Transfer function design based on user selected samples for intuitive multivariate volume exploration

pre-printMultivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets

MeasureIt-ARCH: A Tool for Facilitating Architectural Design in the Open Source Software Blender

This thesis discusses the design and synthesis of MeasureIt-ARCH, a GNU GPL licensed software add-on developed by the author in order to add functionality to the Open Source 3D modeling software Blender that facilitates the creation of architectural drawings. MeasureIt-ARCH adds to Blender simple tools to dimension and annotate 3D models, as well as basic support for the definition and drawing of line work. These tools for the creation of dimensions, annotations and line work are designed to be used in tandem with Blender's existing modelling and rendering tool set. While the drawings that MeasureIt-ARCH produces are fundamentally conventional, as are the majority of the techniques that MeasureIt-ARCH employs to create them, MeasureIt-ARCH does provide two simple and relatively novel methods in its drawing systems. MeasureIt-ARCH provides a new method for the placement of dimension elements in 3D space that draws on the dimension's three dimensional context and surrounding geometry order to determine a placement that optimizes legibility. This dimension placement method does not depend on a 2D work plane, a convention that is common in industry standard Computer Aided Design software. MeasureIt-ARCH also implements a new approach for drawing silhouette lines that operates by transforming the silhouetted models geometry in 4D 'Clip Space'. The hope of this work is that MeasureIt-ARCH might be a small step towards creating an Open Source design pipeline for Architects. A step towards creating architectural drawings that can be shared, read, and modified by anyone, within a platform that is itself free to be changed and improved. The creation of MeasureIt-ARCH is motivated by two goals. First, the work aims to create a basic functioning Open Source platform for the creation of architectural drawings within Blender that is publicly and freely available for use. Second, MeasureIt-ARCH's development served as an opportunity to engage in an interdisciplinary act of craft, providing the author an opportunity to explore the act of digital tool making and gain a basic competency in this intersection between Architecture and Computer Science. To achieve these goals, MeasureIt-ARCH's development draws on references from the history of line drawing and dimensioning within Architecture and Computer Science. On the Architectural side, we make use of the history of architectural drawing and dimensioning conventions as described by Mario Carpo, Alberto Pérez Gómez and others, as well as more contemporary frameworks for the classification of architectural software, such as Mark Bew and Mervyn Richard's BIM Levels framework, in order to help determine the scope of MeasureIt-ARCH's feature set. When crafting MeasureIt-ARCH, precedent works from the field of Computer Science that implement methods for producing line drawings from 3D models helped inform the author’s approach to line drawing. In particular this work draws on the overview of line drawing methods produced by Bénard Pierre and Aaron Hertzmann, Arthur Appel's method for line drawing using 'Quantitative Invisibility', the techniques employed in the Freestyle line drawing system created by Grabli et al. as well as other to help inform MeasureIt-ARCH's simple drawing tools. Beyond discussing MeasureIt-ARCH's development and its motivations, this thesis also provides three small speculative discussions about the implications that an Open Source design tool might have on the architectural profession. We investigate MeasureIt-ARCH's use for small scale architectural projects in a practical setting, using it's tool set to produce conceptual design and renovation drawings for cottages at the Lodge at Pine Cove. We provide a demonstration of how MeasureIt-ARCH and Blender can integrate with external systems and other Blender add-ons to produce a proof of concept, dynamic data visualization of the Noosphere installation at the Futurium center in Berlin by the Living Architecture Systems Group. Finally, we discuss the tool's potential to facilitate greater engagement with the Open Source Architecture (OSArc) movement by illustrating a case study of the work done by Alastair Parvin and Clayton Prest on the WikiHouse project, and by highlighting the challenges that face OSArc projects as they try to produce Open Source Architecture without an Open Source design software

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists

Astronomy with integral field spectroscopy:: observation, data analysis and results

With a new generation of facility instruments being commissioned for 8 metre telescopes, integral field spectroscopy will soon be a standard tool in astronomy, opening a range of exciting new research opportunities. It is clear, however, that reducing and analyzing integral field data is a complex problem, which will need considerable attention before the full potential of the hardware can be realized. The purpose of this thesis is therefore to explore some of the scientific capabilities of integral field spectroscopy, developing the techniques needed to produce astrophysical results from the data. Two chapters are dedicated to the problem of analyzing observations from the densely-packed optical fibre instruments pioneered at Durham. It is shown that, in the limit where each spectrum is sampled by only one detector row, data maybe treated in a similar way to those from an image slicer. The properties of raw fibre data are considered in the context of the Sampling Theorem and methods for three dimensional image reconstruction are discussed. These ideas are implemented in an IRAF data reduction package for the Thousand Element Integral Field Unit (TEIFU), with source code provided on the accompanying compact disc. Two observational studies are also presented. In the first case, the 3D infrared image slicer has been used to test for the presence of a super-massive black hole in the giant early-type galaxy NGC 1316. Measurements of the stellar kinematics do not reveal a black hole of mass 5 x l0(^9)M©, as predicted from bulge luminosity using the relationship of Kormendy & Richstone (1995). The second study is an investigation into the origin of [Fell] line emission in the Seyfert galaxy NGC4151, using Durham University's SMIRFS-IFU. By mapping [Fell] line strength and velocity at the galaxy centre, it is shown that the emission is associated with the optical narrow line region, rather than the radio jet, indicating that the excitation is primarily due to photoionizing X-rays.Finally, a report is given on the performance of TEIFU, which was commissioned at the William Herschel Telescope in 1999. Measurements of throughput and fibre response variation are given and a reconstructed test observation of the radio galaxy 3C 327 is shown, demonstrating the functionality of the instrument and software

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Autonomous Pathfinding for Planetary Rover by Implementing A* Algorithm on an Aerial Map Processed Using MATLAB Image Processing Tool

Human curiosity to discover new things and exploring unknown regions, have continually to development of robots, which became a powerful tools for accessing dangerous environments or exploring regions too distant for human. Previous robot technology functioned under continues human supervision, limiting the robot to confined area and pre-programmed task. However,as exploration moved to regions where communication is ineffective or unviable, robots were used to carry out complex tasks without human supervision. To empower such capacities, robots are being upgraded by advances extending from new sensor improvement to automated mission planning software, circulated automated control, and more proficient power systems. With the advancement of autonomy science robotics technology developed and the robots became more and more capable of operating multi task, under minimal human supervision. In this project work we aim at designing an ONS (Offline Navigation System) system for the planetary rover which will use aerial map taken from satellite and pre-process into a grid map which is then will be used by the rover to travel from one place to another place and completing its mission. The aerial map is processed using Matlab image processing tool to convert into a grid map and search for shortest route is implemented using A* algorithm. The shortest route result is then converted into microcontroller signal to move the rover. With this system the rovers will have the ability to predict the best possible path even if the communication to the satellite is broken

Massive Computation for Understanding Core-Collapse Supernova Explosions

How do massive stars explode? Progress toward the answer is driven by increases in compute power. Petascale supercomputers are enabling detailed 3D simulations of core-collapse supernovae that are elucidating the role of fluid instabilities, turbulence, and magnetic field amplification in supernova engines