Matching Items (256)
Filtering by
- Status: Published
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
As the 3rd generation solar cell, quantum dot solar cells are expected to outperform the first 2 generations with higher efficiency and lower manufacture cost. Currently the main problems for QD cells are the low conversion efficiency and stability. This work is trying to improve the reliability as well as the device performance by inserting an interlayer between the metal cathode and the active layer. Titanium oxide and a novel nitrogen doped titanium oxide were compared and TiOxNy capped device shown a superior performance and stability to TiOx capped one. A unique light anneal effect on the interfacial layer was discovered first time and proved to be the trigger of the enhancement of both device reliability and efficiency. The efficiency was improved by 300% and the device can retain 73.1% of the efficiency with TiOxNy when normal device completely failed after kept for long time. Photoluminescence indicted an increased charge disassociation rate at TiOxNy interface. External quantum efficiency measurement also inferred a significant performance enhancement in TiOxNy capped device, which resulted in a higher photocurrent. X-ray photoelectron spectrometry was performed to explain the impact of light doping on optical band gap. Atomic force microscopy illustrated the effect of light anneal on quantum dot polymer surface. The particle size is increased and the surface composition is changed after irradiation. The mechanism for performance improvement via a TiOx based interlayer was discussed based on a trap filling model. Then Tunneling AFM was performed to further confirm the reliability of interlayer capped organic photovoltaic devices. As a powerful tool based on SPM technique, tunneling AFM was able to explain the reason for low efficiency in non-capped inverted organic photovoltaic devices. The local injection properties as well as the correspondent topography were compared in organic solar cells with or without TiOx interlayer. The current-voltage characteristics were also tested at a single interested point. A severe short-circuit was discovered in non capped devices and a slight reverse bias leakage current was also revealed in TiOx capped device though tunneling AFM results. The failure reason for low stability in normal devices was also discussed comparing to capped devices.
ContributorsYu, Jialin (Author) / Jabbour, Ghassan E. (Thesis advisor) / Alford, Terry L. (Thesis advisor) / Yu, Hongbin (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
Semiconductor nanowires are featured by their unique one-dimensional structure which makes them promising for small scale electronic and photonic device applications. Among them, III-V material nanowires are particularly outstanding due to their good electronic properties. In bulk, these materials reveal electron mobility much higher than conventional silicon based devices, for example at room temperature, InAs field effect transistor (FET) has electron mobility of 40,000 cm2/Vs more than 10 times of Si FET. This makes such materials promising for high speed nanowire FETs. With small bandgap, such as 0.354 eV for InAs and 1.52 eV for GaAs, it does not need high voltage to turn on such devices which leads to low power consumption devices. Another feature of direct bandgap allows their applications of optoelectronic devices such as avalanche photodiodes. However, there are challenges to face up. Due to their large surface to volume ratio, nanowire devices typically are strongly affected by the surface states. Although nanowires can be grown into single crystal structure, people observe crystal defects along the wires which can significantly affect the performance of devices. In this work, FETs made of two types of III-V nanowire, GaAs and InAs, are demonstrated. These nanowires are grown by catalyst-free MOCVD growth method. Vertically nanowires are transferred onto patterned substrates for coordinate calibration. Then electrodes are defined by e-beam lithography followed by deposition of contact metals. Prior to metal deposition, however, the substrates are dipped in ammonium hydroxide solution to remove native oxide layer formed on nanowire surface. Current vs. source-drain voltage with different gate bias are measured at room temperature. GaAs nanowire FETs show photo response while InAs nanowire FETs do not show that. Surface passivation is performed on GaAs FETs by using ammonium surfide solution. The best results on current increase is observed with around 20-30 minutes chemical treatment time. Gate response measurements are performed at room temperature, from which field effect mobility as high as 1490 cm2/Vs is extracted for InAs FETs. One major contributor for this is stacking faults defect existing along nanowires. For InAs FETs, thermal excitations observed from temperature dependent results which leads us to investigate potential barriers.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Ferry, David (Committee member) / Tracy, Clarence (Committee member) / Arizona State University (Publisher)
Created2011
Description
A workload-aware low-power neuromorphic controller for dynamic power and thermal management in VLSI systems is presented. The neuromorphic controller predicts future workload and temperature values based on the past values and CPU performance counters and preemptively regulates supply voltage and frequency. System-level measurements from stateof-the-art commercial microprocessors are used to get workload, temperature and CPU performance counter values. The controller is designed and simulated using circuit-design and synthesis tools. At device-level, on-chip planar inductors suffer from low inductance occupying large chip area. On-chip inductors with integrated magnetic materials are designed, simulated and fabricated to explore performance-efficiency trade offs and explore potential applications such as resonant clocking and on-chip voltage regulation. A system level study is conducted to evaluate the effect of on-chip voltage regulator employing magnetic inductors as the output filter. It is concluded that neuromorphic power controller is beneficial for fine-grained per-core power management in conjunction with on-chip voltage regulators utilizing scaled magnetic inductors.
ContributorsSinha, Saurabh (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Yu, Hongbin (Committee member) / Christen, Jennifer B. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical options, in order to narrow down the search and reach the desired result. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. These empirical methods fail to cover all semantics of categories present in the query results. More importantly these methods do not consider the semantic relationship between the keywords featured in an expanded query. Contrary to a normal keyword search setting, these factors are non-trivial in an exploratory and ambiguous query setting where the user's precise discernment of different categories present in the query results is more important for making subsequent search decisions. In this thesis, I propose a new framework for keyword query expansion: generating a set of queries that correspond to the categorization of original query results, which is referred as Categorizing query expansion. Two approaches of algorithms are proposed, one that performs clustering as pre-processing step and then generates categorizing expanded queries based on the clusters. The other category of algorithms handle the case of generating quality expanded queries in the presence of imperfect clusters.
ContributorsNatarajan, Sivaramakrishnan (Author) / Chen, Yi (Thesis advisor) / Candan, Selcuk (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Most existing approaches to complex event processing over streaming data rely on the assumption that the matches to the queries are rare and that the goal of the system is to identify these few matches within the incoming deluge of data. In many applications, such as stock market analysis and user credit card purchase pattern monitoring, however the matches to the user queries are in fact plentiful and the system has to efficiently sift through these many matches to locate only the few most preferable matches. In this work, we propose a complex pattern ranking (CPR) framework for specifying top-k pattern queries over streaming data, present new algorithms to support top-k pattern queries in data streaming environments, and verify the effectiveness and efficiency of the proposed algorithms. The developed algorithms identify top-k matching results satisfying both patterns as well as additional criteria. To support real-time processing of the data streams, instead of computing top-k results from scratch for each time window, we maintain top-k results dynamically as new events come and old ones expire. We also develop new top-k join execution strategies that are able to adapt to the changing situations (e.g., sorted and random access costs, join rates) without having to assume a priori presence of data statistics. Experiments show significant improvements over existing approaches.
ContributorsWang, Xinxin (Author) / Candan, K. Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
CMOS technology is expected to enter the 10nm regime for future integrated circuits (IC). Such aggressive scaling leads to vastly increased variability, posing a grand challenge to robust IC design. Variations in CMOS are often divided into two types: intrinsic variations and process-induced variations. Intrinsic variations are limited by fundamental physics. They are inherent to CMOS structure, considered as one of the ultimate barriers to the continual scaling of CMOS devices. In this work the three primary intrinsic variations sources are studied, including random dopant fluctuation (RDF), line-edge roughness (LER) and oxide thickness fluctuation (OTF). The research is focused on the modeling and simulation of those variations and their scaling trends. Besides the three variations, a time dependent variation source, Random Telegraph Noise (RTN) is also studied. Different from the other three variations, RTN does not contribute much to the total variation amount, but aggregate the worst case of Vth variations in CMOS. In this work a TCAD based simulation study on RTN is presented, and a new SPICE based simulation method for RTN is proposed for time domain circuit analysis. Process-induced variations arise from the imperfection in silicon fabrication, and vary from foundries to foundries. In this work the layout dependent Vth shift due to Rapid-Thermal Annealing (RTA) are investigated. In this work, we develop joint thermal/TCAD simulation and compact modeling tools to analyze performance variability under various layout pattern densities and RTA conditions. Moreover, we propose a suite of compact models that bridge the underlying RTA process with device parameter change for efficient design optimization.
ContributorsYe, Yun, Ph.D (Author) / Cao, Yu (Thesis advisor) / Yu, Hongbin (Committee member) / Song, Hongjiang (Committee member) / Clark, Lawrence (Committee member) / Arizona State University (Publisher)
Created2011
Description
K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and LSH-Forest improve upon the basic LSH algorithm by varying hash bucket size dynamically at query time, so these two algorithms can answer different KNN queries adaptively. However, these two algorithms need a data access post-processing step after candidates' collection in order to get the final answer to the KNN query. In this thesis, Multi-Probe LSH with data access post-processing (Multi-Probe LSH with DAPP) algorithm and LSH-Forest with data access post-processing (LSH-Forest with DAPP) algorithm are improved by replacing the costly data access post-processing (DAPP) step with a much faster histogram-based post-processing (HBPP). Two HBPP algorithms: LSH-Forest with HBPP and Multi- Probe LSH with HBPP are presented in this thesis, both of them achieve the three goals for KNN search in large scale high dimensional data set: high search quality, high time efficiency, high space efficiency. None of the previous KNN algorithms can achieve all three goals. More specifically, it is shown that HBPP algorithms can always achieve high search quality (as good as LSH-Forest with DAPP and Multi-Probe LSH with DAPP) with much less time cost (one to several orders of magnitude speedup) and same memory usage. It is also shown that with almost same time cost and memory usage, HBPP algorithms can always achieve better search quality than LSH-Forest with random pick (LSH-Forest with RP) and Multi-Probe LSH with random pick (Multi-Probe LSH with RP). Moreover, to achieve a very high search quality, Multi-Probe with HBPP is always a better choice than LSH-Forest with HBPP, regardless of the distribution, size and dimension number of the data set.
ContributorsYu, Renwei (Author) / Candan, Kasim S (Thesis advisor) / Sapino, Maria L (Committee member) / Chen, Yi (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2011