Lindenmayer Systems, Fractals, and Their Mathematics
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Using Fractal Dimension for Target Detection in Clutter
KIM T. CONSTANTIKES USING FRACTAL DIMENSION FOR TARGET DETECTION IN CLUTTER The detection of targets in natural backgrounds requires that we be able to compute some characteristic of target that is distinct from background clutter. We assume that natural objects are fractals and that the irregularity or roughness of the natural objects can be characterized with fractal dimension estimates. Since man-made objects such as aircraft or ships are comparatively regular and smooth in shape, fractal dimension estimates may be used to distinguish natural from man-made objects. INTRODUCTION Image processing associated with weapons systems is fractal. Falconer1 defines fractals as objects with some or often concerned with methods to distinguish natural ob all of the following properties: fine structure (i.e., detail jects from man-made objects. Infrared seekers in clut on arbitrarily small scales) too irregular to be described tered environments need to distinguish the clutter of with Euclidean geometry; self-similar structure, with clouds or solar sea glint from the signature of the intend fractal dimension greater than its topological dimension; ed target of the weapon. The discrimination of target and recursively defined. This definition extends fractal from clutter falls into a category of methods generally into a more physical and intuitive domain than the orig called segmentation, which derives localized parameters inal Mandelbrot definition whereby a fractal was a set (e.g.,texture) from the observed image intensity in order whose "Hausdorff-Besicovitch dimension strictly exceeds to discriminate objects from background. Essentially, one its topological dimension.,,2 The fine, irregular, and self wants these parameters to be insensitive, or invariant, to similar structure of fractals can be experienced firsthand the kinds of variation that the objects and background by looking at the Mandelbrot set at several locations and might naturally undergo because of changes in how they magnifications. -
Turbulence, Fractals, and Mixing
Turbulence, fractals, and mixing Paul E. Dimotakis and Haris J. Catrakis GALCIT Report FM97-1 17 January 1997 Firestone Flight Sciences Laboratory Guggenheim Aeronautical Laboratory Karman Laboratory of Fluid Mechanics and Jet Propulsion Pasadena Turbulence, fractals, and mixing* Paul E. Dimotakis and Haris J. Catrakis Graduate Aeronautical Laboratories California Institute of Technology Pasadena, California 91125 Abstract Proposals and experimenta1 evidence, from both numerical simulations and laboratory experiments, regarding the behavior of level sets in turbulent flows are reviewed. Isoscalar surfaces in turbulent flows, at least in liquid-phase turbulent jets, where extensive experiments have been undertaken, appear to have a geom- etry that is more complex than (constant-D) fractal. Their description requires an extension of the original, scale-invariant, fractal framework that can be cast in terms of a variable (scale-dependent) coverage dimension, Dd(X). The extension to a scale-dependent framework allows level-set coverage statistics to be related to other quantities of interest. In addition to the pdf of point-spacings (in 1-D), it can be related to the scale-dependent surface-to-volume (perimeter-to-area in 2-D) ratio, as well as the distribution of distances to the level set. The application of this framework to the study of turbulent -jet mixing indicates that isoscalar geometric measures are both threshold and Reynolds-number dependent. As regards mixing, the analysis facilitated by the new tools, as well as by other criteria, indicates en- hanced mixing with increasing Reynolds number, at least for the range of Reynolds numbers investigated. This results in a progressively less-complex level-set geom- etry, at least in liquid-phase turbulent jets, with increasing Reynolds number. -
Fractal Curves and Complexity
Perception & Psychophysics 1987, 42 (4), 365-370 Fractal curves and complexity JAMES E. CUTI'ING and JEFFREY J. GARVIN Cornell University, Ithaca, New York Fractal curves were generated on square initiators and rated in terms of complexity by eight viewers. The stimuli differed in fractional dimension, recursion, and number of segments in their generators. Across six stimulus sets, recursion accounted for most of the variance in complexity judgments, but among stimuli with the most recursive depth, fractal dimension was a respect able predictor. Six variables from previous psychophysical literature known to effect complexity judgments were compared with these fractal variables: symmetry, moments of spatial distribu tion, angular variance, number of sides, P2/A, and Leeuwenberg codes. The latter three provided reliable predictive value and were highly correlated with recursive depth, fractal dimension, and number of segments in the generator, respectively. Thus, the measures from the previous litera ture and those of fractal parameters provide equal predictive value in judgments of these stimuli. Fractals are mathematicalobjectsthat have recently cap determine the fractional dimension by dividing the loga tured the imaginations of artists, computer graphics en rithm of the number of unit lengths in the generator by gineers, and psychologists. Synthesized and popularized the logarithm of the number of unit lengths across the ini by Mandelbrot (1977, 1983), with ever-widening appeal tiator. Since there are five segments in this generator and (e.g., Peitgen & Richter, 1986), fractals have many curi three unit lengths across the initiator, the fractionaldimen ous and fascinating properties. Consider four. sion is log(5)/log(3), or about 1.47. -
Fractal Dimension of Self-Affine Sets: Some Examples
FRACTAL DIMENSION OF SELF-AFFINE SETS: SOME EXAMPLES G. A. EDGAR One of the most common mathematical ways to construct a fractal is as a \self-similar" set. A similarity in Rd is a function f : Rd ! Rd satisfying kf(x) − f(y)k = r kx − yk for some constant r. We call r the ratio of the map f. If f1; f2; ··· ; fn is a finite list of similarities, then the invariant set or attractor of the iterated function system is the compact nonempty set K satisfying K = f1[K] [ f2[K] [···[ fn[K]: The set K obtained in this way is said to be self-similar. If fi has ratio ri < 1, then there is a unique attractor K. The similarity dimension of the attractor K is the solution s of the equation n X s (1) ri = 1: i=1 This theory is due to Hausdorff [13], Moran [16], and Hutchinson [14]. The similarity dimension defined by (1) is the Hausdorff dimension of K, provided there is not \too much" overlap, as specified by Moran's open set condition. See [14], [6], [10]. REPRINT From: Measure Theory, Oberwolfach 1990, in Supplemento ai Rendiconti del Circolo Matematico di Palermo, Serie II, numero 28, anno 1992, pp. 341{358. This research was supported in part by National Science Foundation grant DMS 87-01120. Typeset by AMS-TEX. G. A. EDGAR I will be interested here in a generalization of self-similar sets, called self-affine sets. In particular, I will be interested in the computation of the Hausdorff dimension of such sets. -
Fractal Geometry and Applications in Forest Science
ACKNOWLEDGMENTS Egolfs V. Bakuzis, Professor Emeritus at the University of Minnesota, College of Natural Resources, collected most of the information upon which this review is based. We express our sincere appreciation for his investment of time and energy in collecting these articles and books, in organizing the diverse material collected, and in sacrificing his personal research time to have weekly meetings with one of us (N.L.) to discuss the relevance and importance of each refer- enced paper and many not included here. Besides his interdisciplinary ap- proach to the scientific literature, his extensive knowledge of forest ecosystems and his early interest in nonlinear dynamics have helped us greatly. We express appreciation to Kevin Nimerfro for generating Diagrams 1, 3, 4, 5, and the cover using the programming package Mathematica. Craig Loehle and Boris Zeide provided review comments that significantly improved the paper. Funded by cooperative agreement #23-91-21, USDA Forest Service, North Central Forest Experiment Station, St. Paul, Minnesota. Yg._. t NAVE A THREE--PART QUE_.gTION,, F_-ACHPARToF:WHICH HA# "THREEPAP,T_.<.,EACFi PART" Of:: F_.AC.HPART oF wHIct4 HA.5 __ "1t4REE MORE PARTS... t_! c_4a EL o. EP-.ACTAL G EOPAgTI_YCoh_FERENCE I G;:_.4-A.-Ti_E AT THB Reprinted courtesy of Omni magazine, June 1994. VoL 16, No. 9. CONTENTS i_ Introduction ....................................................................................................... I 2° Description of Fractals .................................................................................... -
Fractal-Based Methods As a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: a Survey
INFORMATICA, 2016, Vol. 27, No. 2, 257–281 257 2016 Vilnius University DOI: http://dx.doi.org/10.15388/Informatica.2016.84 Fractal-Based Methods as a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: A Survey Rasa KARBAUSKAITE˙ ∗, Gintautas DZEMYDA Institute of Mathematics and Informatics, Vilnius University Akademijos 4, LT-08663, Vilnius, Lithuania e-mail: [email protected], [email protected] Received: December 2015; accepted: April 2016 Abstract. The estimation of intrinsic dimensionality of high-dimensional data still remains a chal- lenging issue. Various approaches to interpret and estimate the intrinsic dimensionality are deve- loped. Referring to the following two classifications of estimators of the intrinsic dimensionality – local/global estimators and projection techniques/geometric approaches – we focus on the fractal- based methods that are assigned to the global estimators and geometric approaches. The compu- tational aspects of estimating the intrinsic dimensionality of high-dimensional data are the core issue in this paper. The advantages and disadvantages of the fractal-based methods are disclosed and applications of these methods are presented briefly. Key words: high-dimensional data, intrinsic dimensionality, topological dimension, fractal dimension, fractal-based methods, box-counting dimension, information dimension, correlation dimension, packing dimension. 1. Introduction In real applications, we confront with data that are of a very high dimensionality. For ex- ample, in image analysis, each image is described by a large number of pixels of different colour. The analysis of DNA microarray data (Kriukien˙e et al., 2013) deals with a high dimensionality, too. The analysis of high-dimensional data is usually challenging. -
An Investigation of Fractals and Fractal Dimension
An Investigation of Fractals and Fractal Dimension Student: Ian Friesen Advisor: Dr. Andrew J. Dean April 10, 2018 Contents 1 Introduction 2 1.1 Fractals in Nature . 2 1.2 Mathematically Constructed Fractals . 2 1.3 Motivation and Method . 3 2 Basic Topology 5 2.1 Metric Spaces . 5 2.2 String Spaces . 7 2.3 Hausdorff Metric . 8 3 Topological Dimension 9 3.1 Refinement . 9 3.2 Zero-dimensional Spaces . 9 3.3 Covering Dimension . 10 4 Measure Theory 12 4.1 Outer/Inner Lebesgue Measure . 12 4.2 Carath´eodory Measurability . 13 4.3 Two dimensional Lebesgue Measure . 14 5 Self-similarity 14 5.1 Ratios . 14 5.2 Examples and Calculations . 15 5.3 Chaos Game . 17 6 Fractal Dimension 18 6.1 Hausdorff Measure . 18 6.2 Packing Measure . 21 6.3 Fractal Definition . 24 7 Fractals and the Complex Plane 25 7.1 Julia Sets . 25 7.2 Mandelbrot Set . 26 8 Conclusion 27 1 1 Introduction 1.1 Fractals in Nature There are many fractals in nature. Most of these have some level of self-similarity, and are called self-similar objects. Basic examples of these include the surface of cauliflower, the pat- tern of a fern, or the edge of a snowflake. Benoit Mandelbrot (considered to be the father of frac- tals) thought to measure the coastline of Britain and concluded that using smaller units of mea- surement led to larger perimeter measurements. Mathematically, as the unit of measurement de- creases in length and becomes more precise, the perimeter increases in length. -
Irena Ivancic Fraktalna Geometrija I Teorija Dimenzije
SveuˇcilišteJ.J. Strossmayera u Osijeku Odjel za matematiku Sveuˇcilišninastavniˇckistudij matematike i informatike Irena Ivanˇci´c Fraktalna geometrija i teorija dimenzije Diplomski rad Osijek, 2019. SveuˇcilišteJ.J. Strossmayera u Osijeku Odjel za matematiku Sveuˇcilišninastavniˇckistudij matematike i informatike Irena Ivanˇci´c Fraktalna geometrija i teorija dimenzije Diplomski rad Mentor: izv. prof. dr. sc. Tomislav Maroševi´c Osijek, 2019. Sadržaj 1 Uvod 3 2 Povijest fraktala- pojava ˇcudovišnih funkcija 4 2.1 Krivulje koje popunjavaju ravninu . 5 2.1.1 Peanova krivulja . 5 2.1.2 Hilbertova krivulja . 5 2.1.3 Drugi primjeri krivulja koje popunjavaju prostor . 6 3 Karakterizacija 7 3.1 Fraktali su hrapavi . 7 3.2 Fraktali su sami sebi sliˇcnii beskonaˇcnokompleksni . 7 4 Podjela fraktala prema stupnju samosliˇcnosti 8 5 Podjela fraktala prema naˇcinukonstrukcije 9 5.1 Fraktali nastali iteracijom generatora . 9 5.1.1 Cantorov skup . 9 5.1.2 Trokut i tepih Sierpi´nskog. 10 5.1.3 Mengerova spužva . 11 5.1.4 Kochova pahulja . 11 5.2 Kochove plohe . 12 5.2.1 Pitagorino stablo . 12 5.3 Sustav iteriranih funkcija (IFS) . 13 5.3.1 Lindenmayer sustavi . 16 5.4 Fraktali nastali iteriranjem funkcije . 17 5.4.1 Mandelbrotov skup . 17 5.4.2 Julijevi skupovi . 20 5.4.3 Cudniˇ atraktori . 22 6 Fraktalna dimenzija 23 6.1 Zahtjevi za dobru definiciju dimenzije . 23 6.2 Dimenzija šestara . 24 6.3 Dimenzija samosliˇcnosti . 27 6.4 Box-counting dimenzija . 28 6.5 Hausdorffova dimenzija . 28 6.6 Veza izmedu¯ dimenzija . 30 7 Primjena fraktala i teorije dimenzije 31 7.1 Fraktalna priroda geografskih fenomena . -
Fractals Lindenmayer Systems
FRACTALS LINDENMAYER SYSTEMS November 22, 2013 Rolf Pfeifer Rudolf M. Füchslin RECAP HIDDEN MARKOV MODELS What Letter Is Written Here? What Letter Is Written Here? What Letter Is Written Here? The Idea Behind Hidden Markov Models First letter: Maybe „a“, maybe „q“ Second letter: Maybe „r“ or „v“ or „u“ Take the most probable combination as a guess! Hidden Markov Models Sometimes, you don‘t see the states, but only a mapping of the states. A main task is then to derive, from the visible mapped sequence of states, the actual underlying sequence of „hidden“ states. HMM: A Fundamental Question What you see are the observables. But what are the actual states behind the observables? What is the most probable sequence of states leading to a given sequence of observations? The Viterbi-Algorithm We are looking for indices M1,M2,...MT, such that P(qM1,...qMT) = Pmax,T is maximal. 1. Initialization ()ib 1 i i k1 1(i ) 0 2. Recursion (1 t T-1) t1(j ) max( t ( i ) a i j ) b j k i t1 t1(j ) i : t ( i ) a i j max. 3. Termination Pimax,TT max( ( )) qmax,T q i: T ( i ) max. 4. Backtracking MMt t11() t Efficiency of the Viterbi Algorithm • The brute force approach takes O(TNT) steps. This is even for N = 2 and T = 100 difficult to do. • The Viterbi – algorithm in contrast takes only O(TN2) which is easy to do with todays computational means. Applications of HMM • Analysis of handwriting. • Speech analysis. • Construction of models for prediction. -
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. -
Fractal Modeling and Fractal Dimension Description of Urban Morphology
Fractal Modeling and Fractal Dimension Description of Urban Morphology Yanguang Chen (Department of Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, P. R. China. E-mail: [email protected]) Abstract: The conventional mathematical methods are based on characteristic length, while urban form has no characteristic length in many aspects. Urban area is a measure of scale dependence, which indicates the scale-free distribution of urban patterns. Thus, the urban description based on characteristic lengths should be replaced by urban characterization based on scaling. Fractal geometry is one powerful tool for scaling analysis of cities. Fractal parameters can be defined by entropy and correlation functions. However, how to understand city fractals is still a pending question. By means of logic deduction and ideas from fractal theory, this paper is devoted to discussing fractals and fractal dimensions of urban landscape. The main points of this work are as follows. First, urban form can be treated as pre-fractals rather than real fractals, and fractal properties of cities are only valid within certain scaling ranges. Second, the topological dimension of city fractals based on urban area is 0, thus the minimum fractal dimension value of fractal cities is equal to or greater than 0. Third, fractal dimension of urban form is used to substitute urban area, and it is better to define city fractals in a 2-dimensional embedding space, thus the maximum fractal dimension value of urban form is 2. A conclusion can be reached that urban form can be explored as fractals within certain ranges of scales and fractal geometry can be applied to the spatial analysis of the scale-free aspects of urban morphology. -
New Methods for Estimating Fractal Dimensions of Coastlines
New Methods for Estimating Fractal Dimensions of Coastlines by Jonathan David Klotzbach A Thesis Submitted to the Faculty of The College of Science in Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, Florida May 1998 New Methods for Estimating Fractal Dimensions of Coastlines by Jonathan David Klotzbach This thesis was prepared under the direction of the candidate's thesis advisor, Dr. Richard Voss, Department of Mathematical Sciences, and has been approved by the members of his supervisory committee. It was submitted to the faculty of The College of Science and was accepted in partial fulfillment of the requirements for the degree of Master of Science. SUPERVISORY COMMITTEE: ~~~y;::ThesisAMo/ ~-= Mathematical Sciences Date 11 Acknowledgements I would like to thank my advisor, Dr. Richard Voss, for all of his help and insight into the world of fractals and in the world of computer programming. You have given me guidance and support during this process and without you, this never would have been completed. You have been an inspiration to me. I would also like to thank my committee members, Dr. Heinz- Otto Peitgen and Dr. Mingzhou Ding, for their help and support. Thanks to Rich Roberts, a graduate student in the Geography Department at Florida Atlantic University, for all of his help converting the data from the NOAA CD into a format which I could use in my analysis. Without his help, I would have been lost. Thanks to all of the faculty an? staff in the Math Department who made my stay here so enjoyable.