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  Partner: UNT Libraries
 Degree Discipline: Computer Science and Engineering
 Degree Level: Doctoral
 Collection: UNT Theses and Dissertations
Joint Schemes for Physical Layer Security and Error Correction

Joint Schemes for Physical Layer Security and Error Correction

Date: August 2011
Creator: Adamo, Oluwayomi Bamidele
Description: The major challenges facing resource constraint wireless devices are error resilience, security and speed. Three joint schemes are presented in this research which could be broadly divided into error correction based and cipher based. The error correction based ciphers take advantage of the properties of LDPC codes and Nordstrom Robinson code. A cipher-based cryptosystem is also presented in this research. The complexity of this scheme is reduced compared to conventional schemes. The securities of the ciphers are analyzed against known-plaintext and chosen-plaintext attacks and are found to be secure. Randomization test was also conducted on these schemes and the results are presented. For the proof of concept, the schemes were implemented in software and hardware and these shows a reduction in hardware usage compared to conventional schemes. As a result, joint schemes for error correction and security provide security to the physical layer of wireless communication systems, a layer in the protocol stack where currently little or no security is implemented. In this physical layer security approach, the properties of powerful error correcting codes are exploited to deliver reliability to the intended parties, high security against eavesdroppers and efficiency in communication system. The notion of a highly secure and reliable ...
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Space and Spectrum Engineered High Frequency Components and Circuits

Space and Spectrum Engineered High Frequency Components and Circuits

Access: Use of this item is restricted to the UNT Community.
Date: May 2015
Creator: Arigong, Bayaner
Description: With the increasing demand on wireless and portable devices, the radio frequency front end blocks are required to feature properties such as wideband, high frequency, multiple operating frequencies, low cost and compact size. However, the current radio frequency system blocks are designed by combining several individual frequency band blocks into one functional block, which increase the cost and size of devices. To address these issues, it is important to develop novel approaches to further advance the current design methodologies in both space and spectrum domains. In recent years, the concept of artificial materials has been proposed and studied intensively in RF/Microwave, Terahertz, and optical frequency range. It is a combination of conventional materials such as air, wood, metal and plastic. It can achieve the material properties that have not been found in nature. Therefore, the artificial material (i.e. meta-materials) provides design freedoms to control both the spectrum performance and geometrical structures of radio frequency front end blocks and other high frequency systems. In this dissertation, several artificial materials are proposed and designed by different methods, and their applications to different high frequency components and circuits are studied. First, quasi-conformal mapping (QCM) method is applied to design plasmonic wave-adapters and couplers ...
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Statistical Strategies for Efficient Signal Detection and Parameter Estimation in Wireless Sensor Networks

Statistical Strategies for Efficient Signal Detection and Parameter Estimation in Wireless Sensor Networks

Date: December 2013
Creator: Ayeh, Eric
Description: This dissertation investigates data reduction strategies from a signal processing perspective in centralized detection and estimation applications. First, it considers a deterministic source observed by a network of sensors and develops an analytical strategy for ranking sensor transmissions based on the magnitude of their test statistics. The benefit of the proposed strategy is that the decision to transmit or not to transmit observations to the fusion center can be made at the sensor level resulting in significant savings in transmission costs. A sensor network based on target tracking application is simulated to demonstrate the benefits of the proposed strategy over the unconstrained energy approach. Second, it considers the detection of random signals in noisy measurements and evaluates the performance of eigenvalue-based signal detectors. Due to their computational simplicity, robustness and performance, these detectors have recently received a lot of attention. When the observed random signal is correlated, several researchers claim that the performance of eigenvalue-based detectors exceeds that of the classical energy detector. However, such claims fail to consider the fact that when the signal is correlated, the optimal detector is the estimator-correlator and not the energy detector. In this dissertation, through theoretical analyses and Monte Carlo simulations, eigenvalue-based detectors ...
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Extrapolating Subjectivity Research to Other Languages

Extrapolating Subjectivity Research to Other Languages

Date: May 2013
Creator: Banea, Carmen
Description: Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the ...
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A New Look at Retargetable Compilers

A New Look at Retargetable Compilers

Date: December 2014
Creator: Burke, Patrick William
Description: Consumers demand new and innovative personal computing devices every 2 years when their cellular phone service contracts are renewed. Yet, a 2 year development cycle for the concurrent development of both hardware and software is nearly impossible. As more components and features are added to the devices, maintaining this 2 year cycle with current tools will become commensurately harder. This dissertation delves into the feasibility of simplifying the development of such systems by employing heterogeneous systems on a chip in conjunction with a retargetable compiler such as the hybrid computer retargetable compiler (Hy-C). An example of a simple architecture description of sufficient detail for use with a retargetable compiler like Hy-C is provided. As a software engineer with 30 years of experience, I have witnessed numerous system failures. A plethora of software development paradigms and tools have been employed to prevent software errors, but none have been completely successful. Much discussion centers on software development in the military contracting market, as that is my background. The dissertation reviews those tools, as well as some existing retargetable compilers, in an attempt to determine how those errors occurred and how a system like Hy-C could assist in reducing future software errors. In ...
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Monitoring Dengue Outbreaks Using Online Data

Monitoring Dengue Outbreaks Using Online Data

Date: May 2014
Creator: Chartree, Jedsada
Description: Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.
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Social Network Simulation and Mining Social Media to Advance Epidemiology

Social Network Simulation and Mining Social Media to Advance Epidemiology

Date: August 2009
Creator: Corley, Courtney David
Description: Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.
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The Value of Everything: Ranking and Association with Encyclopedic Knowledge

The Value of Everything: Ranking and Association with Encyclopedic Knowledge

Date: December 2009
Creator: Coursey, Kino High
Description: This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the PageRank algorithm. WikiRank is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, WikiRank is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, WikiRank is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus ...
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Procedural Generation of Content for Online Role Playing Games

Procedural Generation of Content for Online Role Playing Games

Date: August 2014
Creator: Doran, Jonathon
Description: Video game players demand a volume of content far in excess of the ability of game designers to create it. For example, a single quest might take a week to develop and test, which means that companies such as Blizzard are spending millions of dollars each month on new content for their games. As a result, both players and developers are frustrated with the inability to meet the demand for new content. By generating content on-demand, it is possible to create custom content for each player based on player preferences. It is also possible to make use of the current world state during generation, something which cannot be done with current techniques. Using developers to create rules and assets for a content generator instead of creating content directly will lower development costs as well as reduce the development time for new game content to seconds rather than days. This work is part of the field of computational creativity, and involves the use of computers to create aesthetically pleasing game content, such as terrain, characters, and quests. I demonstrate agent-based terrain generation, and economic modeling of game spaces. I also demonstrate the autonomous generation of quests for online role playing games, ...
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Metamodeling-based Fast Optimization of  Nanoscale Ams-socs

Metamodeling-based Fast Optimization of Nanoscale Ams-socs

Date: May 2012
Creator: Garitselov, Oleg
Description: Modern consumer electronic systems are mostly based on analog and digital circuits and are designed as analog/mixed-signal systems on chip (AMS-SoCs). the integration of analog and digital circuits on the same die makes the system cost effective. in AMS-SoCs, analog and mixed-signal portions have not traditionally received much attention due to their complexity. As the fabrication technology advances, the simulation times for AMS-SoC circuits become more complex and take significant amounts of time. the time allocated for the circuit design and optimization creates a need to reduce the simulation time. the time constraints placed on designers are imposed by the ever-shortening time to market and non-recurrent cost of the chip. This dissertation proposes the use of a novel method, called metamodeling, and intelligent optimization algorithms to reduce the design time. Metamodel-based ultra-fast design flows are proposed and investigated. Metamodel creation is a one time process and relies on fast sampling through accurate parasitic-aware simulations. One of the targets of this dissertation is to minimize the sample size while retaining the accuracy of the model. in order to achieve this goal, different statistical sampling techniques are explored and applied to various AMS-SoC circuits. Also, different metamodel functions are explored for their ...
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