This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java

Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
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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

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
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Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications

With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
The demand for handheld portable computing in education, business and research has resulted in advanced mobile devices with powerful processors and large multi-touch screens. Such devices are capable of handling tasks of moderate computational complexity such as word processing, complex Internet transactions, and even human motion analysis. Apple's iOS devices,

The demand for handheld portable computing in education, business and research has resulted in advanced mobile devices with powerful processors and large multi-touch screens. Such devices are capable of handling tasks of moderate computational complexity such as word processing, complex Internet transactions, and even human motion analysis. Apple's iOS devices, including the iPhone, iPod touch and the latest in the family - the iPad, are among the well-known and widely used mobile devices today. Their advanced multi-touch interface and improved processing power can be exploited for engineering and STEM demonstrations. Moreover, these devices have become a part of everyday student life. Hence, the design of exciting mobile applications and software represents a great opportunity to build student interest and enthusiasm in science and engineering. This thesis presents the design and implementation of a portable interactive signal processing simulation software on the iOS platform. The iOS-based object-oriented application is called i-JDSP and is based on the award winning Java-DSP concept. It is implemented in Objective-C and C as a native Cocoa Touch application that can be run on any iOS device. i-JDSP offers basic signal processing simulation functions such as Fast Fourier Transform, filtering, spectral analysis on a compact and convenient graphical user interface and provides a very compelling multi-touch programming experience. Built-in modules also demonstrate concepts such as the Pole-Zero Placement. i-JDSP also incorporates sound capture and playback options that can be used in near real-time analysis of speech and audio signals. All simulations can be visually established by forming interactive block diagrams through multi-touch and drag-and-drop. Computations are performed on the mobile device when necessary, making the block diagram execution fast. Furthermore, the extensive support for user interactivity provides scope for improved learning. The results of i-JDSP assessment among senior undergraduate and first year graduate students revealed that the software created a significant positive impact and increased the students' interest and motivation and in understanding basic DSP concepts.
ContributorsLiu, Jinru (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Qian, Gang (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Great advances have been made in the construction of photovoltaic (PV) cells and modules, but array level management remains much the same as it has been in previous decades. Conventionally, the PV array is connected in a fixed topology which is not always appropriate in the presence of faults in

Great advances have been made in the construction of photovoltaic (PV) cells and modules, but array level management remains much the same as it has been in previous decades. Conventionally, the PV array is connected in a fixed topology which is not always appropriate in the presence of faults in the array, and varying weather conditions. With the introduction of smarter inverters and solar modules, the data obtained from the photovoltaic array can be used to dynamically modify the array topology and improve the array power output. This is beneficial especially when module mismatches such as shading, soiling and aging occur in the photovoltaic array. This research focuses on the topology optimization of PV arrays under shading conditions using measurements obtained from a PV array set-up. A scheme known as topology reconfiguration method is proposed to find the optimal array topology for a given weather condition and faulty module information. Various topologies such as the series-parallel (SP), the total cross-tied (TCT), the bridge link (BL) and their bypassed versions are considered. The topology reconfiguration method compares the efficiencies of the topologies, evaluates the percentage gain in the generated power that would be obtained by reconfiguration of the array and other factors to find the optimal topology. This method is employed for various possible shading patterns to predict the best topology. The results demonstrate the benefit of having an electrically reconfigurable array topology. The effects of irradiance and shading on the array performance are also studied. The simulations are carried out using a SPICE simulator. The simulation results are validated with the experimental data provided by the PACECO Company.
ContributorsBuddha, Santoshi Tejasri (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
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Description
There are many wireless communication and networking applications that require high transmission rates and reliability with only limited resources in terms of bandwidth, power, hardware complexity etc.. Real-time video streaming, gaming and social networking are a few such examples. Over the years many problems have been addressed towards the goal

There are many wireless communication and networking applications that require high transmission rates and reliability with only limited resources in terms of bandwidth, power, hardware complexity etc.. Real-time video streaming, gaming and social networking are a few such examples. Over the years many problems have been addressed towards the goal of enabling such applications; however, significant challenges still remain, particularly, in the context of multi-user communications. With the motivation of addressing some of these challenges, the main focus of this dissertation is the design and analysis of capacity approaching coding schemes for several (wireless) multi-user communication scenarios. Specifically, three main themes are studied: superposition coding over broadcast channels, practical coding for binary-input binary-output broadcast channels, and signalling schemes for two-way relay channels. As the first contribution, we propose an analytical tool that allows for reliable comparison of different practical codes and decoding strategies over degraded broadcast channels, even for very low error rates for which simulations are impractical. The second contribution deals with binary-input binary-output degraded broadcast channels, for which an optimal encoding scheme that achieves the capacity boundary is found, and a practical coding scheme is given by concatenation of an outer low density parity check code and an inner (non-linear) mapper that induces desired distribution of "one" in a codeword. The third contribution considers two-way relay channels where the information exchange between two nodes takes place in two transmission phases using a coding scheme called physical-layer network coding. At the relay, a near optimal decoding strategy is derived using a list decoding algorithm, and an approximation is obtained by a joint decoding approach. For the latter scheme, an analytical approximation of the word error rate based on a union bounding technique is computed under the assumption that linear codes are employed at the two nodes exchanging data. Further, when the wireless channel is frequency selective, two decoding strategies at the relay are developed, namely, a near optimal decoding scheme implemented using list decoding, and a reduced complexity detection/decoding scheme utilizing a linear minimum mean squared error based detector followed by a network coded sequence decoder.
ContributorsBhat, Uttam (Author) / Duman, Tolga M. (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
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Description

This 15-week long course is designed to introduce students, specifically in Arizona, to basic sustainability and conservation principles in the context of local reptile wildlife. Throughout the course, the students work on identifying the problem, creating visions for the desired future, and finally developing a strategy to help with reptile

This 15-week long course is designed to introduce students, specifically in Arizona, to basic sustainability and conservation principles in the context of local reptile wildlife. Throughout the course, the students work on identifying the problem, creating visions for the desired future, and finally developing a strategy to help with reptile species survival in the valley. Research shows that animals in the classroom have led to improved academic success for students. Thus, through creating this course I was able to combine conservation and sustainability curriculum with real-life animals whose survival is directly being affected in the valley. My hope is that this course will help students identify a newfound passion and call to action to protect native wildlife. The more awareness and actionable knowledge which can be brought to students in Arizona about challenges to species survival the more likely we are to see a change in the future and a stronger sense of urgency for protecting wildlife. In order to accomplish these goals, the curriculum was developed to begin with basic concepts of species needs such as food and shelter and basic principles of sustainability. As the course progresses the students analyze current challenges reptile wildlife faces, like urban sprawl, and explore options to address these challenges. The course concludes with a pilot pitch where students present their solution projects to the school.

ContributorsGoethe, Emma Rae (Author) / Brundiers, Katja (Thesis director) / Bouges, Olivia (Committee member) / School of Sustainability (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Universal Basic Income is a proposed policy where the government would regularly pay all citizens in cash. The idea of a Universal Basic Income (UBI) has had a resurgence in recent years because of popular figures like Andrew Yang and Elon Musk, but its history and potential implications go dee

Universal Basic Income is a proposed policy where the government would regularly pay all citizens in cash. The idea of a Universal Basic Income (UBI) has had a resurgence in recent years because of popular figures like Andrew Yang and Elon Musk, but its history and potential implications go deep into the structure of human society. This thesis delves into how a basic income would transform social concepts of work and disrupt the personal economic model. With the bargaining power and freedom granted by a basic income, workers would find themselves in a position of work freedom and choice that has never existed in human history. With new freedom to do as they wish, the place of work in people’s lives needs to be reimagined as a source of fulfillment instead of an unlikeable but necessary part of everyday life. Workers will be given the choice to leave unfair or unfulfilling work and decide for themselves how they want to contribute within society. From increasing mental and economic well-being for most Americans to serving as a response to unemployment trends in the automated future, to encouraging greater business innovation, there are myriad ways in which basic incomes have the potential to benefit society. Framed by Martin Luther King Jr. and Franklin Delano Roosevelt as the only policy capable of abolishing poverty forever, Universal Basic income will be an important feature of transformative innovative policy advocacy until it is adopted by a major world government at which point the effects in practice will become clear.

ContributorsSimm, Michael Anthony (Author) / Soares, Rebecca (Thesis director) / Voorhees, Matthew (Committee member) / School of Sustainability (Contributor) / School of Politics and Global Studies (Contributor) / School for the Future of Innovation in Society (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Globally, the incidental capture of non-target species in fisheries (bycatch) has been linked to declines of ecologically, economically, and culturally important marine species. Gillnet fisheries have especially high bycatch due to their non-selective nature, necessitating the development of new bycatch reduction technologies (BRTs). Net illumination is an emerging BRT that

Globally, the incidental capture of non-target species in fisheries (bycatch) has been linked to declines of ecologically, economically, and culturally important marine species. Gillnet fisheries have especially high bycatch due to their non-selective nature, necessitating the development of new bycatch reduction technologies (BRTs). Net illumination is an emerging BRT that has shown promise in reducing bycatch of marine megafauna, including sea turtles, cetaceans, and seabirds. However, little research has been conducted to understand the effects of net illumination on fish assemblages, including bony fish and elasmobranchs (i.e. sharks, rays, and skates). Here, I assessed a 7-year dataset of paired net illumination trials using four different types of light (green LEDs, green chemical glowsticks, ultraviolet (UV) lights, and orange lights) to examine the effects of net illumination on fish catch and bycatch in a gillnet fishery at Baja California Sur, Mexico. Analysis revealed no significant effect on bony fish target catch or bycatch for any light type. There was a significant decrease in elasmobranch bycatch using UV and orange lights, with orange lights showing the most promise for decreasing elasmobranch bycatch, resulting in a 50% reduction in bycatch rates. Analysis of the effects of net illumination on elasmobranch target catch was limited due to insufficient data. These results indicate that the illumination of gillnets may offer a practical solution for reducing fish bycatch while maintaining target catch. More research should be conducted to understand the effects of net illumination in different fisheries, how net illumination affects fisher profit and efficiency, and how net illumination affects fish behavior. Further optimization of net illumination is also necessary before the technology can be recommended on a broader scale.

ContributorsBurgher, Kayla Marie (Author) / Senko, Jesse (Thesis director) / Throop, Heather (Committee member) / School of Life Sciences (Contributor) / School of Sustainability (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05