Chapter 15: Per-Pixel Operations

Total Page:16

File Type:pdf, Size:1020Kb

Chapter 15: Per-Pixel Operations Chapter 15: Per-Pixel Operations Introduction So far in the book we have focused on graphics APIs that are strongly polygon-oriented, and have learned how to use this kind of tools to make effective images. However, this is not the only kind of computer graphics that can be done, and it is also not the only kind of computer graphics that can be effective. Another kind of computer graphics works with a geometric model to determine the color of each pixel in a scene independently. We call this per-pixel graphics, and there are several different approaches that can be taken to this. There is ray casting, a straightforward approach to generating an image in which rays are generated from the eye point through each pixel of a virtual screen, and the closest intersection of each ray with the geometry of the scene defines the color of that pixel. There is ray tracing, a more sophisticated approach that begins in the same way as ray casting, but you may generate more than one ray for each pixel and when the rays intersect with the geometry, they may define secondary rays by reflection or refraction that will help define the color that the pixel will eventually be given. There are also several uses of pixels as ways to record some computational value, such as one might find in a study of iterated functions or of fractals. With a wide variety of ways to take advantage of this kind of graphics, we believe it should be included in a beginning course. In this chapter you will see examples of several per-pixel operations that can be used to handle several kinds of computer graphics problems. We will not go into these operations in great depth, but we will introduce them to you so you can understand their capabilities and decide whether you want to go into them in more depth with other reading. To get the best from this chapter, you should be familiar with geometric operations in an algebraic setting, such as were described in the earlier chapter on mathematical concepts for modeling. Definitions The terms ray tracing and ray casting are not always well-defined in computer graphics. We will define ray casting as the process of generating an image from a model by sending out one ray per pixel and computing the color of that pixel by considering its intersection with a model with no shadow, reflection, or refraction computation. We will define ray tracing as an extension of ray casting without limitations: multiple rays may be cast for each pixel as needed, and may use any kind of shadow, refraction, and/or reflection computation as you wish. We will discuss both these processes, although we will not go into much depth on either and will refer you to a resource such as the Glassner text or a public-domain ray tracer such as POVRay to get more experience. Ray casting If we recall the standard viewing setup and viewing volume for the perspective transformation from the chapter on viewing and projection, we see that the front of the viewing frustum can be an effective equivalent of the actual display screen. We can make this more concrete by applying a reverse window-to-viewport operation and, in effect, create a screen-to-viewplane operation that will give us a virtual screen at the front of the frustum. To do this, we can take advantage of the idea of the perspective viewing volume that we saw in the viewing and projection chapter. We can create the viewing transformation as we illustrated in the chapter on mathematics for modeling, which allows us to consider the eye point at the origin and the front of the viewing volume in the plane z = –1. Just as the perspective projection in OpenGL is defined in terms of the field of view a and the aspect ratio r , we can determine the coordinates of the front of the view volume as x = tan(a ) and y = r *x. This space is the virtual screen, and you can divide up the screen into pixels by using a step size of 2*x/N where N is the number of pixels across the horizontal screen. A virtual screen in world space, along with the eyepoint and a sample ray, is shown in Figure 15.1. In order to manage the aspect ratio you have set, you should also use the same step size for the vertical screen. The final parameters of the OpenGL perspective projection are the front and back clipping planes for the view; you may use these parameters to limit the range where you will check for intersections or you may choose to use your model with no limits. Figure 15.1: the eye point and virtual screen in 3D space The key point of ray casting is to generate a ray, which you can model as a parametric line segment with only positive parameter values, from the eye point through each pixel in the virtual screen. You may choose what point you want to use in the pixel because each pixel actually corresponds to a small quad determined by adjacent horizontal and vertical lines in the virtual grid. In the general discussion it doesn’t matter which point you choose, but you might start with the middle point in the quad. The ray you generate will start at the origin and will go through a point (x,y,1), so the parametric equations for the ray are (xt, yt, t). Once you have the parametric equation for a ray, you must determine the point in your model where the ray intersects some object. If there is no such point, then you simply get the background color; if there is an intersection, then you must determine the nearest intersection point. The pixel is then given the color of your model at that point of intersection. The ray-object intersection problem is the heart of the ray casting problem, and a great deal of time is spent in calculating intersections. We discussed collision testing in the chapter on mathematics for modeling, and the ray-object intersection is just a special case of collision testing. The easiest intersection test is made with a sphere, where you can use the formula for the distance between a point and a line to see how close the line comes to the center of the sphere; if that distance is less than the radius of the sphere, there is an intersection and a simple quadratic equation will give you the intersection points. Determining the color depends a great deal on the model you are viewing. If your model does not use a lighting model, then each object will have its own color, and the color of the pixel will simply be the color of the object the ray intersects. If you do have lighting, however, the local lighting model is not provided by the system; you must develop it yourself. This means that at the point where a ray meets an object, you must do the computations for ambient, diffuse, and specular lighting that we described in the chapter on lighting and shading. This will require computing the Page 15.2 four vectors involved in local lighting: the normal vector, eye vector, reflection vector and light vector. The eye vector is simple because this is the ray we are using; the normal vector is calculated using analytic techniques or geometric computations just as it was for the OpenGL glNormal*(...) function, the reflection vector is calculated as in the discussion on mathematics for modeling, and it is straightforward to create a vector from the point to each of the lights in the scene. Details of the lighting computations are sketched in the chapter on lighting and shading, so we will not go into them in more detail here. Because you are creating your own lighting, you have the opportunity to use more sophisticated shading models than are provided by OpenGL. These shading models generally use the standard ambient and diffuse light, but they can change the way specular highlights appear on an object by modifying the way light reflects from a surface. This is called anisotropic shading and involves creating a different reflection vector through a bidirectional reflection distribution function or BRDF. This kind of function takes the x- and y-components of the light vector and determines a direction that light will be reflected; the standard specular lighting computation is then done with that reflection direction vector. Details are much more complex than are appropriate here, but you can consult advanced references for more information. Ray casting can also be used to view pre-computed models. Where each ray intersects the model, the color of that point is returned to the pixel in the image. This technique, or a more sophisticated ray-tracing equivalent, is often used to view a model that is lighted with a global lighting model such as radiosity or photon mapping. The lighting process is used to calculate the light at each surface in the model, and other visual properties such as texture mapping are applied, and then for each ray, the color of the intersection point is read from the model or is computed from information in the model. The resulting image carries all the sophistication of the model, even though it is created by a simple ray casting process. Ray casting has aliasing problems because we sample with only one ray per pixel; the screen coordinates are quite coarse when compared to the real-valued fine detail of the real world.
Recommended publications
  • Image Encryption and Decryption Schemes Using Linear and Quadratic Fractal Algorithms and Their Systems
    Image Encryption and Decryption Schemes Using Linear and Quadratic Fractal Algorithms and Their Systems Anatoliy Kovalchuk 1 [0000-0001-5910-4734], Ivan Izonin 1 [0000-0002-9761-0096] Christine Strauss 2 [0000-0003-0276-3610], Mariia Podavalkina 1 [0000-0001-6544-0654], Natalia Lotoshynska 1 [0000-0002-6618-0070] and Nataliya Kustra 1 [0000-0002-3562-2032] 1 Department of Publishing Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine [email protected], [email protected], [email protected], [email protected], [email protected] 2 Department of Electronic Business, University of Vienna, Vienna, Austria [email protected] Abstract. Image protection and organizing the associated processes is based on the assumption that an image is a stochastic signal. This results in the transition of the classic encryption methods into the image perspective. However the image is some specific signal that, in addition to the typical informativeness (informative data), also involves visual informativeness. The visual informativeness implies additional and new challenges for the issue of protection. As it involves the highly sophisticated modern image processing techniques, this informativeness enables unauthorized access. In fact, the organization of the attack on an encrypted image is possible in two ways: through the traditional hacking of encryption methods or through the methods of visual image processing (filtering methods, contour separation, etc.). Although the methods mentioned above do not fully reproduce the encrypted image, they can provide an opportunity to obtain some information from the image. In this regard, the encryption methods, when used in images, have another task - the complete noise of the encrypted image.
    [Show full text]
  • A Gallery of De Rham Curves
    A Gallery of de Rham Curves Linas Vepstas <[email protected]> 20 August 2006 Abstract The de Rham curves are a set of fairly generic fractal curves exhibiting dyadic symmetry. Described by Georges de Rham in 1957[3], this set includes a number of the famous classical fractals, including the Koch snowflake, the Peano space- filling curve, the Cesàro-Faber curves, the Takagi-Landsberg[4] or blancmange curve, and the Lévy C-curve. This paper gives a brief review of the construction of these curves, demonstrates that the complete collection of linear affine deRham curves is a five-dimensional space, and then presents a collection of four dozen images exploring this space. These curves are interesting because they exhibit dyadic symmetry, with the dyadic symmetry monoid being an interesting subset of the group GL(2,Z). This is a companion article to several others[5][6] exploring the nature of this monoid in greater detail. 1 Introduction In a classic 1957 paper[3], Georges de Rham constructs a class of curves, and proves that these curves are everywhere continuous but are nowhere differentiable (more pre- cisely, are not differentiable at the rationals). In addition, he shows how the curves may be parameterized by a real number in the unit interval. The construction is simple. This section illustrates some of these curves. 2 2 2 2 Consider a pair of contracting maps of the plane d0 : R → R and d1 : R → R . By the Banach fixed point theorem, such contracting maps should have fixed points p0 and p1. Assume that each fixed point lies in the basin of attraction of the other map, and furthermore, that the one map applied to the fixed point of the other yields the same point, that is, d1(p0) = d0(p1) (1) These maps can then be used to construct a certain continuous curve between p0and p1.
    [Show full text]
  • Riemann-Liouville Fractional Calculus of Blancmange Curve and Cantor Functions
    Journal of Applied Mathematics and Computation, 2020, 4(4), 123-129 https://www.hillpublisher.com/journals/JAMC/ ISSN Online: 2576-0653 ISSN Print: 2576-0645 Riemann-Liouville Fractional Calculus of Blancmange Curve and Cantor Functions Srijanani Anurag Prasad Department of Mathematics and Statistics, Indian Institute of Technology Tirupati, India. How to cite this paper: Srijanani Anurag Prasad. (2020) Riemann-Liouville Frac- Abstract tional Calculus of Blancmange Curve and Riemann-Liouville fractional calculus of Blancmange Curve and Cantor Func- Cantor Functions. Journal of Applied Ma- thematics and Computation, 4(4), 123-129. tions are studied in this paper. In this paper, Blancmange Curve and Cantor func- DOI: 10.26855/jamc.2020.12.003 tion defined on the interval is shown to be Fractal Interpolation Functions with appropriate interpolation points and parameters. Then, using the properties of Received: September 15, 2020 Fractal Interpolation Function, the Riemann-Liouville fractional integral of Accepted: October 10, 2020 Published: October 22, 2020 Blancmange Curve and Cantor function are described to be Fractal Interpolation Function passing through a different set of points. Finally, using the conditions for *Corresponding author: Srijanani the fractional derivative of order ν of a FIF, it is shown that the fractional deriva- Anurag Prasad, Department of Mathe- tive of Blancmange Curve and Cantor function is not a FIF for any value of ν. matics, Indian Institute of Technology Tirupati, India. Email: [email protected] Keywords Fractal, Interpolation, Iterated Function System, fractional integral, fractional de- rivative, Blancmange Curve, Cantor function 1. Introduction Fractal geometry is a subject in which irregular and complex functions and structures are researched.
    [Show full text]
  • Classical Math Fractals in Postscript Fractal Geometry I
    Kees van der Laan VOORJAAR 2013 49 Classical Math Fractals in PostScript Fractal Geometry I Abstract Classical mathematical fractals in BASIC are explained and converted into mean-and-lean EPSF defs, of which the .eps pictures are delivered in .pdf format and cropped to the prescribed BoundingBox when processed by Acrobat Pro, to be included easily in pdf(La)TEX, Word, … documents. The EPSF fractals are transcriptions of the Turtle Graphics BASIC codes or pro- grammed anew, recursively, based on the production rules of oriented objects. The Linden- mayer production rules are enriched by PostScript concepts. Experience gained in converting a TEX script into WYSIWYG Word is communicated. Keywords Acrobat Pro, Adobe, art, attractor, backtracking, BASIC, Cantor Dust, C curve, dragon curve, EPSF, FIFO, fractal, fractal dimension, fractal geometry, Game of Life, Hilbert curve, IDE (In- tegrated development Environment), IFS (Iterated Function System), infinity, kronkel (twist), Lauwerier, Lévy, LIFO, Lindenmayer, minimal encapsulated PostScript, minimal plain TeX, Minkowski, Monte Carlo, Photoshop, production rule, PSlib, self-similarity, Sierpiński (island, carpet), Star fractals, TACP,TEXworks, Turtle Graphics, (adaptable) user space, von Koch (island), Word Contents - Introduction - Lévy (Properties, PostScript program, Run the program, Turtle Graphics) Cantor - Lindenmayer enriched by PostScript concepts for the Lévy fractal 0 - von Koch (Properties, PostScript def, Turtle Graphics, von Koch island) Cantor1 - Lindenmayer enriched by PostScript concepts for the von Koch fractal Cantor2 - Kronkel Cantor - Minkowski 3 - Dragon figures - Stars - Game of Life - Annotated References - Conclusions (TEX mark up, Conversion into Word) - Acknowledgements (IDE) - Appendix: Fractal Dimension - Appendix: Cantor Dust Peano curves: order 1, 2, 3 - Appendix: Hilbert Curve - Appendix: Sierpiński islands Introduction My late professor Hans Lauwerier published nice, inspiring booklets about fractals with programs in BASIC.
    [Show full text]
  • Fractals a Fractal Is a Shape That Seem to Have the Same Structure No Matter How Far You Zoom In, Like the figure Below
    Fractals A fractal is a shape that seem to have the same structure no matter how far you zoom in, like the figure below. Sometimes it's only part of the shape that repeats. In the figure below, called an Apollonian Gasket, no part looks like the whole shape, but the parts near the edges still repeat when you zoom in. Today you'll learn how to construct a few fractals: • The Snowflake • The Sierpinski Carpet • The Sierpinski Triangle • The Pythagoras Tree • The Dragon Curve After you make a few of those, try constructing some fractals of your own design! There's more on the back. ! Challenge Problems In order to solve some of the more difficult problems today, you'll need to know about the geometric series. In a geometric series, we add up a sequence of terms, 1 each of which is a fixed multiple of the previous one. For example, if the ratio is 2 , then a geometric series looks like 1 1 1 1 1 1 1 + + · + · · + ::: 2 2 2 2 2 2 1 12 13 = 1 + + + + ::: 2 2 2 The geometric series has the incredibly useful property that we have a good way of 1 figuring out what the sum equals. Let's let r equal the common ratio (like 2 above) and n be the number of terms we're adding up. Our series looks like 1 + r + r2 + ::: + rn−2 + rn−1 If we multiply this by 1 − r we get something rather simple. (1 − r)(1 + r + r2 + ::: + rn−2 + rn−1) = 1 + r + r2 + ::: + rn−2 + rn−1 − ( r + r2 + ::: + rn−2 + rn−1 + rn ) = 1 − rn Thus 1 − rn 1 + r + r2 + ::: + rn−2 + rn−1 = : 1 − r If we're clever, we can use this formula to compute the areas and perimeters of some of the shapes we create.
    [Show full text]
  • Math Morphing Proximate and Evolutionary Mechanisms
    Curriculum Units by Fellows of the Yale-New Haven Teachers Institute 2009 Volume V: Evolutionary Medicine Math Morphing Proximate and Evolutionary Mechanisms Curriculum Unit 09.05.09 by Kenneth William Spinka Introduction Background Essential Questions Lesson Plans Website Student Resources Glossary Of Terms Bibliography Appendix Introduction An important theoretical development was Nikolaas Tinbergen's distinction made originally in ethology between evolutionary and proximate mechanisms; Randolph M. Nesse and George C. Williams summarize its relevance to medicine: All biological traits need two kinds of explanation: proximate and evolutionary. The proximate explanation for a disease describes what is wrong in the bodily mechanism of individuals affected Curriculum Unit 09.05.09 1 of 27 by it. An evolutionary explanation is completely different. Instead of explaining why people are different, it explains why we are all the same in ways that leave us vulnerable to disease. Why do we all have wisdom teeth, an appendix, and cells that if triggered can rampantly multiply out of control? [1] A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole," a property called self-similarity. The term was coined by Beno?t Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion. http://www.kwsi.com/ynhti2009/image01.html A fractal often has the following features: 1. It has a fine structure at arbitrarily small scales.
    [Show full text]
  • Understanding the Mandelbrot and Julia Set
    Understanding the Mandelbrot and Julia Set Jake Zyons Wood August 31, 2015 Introduction Fractals infiltrate the disciplinary spectra of set theory, complex algebra, generative art, computer science, chaos theory, and more. Fractals visually embody recursive structures endowing them with the ability of nigh infinite complexity. The Sierpinski Triangle, Koch Snowflake, and Dragon Curve comprise a few of the more widely recognized iterated function fractals. These recursive structures possess an intuitive geometric simplicity which makes their creation, at least at a shallow recursive depth, easy to do by hand with pencil and paper. The Mandelbrot and Julia set, on the other hand, allow no such convenience. These fractals are part of the class: escape-time fractals, and have only really entered mathematicians’ consciousness in the late 1970’s[1]. The purpose of this paper is to clearly explain the logical procedures of creating escape-time fractals. This will include reviewing the necessary math for this type of fractal, then specifically explaining the algorithms commonly used in the Mandelbrot Set as well as its variations. By the end, the careful reader should, without too much effort, feel totally at ease with the underlying principles of these fractals. What Makes The Mandelbrot Set a set? 1 Figure 1: Black and white Mandelbrot visualization The Mandelbrot Set truly is a set in the mathematica sense of the word. A set is a collection of anything with a specific property, the Mandelbrot Set, for instance, is a collection of complex numbers which all share a common property (explained in Part II). All complex numbers can in fact be labeled as either a member of the Mandelbrot set, or not.
    [Show full text]
  • On the Beta Transformation
    On the Beta Transformation Linas Vepstas December 2017 (Updated Feb 2018 and Dec 2018) [email protected] doi:10.13140/RG.2.2.17132.26248 Abstract The beta transformation is the iterated map bx mod 1. The special case of b = 2 is known as the Bernoulli map, and is exactly solvable. The Bernoulli map provides a model for pure, unrestrained chaotic (ergodic) behavior: it is the full invariant shift on the Cantor space f0;1gw . The Cantor space consists of infinite strings of binary digits; it is notable for many properties, including that it can represent the real number line. The beta transformation defines a subshift: iterated on the unit interval, it sin- gles out a subspace of the Cantor space that is invariant under the action of the left-shift operator. That is, lopping off one bit at a time gives back the same sub- space. The beta transform seems to capture something basic about the multiplication of two real numbers: b and x. It offers insight into the nature of multiplication. Iterating on multiplication, one would get b nx – that is, exponentiation; the mod 1 of the beta transform contorts this in strange ways. Analyzing the beta transform is difficult. The work presented here is more-or- less a research diary: a pastiche of observations and some shallow insights. One is that chaos seems to be rooted in how the carry bit behaves during multiplication. Another is that one can surgically insert “islands of stability” into chaotic (ergodic) systems, and have some fair amount of control over how those islands of stability behave.
    [Show full text]
  • Exploring the Effect of Direction on Vector-Based Fractals
    Exploring the Effect of Direction on Vector-Based Fractals Magdy Ibrahim and Robert J. Krawczyk College of Architecture Illinois Institute of Technology 3360 S. State St. Chicago, IL, 60616, USA Email: [email protected], [email protected] Abstract This paper investigates an approach to begin the study of fractals in architectural design. Vector-based fractals are studied to determine if the modification of vector direction in either the generator or the initiator will develop alternate fractal forms. The Koch Snowflake is used as the demonstrating fractal. Introduction A fractal is an object or quantity that displays self-similarity on all scales. The object need not exhibit exactly the same structure at all scales, but the same “type” of structures must appear on all scales [7]. Fractals were first discussed by Mandelbrot in the 1970s [4], but the idea was identified as early as 1925. Fractals have been investigated for their visual qualities as art, their relationship to explain natural processes, music, medicine, and in mathematics [5]. Javier Barrallo classified fractals [2] into six main groups depending on their type: 1. Fractals derived from standard geometry by using iterative transformations on an initial common figure. 2. IFS (Iterated Function Systems), this is a type of fractal introduced by Michael Barnsley. 3. Strange attractors. 4. Plasma fractals. Created with techniques like fractional Brownian motion. 5. L-Systems, also called Lindenmayer systems, were not invented to create fractals but to model cellular growth and interactions. 6. Fractals created by the iteration of complex polynomials. From the mathematical point of view, we can classify fractals into three major categories.
    [Show full text]
  • Furniture Design Inspired from Fractals
    169 Rania Mosaad Saad Furniture design inspired from fractals. Dr. Rania Mosaad Saad Assistant Professor, Interior Design and Furniture Department, Faculty of Applied Arts, Helwan University, Cairo, Egypt Abstract: Keywords: Fractals are a new branch of mathematics and art. But what are they really?, How Fractals can they affect on Furniture design?, How to benefit from the formation values and Mandelbrot Set properties of fractals in Furniture Design?, these were the research problem . Julia Sets This research consists of two axis .The first axe describes the most famous fractals IFS fractals were created, studies the Fractals structure, explains the most important fractal L-system fractals properties and their reflections on furniture design. The second axe applying Fractal flame functional and aesthetic values of deferent Fractals formations in furniture design Newton fractals inspired from them to achieve the research objectives. Furniture Design The research follows the descriptive methodology to describe the fractals kinds and properties, and applied methodology in furniture design inspired from fractals. Paper received 12th July 2016, Accepted 22th September 2016 , Published 15st of October 2016 nearly identical starting positions, and have real Introduction: world applications in nature and human creations. Nature is the artist's inspiring since the beginning Some architectures and interior designers turn to of creation. Despite the different artistic trends draw inspiration from the decorative formations, across different eras- ancient and modern- and the geometric and dynamic properties of fractals in artists perception of reality, but that all of these their designs which enriched the field of trends were united in the basic inspiration (the architecture and interior design, and benefited nature).
    [Show full text]
  • Ontologia De Domínio Fractal
    MÉTODOS COMPUTACIONAIS PARA A CONSTRUÇÃO DA ONTOLOGIA DE DOMÍNIO FRACTAL Ivo Wolff Gersberg Dissertação de Mestrado apresentada ao Programa de Pós-graduação em Engenharia Civil, COPPE, da Universidade Federal do Rio de Janeiro, como parte dos requisitos necessários à obtenção do título de Mestre em Engenharia Civil. Orientadores: Nelson Francisco Favilla Ebecken Luiz Bevilacqua Rio de Janeiro Agosto de 2011 MÉTODOS COMPUTACIONAIS PARA CONSTRUÇÃO DA ONTOLOGIA DE DOMÍNIO FRACTAL Ivo Wolff Gersberg DISSERTAÇÃO SUBMETIDA AO CORPO DOCENTE DO INSTITUTO ALBERTO LUIZ COIMBRA DE PÓS-GRADUAÇÃO E PESQUISA DE ENGENHARIA (COPPE) DA UNIVERSIDADE FEDERAL DO RIO DE JANEIRO COMO PARTE DOS REQUISITOS NECESSÁRIOS PARA A OBTENÇÃO DO GRAU DE MESTRE EM CIÊNCIAS EM ENGENHARIA CIVIL. Examinada por: ________________________________________________ Prof. Nelson Francisco Favilla Ebecken, D.Sc. ________________________________________________ Prof. Luiz Bevilacqua, Ph.D. ________________________________________________ Prof. Marta Lima de Queirós Mattoso, D.Sc. ________________________________________________ Prof. Fernanda Araújo Baião, D.Sc. RIO DE JANEIRO, RJ - BRASIL AGOSTO DE 2011 Gersberg, Ivo Wolff Métodos computacionais para a construção da Ontologia de Domínio Fractal/ Ivo Wolff Gersberg. – Rio de Janeiro: UFRJ/COPPE, 2011. XIII, 144 p.: il.; 29,7 cm. Orientador: Nelson Francisco Favilla Ebecken Luiz Bevilacqua Dissertação (mestrado) – UFRJ/ COPPE/ Programa de Engenharia Civil, 2011. Referências Bibliográficas: p. 130-133. 1. Ontologias. 2. Mineração de Textos. 3. Fractal. 4. Metodologia para Construção de Ontologias de Domínio. I. Ebecken, Nelson Francisco Favilla et al . II. Universidade Federal do Rio de Janeiro, COPPE, Programa de Engenharia Civil. III. Titulo. iii À minha mãe e meu pai, Basia e Jayme Gersberg. iv AGRADECIMENTOS Agradeço aos meus orientadores, professores Nelson Ebecken e Luiz Bevilacqua, pelo incentivo e paciência.
    [Show full text]
  • Rafael De Andrade Sousa Utilização De Múltiplas
    RAFAEL DE ANDRADE SOUSA UTILIZAÇÃO DE MÚLTIPLAS REPRESENTAÇÕES EXTERNAS PARA CONSTRUÇÃO DE FRACTAIS EM AMBIENTES EXPLORATÓRIOS DE APRENDIZAGEM Proposta de Dissertação de Mestrado apre- sentada ao Programa de Pós-Graduação em Informática, Setor de Ciências Exatas, Uni- versidade Federal do Paraná. Orientador: Prof. Dr. Alexandre Ibrahim Direne CURITIBA 2010 RAFAEL DE ANDRADE SOUSA UTILIZAÇÃO DE MÚLTIPLAS REPRESENTAÇÕES EXTERNAS PARA CONSTRUÇÃO DE FRACTAIS EM AMBIENTES EXPLORATÓRIOS DE APRENDIZAGEM Dissertação apresentada como requisito par- cial à obtenção do grau de Mestre. Pro- grama de Pós-Graduação em Informática, Setor de Ciências Exatas, Universidade Fe- deral do Paraná. Orientador: Prof. Dr. Alexandre Ibrahim Direne CURITIBA 2010 RAFAEL DE ANDRADE SOUSA UTILIZAÇÃO DE MÚLTIPLAS REPRESENTAÇÕES EXTERNAS PARA CONSTRUÇÃO DE FRACTAIS EM AMBIENTES EXPLORATÓRIOS DE APRENDIZAGEM Dissertação apresentada como requisito par- cial à obtenção do grau de Mestre. Pro- grama de Pós-Graduação em Informática, Setor de Ciências Exatas, Universidade Fe- deral do Paraná. Orientador: Prof. Dr. Alexandre Ibrahim Direne CURITIBA 2010 RAFAEL DE ANDRADE SOUSA UTILIZAÇÃO DE MÚLTIPLAS REPRESENTAÇÕES EXTERNAS PARA CONSTRUÇÃO DE FRACTAIS EM AMBIENTES EXPLORATÓRIOS DE APRENDIZAGEM Dissertação aprovada como requisito parcial à obtenção do grau de Mestre no Programa de Pós-Graduação em Informática da Universidade Federal do Paraná, pela Comissão formada pelos professores: Orientador: Prof. Dr. Alexandre Ibrahim Direne Departamento de Informática, UFPR Prof. Dr. Davidson Cury Departamento de Informática, Universidade Federal do Espírito Santo Prof. Dr. Andrey Ricardo Pimentel Departamento de Informática, Universidade Federal do Paraná Curitiba, 30 de agosto de 2010 i AGRADECIMENTOS A Deus, pela imerecida Graça que tenho recebido desde o meu nascimento pois até aqui me ajudou o Senhor.
    [Show full text]