Triangulation of 3D Domains (With Quad-Hexa Meshing Support)
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												  Relative Curve Orientation in the Alignment of Inconsistent Linear DatasetsRelative Curve Orientation in the Alignment of Inconsistent Linear Datasets David N. Siriba, Daniel Eggert, Monika Sester Institute of Cartography and Geoinformatics (IKG), Leibniz University of Hannover, Germany Appelstraße 9a, 30167 Hannover, Germany. E-mail: {david.siriba, daniel.eggert, monika.sester}@ikg.uni-hannover.de ABSTRACT An approach for the alignment of a linear dataset of inconsistent positional accuracy with a more accurate one is presented. The approach consists of two main steps: path-matching and alignment based on relative curve orientations of the corresponding feature followed by a non-rigid transformation based on Thin plate Splines (TPS). The results obtained with this approach on two experimental cases show significant improvement in alignment and geometric accuracy of the lower accuracy datasets. 1. INTRODUCTION Geometric alignment of spatial features is one of the typical problems of data integration and usually carried out so that spatial features from two datasets that describe the same object coincide. It is achieved through point-based transformation that utilizes corresponding point features, which represent either some identified point landmarks or nodes mainly in a road network. Point-based transformation is adequate only for simple cases where positional discrepancies between the datasets are low and almost evenly distributed. In case the discrepancies are high and unevenly distributed, point-based approaches are not sufficient for aligning linear and polygonal features as they only guarantee that the control points will coincide. This problem is often encountered when integrating legacy datasets that contain large positional distortions with more geometrically accurate and newer datasets. Therefore corresponding linear features could be used to determine a more suitable geometric transformation between the datasets, in addition to point features.
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												  Limits of Pgl(3)-Translates of Plane Curves, ILIMITS OF PGL(3)-TRANSLATES OF PLANE CURVES, I PAOLO ALUFFI, CAREL FABER Abstract. We classify all possible limits of families of translates of a fixed, arbitrary complex plane curve. We do this by giving a set-theoretic description of the projective normal cone (PNC) of the base scheme of a natural rational map, determined by the curve, 8 N from the P of 3 × 3 matrices to the P of plane curves of degree d. In a sequel to this paper we determine the multiplicities of the components of the PNC. The knowledge of the PNC as a cycle is essential in our computation of the degree of the PGL(3)-orbit closure of an arbitrary plane curve, performed in [5]. 1. Introduction In this paper we determine the possible limits of a fixed, arbitrary complex plane curve C , obtained by applying to it a family of translations α(t) centered at a singular transformation of the plane. In other words, we describe the curves in the boundary of the PGL(3)-orbit closure of a given curve C . Our main motivation for this work comes from enumerative geometry. In [5] we have determined the degree of the PGL(3)-orbit closure of an arbitrary (possibly singular, re- ducible, non-reduced) plane curve; this includes as special cases the determination of several characteristic numbers of families of plane curves, the degrees of certain maps to moduli spaces of plane curves, and isotrivial versions of the Gromov-Witten invariants of the plane. A description of the limits of a curve, and in fact a more refined type of information is an essential ingredient of our approach.
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												  Unsupervised Smooth Contour DetectionPublished in Image Processing On Line on 2016–11–18. Submitted on 2016–04–21, accepted on 2016–10–03. ISSN 2105–1232 c 2016 IPOL & the authors CC–BY–NC–SA This article is available online with supplementary materials, software, datasets and online demo at https://doi.org/10.5201/ipol.2016.175 2015/06/16 v0.5.1 IPOL article class Unsupervised Smooth Contour Detection Rafael Grompone von Gioi1, Gregory Randall2 1 CMLA, ENS Cachan, France ([email protected]) 2 IIE, Universidad de la Rep´ublica, Uruguay ([email protected]) Abstract An unsupervised method for detecting smooth contours in digital images is proposed. Following the a contrario approach, the starting point is defining the conditions where contours should not be detected: soft gradient regions contaminated by noise. To achieve this, low frequencies are removed from the input image. Then, contours are validated as the frontiers separating two adjacent regions, one with significantly larger values than the other. Significance is evalu- ated using the Mann-Whitney U test to determine whether the samples were drawn from the same distribution or not. This test makes no assumption on the distributions. The resulting algorithm is similar to the classic Marr-Hildreth edge detector, with the addition of the statis- tical validation step. Combined with heuristics based on the Canny and Devernay methods, an efficient algorithm is derived producing sub-pixel contours. Source Code The ANSI C source code for this algorithm, which is part of this publication, and an online demo are available from the web page of this article1.
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												  (Iso19107) of the Open Geospatial ConsortiumTechnical Report A Java Implementation of the OpenGIS™ Feature Geometry Abstract Specification (ISO 19107 Spatial Schema) Sanjay Dominik Jena, Jackson Roehrig {[email protected], [email protected]} University of Applied Sciences Cologne Institute for Technology in the Tropics Version 0.1 July 2007 ABSTRACT The Open Geospatial Consortium’s (OGC) Feature Geometry Abstract Specification (ISO/TC211 19107) describes a geometric and topological data structure for two and three dimensional representations of vector data. GeoAPI, an OGC working group, defines inter- face APIs derived from the ISO 19107. GeoTools provides an open source Java code library, which implements (OGC) specifications in close collaboration with GeoAPI projects. This work describes a partial but serviceable implementation of the ISO 19107 specifi- cation and its corresponding GeoAPI interfaces considering previous implementations and related specifications. It is intended to be a first impulse to the GeoTools project towards a full implementation of the Feature Geometry Abstract Specification. It focuses on aspects of spatial operations, such as robustness, precision, persistence and performance. A JUnit Test Suite was developed to verify the compliance of the implementation with the GeoAPI. The ISO 19107 is discussed and proposals for improvement of the GeoAPI are presented. II © Copyright by Sanjay Dominik Jena and Jackson Roehrig 2007 ACKNOWLEDGMENTS Our appreciation goes to the whole of the GeoTools and GeoAPI communities, in par- ticular to Martin Desruisseaux, Bryce Nordgren, Jody Garnett and Graham Davis for their extensive support and several discussions, and to the JTS developers, the JTS developer mail- ing list and to those, who will make use of and continue the implementation accomplished in this work.
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												  Calculus-Free Derivatives of Sine and CosineCalculus-free Derivatives of Sine and Cosine B.D.S. McConnell [email protected] Wrap a square smoothly around a cylinder, and the curved images of the square’s diagonals will trace out helices, with the (original, uncurved) diag- onals tangent to those curves. Projecting these elements into appropriate planes reveals an intuitive, geometric development of the formulas for the derivatives of sine and cosine, with no quotient of vanishing differences re- quired. Indeed, a single —almost-“Behold!”-worthy— diagram and a short notational summary (see the last page of this note) give everything away. The setup. Consider a cylinder (“C”) of radius 1 and a unit square (“S”), with a pair of S’s edges parallel to C’s axis, and with S’s center (“P ”) a point of tangency with C’s surface. We may assume that the cylinder’s axis coincides with the z axis; we may also assume that, measuring along the surface of the cylinder, the (straight-line) distance from P to the xy-plane is equal to the (arc-of-circle) distance from P to the zx-plane. That is, we take P to have coordinates (cos θ0, sin θ0, θ0) for some θ0, where cos() and sin() take radian arguments. Wrapping S around C causes the image of an extended diagonal of S to trace out the helix (“H”) containing all (and only) points of the form (cos θ, sin θ, θ). Moreover, the original, uncurved diagonal segment of S de- termines a vector, v :=< vx, vy, vz >, tangent to H at P ; taking vz = 1, we assure that the vector conveniently points “forward” (that is, in the direction of increasing θ) along the helix.
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												  Geometry of Algebraic CurvesGeometry of Algebraic Curves Lectures delivered by Joe Harris Notes by Akhil Mathew Fall 2011, Harvard Contents Lecture 1 9/2 x1 Introduction 5 x2 Topics 5 x3 Basics 6 x4 Homework 11 Lecture 2 9/7 x1 Riemann surfaces associated to a polynomial 11 x2 IOUs from last time: the degree of KX , the Riemann-Hurwitz relation 13 x3 Maps to projective space 15 x4 Trefoils 16 Lecture 3 9/9 x1 The criterion for very ampleness 17 x2 Hyperelliptic curves 18 x3 Properties of projective varieties 19 x4 The adjunction formula 20 x5 Starting the course proper 21 Lecture 4 9/12 x1 Motivation 23 x2 A really horrible answer 24 x3 Plane curves birational to a given curve 25 x4 Statement of the result 26 Lecture 5 9/16 x1 Homework 27 x2 Abel's theorem 27 x3 Consequences of Abel's theorem 29 x4 Curves of genus one 31 x5 Genus two, beginnings 32 Lecture 6 9/21 x1 Differentials on smooth plane curves 34 x2 The more general problem 36 x3 Differentials on general curves 37 x4 Finding L(D) on a general curve 39 Lecture 7 9/23 x1 More on L(D) 40 x2 Riemann-Roch 41 x3 Sheaf cohomology 43 Lecture 8 9/28 x1 Divisors for g = 3; hyperelliptic curves 46 x2 g = 4 48 x3 g = 5 50 1 Lecture 9 9/30 x1 Low genus examples 51 x2 The Hurwitz bound 52 2.1 Step 1 . 53 2.2 Step 10 ................................. 54 2.3 Step 100 ................................ 54 2.4 Step 2 .
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												  Perylene Diimide: a Versatile Building Block for Complex Molecular Architectures and a Stable Charge Storage MaterialPerylene Diimide: A Versatile Building Block for Complex Molecular Architectures and a Stable Charge Storage Material Margarita Milton Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2018 © 2018 Margarita Milton All rights reserved ABSTRACT Perylene Diimide: A Versatile Building Block for Complex Molecular Architectures and a Stable Charge Storage Material Margarita Milton Properties such as chemical robustness, potential for synthetic tunability, and superior electron-accepting character describe the chromophore perylene-3,4,9,10-tetracarboxylic diimide (PDI) and have enabled its penetration into organic photovoltaics. The ability to extend what is already a large aromatic core allows for synthesis of graphene ribbon PDI oligomers. Functionalization with polar and ionic groups leads to liquid crystalline phases or immense supramolecular architectures. Significantly, PDI dianions can survive in water for two months with no decomposition, an important property for charge storage materials. We realized the potential of PDI as an efficient negative-side material for Redox Flow Batteries (RFBs). The synthetic tunability of PDI allowed for screening of several derivatives with side chains that enhanced solubility in polar solvents. The optimized molecule, PDI[TFSI]2, dissolved in acetonitrile up to 0.5 M. For the positive-side, we synthesized the ferrocene oil [Fc4] in high yield. The large hydrodynamic radii of PDI[TFSI]2 and [Fc4] preclude their ability to cross a size exclusion membrane, which is a cheap alternative to the typical RFB membranes. We show that this cellulose-based membrane can support high voltages in excess of 3 V and extreme temperatures (−20 to 110 °C).
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												  Prism User's Guide 10 Copyright (C) 1999 Graphpad Software IncUser's Guide Version 3 The fast, organized way to analyze and graph scientific data ã 1994-1999, GraphPad Software, Inc. All rights reserved. GraphPad Prism, Prism and InStat are registered trademarks of GraphPad When you opened the disk envelope, or downloaded the Software, Inc. GraphPad is a trademark of GraphPad Software, Inc. purchased software, you agreed to the Software License Agreement reprinted on page 197. GraphPad Software, Inc. Use of the software is subject to the restrictions contained in the does not guarantee that the program is error-free, and cannot accompanying software license agreement. be held liable for any damages or inconvenience caused by er- rors. How to reach GraphPad Software, Inc: Although we have tested Prism carefully, the possibility of software errors exists in any complex computer program. You Phone: 858-457-3909 (619-457-3909 before June 12, 1999) should check important results carefully before drawing con- Fax: 858-457-8141 (619-457-8141 before June 12, 1999) clusions. GraphPad Prism is designed for research purposes Email: [email protected] or [email protected] only, and should not be used for the diagnosis or treatment of Web: www.graphpad.com patients. Mail: GraphPad Software, Inc. If you find that Prism does not fit your needs, you may return it 5755 Oberlin Drive #110 for a full refund (less shipping fees) within 90 days. You do not San Diego, CA 92121 USA need to contact us first. Simply ship the package to us with a note explaining why you are returning the program. Include your phone and fax numbers, and a copy of the invoice.
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												  Analytic Rasterization of Curves with Polynomial FiltersEUROGRAPHICS ’0x / N.N. and N.N. Volume 0 (1981), Number 0 (Editors) Analytic Rasterization of Curves with Polynomial Filters Josiah Manson and Scott Schaefer Texas A&M University Figure 1: Vector graphic art of butterflies represented by cubic curves, scaled by the golden ratio. The images were analytically rasterized using our method with a radial filter of radius three. Abstract We present a method of analytically rasterizing shapes that have curved boundaries and linear color gradients using piecewise polynomial prefilters. By transforming the convolution of filters with the image from an integral over area into a boundary integral, we find closed-form expressions for rasterizing shapes. We show that a poly- nomial expression can be used to rasterize any combination of polynomial curves and filters. Our rasterizer also handles rational quadratic boundaries, which allows us to evaluate circles and ellipses. We apply our technique to rasterizing vector graphics and show that our derivation gives an efficient implementation as a scanline rasterizer. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture 1. Introduction Although many shapes are stored in vector format, dis- plays are typically raster devices. This means that vector im- There are two basic ways of representing images: raster ages must be converted to pixel intensities to be displayed. graphics and vector graphics. Raster graphics are images that A major benefit of vector images is that they can be drawn are stored as an array of pixel values, whereas vector graph- accurately at different resolutions.
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												  UNIT 2. Curvatures of a CurveUNIT 2. Curvatures of a Curve ========================================================================================================= ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Convergence of k-planes, the osculating k-plane, curves of general type in Rn, the osculating flag, vector fields, moving frames and Frenet frames along a curve, orientation of a vector space, the standard orientation of Rn, the distinguished Frenet frame, Gram-Schmidt orthogonalization process, Frenet formulas, curvatures, invariance theorems, curves with prescribed curvatures. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- One of the most important tools of analysis is linearization, or more generally, the approximation of general objects with easily treatable ones. E.g. the derivative of a function is the best linear approximation, Taylor’s polynomials are the best polynomial approximations
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												  Vector-Valued Functions12 Vector-Valued Functions 12.1 Vector-Valued Functions Copyright © Cengage Learning. All rights reserved. Copyright © Cengage Learning. All rights reserved. Objectives ! Analyze and sketch a space curve given by a vector-valued function. ! Extend the concepts of limits and continuity to vector-valued functions. Space Curves and Vector-Valued Functions 3 4 Space Curves and Vector-Valued Functions Space Curves and Vector-Valued Functions A plane curve is defined as the set of ordered pairs (f(t), g(t)) This definition can be extended naturally to three-dimensional together with their defining parametric equations space as follows. x = f(t) and y = g(t) A space curve C is the set of all ordered triples (f(t), g(t), h(t)) together with their defining parametric equations where f and g are continuous functions of t on an interval I. x = f(t), y = g(t), and z = h(t) where f, g and h are continuous functions of t on an interval I. A new type of function, called a vector-valued function, is introduced. This type of function maps real numbers to vectors. 5 6 Space Curves and Vector-Valued Functions Space Curves and Vector-Valued Functions Technically, a curve in the plane or in space consists of a A collection of points and the defining parametric equations. Two different curves can have the same graph. For instance, each of the curves given by r(t) = sin t i + cos t j and r(t) = sin t2 i + cos t2 j has the unit circle as its graph, but these equations do not represent the same curve—because the circle is traced out in different ways on the graphs.
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												  A Database of Genus 2 Curves Over the Rational NumbersSubmitted exclusively to the London Mathematical Society doi:10.1112/0000/000000 A database of genus 2 curves over the rational numbers Andrew R. Booker, Jeroen Sijsling, Andrew V. Sutherland, John Voight and Dan Yasaki Abstract We describe the construction of a database of genus 2 curves of small discriminant that includes geometric and arithmetic invariants of each curve, its Jacobian, and the associated L-function. This data has been incorporated into the L-Functions and Modular Forms Database (LMFDB). 1. Introduction The history of computing tables of elliptic curves over the rational numbers extends as far back as some of the earliest machine-aided computations in number theory. The first of these tables appeared in the proceedings of the 1972 conference held in Antwerp [3]. Vast tables of elliptic curves now exist, as computed by Cremona [12, 13, 14] and Stein–Watkins [45]. These tables have been used extensively in research in arithmetic geometry to test and formulate conjectures, and have thereby motivated many important advances. In this article, we continue this tradition by constructing a table of genus 2 curves over the rational numbers. We find a total of 66,158 isomorphism classes of curves with absolute discriminant at most 106; for each curve, we compute an array of geometric and arithmetic invariants of the curve and its Jacobian, as well as information about rational points and the associated L-function. This data significantly extends previously existing tables (see section 3.4 for a comparison and discussion of completeness), and it is available online at the L-functions and Modular Forms Database (LMFDB) [34].