Pseudo-Random Number Generator Based on Logistic Chaotic System
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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. -
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. -
STRONG REGULARITY 3 Ergodic Theorem Produces Lyapunov Exponents (W.R.T
STRONG REGULARITY by Pierre Berger and Jean-Christophe Yoccoz 1. Uniformly hyperbolic dynamical systems The theory of uniformly hyperbolic dynamical systems was constructed in the 1960’s under the dual leadership of Smale in the USA and Anosov and Sinai in the Soviet Union. It is nowadays almost complete. It encompasses various examples [Sma67]: expanding maps, horseshoes, solenoid maps, Plykin attractors, Anosov maps and DA, all of which are basic pieces. We recall standard definitions. Let f be a C1-diffeomorphism f of a finite dimen- sional manifold M. A compact f-invariant subset Λ ⊂ M is uniformly hyperbolic if the restriction to Λ of the tangent bundle TM splits into two continuous invariant subbundles TM|Λ= Es ⊕ Eu, Es being uniformly contracted and Eu being uniformly expanded. Then for every z ∈ Λ, the sets W s(z)= {z′ ∈ M : lim d(f n(z),f n(z′))=0}, n→+∞ W u(z)= {z′ ∈ M : lim d(f n(z),f n(z′))=0} n→−∞ are called the stable and unstable manifolds of z. They are immersed manifolds tangent at z to respectively Es(z) and Eu(z). s arXiv:1901.09430v1 [math.DS] 27 Jan 2019 The ǫ-local stable manifold Wǫ (z) of z is the connected component of z in the intersection of W s(z) with a ǫ-neighborhood of z. The ǫ-local unstable manifold u Wǫ (z) is defined likewise. Definition 1.1. — A basic set is a compact, f-invariant, uniformly hyperbolic set Λ which is transitive and locally maximal: there exists a neighborhood N of Λ such that n Λ= ∩n∈Zf (N). -
Complex Bifurcation Structures in the Hindmarsh–Rose Neuron Model
International Journal of Bifurcation and Chaos, Vol. 17, No. 9 (2007) 3071–3083 c World Scientific Publishing Company COMPLEX BIFURCATION STRUCTURES IN THE HINDMARSH–ROSE NEURON MODEL J. M. GONZALEZ-MIRANDA´ Departamento de F´ısica Fundamental, Universidad de Barcelona, Avenida Diagonal 647, Barcelona 08028, Spain Received June 12, 2006; Revised October 25, 2006 The results of a study of the bifurcation diagram of the Hindmarsh–Rose neuron model in a two-dimensional parameter space are reported. This diagram shows the existence and extent of complex bifurcation structures that might be useful to understand the mechanisms used by the neurons to encode information and give rapid responses to stimulus. Moreover, the information contained in this phase diagram provides a background to develop our understanding of the dynamics of interacting neurons. Keywords: Neuronal dynamics; neuronal coding; deterministic chaos; bifurcation diagram. 1. Introduction 1961] has proven to give a good qualitative descrip- There are two main problems in neuroscience whose tion of the dynamics of the membrane potential of solution is linked to the research in nonlinear a single neuron, which is the most relevant experi- dynamics and chaos. One is the problem of inte- mental output of the neuron dynamics. Because of grated behavior of the nervous system [Varela et al., the enormous development that the field of dynam- 2001], which deals with the understanding of the ical systems and chaos has undergone in the last mechanisms that allow the different parts and units thirty years [Hirsch et al., 2004; Ott, 2002], the of the nervous system to work together, and appears study of meaningful mathematical systems like the related to the synchronization of nonlinear dynami- Hindmarsh–Rose model have acquired new interest cal oscillators. -
DOUBLE STANDARD MAPS 1. Introduction It Is a Usual
DOUBLE STANDARD MAPS MICHALMISIUREWICZ AND ANA RODRIGUES Abstract. We investigate the family of double standard maps of the circle onto itself, given by fa,b(x) = 2x + a + (b/π) sin(2πx) (mod 1), where the parameters a, b are real and 0 ≤ b ≤ 1. Similarly to the well known family of (Arnold) standard maps of the circle, Aa,b(x) = x + a + (b/(2π)) sin(2πx) (mod 1), any such map has at most one attracting periodic orbit and the set of parameters (a, b) for which such orbit exists is divided into tongues. However, unlike the classical Arnold tongues, that begin at the level b = 0, for double standard maps the tongues begin at higher levels, depending on the tongue. Moreover, the order of the tongues is different. For the standard maps it is governed by the continued fraction expansions of rational numbers; for the double standard maps it is governed by their binary expansions. We investigate closer two families of tongues with different behavior. 1. Introduction It is a usual procedure that in order to understand the behavior of a system in higher dimension one investigates first a one-dimensional system that is somewhat similar. The classical example is the H´enonmap and similar systems, where a serious progress occurred only after unimodal interval maps have been thoroughly understood. Another example of this type was investigation by V. Arnold of the family of standard maps of the circle, given by the formula b (1.1) A (x) = x + a + sin(2πx) (mod 1) a,b 2π (when we write “mod 1,” we mean that both the arguments and the values are taken modulo 1). -
An Entire Transcendental Family with a Persistent Siegel Disk
An entire transcendental family with a persistent Siegel disk Rub´en Berenguel, N´uria Fagella March 12, 2009 Abstract We study the class of entire transcendental maps of finite order with one critical point and one asymptotic value, which has exactly one finite pre-image, and having a persistent Siegel disk. After normalization ∗ this is a one parameter family fa with a ∈ C which includes the semi- standard map λzez at a = 1, approaches the exponential map when a → 0 and a quadratic polynomial when a → ∞. We investigate the stable components of the parameter plane (capture components and semi-hyperbolic components) and also some topological properties of the Siegel disk in terms of the parameter. 1 Introduction Given a holomorphic endomorphism f : S → S on a Riemann surface S we consider the dynamical system generated by the iterates of f, denoted by n) n f = f◦ ··· ◦f. The orbit of an initial condition z0 ∈ S is the sequence + n O (z0)= {f (z0)}n N and we are interested in classifying the initial condi- ∈ tions in the phase space or dynamical plane S, according to the asymptotic behaviour of their orbits when n tends to infinity. There is a dynamically natural partition of the phase space S into the Fatou set F (f) (open) where the iterates of f form a normal family and the Julia set J (f)= S\F (f) which is its complement (closed). If S = C = C ∪∞ then f is a rational map. If S = C and f does not extend to the point at infinity, then f is an entire transcendental map, b that is, infinity is an essential singularity. -
PHY411 Lecture Notes Part 4
PHY411 Lecture notes Part 4 Alice Quillen February 6, 2019 Contents 0.1 Introduction . 2 1 Bifurcations of one-dimensional dynamical systems 2 1.1 Saddle-node bifurcation . 2 1.1.1 Example of a saddle-node bifurcation . 4 1.1.2 Another example . 6 1.2 Pitchfork bifurcations . 7 1.3 Trans-critical bifurcations . 10 1.4 Imperfect bifurcations . 10 1.5 Relation to the fixed points of a simple Hamiltonian system . 13 2 Iteratively applied maps 14 2.1 Cobweb plots . 14 2.2 Stability of a fixed point . 16 2.3 The Logistic map . 16 2.4 Two-cycles and period doubling . 17 2.5 Sarkovskii's Theorem and Ordering of periodic orbits . 22 2.6 A Cantor set for r > 4.............................. 24 3 Lyapunov exponents 26 3.1 The Tent Map . 28 3.2 Computing the Lyapunov exponent for a map . 28 3.3 The Maximum Lyapunov Exponent . 29 3.4 Numerical Computation of the Maximal Lyapunov Exponent . 30 3.5 Indicators related to the Lyapunov exponent . 32 1 0.1 Introduction For bifurcations and maps we are following early and late chapters in the lovely book by Steven Strogatz (1994) called `Non-linear Dynamics and Chaos' and the extremely clear book by Richard A. Holmgren (1994) called `A First Course in Discrete Dynamical Systems'. On Lyapunov exponents, we include some notes from Allesandro Morbidelli's book `Modern Celestial Mechanics, Aspects of Solar System Dynamics.' In future I would like to add more examples from the book called Bifurcations in Hamiltonian systems (Broer et al.). Renormalization in logistic map is lacking. -
Elliptic Bubbles in Moser's 4D Quadratic Map: the Quadfurcation
Elliptic Bubbles in Moser's 4DQuadratic Map: the Quadfurcation ∗ Arnd B¨ackery and James D. Meissz Abstract. Moser derived a normal form for the family of four-dimensional, quadratic, symplectic maps in 1994. This six-parameter family generalizes H´enon'subiquitous 2d map and provides a local approximation for the dynamics of more general 4D maps. We show that the bounded dynamics of Moser's family is organized by a codimension-three bifurcation that creates four fixed points|a bifurcation analogous to a doubled, saddle-center|which we call a quadfurcation. In some sectors of parameter space a quadfurcation creates four fixed points from none, and in others it is the collision of a pair of fixed points that re-emerge as two or possibly four. In the simplest case the dynamics is similar to the cross product of a pair of H´enonmaps, but more typically the stability of the created fixed points does not have this simple form. Up to two of the fixed points can be doubly-elliptic and be surrounded by bubbles of invariant two-tori; these dominate the set of bounded orbits. The quadfurcation can also create one or two complex-unstable (Krein) fixed points. Special cases of the quadfurcation correspond to a pair of weakly coupled H´enonmaps near their saddle-center bifurcations. Key words. H´enonmap, symplectic maps, saddle-center bifurcation, Krein bifurcation, invariant tori AMS subject classifications. 37J10 37J20 37J25 70K43 1. Introduction. Multi-dimensional Hamiltonian systems model dynamics on scales rang- ing from zettameters, for the dynamics of stars in galaxies [1, 2], to nanometers, in atoms and molecules [3, 4]. -
Chaos: the Mathematics Behind the Butterfly Effect
Chaos: The Mathematics Behind the Butterfly E↵ect James Manning Advisor: Jan Holly Colby College Mathematics Spring, 2017 1 1. Introduction A butterfly flaps its wings, and a hurricane hits somewhere many miles away. Can these two events possibly be related? This is an adage known to many but understood by few. That fact is based on the difficulty of the mathematics behind the adage. Now, it must be stated that, in fact, the flapping of a butterfly’s wings is not actually known to be the reason for any natural disasters, but the idea of it does get at the driving force of Chaos Theory. The common theme among the two is sensitive dependence on initial conditions. This is an idea that will be revisited later in the paper, because we must first cover the concepts necessary to frame chaos. This paper will explore one, two, and three dimensional systems, maps, bifurcations, limit cycles, attractors, and strange attractors before looking into the mechanics of chaos. Once chaos is introduced, we will look in depth at the Lorenz Equations. 2. One Dimensional Systems We begin our study by looking at nonlinear systems in one dimen- sion. One of the important features of these is the nonlinearity. Non- linearity in an equation evokes behavior that is not easily predicted due to the disproportionate nature of inputs and outputs. Also, the term “system”isoftenamisnomerbecauseitoftenevokestheideaof asystemofequations.Thiswillbethecaseaswemoveourfocuso↵ of one dimension, but for now we do not want to think of a system of equations. In this case, the type of system we want to consider is a first-order system of a single equation. -
A Partial Justification of Greene's Criterion For
A PARTIAL JUSTIFICATION OF GREENE'S CRITERION FOR CONFORMALLY SYMPLECTIC SYSTEMS RENATO C. CALLEJA, ALESSANDRA CELLETTI, CORRADO FALCOLINI, AND RAFAEL DE LA LLAVE Abstract. Greene's criterion for twist mappings asserts the existence of smooth in- variant circles with preassigned rotation number if and only if the periodic trajectories with frequency approaching that of the quasi-periodic orbit are linearly stable. We formulate an extension of this criterion for conformally symplectic systems in any dimension and prove one direction of the implication, namely that if there is a smooth invariant attractor, we can predict the eigenvalues of the periodic orbits whose frequencies approximate that of the tori. The proof of this result is very different from the proof in the area preserving case, since in the conformally symplectic case the existence of periodic orbits requires adjusting parameters. Also, as shown in [13], in the conformally symplectic case there are no Birkhoff invariants giving obstructions to linearization near an invariant torus. As a byproduct of the techniques developed here, we obtain quantitative information on the existence of periodic orbits in the neighborhood of quasi-periodic tori and we provide upper and lower bounds on the width of the Arnold tongues in n-degrees of freedom conformally symplectic systems. Contents 1. Introduction 2 1.1. The Greene's criterion 2 1.2. Greene's criterion for conformally symplectic systems 3 1.3. Organization of the paper 6 2. Local behavior near rotational Lagrangian tori in conformally symplectic systems 6 2.1. Conformally symplectic systems 6 2.2. Local behavior in a neighborhood of a KAM torus 7 3. -
Bifurcations, Mode Locking, and Chaos in a Dissipative System
The Bogdanov Map: Bifurcations, Mode Locking, and Chaos in a Dissipative System David K. Arrowsmith, School of Mathematical Sciences Queen Mary and Westfield College University of London Mile End Road London E1 4NS, UK Julyan H. E. Cartwright, Departament de F´ısica Universitat de les Illes Balears 07071 Palma de Mallorca, Spain Alexis N. Lansbury, Department of Physics Brunel, The University of West London Uxbridge, Middlesex UB8 3PH, UK &ColinM.Place.∗ Int. J. Bifurcation and Chaos, 3,803–842,1993 We investigate the bifurcations and basins of attraction in the Bog- danov map, a planar quadratic map which is conjugate to the Henon´ area-preserving map in its conservative limit. It undergoesaHopf bifurcation as dissipation is added, and exhibits the panoply of mode locking, Arnold tongues, and chaos as an invariant circle grows out, finally to be destroyed in the homoclinic tangency of the manifolds of aremotesaddlepoint.TheBogdanovmapistheEulermapofatwo- dimensional system of ordinary differential equations firstconsidered by Bogdanov and Arnold in their study of the versal unfolding of the double-zero-eigenvalue singularity, and equivalently of avectorfield invariant under rotation of the plane by an angle 2π.Itisauseful system in which to observe the effect of dissipative perturbations on Hamiltonian structure. In addition, we argue that the Bogdanov map provides a good approximation to the dynamics of the Poincaremaps´ of periodically forced oscillators. ∗Formerly: Department of Mathematics, Westfield College, University of London, UK. 1 1. Introduction Tisnowwellknownthatthestudyofsystemsofordinarydifferential equations, which commonly arise in dynamical systems investigated in I many fields of science, can be aided by utilizing the surface-of-section technique of Poincaremaps.The´ Poincar´e or return map of a system of or- dinary differential equations reduces the dimension of the problem, replac- ing an n-dimensional set of ordinary differential equations with an (n 1)- dimensional set of difference equations. -
Synchronization in Nonlinear Systems and Networks
Synchronization in Nonlinear Systems and Networks Yuri Maistrenko E-mail: [email protected] you can find me in the room EW 632, Wednesday 13:00-14:00 Lecture 5-6 30.11, 07.12.2011 1 Attractor Attractor A is a limiting set in phase space, towards which a dynamical system evolves over time. This limiting set A can be: 1) point (equilibrium) 2) curve (periodic orbit) 3) torus (quasiperiodic orbit ) 4) fractal (strange attractor = CHAOS) Attractor is a closed set with the following properties: S. Strogatz LORENTZ ATTRACTOR (1963) butterfly effect a trajectory in the phase space The Lorenz attractor is generated by the system of 3 differential equations dx/dt= -10x +10y dy/dt= 28x -y -xz dz/dt= -8/3z +xy. ROSSLER ATTRACTOR (1976) A trajectory of the Rossler system, t=250 What can we learn from the two exemplary 3-dimensional flow? If a flow is locally unstable in each point but globally bounded, any open ball of initial points will be stretched out and then folded back. If the equilibria are hyperbolic, the trajectory will be attracted along some eigen-directions and ejected along others. Reducing to discrete dynamics. Lorenz map Lorenz attractor Continues dynamics . Variable z(t) x Lorenz one-dimensional map Poincare section and Poincare return map Rossler attractor Rossler one-dimensional map Tent map and logistic map How common is chaos in dynamical systems? To answer the question, we need discrete dynamical systems given by one-dimensional maps Simplest examples of chaotic maps xn1 f (xn ), n 0, 1, 2,..