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- All Subjects: Computer Science
- Creators: Arizona State University
Description
Emergent environmental issues, ever-shrinking petroleum reserves, and rising fossil fuel costs continue to spur interest in the development of sustainable biofuels from renewable feed-stocks. Meanwhile, however, the development and viability of biofuel fermentations remain limited by numerous factors such as feedback inhibition and inefficient and generally energy intensive product recovery processes. To circumvent both feedback inhibition and recovery issues, researchers have turned their attention to incorporating energy efficient separation techniques such as adsorption in in situ product recovery (ISPR) approaches. This thesis focused on the characterization of two novel adsorbents for the recovery of alcohol biofuels from model aqueous solutions. First, a hydrophobic silica aerogel was evaluated as a biofuel adsorbent through characterization of equilibrium behavior for conventional second generation biofuels (e.g., ethanol and n-butanol). Longer chain and accordingly more hydrophobic alcohols (i.e., n-butanol and 2-pentanol) were more effectively adsorbed than shorter chain alcohols (i.e., ethanol and i-propanol), suggesting a mechanism of hydrophobic adsorption. Still, the adsorbed alcohol capacity at biologically relevant conditions were low relative to other `model' biofuel adsorbents as a result of poor interfacial contact between the aqueous and sorbent. However, sorbent wettability and adsorption is greatly enhanced at high concentrations of alcohol in the aqueous. Consequently, the sorbent exhibits Type IV adsorption isotherms for all biofuels studied, which results from significant multilayer adsorption at elevated alcohol concentrations in the aqueous. Additionally, sorbent wettability significantly affects the dynamic binding efficiency within a packed adsorption column. Second, mesoporous carbons were evaluated as biofuel adsorbents through characterization of equilibrium and kinetic behavior. Variations in synthetic conditions enabled tuning of specific surface area and pore morphology of adsorbents. The adsorbed alcohol capacity increased with elevated specific surface area of the adsorbents. While their adsorption capacity is comparable to polymeric adsorbents of similar surface area, pore morphology and structure of mesoporous carbons greatly influenced adsorption rates. Multiple cycles of adsorbent regeneration rendered no impact on adsorption equilibrium or kinetics. The high chemical and thermal stability of mesoporous carbons provide potential significant advantages over other commonly examined biofuel adsorbents. Correspondingly, mesoporous carbons should be further studied for biofuel ISPR applications.
ContributorsLevario, Thomas (Author) / Nielsen, David R (Thesis advisor) / Vogt, Bryan D (Committee member) / Lind, Mary L (Committee member) / Arizona State University (Publisher)
Created2011
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 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
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
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 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
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
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 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
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Proton exchange membrane fuel cells (PEMFCs) run on pure hydrogen and oxygen (or air), producing electricity, water, and some heat. This makes PEMFC an attractive option for clean power generation. PEMFCs also operate at low temperature which makes them quick to start up and easy to handle. PEMFCs have several important limitations which must be overcome before commercial viability can be achieved. Active areas of research into making them commercially viable include reducing the cost, size and weight of fuel cells while also increasing their durability and performance. A growing and important part of this research involves the computer modeling of fuel cells. High quality computer modeling and simulation of fuel cells can help speed up the discovery of optimized fuel cell components. Computer modeling can also help improve fundamental understanding of the mechanisms and reactions that take place within the fuel cell. The work presented in this thesis describes a procedure for utilizing computer modeling to create high quality fuel cell simulations using Ansys Fluent 12.1. Methods for creating computer aided design (CAD) models of fuel cells are discussed. Detailed simulation parameters are described and emphasis is placed on establishing convergence criteria which are essential for producing consistent results. A mesh sensitivity study of the catalyst and membrane layers is presented showing the importance of adhering to strictly defined convergence criteria. A study of iteration sensitivity of the simulation at low and high current densities is performed which demonstrates the variance in the rate of convergence and the absolute difference between solution values derived at low numbers of iterations and high numbers of iterations.
ContributorsArvay, Adam (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Liang, Yong (Committee member) / Subach, James (Committee member) / Arizona State University (Publisher)
Created2011
Description
Service based software (SBS) systems are software systems consisting of services based on the service oriented architecture (SOA). Each service in SBS systems provides partial functionalities and collaborates with other services as workflows to provide the functionalities required by the systems. These services may be developed and/or owned by different entities and physically distributed across the Internet. Compared with traditional software system components which are usually specifically designed for the target systems and bound tightly, the interfaces of services and their communication protocols are standardized, which allow SBS systems to support late binding, provide better interoperability, better flexibility in dynamic business logics, and higher fault tolerance. The development process of SBS systems can be divided to three major phases: 1) SBS specification, 2) service discovery and matching, and 3) service composition and workflow execution. This dissertation focuses on the second phase, and presents a privacy preserving service discovery and ranking approach for multiple user QoS requirements. This approach helps service providers to register services and service users to search services through public, but untrusted service directories with the protection of their privacy against the service directories. The service directories can match the registered services with service requests, but do not learn any information about them. Our approach also enforces access control on services during the matching process, which prevents unauthorized users from discovering services. After the service directories match a set of services that satisfy the service users' functionality requirements, the service discovery approach presented in this dissertation further considers service users' QoS requirements in two steps. First, this approach optimizes services' QoS by making tradeoff among various QoS aspects with users' QoS requirements and preferences. Second, this approach ranks services based on how well they satisfy users' QoS requirements to help service users select the most suitable service to develop their SBSs.
ContributorsYin, Yin (Author) / Yau, Stephen S. (Thesis advisor) / Candan, Kasim (Committee member) / Dasgupta, Partha (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2011
Description
Infant mortality rate of field deployed photovoltaic (PV) modules may be expected to be higher than that estimated by standard qualification tests. The reason for increased failure rates may be attributed to the high system voltages. High voltages (HV) in grid connected modules induce additional stress factors that cause new degradation mechanisms. These new degradation mechanisms are not recognized by qualification stress tests. To study and model the effect of high system voltages, recently, potential induced degradation (PID) test method has been introduced. Using PID studies, it has been reported that high voltage failure rates are essentially due to increased leakage currents from active semiconducting layer to the grounded module frame, through encapsulant and/or glass. This project involved designing and commissioning of a new PID test bed at Photovoltaic Reliability Laboratory (PRL) of Arizona State University (ASU) to study the mechanisms of HV induced degradation. In this study, PID stress tests have been performed on accelerated stress modules, in addition to fresh modules of crystalline silicon technology. Accelerated stressing includes thermal cycling (TC200 cycles) and damp heat (1000 hours) tests as per IEC 61215. Failure rates in field deployed modules that are exposed to long term weather conditions are better simulated by conducting HV tests on prior accelerated stress tested modules. The PID testing was performed in 3 phases on a set of 5 mono crystalline silicon modules. In Phase-I of PID test, a positive bias of +600 V was applied, between shorted leads and frame of each module, on 3 modules with conducting carbon coating on glass superstrate. The 3 module set was comprised of: 1 fresh control, TC200 and DH1000. The PID test was conducted in an environmental chamber by stressing the modules at 85°C, for 35 hours with an intermittent evaluation for Arrhenius effects. In the Phase-II, a negative bias of -600 V was applied on a set of 3 modules in the chamber as defined above. The 3 module set in phase-II was comprised of: control module from phase-I, TC200 and DH1000. In the Phase-III, the same set of 3 modules which were used in the phase-II again subjected to +600 V bias to observe the recovery of lost power during the Phase-II. Electrical performance, infrared (IR) and electroluminescence (EL) were done prior and post PID testing. It was observed that high voltage positive bias in the first phase resulted in little
o power loss, high voltage negative bias in the second phase caused significant power loss and the high voltage positive bias in the third phase resulted in major recovery of lost power.
o power loss, high voltage negative bias in the second phase caused significant power loss and the high voltage positive bias in the third phase resulted in major recovery of lost power.
ContributorsGoranti, Sandhya (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011