The Graph Structure of the Generalized Discrete Arnold's Cat
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Construction and Chaos Properties Analysis of a Quadratic Polynomial Surjective Map
INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING Volume 11, 2017 Construction and Chaos Properties Analysis of a Quadratic Polynomial Surjective Map Jun Tang, Jianghong Shi, Zhenmiao Deng randomly distributed sequences [16]–[18]. Abstract—In this paper, a kind of quadratic polynomial surjective In this paper, we deduce the relationships between the map (QPSM) is constructed, and the topological conjugation of the coefficients of a quadratic polynomial surjective map (QPSM) QPSM and tent map is proven. With the probability density function and prove the topological conjugacy of the QPSM and tent map. (PDF) of the QPSM being deduced, an anti-trigonometric transform We give the PDF of the QPSM, which is then used to design a function is proposed to homogenize the QPSM. The information entropy, Kolmogorov entropy (KE), and discrete entropy (DE) of the transform function to homogenize the QPSM sequence. Finally, QPSM are calculated for both the original and homogenized maps we estimate entropies of the QPSM such as the information, with respect to different parameters. Simulation results show that the Kolmogorov, and discrete entropies for both the original and information entropy of the homogenized sequence is close to the homogenized map. theoretical limit and the discrete entropy remains unchanged, which This paper is organized as follows. Section II presents a suggest that the homogenization method is effective. Thus, the method for determining the QPSM coefficients, and the homogenized map not only inherits the diverse properties of the original QPSM but also possesses better uniformity. These features topological conjugation of the QPSM and tent map is proven. make it more suitable to secure communication and noise radar. -
Fractal Growth on the Surface of a Planet and in Orbit Around It
Wilfrid Laurier University Scholars Commons @ Laurier Physics and Computer Science Faculty Publications Physics and Computer Science 10-2014 Fractal Growth on the Surface of a Planet and in Orbit around It Ioannis Haranas Wilfrid Laurier University, [email protected] Ioannis Gkigkitzis East Carolina University, [email protected] Athanasios Alexiou Ionian University Follow this and additional works at: https://scholars.wlu.ca/phys_faculty Part of the Mathematics Commons, and the Physics Commons Recommended Citation Haranas, I., Gkigkitzis, I., Alexiou, A. Fractal Growth on the Surface of a Planet and in Orbit around it. Microgravity Sci. Technol. (2014) 26:313–325. DOI: 10.1007/s12217-014-9397-6 This Article is brought to you for free and open access by the Physics and Computer Science at Scholars Commons @ Laurier. It has been accepted for inclusion in Physics and Computer Science Faculty Publications by an authorized administrator of Scholars Commons @ Laurier. For more information, please contact [email protected]. 1 Fractal Growth on the Surface of a Planet and in Orbit around it 1Ioannis Haranas, 2Ioannis Gkigkitzis, 3Athanasios Alexiou 1Dept. of Physics and Astronomy, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada 2Departments of Mathematics and Biomedical Physics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC 27858-4353, USA 3Department of Informatics, Ionian University, Plateia Tsirigoti 7, Corfu, 49100, Greece Abstract: Fractals are defined as geometric shapes that exhibit symmetry of scale. This simply implies that fractal is a shape that it would still look the same even if somebody could zoom in on one of its parts an infinite number of times. -
Chapter 6: Ensemble Forecasting and Atmospheric Predictability
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors and errors in the initial conditions… In the 1960’s the forecasts were skillful for only one day or so. Statistical prediction was equal or better than dynamical predictions, Like it was until now for ENSO predictions! Lorenz wanted to show that statistical prediction could not match prediction with a nonlinear model for the Tokyo (1960) NWP conference So, he tried to find a model that was not periodic (otherwise stats would win!) He programmed in machine language on a 4K memory, 60 ops/sec Royal McBee computer He developed a low-order model (12 d.o.f) and changed the parameters and eventually found a nonperiodic solution Printed results with 3 significant digits (plenty!) Tried to reproduce results, went for a coffee and OOPS! Lorenz (1963) discovered that even with a perfect model and almost perfect initial conditions the forecast loses all skill in a finite time interval: “A butterfly in Brazil can change the forecast in Texas after one or two weeks”. In the 1960’s this was only of academic interest: forecasts were useless in two days Now, we are getting closer to the 2 week limit of predictability, and we have to extract the maximum information Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability (chaos) b) Stable systems are infinitely predictable a) Unstable dynamical system b) Stable dynamical -
Topological Entropy of Quadratic Polynomials and Sections of the Mandelbrot Set
Topological entropy of quadratic polynomials and sections of the Mandelbrot set Giulio Tiozzo Harvard University Warwick, April 2012 2. External rays 3. Main theorem 4. Ideas of proof (maybe) 5. Complex version Summary 1. Topological entropy 3. Main theorem 4. Ideas of proof (maybe) 5. Complex version Summary 1. Topological entropy 2. External rays 4. Ideas of proof (maybe) 5. Complex version Summary 1. Topological entropy 2. External rays 3. Main theorem 5. Complex version Summary 1. Topological entropy 2. External rays 3. Main theorem 4. Ideas of proof (maybe) Summary 1. Topological entropy 2. External rays 3. Main theorem 4. Ideas of proof (maybe) 5. Complex version Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. logf#laps of f ng htop(f ; R) := lim n!1 n Topological entropy of real maps Let f : I ! I, continuous. -
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music. Talk given at PoRT workshop, Glasgow, July 2012 Fred Cummins, University College Dublin [1] Dynamical Systems Theory (DST) is the lingua franca of Physics (both Newtonian and modern), Biology, Chemistry, and many other sciences and non-sciences, such as Economics. To employ the tools of DST is to take an explanatory stance with respect to observed phenomena. DST is thus not just another tool in the box. Its use is a different way of doing science. DST is increasingly used in non-computational, non-representational, non-cognitivist approaches to understanding behavior (and perhaps brains). (Embodied, embedded, ecological, enactive theories within cognitive science.) [2] DST originates in the science of mechanics, developed by the (co-)inventor of the calculus: Isaac Newton. This revolutionary science gave us the seductive concept of the mechanism. Mechanics seeks to provide a deterministic account of the relation between the motions of massive bodies and the forces that act upon them. A dynamical system comprises • A state description that indexes the components at time t, and • A dynamic, which is a rule governing state change over time The choice of variables defines the state space. The dynamic associates an instantaneous rate of change with each point in the state space. Any specific instance of a dynamical system will trace out a single trajectory in state space. (This is often, misleadingly, called a solution to the underlying equations.) Description of a specific system therefore also requires specification of the initial conditions. In the domain of mechanics, where we seek to account for the motion of massive bodies, we know which variables to choose (position and velocity). -
Mixing, Chaotic Advection, and Turbulence
Annual Reviews www.annualreviews.org/aronline Annu. Rev. Fluid Mech. 1990.22:207-53 Copyright © 1990 hV Annual Reviews Inc. All r~hts reserved MIXING, CHAOTIC ADVECTION, AND TURBULENCE J. M. Ottino Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts 01003 1. INTRODUCTION 1.1 Setting The establishment of a paradigm for mixing of fluids can substantially affect the developmentof various branches of physical sciences and tech- nology. Mixing is intimately connected with turbulence, Earth and natural sciences, and various branches of engineering. However, in spite of its universality, there have been surprisingly few attempts to construct a general frameworkfor analysis. An examination of any library index reveals that there are almost no works textbooks or monographs focusing on the fluid mechanics of mixing from a global perspective. In fact, there have been few articles published in these pages devoted exclusively to mixing since the first issue in 1969 [one possible exception is Hill’s "Homogeneous Turbulent Mixing With Chemical Reaction" (1976), which is largely based on statistical theory]. However,mixing has been an important component in various other articles; a few of them are "Mixing-Controlled Supersonic Combustion" (Ferri 1973), "Turbulence and Mixing in Stably Stratified Waters" (Sherman et al. 1978), "Eddies, Waves, Circulation, and Mixing: Statistical Geofluid Mechanics" (Holloway 1986), and "Ocean Tur- bulence" (Gargett 1989). It is apparent that mixing appears in both indus- try and nature and the problems span an enormous range of time and length scales; the Reynolds numberin problems where mixing is important varies by 40 orders of magnitude(see Figure 1). -
An Image Cryptography Using Henon Map and Arnold Cat Map
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072 An Image Cryptography using Henon Map and Arnold Cat Map. Pranjali Sankhe1, Shruti Pimple2, Surabhi Singh3, Anita Lahane4 1,2,3 UG Student VIII SEM, B.E., Computer Engg., RGIT, Mumbai, India 4Assistant Professor, Department of Computer Engg., RGIT, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this digital world i.e. the transmission of non- 2. METHODOLOGY physical data that has been encoded digitally for the purpose of storage Security is a continuous process via which data can 2.1 HENON MAP be secured from several active and passive attacks. Encryption technique protects the confidentiality of a message or 1. The Henon map is a discrete time dynamic system information which is in the form of multimedia (text, image, introduces by michel henon. and video).In this paper, a new symmetric image encryption 2. The map depends on two parameters, a and b, which algorithm is proposed based on Henon’s chaotic system with for the classical Henon map have values of a = 1.4 and byte sequences applied with a novel approach of pixel shuffling b = 0.3. For the classical values the Henon map is of an image which results in an effective and efficient chaotic. For other values of a and b the map may be encryption of images. The Arnold Cat Map is a discrete system chaotic, intermittent, or converge to a periodic orbit. that stretches and folds its trajectories in phase space. Cryptography is the process of encryption and decryption of 3. -
WHAT IS a CHAOTIC ATTRACTOR? 1. Introduction J. Yorke Coined the Word 'Chaos' As Applied to Deterministic Systems. R. Devane
WHAT IS A CHAOTIC ATTRACTOR? CLARK ROBINSON Abstract. Devaney gave a mathematical definition of the term chaos, which had earlier been introduced by Yorke. We discuss issues involved in choosing the properties that characterize chaos. We also discuss how this term can be combined with the definition of an attractor. 1. Introduction J. Yorke coined the word `chaos' as applied to deterministic systems. R. Devaney gave the first mathematical definition for a map to be chaotic on the whole space where a map is defined. Since that time, there have been several different definitions of chaos which emphasize different aspects of the map. Some of these are more computable and others are more mathematical. See [9] a comparison of many of these definitions. There is probably no one best or correct definition of chaos. In this paper, we discuss what we feel is one of better mathematical definition. (It may not be as computable as some of the other definitions, e.g., the one by Alligood, Sauer, and Yorke.) Our definition is very similar to the one given by Martelli in [8] and [9]. We also combine the concepts of chaos and attractors and discuss chaotic attractors. 2. Basic definitions We start by giving the basic definitions needed to define a chaotic attractor. We give the definitions for a diffeomorphism (or map), but those for a system of differential equations are similar. The orbit of a point x∗ by F is the set O(x∗; F) = f Fi(x∗) : i 2 Z g. An invariant set for a diffeomorphism F is an set A in the domain such that F(A) = A. -
Ergodic Theory
MATH41112/61112 Ergodic Theory Charles Walkden 4th January, 2018 MATH4/61112 Contents Contents 0 Preliminaries 2 1 An introduction to ergodic theory. Uniform distribution of real se- quences 4 2 More on uniform distribution mod 1. Measure spaces. 13 3 Lebesgue integration. Invariant measures 23 4 More examples of invariant measures 38 5 Ergodic measures: definition, criteria, and basic examples 43 6 Ergodic measures: Using the Hahn-Kolmogorov Extension Theorem to prove ergodicity 53 7 Continuous transformations on compact metric spaces 62 8 Ergodic measures for continuous transformations 72 9 Recurrence 83 10 Birkhoff’s Ergodic Theorem 89 11 Applications of Birkhoff’s Ergodic Theorem 99 12 Solutions to the Exercises 108 1 MATH4/61112 0. Preliminaries 0. Preliminaries 0.1 Contact details § The lecturer is Dr Charles Walkden, Room 2.241, Tel: 0161 275 5805, Email: [email protected]. My office hour is: Monday 2pm-3pm. If you want to see me at another time then please email me first to arrange a mutually convenient time. 0.2 Course structure § This is a reading course, supported by one lecture per week. I have split the notes into weekly sections. You are expected to have read through the material before the lecture, and then go over it again afterwards in your own time. In the lectures I will highlight the most important parts, explain the statements of the theorems and what they mean in practice, and point out common misunderstandings. As a general rule, I will not normally go through the proofs in great detail (but they are examinable unless indicated otherwise). -
The Transition Between the Complex Quadratic Map and the Hénon Map
The Transition Between the Complex Quadratic Map and the Hénon Map by Sarah N. Kabes Technical Report Department of Mathematics and Statistics University of Minnesota Duluth, MN 55812 July 2012 The Transition Between the Complex Quadratic Map and the Hénon map A PROJECT SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Sarah Kabes in partial fulfillment of the requirements for the degree of Master of Science July 2012 Acknowledgements Thank you first and foremost to my advisor Dr. Bruce Peckham. Your patience, encouragement, excitement, and support while teaching have made this experience not only possible but enjoyable as well. Additional thanks to my committee members, Dr. Marshall Hampton and Dr. John Pastor for reading and providing suggestions for this project. Furthermore, without the additional assistance from two individuals my project would not be as complete as it is today. Thank you Dr. Harlan Stech for finding the critical value , and thank you Scot Halverson for working with me and the open source code to produce the movie. Of course none of this would be possibly without the continued support of my family and friends. Thank you all for believing in me. i Abstract This paper investigates the transition between two well known dynamical systems in the plane, the complex quadratic map and the Hénon map. Using bifurcation theory, an analysis of the dynamical changes the family of maps undergoes as we follow a “homotopy” from one map to the other is presented. Along with locating common local bifurcations, an additional un-familiar bifurcation at infinity is discussed. -
Stat 8112 Lecture Notes Stationary Stochastic Processes Charles J
Stat 8112 Lecture Notes Stationary Stochastic Processes Charles J. Geyer April 29, 2012 1 Stationary Processes A sequence of random variables X1, X2, ::: is called a time series in the statistics literature and a (discrete time) stochastic process in the probability literature. A stochastic process is strictly stationary if for each fixed positive integer k the distribution of the random vector (Xn+1;:::;Xn+k) has the same distribution for all nonnegative integers n. A stochastic process having second moments is weakly stationary or sec- ond order stationary if the expectation of Xn is the same for all positive integers n and for each nonnegative integer k the covariance of Xn and Xn+k is the same for all positive integers n. 2 The Birkhoff Ergodic Theorem The Birkhoff ergodic theorem is to strictly stationary stochastic pro- cesses what the strong law of large numbers (SLLN) is to independent and identically distributed (IID) sequences. In effect, despite the different name, it is the SLLN for stationary stochastic processes. Suppose X1, X2, ::: is a strictly stationary stochastic process and X1 has expectation (so by stationary so do the rest of the variables). Write n 1 X X = X : n n i i=1 To introduce the Birkhoff ergodic theorem, it says a.s. Xn −! Y; (1) where Y is a random variable satisfying E(Y ) = E(X1). More can be said about Y , but we will have to develop some theory first. 1 The SSLN for IID sequences says the same thing as the Birkhoff ergodic theorem (1) except that in the SLLN for IID sequences the limit Y = E(X1) is constant. -
PERIODIC ORBITS of PIECEWISE MONOTONE MAPS a Dissertation
PERIODIC ORBITS OF PIECEWISE MONOTONE MAPS A Dissertation Submitted to the Faculty of Purdue University by David Cosper In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2018 Purdue University Indianapolis, Indiana ii ACKNOWLEDGMENTS I would like to thank my advisor Micha l Misiurewicz for sticking with me all these years. iii TABLE OF CONTENTS Page LIST OF FIGURES ::::::::::::::::::::::::::::::: iv ABSTRACT ::::::::::::::::::::::::::::::::::: v 1 INTRODUCTION :::::::::::::::::::::::::::::: 1 2 PRELIMINARIES :::::::::::::::::::::::::::::: 4 2.1 Kneading Theory :::::::::::::::::::::::::::: 4 2.2 Sharkovsky's Theorem ::::::::::::::::::::::::: 12 2.3 Periodic Orbits of Interval Maps :::::::::::::::::::: 16 3 PERIODIC ORBITS OF PIECEWISE MONOTONE MAPS ::::::: 20 3.1 Horizontal/Vertical Families and Order Type ::::::::::::: 23 3.2 Extremal Points ::::::::::::::::::::::::::::: 35 3.3 Characterizing P :::::::::::::::::::::::::::: 46 4 MATCHING :::::::::::::::::::::::::::::::::: 55 4.1 Matching :::::::::::::::::::::::::::::::: 58 4.2 Topological entropy ::::::::::::::::::::::::::: 62 4.3 Transitivity ::::::::::::::::::::::::::::::: 64 4.4 Beyond transitivity ::::::::::::::::::::::::::: 67 LIST OF REFERENCES :::::::::::::::::::::::::::: 71 VITA ::::::::::::::::::::::::::::::::::::::: 73 iv LIST OF FIGURES Figure Page 1.1 An example of a two-sided truncated tent map. ::::::::::::: 2 2.1 This truncated tent map cannot have the min-max periodic orbit (RLR)1, and hence does not have a period 3 periodic point. ::::::::::: 15 2.2 The period 6 orbit with itinerary [R ∗ (RLR)]1. Notice that the first return map on the blue interval is f 2 and that the three points form a periodic orbit under f 2. :::::::::::::::::::::::::: 19 3.1 This truncated tent map cannot have the orbit (RLR)1. However, it does have the orbit (RLL)1. :::::::::::::::::::::::::: 21 3.2 This truncated tent map cannot have the orbit (RL)1.