SmallKat MQP

The SmallKat MQP is providing a quadrupedal robotic platform to help research and design new gaits, test sensors, and teach engineering students. Current options limit small companies, universities, and hobbyists due to their complexity, large size, and immense cost. SmallKat is a low-cost robotic platform with customizability and adaptability in mind. To allow for a multitude of gait designs, it is designed with 4-DoF legs controlled by powerful custom servo motors, 9-DoF IMUs, and custom microcontrollers. The body is constructed using additive manufacturing with PLA plastics, and even has a continuum tail for added body control. The higher level controller runs on a single-board computer for added performance when computing kinematics and dynamics, and controlling different gaits

Development and evaluation of a digital tool for virtual reconstruction of historic Islamic geometric patterns

For the purpose of cultural heritage preservation, the task of recording and reconstructing visually complicated architectural geometrical patterns is facing many practical challenges. Existing traditional technologies rely heavily on the subjective nature of our perceptual power in understanding its complexity and depicting its color differences. This study explores one possible solution, through utilizing digital techniques for reconstructing detailed historical Islamic geometric patterns. Its main hypothesis is that digital techniques offer many advantages over the human eye in terms of recognizing subtle differences in light and color. The objective of the study is to design, test and evaluate an automatic visual tool for identifying deteriorated or incomplete archaeological Islamic geometrical patterns captured in digital images, and then restoring them digitally, for the purpose of producing accurate 2D reconstructed metric models. An experimental approach is used to develop, test and evaluate the specialized software. The goal of the experiment is to analyze the output reconstructed patterns for the purpose of evaluating the digital tool in respect to reliability and structural accuracy, from the point of view of the researcher in the context of historic preservation. The research encapsulates two approaches within its methodology; Qualitative approach is evident in the process of program design, algorithm selection, and evaluation. Quantitative approach is manifested through using mathematical knowledge of pattern generation to interpret available data and to simulate the rest based on it. The reconstruction process involves induction, deduction and analogy. The proposed method was proven to be successful in capturing the accurate structural geometry of the deteriorated straight-lines patterns generated based on the octagon-square basic grid. This research also concluded that it is possible to apply the same conceptual method to reconstruct all two-dimensional Islamic geometric patterns. Moreover, the same methodology can be applied to reconstruct many other pattern systems. The conceptual framework proposed by this study can serve as a platform for developing professional softwares related to historic documentation. Future research should be directed more towards developing artificial intelligence and pattern recognition techniques that have the ability to suplement human power in accomplishing difficult tasks

Navigation and Exploration in Virtual Environment with Virtual Agent using Java 3D

This project's objective was to investigate the potential used of Java 3D in developing a virtual environment integrated with virtual agent. The methodology of this project is constitutes on four (4) main phases; planning and analysis phase, modeling and development phase, integration construction phase and the last is testing phase. The testing phase indicates that in order to develop a virtual reality (VR) application, the developer must perform hard-coded programming to model the objects as well as to develop a behavior. Navigation and exploration activity in a virtual environment can be perform easily by using keyboard arrows. To develop a joint behavior between the virtual environment and virtual agent, complex and careful programming is required. This project's conclusion indicates that Java 3D provides other libraries to develop a VRapplication to better help user in navigation and exploration in a virtual environment. For that reason, further study and investigation can be conducted to achieve this goal

Doctor of Philosophy

dissertationNumerical simulation of the geometrically complex fractured reservoirs has been a major engineering challenge. The deficiencies of continuum models are often addressed using the discrete fracture network (DFN) models which represent the complex fracture geometry explicitly. The primary goal in this dissertation is to explore ways of applying the DFN methodology to solve a variety of multiphase problems in oil reservoir simulation. Three-dimensional, three-phase simulators using the control-volume finiteelement scheme were used. After completing validation and fracture-property sensitivity studies, the limitation of employing the often-used Oda homogenization method was shown followed by the development of a simpler geometric scheme. The important question of oil recovery from basement reservoirs (Type I) composed of fractures of various sizes was examined in detail. Oil recovery and breakthrough behavior of this system comprised of seismic and subseismic features were investigated for different oil distributions, permeability values, levels of heterogeneity and rate. In general having more oil distributed in smaller systems led to lower recovery and quicker breakthrough. Lower permeabilities in the subseismic features also led to lower recovery. The recovery at given pore volume of water injected was rate dependent in all of the scenarios explored, with the lower rate production leading to about 5% higher oil in place recovery. This phenomenon was consistent when viewed from the point of view of gravity number for each displacement. The mechanism of gravity-dominated oil recovery in two-phase applications was explored, and a "critical rate" concept for obtaining higher recoveries in gravity-dominated flow was developed A multiscale upscaling exercise was performed to match the oil recovery performance from a structured fault zone using a single feature with different sets of relative permeability curves. The effectiveness of using DFN simulations for reservoirs containing matrix and fractures (Type II) was shown using two different systems. It was shown that placing wells either in the fault zone or in the matrix can have significant impact on recovery and breakthrough behavior. It was also demonstrated that fracture networks bring apparent anisotropy, and water-flooding from one direction or the other may affect oil recovery. Fractured reservoir simulation is high-performance computing - data and file management, computation, visualization, etc. are integral components of this exercise. A workflow to facilitate creation of fracture networks, gridding and simulation, and visualization was developed. A fully integrated two-dimensional graphical user interface (java-based) was also built

Inferring Anomalies from Data using Bayesian Networks

Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and numeric databases. However, not much stress has been given on the interestingness of the reported outlier. One way to ascertain interestingness and usefulness of the reported outlier is by making use of domain knowledge. In this thesis, we present measures to discover outliers based on background knowledge, represented by a Bayesian network. Using causal relationships between attributes encoded in the Bayesian framework, we demonstrate that meaningful outliers, i.e., outliers which encode important or new information are those which violate causal relationships encoded in the model. Depending upon nature of data, several approaches are proposed to identify and explain anomalies using Bayesian knowledge. Outliers are often identified as data points which are ``rare'', ''isolated'', or ''far away from their nearest neighbors''. We show that these characteristics may not be an accurate way of describing interesting outliers. Through a critical analysis on several existing outlier detection techniques, we show why there is a mismatch between outliers as entities described by these characteristics and ``real'' outliers as identified using Bayesian approach. We show that the Bayesian approaches presented in this thesis has better accuracy in mining genuine outliers while, keeping a low false positive rate as compared to traditional outlier detection techniques

Utilization of forward error correction (FEC) techniques with extensible markup language (XML) schema-based binary compression (XSBC) technology

In order to plug-in current open sourced, open standard Java programming technology into the building blocks of the US Navy's ForceNet, first, stove-piped systems need to be made extensible to other pertinent applications and then a new paradigm of adopting extensible and cross-platform open technologies will begin to bridge gaps with old and new weapons systems. The battle-space picture in real time and with as much detail, or as little detail needed is now a current vital requirement. Access to this information via wireless laptop technology is here now. Transmission of data to increase the resolution of that battle-space snapshot will invariably be through noisy links. Noisy links such as found in the shallow water littoral regions of interest will be where Autonomous Underwater and Unmanned Underwater Vehicles (AUVs/UUVs) are gathering intelligence for the sea warrior in need of that intelligence. The battle-space picture built from data transmitted within these noisy and unpredictable acoustic regions demands efficiency and reliability features abstract to the user. To realize this efficiency Extensible Markup Language (XML) Schema-based Binary Compression (XSBC), in combination with Vandermode-based Forward Error Correction (FEC) erasure codes, offer the qualities of efficient streaming of plain text XML documents in a highly compressed form, and a data self-healing capability should there be loss of data during transmission in unpredictable transmission mediums. Both the XSBC and FEC libraries detailed in this thesis are open sourced Java Application Program Interfaces (APIs) that can be readily adapted for extensible, cross-platform applications that will be enhanced by these desired features to add functional capability to ForceNet for the sea warrior to access on demand, at sea and in real-time. These features will be presented in the Autonomous Underwater Vehicle (AUV) Workbench (AUVW) Java-based application that will become a valuable tool for warriors involved with Undersea Warfare (UW).http://archive.org/details/utilizationoffor109451247Lieutenant, United States NavyApproved for public release; distribution is unlimited

Data mining and machine learning methods for chromosome conformation data analysis

Sixteen years after the sequencing of the human genome, the Human Genome Project (HGP), and 17 years after the introduction of Chromosome Conformation Capture (3C) technologies, three-dimensional (3-D) inference and big data remains problematic in the field of genomics, and specifically, in the field of 3C data analysis. Three-dimensional inference involves the reconstruction of a genome's 3D structure or, in some cases, ensemble of structures from contact interaction frequencies extracted from a variant of the 3C technology called the Hi-C technology. Further questions remain about chromosome topology and structure; enhancer-promoter interactions; location of genes, gene clusters, and transcription factors; the relationship between gene expression and epigenetics; and chromosome visualization at a higher scale, among others. In this dissertation, four major contributions are described, first, 3DMax, a tool for chromosome and genome 3-D structure prediction from H-C data using optimization algorithm, second, GSDB, a comprehensive and common repository that contains 3D structures for Hi-C datasets from novel 3D structure reconstruction tools developed over the years, third, ClusterTAD, a method for topological associated domains (TAD) extraction from Hi-C data using unsupervised learning algorithm. Finally, we introduce a tool called, GenomeFlow, a comprehensive graphical tool to facilitate the entire process of modeling and analysis of 3D genome organization. It is worth noting that GenomeFlow and GSDB are the first of their kind in the 3D chromosome and genome research field. All the methods are available as software tools that are freely available to the scientific community.Includes bibliographical reference