds, depending on network settings. To understand the benefits of side information in cooperative relaying scenarios, a basic model is explored for two-hop transmissions of two information flows which interfere with each other. While the first hop is a classical interference channel, the second hop can be treated as an interference channel with transmitter side information. Various cooperative relaying strategies are developed to enhance the achievable rate. In another context, a simple sensor network is considered, where a sensor node acts as a relay, and aids fusion center in detecting an event. Two relaying schemes are considered: analog relaying and digital relaying. Sufficient conditions are provided for the optimality of analog relaying over digital relaying in this network. To illustrate the role of information about the signal structure in joint source-channel coding, multicast of compressible signals over lossy channels is studied. The focus is on the network outage from the perspective of signal distortion across all receivers. Based on extreme value theory, the network outage is characterized in terms of key parameters. A new method using subblock network coding is devised, which prioritizes resource allocation based on the signal information structure.
ContributorsPaataguppe Suryanarayan Bhat, Chandrashekhar Thejaswi (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Duman, Tolga (Committee member) / Hui, Yu (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2011