Physical Sciences and Mathematics Commons^™

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, …

Vascular Countercurrent Network For 3d Triple-Layered Skin Structure With Radiation Heating, Xiaoqi Zeng

Doctoral Dissertations

Heat transfer in living tissue has become more and more attention for researchers, because high thermal radiation produced by intense fire, such as wild fires, chemical fires, accidents, warfare, terrorism, etc, is often encountered in human's daily life. Living tissue is a heterogeneous organ consisting of cellular tissue and blood vessels, and heat transfer in cellular tissue and blood vessel is quite different, because the blood vessels provide channels for fast heat transfer. The metabolic heat generation, heat conduction and blood perfusion in soft tissue, convection and perfusion of the arterial-venous blood through the capillary, and interaction with the environment …

Associative Pattern Mining For Supervised Learning, Harpreet Singh

Doctoral Dissertations

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known …

Accurate And Stable Numerical Methods For Solving Micro Heat Transfer Models In An N-Carrier System In Spherical Coordinates, Di Zhao

Doctoral Dissertations

Energy exchange between electrons and phonons in metal provides the best example in describing non-equilibrium heating during the ultrafast transient. In times comparable to the thermalization and relaxation time of electrons and phonons, which are in the range of a few to several tens of picoseconds, heat continuously flows from hot electrons to cold phonons through mutual collisions. Consequently, electron temperature continuously decreases whereas phonon temperature continuously increases until thermal equilibrium is reached. Tien developed the well-known parabolic two-step model for describing the non-equilibrium heating in the electron-phonon system in 1992, and Tzou developed the parabolic model for the non-equilibrium …

Discrete Nondeterministic Modeling Of Biochemical Networks, John R. Jack

Doctoral Dissertations

The ideas expressed in this work pertain to biochemical modeling. We explore our technique, the Nondeterministic Waiting Time algorithm, for modeling molecular signaling cascades. The algorithm is presented with pseudocode along with an explanation of its implementation. The entire source code can be found in the Appendices. This algorithm builds on earlier work from the lab of Dr. Andrei Nun, the advisor for this dissertation. We discuss several important extensions including: (i) a heap with special maintenance functions for sorting reaction waiting times, (ii) a nondeterministic component for handling reaction competition, and (iii) a memory enhancement allowing slower reactions to …

Text Summarization Using Concept Hierarchy, Xiaomei Huang

Doctoral Dissertations

This dissertation aims to create new sentences to summarize text documents. In addition to generating new sentences, this project also generates new concepts and extracts key sentences to summarize documents. This project is the first research work that can generate new key concepts and can create new sentences to summarize documents.

Automatic document summarization is the process of creating a condensed version of the document. The condensed version extracts the key contents from the original document. Most related research uses statistical methods that generate a summary based on word distribution in the document. In this dissertation, we create a summary …

Naïve Bayes And Similarity Based Methods For Identifying Computer Users Using Keystroke Patterns, Shrijit S. Joshi

Doctoral Dissertations

In this dissertation, we present two methods for identifying computer users using keystroke patterns. In the first method "Competition between naïve Bayes models for user identification," a naïve Bayes model is created for each user. In the training phase of this method, the model of a user is trained using maximum likelihood estimation on the key press latency values extracted from the texts typed by the user. In the user identification phase of this method, for each user we determine the probabilistic likelihood that the typed text belongs to a user. Finally, the typed text is assigned to the user …

Integrated Mining Of Feature Spaces For Bioinformatics Domain Discovery, Pradeep Chowriappa

Doctoral Dissertations

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein's surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized …

K-Means+Id3 And Dependence Tree Methods For Supervised Anomaly Detection, Kiran S. Balagani

Doctoral Dissertations

In this dissertation, we present two novel methods for supervised anomaly detection. The first method "K-Means+ID3" performs supervised anomaly detection by partitioning the training data instances into k clusters using Euclidean distance similarity. Then, on each cluster representing a density region of normal or anomaly instances, an ID3 decision tree is built. The ID3 decision tree on each cluster refines the decision boundaries by learning the subgroups within a cluster. To obtain a final decision on detection, the k-Means and ID3 decision trees are combined using two rules: (1) the nearest neighbor rule; and (2) the nearest consensus rule. The …

Wireless Sensor Network Modeling Using Modified Recurrent Neural Network: Application To Fault Detection, Azzam Issam Moustapha

Doctoral Dissertations

Wireless Sensor Networks (WSNs) consist of a large number of sensors, which in turn have their own dynamics. They interact with each other and the base station, which controls the network. In multi-hop wireless sensor networks, information hops from one node to another and finally to the network gateway or base station. Dynamic Recurrent Neural Networks (RNNs) consist of a set of dynamic nodes that provide internal feedback to their own inputs. They can be used to simulate and model dynamic systems such as a network of sensors.

In this dissertation, a dynamic model of wireless sensor networks and its …

Failure Analysis And Reliability -Aware Resource Allocation Of Parallel Applications In High Performance Computing Systems, Narasimha Raju Gottumukkala

Doctoral Dissertations

The demand for more computational power to solve complex scientific problems has been driving the physical size of High Performance Computing (HPC) systems to hundreds and thousands of nodes. Uninterrupted execution of large scale parallel applications naturally becomes a major challenge because a single node failure interrupts the entire application, and the reliability of a job completion decreases with increasing the number of nodes. Accurate reliability knowledge of a HPC system enables runtime systems such as resource management and applications to minimize performance loss due to random failures while also providing better Quality Of Service (QOS) for computational users.

This …

A Finite Difference Method For Studying Thermal Deformation In A Three-Dimensional Microsphere Exposed To Ultrashort-Pulsed Lasers, Xudong Du

Doctoral Dissertations

Ultrashort-pulsed lasers with pulse durations on the order of sub-picoseconds to femtoseconds possess the capabilities in limiting the undesirable spread of the thermal process zone in a heated sample which have been attracting worldwide interest in science and engineering. Success of ultrashort-pulsed lasers in real application relies on: (1) well characterized pulse width, intensity and experimental techniques; (2) reliable microscale heat transfer models; and (3) prevention of thermal damage. Laser damage by ultrashort-pulsed lasers occurs after the heating pulse is over since the pulse duration time is extremely short and the heat flux is essentially limited to the region within …

Reliability -Aware Optimal Checkpoint /Restart Model In High Performance Computing, Yudan Liu

Doctoral Dissertations

Computational power demand for large challenging problems has increasingly driven the physical size of High Performance Computing (HPC) systems. As the system gets larger, it requires more and more components (processor, memory, disk, switch, power supply and so on). Thus, challenges arise in handling reliability of such large-scale systems. In order to minimize the performance loss due to unexpected failures, fault tolerant mechanisms are vital to sustain computational power in such environment. Checkpoint/restart is a common fault tolerant technique which has been widely applied in the single computer system. However, checkpointing in a large-scale HPC environment is much more challenging …

Membrane Systems With Limited Parallelism, Bianca Daniela Popa

Doctoral Dissertations

Membrane computing is an emerging research field that belongs to the more general area of molecular computing, which deals with computational models inspired from bio-molecular processes. Membrane computing aims at defining models, called membrane systems or P systems, which abstract the functioning and structure of the cell. A membrane system consists of a hierarchical arrangement of membranes delimiting regions, which represent various compartments of a cell, and with each region containing bio-chemical elements of various types and having associated evolution rules, which represent bio-chemical processes taking place inside the cell.

This work is a continuation of the investigations aiming to …

Stochastic Propagation Modeling And Early Detection Of Malicious Mobile Code, Xin Xu

Doctoral Dissertations

Epidemic models are commonly used to model the propagation of malicious mobile code like a computer virus or a worm. In this dissertation, we introduce stochastic techniques to describe the propagation behavior of malicious mobile code. We propose a stochastic infection-immunization (INIM) model based on the standard Susceptible-Infected-Removed (SIR) epidemic model, and we get an explicit solution of this model using probability generating function (pgf.). Our experiments simulate the propagation of malicious mobile code with immunization. The simulation results match the theoretical results of the model, which indicates that it is reliable to use INIM model to predict the propagation …

A Novel Computational Framework For Fast, Distributed Computing And Knowledge Integration For Microarray Gene Expression Data Analysis, Prerna Sethi

Doctoral Dissertations

The healthcare burden and suffering due to life-threatening diseases such as cancer would be significantly reduced by the design and refinement of computational interpretation of micro-molecular data collected by bioinformaticians. Rapid technological advancements in the field of microarray analysis, an important component in the design of in-silico molecular medicine methods, have generated enormous amounts of such data, a trend that has been increasing exponentially over the last few years. However, the analysis and handling of these data has become one of the major bottlenecks in the utilization of the technology. The rate of collection of these data has far surpassed …

Availability Modeling And Evaluation On High Performance Cluster Computing Systems, Hertong Song

Doctoral Dissertations

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power, cost effective, and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system's availability requirement.

In this study, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. …

Evaluating Online Trust Using Machine Learning Methods, Weihua Song

Doctoral Dissertations

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation …

The Bipartite Clique: A Topological Paradigm For Web User Search Customization And Web Site Restructuring, Brenda F. Choyce-Miles

Doctoral Dissertations

The objective of this dissertation research is to aid the Web user to achieve his search objective at a host Web site by organizing a strongly connected neighborhood of Web pages that are thematically and spatially related to the user's search interest. Therefore, methods were developed to (1) find all Web pages at a given Web site that are thematically similar to a user's initial choice of a Web page (selected from the set of Web pages returned in response to a query by any popular search engine), and (2) organize these pages hierarchically in terms of their relevance to …

Developing A B -Tagging Algorithm Using Soft Muons At Level-3 For The Dø Detector At Fermilab, Mayukh Das

Doctoral Dissertations

The current data-taking phase of the DØ detector at Fermilab, called Run II, is designed to aid the search for the Higgs Boson. The neutral Higgs is postulated to have a mass of 117 GeV. One of the channels promising the presence of this hypothetical particle is through the decay of b-quark into a muon. The process of identifying a b-quark in a jet using muon as a reference is b-tagging with a muon tag.

At the current data taking and analysis rate, it will take long to reach the process of identifying valid events. The triggering mechanism of the …

Integrated Modeling And Parallel Computation Of Laser-Induced Axisymmetric Rod Growth, Hong Lan

Doctoral Dissertations

To fully investigate a pyrolytic Laser-induced chemical vapor deposition (LCVD) system for growing an axisymmetric rod, a novel integrated three-dimensional mathematical model was developed not only to describe the heat transport in the deposit and substrate, but also to simulate the gas-phase in the heated reaction zone and its effect on growth rate. The integrated model consists of three components: the substrate, rod, and gas-phase domains. Each component is a separate model and the three components are dynamically integrated into one model for simulating the iterative and complex process of rod deposition.

The gas-phase reaction is modeled by the gas-phase …

Computational Approaches To The Design And Analysis Of Stability Of Polypeptide Multilayer Thin Films, Bin Zheng

Doctoral Dissertations

The focus of this research is the development of computational approaches to understanding the physical basis of layer-by-layer assembly (LBL), a key methodology of nanomanufacturing. The results provided detailed information on structure which cannot be obtained directly by experiments.

The model systems chosen for study are polypeptide chains. Reasons for this are that polypeptides are no less polyelectrolytes than the more usual polyions, and one can control the primary structure of a polypeptide on a residue-by-residue basis using modern synthetic methods. Moreover, as peptides constitute one of the four major classes of biological macromolecules, research in this direction is expected …

Sense -Based Text Classification By Semantic Hierarchy Representation, Xiaogang Peng

Doctoral Dissertations

Automatic classification of web pages is an effective way to facilitate the process of retrieving information from the Internet. Currently, two major classification methods are used in this area: keyword-based classification and sense-based classification. For keyword-based classification, keywords often have different semantic meanings, and the correct keyword matching is largely based on using exactly the same keywords. Thus, the classification results of keyword-based classification are not always satisfying. Many sense-based classification algorithms and systems have been presented, but they pay little attention to the relationship between senses. In this dissertation, we present a method to automatically classify documents based on …

Modeling Of The Inverse Heat -Conduction Problem With Application To Laser Chemical Vapor Deposition And Bioheat Transfer, Peng Zhen

Doctoral Dissertations

This dissertation consists of two parts. Part one deals with three-dimensional laser induced chemical vapor deposition (3D-LCVD), whereas part two deals with a Pennes model of a 3D skin structure. LCVD is an important technique in manufacturing complex micro-structures with high aspect ratio. In part one, a numerical model was developed for simulating kinetically-limited growth of an axisymmetric cylindrical rod by pre-specifying the surface temperature distribution required for growing the rod and then by obtaining optimized laser power that gives rise to the pre-specified temperature distribution. The temperature distribution at the surface of the rod was assumed to be at …

Machine Learning Approaches For Determining Effective Seeds For K -Means Algorithm, Kaveephong Lertwachara

Doctoral Dissertations

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead of the neural network. Both NN and GA used in this study are in their simplest-possible forms.

In the simulated data sets, I investigate two …

Asp -Pricing: A Black -Scholes Option Pricing Formulation, Chaitanya Singh

Doctoral Dissertations

The Applications Service Provider (ASP) arrangement has engendered a revolution in the area of corporate information technology (IT) by transforming software from a packaged off-the-shelf product to an on-line virtual service.

The focus of this study is to establish a sound mathematical foundation for evaluating software rental agreements (embedding exit flexibility) by incorporating a real options framework (based upon the Black-Scholes approach) into the traditional capital budgeting technique. The static discounted cash flow or net present value analysis may not adequately serve as a ‘barometer’ of outsourcing value due to its inherent weaknesses. On the other hand, the options approach …

Fuzzy Product -Limit Estimators: Soft Computing In The Presence Of Very Small And Highly Censored Data Sets, Kian Lawrence Pokorny

Doctoral Dissertations

When very few data are available and a high proportion of the data is censored, accurate estimates of reliability are problematic. Standard statistical methods require a more complete data set, and with any fewer data, expert knowledge or heuristic methods are required. In the current research a computational system is developed that obtains a survival curve, point estimate, and confidence interval about the point estimate.

The system uses numerical methods to define fuzzy membership functions about each data point that quantify uncertainty due to censoring. The “fuzzy” data are then used to estimate a survival curve, and the mean survival …

Multi -Mission Attitude Determination System For Balloon Flight, Liping Mo

Doctoral Dissertations

MADS (Multi-mission Attitude Determination System) is a new software package used to determine the attitude of instruments on a high-altitude balloon employed for scientific experiments. There is no existing system for the automated determination of the attitude of instruments in balloon experiments, so we have developed MADS to do the data analysis for balloon experiments to find the location of astrophysical sources such as gamma-ray or x-ray sources.

The two areas that required most work were modeling star trackers and modeling the motion of the balloon. Star trackers are used on satellites, but are far too expensive and sophisticated to …

A Hybrid Finite Element-Finite Difference Method For Thermal Analysis In A Double-Layered Thin Film, Teng Zhu

Doctoral Dissertations

Thin film technology is of vital importance in microtechnology applications. For instance, thin films of metals, of dielectrics such as SiO2, or Si semiconductors are important components of microelectronic devices. The reduction of the device size to the microscale has the advantage of enhancing the switching speed of the device. The reduction, on the other hand, increases the rate of heat generation that leads to a high thermal load on the microdevice. Heat transfer at the microscale with an ultrafast pulsed-laser is also a very important process for thin films. Hence, studying the thermal behavior of thin films or of …

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …