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 70
Description
Micro Electro Mechanical Systems (MEMS) is one of the fastest growing field in silicon industry. Low cost production is key for any company to improve their market share. MEMS testing is challenging since input to test a MEMS device require physical stimulus like acceleration, pressure etc. Also, MEMS device vary with process and requires calibration to make them reliable. This increases test cost and testing time. This challenge can be overcome by combining electrical stimulus based testing along with statistical analysis on MEMS response for electrical stimulus and also limited physical stimulus response data. This thesis proposes electrical stimulus based built in self test(BIST) which can be used to get MEMS data and later this data can be used for statistical analysis. A capacitive MEMS accelerometer is considered to test this BIST approach. This BIST circuit overhead is less and utilizes most of the standard readout circuit. This thesis discusses accelerometer response for electrical stimulus and BIST architecture. As a part of this BIST circuit, a second order sigma delta modulator has been designed. This modulator has a sampling frequency of 1MHz and bandwidth of 6KHz. SNDR of 60dB is achieved with 1Vpp differential input signal and 3.3V supply
ContributorsKundur, Vinay (Author) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Attribute Based Access Control (ABAC) mechanisms have been attracting a lot of interest from the research community in recent times. This is especially because of the flexibility and extensibility it provides by using attributes assigned to subjects as the basis for access control. ABAC enables an administrator of a server to enforce access policies on the data, services and other such resources fairly easily. It also accommodates new policies and changes to existing policies gracefully, thereby making it a potentially good mechanism for implementing access control in large systems, particularly in today's age of Cloud Computing. However management of the attributes in ABAC environment is an area that has been little touched upon. Having a mechanism to allow multiple ABAC based systems to share data and resources can go a long way in making ABAC scalable. At the same time each system should be able to specify their own attribute sets independently. In the research presented in this document a new mechanism is proposed that would enable users to share resources and data in a cloud environment using ABAC techniques in a distributed manner. The focus is mainly on decentralizing the access policy specifications for the shared data so that each data owner can specify the access policy independent of others. The concept of ontologies and semantic web is introduced in the ABAC paradigm that would help in giving a scalable structure to the attributes and also allow systems having different sets of attributes to communicate and share resources.
ContributorsPrabhu Verleker, Ashwin Narayan (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2014
Description
The design and development of analog/mixed-signal (AMS) integrated circuits (ICs) is becoming increasingly expensive, complex, and lengthy. Rapid prototyping and emulation of analog ICs will be significant in the design and testing of complex analog systems. A new approach, Programmable ANalog Device Array (PANDA) that maps any AMS design problem to a transistor-level programmable hardware, is proposed. This approach enables fast system level validation and a reduction in post-Silicon bugs, minimizing design risk and cost. The unique features of the approach include 1) transistor-level programmability that emulates each transistor behavior in an analog design, achieving very fine granularity of reconfiguration; 2) programmable switches that are treated as a design component during analog transistor emulating, and optimized with the reconfiguration matrix; 3) compensation of AC performance degradation through boosting the bias current. Based on these principles, a digitally controlled PANDA platform is designed at 45nm node that can map AMS modules across 22nm to 90nm technology nodes. A systematic emulation approach to map any analog transistor to 45nm PANDA cell is proposed, which achieves transistor level matching accuracy of less than 5% for ID and less than 10% for Rout and Gm. Circuit level analog metrics of a voltage-controlled oscillator (VCO) emulated by PANDA, match to those of the original designs in 22nm and 90nm nodes with less than a 5% error. Several other 90nm and 22nm analog blocks are successfully emulated by the 45nm PANDA platform, including a folded-cascode operational amplifier and a sample-and-hold module (S/H). Further capabilities of PANDA are demonstrated by the first full-chip silicon of PANDA which is implemented on 65nm process This system consists of a 24×25 cell array, reconfigurable interconnect and configuration memory. The voltage and current reference circuits, op amps and a VCO with a phase interpolation circuit are emulated by PANDA.
ContributorsSuh, Jounghyuk (Author) / Bakkaloglu, Bertan (Thesis advisor) / Cao, Yu (Committee member) / Ozev, Sule (Committee member) / Kozicki, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis addresses the ever increasing threat of botnets in the smartphone domain and focuses on the Android platform and the botnets using Online Social Networks (OSNs) as Command and Control (C&C;) medium. With any botnet, C&C; is one of the components on which the survival of botnet depends. Individual bots use the C&C; channel to receive commands and send the data. This thesis develops active host based approach for identifying the presence of bot based on the anomalies in the usage patterns of the user before and after the bot is installed on the user smartphone and alerting the user to the presence of the bot. A profile is constructed for each user based on the regular web usage patterns (achieved by intercepting the http(s) traffic) and implementing machine learning techniques to continuously learn the user's behavior and changes in the behavior and all the while looking for any anomalies in the user behavior above a threshold which will cause the user to be notified of the anomalous traffic. A prototype bot which uses OSN s as C&C; channel is constructed and used for testing. Users are given smartphones(Nexus 4 and Galaxy Nexus) running Application proxy which intercepts http(s) traffic and relay it to a server which uses the traffic and constructs the model for a particular user and look for any signs of anomalies. This approach lays the groundwork for the future host-based counter measures for smartphone botnets using OSN s as C&C; channel.
ContributorsKilari, Vishnu Teja (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2013
Description
The widespread adoption of mobile devices gives rise to new opportunities and challenges for authentication mechanisms. Many traditional authentication mechanisms become unsuitable for smart devices. For example, while password is widely used on computers as user identity authentication, inputting password on small smartphone screen is error-prone and not convenient. In the meantime, there are emerging demands for new types of authentication. Proximity authentication is an example, which is not needed for computers but quite necessary for smart devices. These challenges motivate me to study and develop novel authentication mechanisms specific for smart devices.
In this dissertation, I am interested in the special authentication demands of smart devices and about to satisfy the demands. First, I study how the features of smart devices affect user identity authentications. For identity authentication domain, I aim to design a continuous, forge-resistant authentication mechanism that does not interrupt user-device interactions. I propose a mechanism that authenticates user identity based on the user's finger movement patterns. Next, I study a smart-device-specific authentication, proximity authentication, which authenticates whether two devices are in close proximity. For prox- imity authentication domain, I aim to design a user-friendly authentication mechanism that can defend against relay attacks. In addition, I restrict the authenticated distance to the scale of near field, i.e., a few centimeters. My first design utilizes a user's coherent two-finger movement on smart device screen to restrict the distance. To achieve a fully-automated system, I explore acoustic communications and propose a novel near field authentication system.
In this dissertation, I am interested in the special authentication demands of smart devices and about to satisfy the demands. First, I study how the features of smart devices affect user identity authentications. For identity authentication domain, I aim to design a continuous, forge-resistant authentication mechanism that does not interrupt user-device interactions. I propose a mechanism that authenticates user identity based on the user's finger movement patterns. Next, I study a smart-device-specific authentication, proximity authentication, which authenticates whether two devices are in close proximity. For prox- imity authentication domain, I aim to design a user-friendly authentication mechanism that can defend against relay attacks. In addition, I restrict the authenticated distance to the scale of near field, i.e., a few centimeters. My first design utilizes a user's coherent two-finger movement on smart device screen to restrict the distance. To achieve a fully-automated system, I explore acoustic communications and propose a novel near field authentication system.
ContributorsLi, Lingjun (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2014
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Ozev, Sule (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
High speed current-steering DACs with high linearity are needed in today's applications such as wired and wireless communications, instrumentation, radar, and other direct digital synthesis (DDS) applications. However, a trade-off exists between the speed and resolution of Nyquist rate current-steering DACs. As the resolution increases, more transistor area is required to meet matching requirements for optimal linearity and thus, the overall speed of the DAC is limited.
In this thesis work, a 12-bit current-steering DAC was designed with current sources scaled below the required matching size to decrease the area and increase the overall speed of the DAC. By scaling the current sources, however, errors due to random mismatch between current sources will arise and additional calibration hardware is necessary to ensure 12-bit linearity. This work presents how to implement a self-calibration DAC that works to fix amplitude errors while maintaining a lower overall area. Additionally, the DAC designed in this thesis investigates the implementation feasibility of a data-interleaved architecture. Data interleaving can increase the total bandwidth of the DACs by 2 with an increase in SQNR by an additional 3 dB.
The final results show that the calibration method can effectively improve the linearity of the DAC. The DAC is able to run up to 400 MSPS frequencies with a 75 dB SFDR performance and above 87 dB SFDR performance at update rates of 200 MSPS.
In this thesis work, a 12-bit current-steering DAC was designed with current sources scaled below the required matching size to decrease the area and increase the overall speed of the DAC. By scaling the current sources, however, errors due to random mismatch between current sources will arise and additional calibration hardware is necessary to ensure 12-bit linearity. This work presents how to implement a self-calibration DAC that works to fix amplitude errors while maintaining a lower overall area. Additionally, the DAC designed in this thesis investigates the implementation feasibility of a data-interleaved architecture. Data interleaving can increase the total bandwidth of the DACs by 2 with an increase in SQNR by an additional 3 dB.
The final results show that the calibration method can effectively improve the linearity of the DAC. The DAC is able to run up to 400 MSPS frequencies with a 75 dB SFDR performance and above 87 dB SFDR performance at update rates of 200 MSPS.
ContributorsJankunas, Benjamin (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kitchen, Jennifer (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2014
Description
A firewall is a necessary component for network security and just like any regular equipment it requires maintenance. To keep up with changing cyber security trends and threats, firewall rules are modified frequently. Over time such modifications increase the complexity, size and verbosity of firewall rules. As the rule set grows in size, adding and modifying rule becomes a tedious task. This discourages network administrators to review the work done by previous administrators before and after applying any changes. As a result the quality and efficiency of the firewall goes down.
Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations.
This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules.
Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations.
This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules.
ContributorsKhatkar, Pankaj Kumar (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Users often join an online social networking (OSN) site, like Facebook, to remain social, by either staying connected with friends or expanding social networks. On an OSN site, users generally share variety of personal information which is often expected to be visible to their friends, but sometimes vulnerable to unwarranted access from others. The recent study suggests that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable using users' personal data from an OSN site. The majority of users want to remain socially active, and protect their personal data at the same time. This tension leads to a user's vulnerability, allowing privacy attacks which can cause physical and emotional distress to a user, sometimes with dire consequences. For example, stalkers can make use of personal information available on an OSN site to their personal gain. This dissertation aims to systematically study a user vulnerability against such privacy attacks.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
ContributorsGundecha, Pritam S (Author) / Liu, Huan (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Barbier, Geoffrey (Committee member) / Arizona State University (Publisher)
Created2015