Iterated Function Systems, Ruelle Operators, and Invariant Projective Measures
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Fractal (Mandelbrot and Julia) Zero-Knowledge Proof of Identity
Journal of Computer Science 4 (5): 408-414, 2008 ISSN 1549-3636 © 2008 Science Publications Fractal (Mandelbrot and Julia) Zero-Knowledge Proof of Identity Mohammad Ahmad Alia and Azman Bin Samsudin School of Computer Sciences, University Sains Malaysia, 11800 Penang, Malaysia Abstract: We proposed a new zero-knowledge proof of identity protocol based on Mandelbrot and Julia Fractal sets. The Fractal based zero-knowledge protocol was possible because of the intrinsic connection between the Mandelbrot and Julia Fractal sets. In the proposed protocol, the private key was used as an input parameter for Mandelbrot Fractal function to generate the corresponding public key. Julia Fractal function was then used to calculate the verified value based on the existing private key and the received public key. The proposed protocol was designed to be resistant against attacks. Fractal based zero-knowledge protocol was an attractive alternative to the traditional number theory zero-knowledge protocol. Key words: Zero-knowledge, cryptography, fractal, mandelbrot fractal set and julia fractal set INTRODUCTION Zero-knowledge proof of identity system is a cryptographic protocol between two parties. Whereby, the first party wants to prove that he/she has the identity (secret word) to the second party, without revealing anything about his/her secret to the second party. Following are the three main properties of zero- knowledge proof of identity[1]: Completeness: The honest prover convinces the honest verifier that the secret statement is true. Soundness: Cheating prover can’t convince the honest verifier that a statement is true (if the statement is really false). Fig. 1: Zero-knowledge cave Zero-knowledge: Cheating verifier can’t get anything Zero-knowledge cave: Zero-Knowledge Cave is a other than prover’s public data sent from the honest well-known scenario used to describe the idea of zero- prover. -
Homeomorphisms Group of Normed Vector Space: Conjugacy Problems
HOMEOMORPHISMS GROUP OF NORMED VECTOR SPACE: CONJUGACY PROBLEMS AND THE KOOPMAN OPERATOR MICKAEL¨ D. CHEKROUN AND JEAN ROUX Abstract. This article is concerned with conjugacy problems arising in the homeomorphisms group, Hom(F ), of unbounded subsets F of normed vector spaces E. Given two homeomor- phisms f and g in Hom(F ), it is shown how the existence of a conjugacy may be related to the existence of a common generalized eigenfunction of the associated Koopman operators. This common eigenfunction serves to build a topology on Hom(F ), where the conjugacy is obtained as limit of a sequence generated by the conjugacy operator, when this limit exists. The main conjugacy theorem is presented in a class of generalized Lipeomorphisms. 1. Introduction In this article we consider the conjugacy problem in the homeomorphisms group of a finite dimensional normed vector space E. It is out of the scope of the present work to review the problem of conjugacy in general, and the reader may consult for instance [13, 16, 29, 33, 26, 42, 45, 51, 52] and references therein, to get a partial survey of the question from a dynamical point of view. The present work raises the problem of conjugacy in the group Hom(F ) consisting of homeomorphisms of an unbounded subset F of E and is intended to demonstrate how the conjugacy problem, in such a case, may be related to spectral properties of the associated Koopman operators. In this sense, this paper provides new insights on the relations between the spectral theory of dynamical systems [5, 17, 23, 36] and the topological conjugacy problem [51, 52]1. -
Rendering Hypercomplex Fractals Anthony Atella [email protected]
Rhode Island College Digital Commons @ RIC Honors Projects Overview Honors Projects 2018 Rendering Hypercomplex Fractals Anthony Atella [email protected] Follow this and additional works at: https://digitalcommons.ric.edu/honors_projects Part of the Computer Sciences Commons, and the Other Mathematics Commons Recommended Citation Atella, Anthony, "Rendering Hypercomplex Fractals" (2018). Honors Projects Overview. 136. https://digitalcommons.ric.edu/honors_projects/136 This Honors is brought to you for free and open access by the Honors Projects at Digital Commons @ RIC. It has been accepted for inclusion in Honors Projects Overview by an authorized administrator of Digital Commons @ RIC. For more information, please contact [email protected]. Rendering Hypercomplex Fractals by Anthony Atella An Honors Project Submitted in Partial Fulfillment of the Requirements for Honors in The Department of Mathematics and Computer Science The School of Arts and Sciences Rhode Island College 2018 Abstract Fractal mathematics and geometry are useful for applications in science, engineering, and art, but acquiring the tools to explore and graph fractals can be frustrating. Tools available online have limited fractals, rendering methods, and shaders. They often fail to abstract these concepts in a reusable way. This means that multiple programs and interfaces must be learned and used to fully explore the topic. Chaos is an abstract fractal geometry rendering program created to solve this problem. This application builds off previous work done by myself and others [1] to create an extensible, abstract solution to rendering fractals. This paper covers what fractals are, how they are rendered and colored, implementation, issues that were encountered, and finally planned future improvements. -
Generating Fractals Using Complex Functions
Generating Fractals Using Complex Functions Avery Wengler and Eric Wasser What is a Fractal? ● Fractals are infinitely complex patterns that are self-similar across different scales. ● Created by repeating simple processes over and over in a feedback loop. ● Often represented on the complex plane as 2-dimensional images Where do we find fractals? Fractals in Nature Lungs Neurons from the Oak Tree human cortex Regardless of scale, these patterns are all formed by repeating a simple branching process. Geometric Fractals “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.” Mandelbrot (1983) The Sierpinski Triangle Algebraic Fractals ● Fractals created by repeatedly calculating a simple equation over and over. ● Were discovered later because computers were needed to explore them ● Examples: ○ Mandelbrot Set ○ Julia Set ○ Burning Ship Fractal Mandelbrot Set ● Benoit Mandelbrot discovered this set in 1980, shortly after the invention of the personal computer 2 ● zn+1=zn + c ● That is, a complex number c is part of the Mandelbrot set if, when starting with z0 = 0 and applying the iteration repeatedly, the absolute value of zn remains bounded however large n gets. Animation based on a static number of iterations per pixel. The Mandelbrot set is the complex numbers c for which the sequence ( c, c² + c, (c²+c)² + c, ((c²+c)²+c)² + c, (((c²+c)²+c)²+c)² + c, ...) does not approach infinity. Julia Set ● Closely related to the Mandelbrot fractal ● Complementary to the Fatou Set Featherino Fractal Newton’s method for the roots of a real valued function Burning Ship Fractal z2 Mandelbrot Render Generic Mandelbrot set. -
Recursion, Writing, Iteration. a Proposal for a Graphics Foundation of Computational Reason Luca M
Recursion, Writing, Iteration. A Proposal for a Graphics Foundation of Computational Reason Luca M. Possati To cite this version: Luca M. Possati. Recursion, Writing, Iteration. A Proposal for a Graphics Foundation of Computa- tional Reason. 2015. hal-01321076 HAL Id: hal-01321076 https://hal.archives-ouvertes.fr/hal-01321076 Preprint submitted on 24 May 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1/16 Recursion, Writing, Iteration A Proposal for a Graphics Foundation of Computational Reason Luca M. Possati In this paper we present a set of philosophical analyses to defend the thesis that computational reason is founded in writing; only what can be written is computable. We will focus on the relations among three main concepts: recursion, writing and iteration. The most important questions we will address are: • What does it mean to compute something? • What is a recursive structure? • Can we clarify the nature of recursion by investigating writing? • What kind of identity is presupposed by a recursive structure and by computation? Our theoretical path will lead us to a radical revision of the philosophical notion of identity. The act of iterating is rooted in an abstract space – we will try to outline a topological description of iteration. -
Bézier Curves Are Attractors of Iterated Function Systems
New York Journal of Mathematics New York J. Math. 13 (2007) 107–115. All B´ezier curves are attractors of iterated function systems Chand T. John Abstract. The fields of computer aided geometric design and fractal geom- etry have evolved independently of each other over the past several decades. However, the existence of so-called smooth fractals, i.e., smooth curves or sur- faces that have a self-similar nature, is now well-known. Here we describe the self-affine nature of quadratic B´ezier curves in detail and discuss how these self-affine properties can be extended to other types of polynomial and ra- tional curves. We also show how these properties can be used to control shape changes in complex fractal shapes by performing simple perturbations to smooth curves. Contents 1. Introduction 107 2. Quadratic B´ezier curves 108 3. Iterated function systems 109 4. An IFS with a QBC attractor 110 5. All QBCs are attractors of IFSs 111 6. Controlling fractals with B´ezier curves 112 7. Conclusion and future work 114 References 114 1. Introduction In the late 1950s, advancements in hardware technology made it possible to effi- ciently manufacture curved 3D shapes out of blocks of wood or steel. It soon became apparent that the bottleneck in mass production of curved 3D shapes was the lack of adequate software for designing these shapes. B´ezier curves were first introduced in the 1960s independently by two engineers in separate French automotive compa- nies: first by Paul de Casteljau at Citro¨en, and then by Pierre B´ezier at R´enault. -
Summary of Unit 1: Iterated Functions 1
Summary of Unit 1: Iterated Functions 1 Summary of Unit 1: Iterated Functions David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 2 Functions • A function is a rule that takes a number as input and outputs another number. • A function is an action. • Functions are deterministic. The output is determined only by the input. x f(x) f David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 3 Iteration and Dynamical Systems • We iterate a function by turning it into a feedback loop. • The output of one step is used as the input for the next. • An iterated function is a dynamical system, a system that evolves in time according to a well-defined, unchanging rule. x f(x) f David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 4 Itineraries and Seeds • We iterate a function by applying it again and again to a number. • The number we start with is called the seed or initial condition and is usually denoted x0. • The resulting sequence of numbers is called the itinerary or orbit. • It is also sometimes called a time series or a trajectory. • The iterates are denoted xt. Ex: x5 is the fifth iterate. David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 5 Time Series Plots • A useful way to visualize an itinerary is with a time series plot. 0.8 0.7 0.6 0.5 t 0.4 x 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 9 10 time t • The time series plotted above is: 0.123, 0.189, 0.268, 0.343, 0.395, 0.418, 0.428, 0.426, 0.428, 0.429, 0.429. -
Solving Iterated Functions Using Genetic Programming Michael D
Solving Iterated Functions Using Genetic Programming Michael D. Schmidt Hod Lipson Computational Synthesis Lab Computational Synthesis Lab Cornell University Cornell University Ithaca, NY 14853 Ithaca, NY 14853 [email protected] [email protected] ABSTRACT various communities. Renowned physicist Michael Fisher is An iterated function f(x) is a function that when composed with rumored to have solved the puzzle within five minutes [2]; itself, produces a given expression f(f(x))=g(x). Iterated functions however, few have matched this feat. are essential constructs in fractal theory and dynamical systems, The problem is enticing because of its apparent simplicity. Similar but few analysis techniques exist for solving them analytically. problems such as f(f(x)) = x2, or f(f(x)) = x4 + b are straightforward Here we propose using genetic programming to find analytical (see Table 1). The fact that the slight modification from these solutions to iterated functions of arbitrary form. We demonstrate easier functions makes the problem much more challenging this technique on the notoriously hard iterated function problem of highlights the difficulty in solving iterated function problems. finding f(x) such that f(f(x))=x2–2. While some analytical techniques have been developed to find a specific solution to Table 1. A few example iterated functions problems. problems of this form, we show that it can be readily solved using genetic programming without recourse to deep mathematical Iterated Function Solution insight. We find a previously unknown solution to this problem, suggesting that genetic programming may be an essential tool for f(f(x)) = x f(x) = x finding solutions to arbitrary iterated functions. -
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. -
Iterated Function System
IARJSET ISSN (Online) 2393-8021 ISSN (Print) 2394-1588 International Advanced Research Journal in Science, Engineering and Technology ISO 3297:2007 Certified Vol. 3, Issue 8, August 2016 Iterated Function System S. C. Shrivastava Department of Applied Mathematics, Rungta College of Engineering and Technology, Bhilai C.G., India Abstract: Fractal image compression through IFS is very important for the efficient transmission and storage of digital data. Fractal is made up of the union of several copies of itself and IFS is defined by a finite number of affine transformation which characterized by Translation, scaling, shearing and rotat ion. In this paper we describe the necessary conditions to form an Iterated Function System and how fractals are generated through affine transformations. Keywords: Iterated Function System; Contraction Mapping. 1. INTRODUCTION The exploration of fractal geometry is usually traced back Metric Spaces definition: A space X with a real-valued to the publication of the book “The Fractal Geometry of function d: X × X → ℜ is called a metric space (X, d) if d Nature” [1] by the IBM mathematician Benoit B. possess the following properties: Mandelbrot. Iterated Function System is a method of constructing fractals, which consists of a set of maps that 1. d(x, y) ≥ 0 for ∀ x, y ∈ X explicitly list the similarities of the shape. Though the 2. d(x, y) = d(y, x) ∀ x, y ∈ X formal name Iterated Function Systems or IFS was coined 3. d x, y ≤ d x, z + d z, y ∀ x, y, z ∈ X . (triangle by Barnsley and Demko [2] in 1985, the basic concept is inequality). -
Aspects of Functional Iteration Milton Eugene Winger Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1972 Aspects of functional iteration Milton Eugene Winger Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Statistics and Probability Commons Recommended Citation Winger, Milton Eugene, "Aspects of functional iteration " (1972). Retrospective Theses and Dissertations. 5876. https://lib.dr.iastate.edu/rtd/5876 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This dissertation was produced from a microfilm copy of the original document. While the most advanced technological means to photograph and reproduce this document have been used, the quality is heavily dependent upon the quality of the original submitted. The following explanation of techniques is provided to help you understand markings or patterns which may appear on this reproduction. 1. The sign or "target" for pages apparently lacking from the document photographed is "Missing Page(s)". If it was possible to obtain the missing page(s) or section, they are spliced into the film along with adjacent pages. This may have necessitated cutting thru an image and duplicating adjacent pages to insure you complete continuity. 2. When an image on the film is obliterated with a large round black mark, it is an indication that the photographer suspected that the copy may have moved during exposure and thus cause a blurred image. -
New Key Exchange Protocol Based on Mandelbrot and Julia Fractal Sets Mohammad Ahmad Alia and Azman Bin Samsudin
302 IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.2, February 2007 New Key Exchange Protocol Based on Mandelbrot and Julia Fractal Sets Mohammad Ahmad Alia and Azman Bin Samsudin, School of Computer Sciences, Universiti Sains Malaysia. Summary by using two keys, a private and a public key. In most In this paper, we propose a new cryptographic key exchange cases, by exchanging the public keys, both parties can protocol based on Mandelbrot and Julia Fractal sets. The Fractal calculate a unique shared key, known only to both of them based key exchange protocol is possible because of the intrinsic [2, 3, 4]. connection between the Mandelbrot and Julia Fractal sets. In the proposed protocol, Mandelbrot Fractal function takes the chosen This paper proposed a new Fractal (Mandelbrot and private key as the input parameter and generates the corresponding public key. Julia Fractal function is then used to Julia Fractal sets) key exchange protocol as a method to calculate the shared key based on the existing private key and the securely agree on a shared key. The proposed method received public key. The proposed protocol is designed to be achieved the secure exchange protocol by creating a resistant against attacks, utilizes small key size and shared secret through the use of the Mandelbrot and Julia comparatively performs faster then the existing Diffie-Hellman Fractal sets. key exchange protocol. The proposed Fractal key exchange protocol is therefore an attractive alternative to traditional 2.1. Fractals number theory based key exchange protocols. A complex number consists of a real and an imaginary Key words: Fractals Cryptography, Key- exchange protocol, Mandelbrot component.