A Very Simple Approach for 3-D to 2-D Mapping

Size: px
Start display at page:

Download "A Very Simple Approach for 3-D to 2-D Mapping"

Transcription

1 A Very Simple Approach for -D to -D appig Sadipa Dey (1 Ajith Abraham ( Sugata Sayal ( Sadipa Dey (1 Ashi Software Private Limited INFINITY Tower II 10 th Floor Plot No Block GP Salt Lake Electroics Complex Sector V Kolkata Ajith Abraham ( IITA Professorship Program School of Computer Sciece Yosei Uiversity 14 Shicho-dog Sudaemoo-ku Seoul Republic of Korea ajith.abraham@ieee.org Sugata Sayal ( School of Techology & Computer Sciece Tata Istitute of Fudametal Research Homi Bhabha Road umbai INDIA sayal@tifr.res.i

2 Abstract ay times we eed to plot -D fuctios e.g. i may scietific experimets. To plot the -D fuctios o -D scree it requires some kid of mappig. Though OpeGL DirectX etc -D rederig libraries have made this job very simple still these libraries come with may complex pre-operatios that are simply ot iteded also to itegrate these libraries with ay kid of system is ofte a tough trial. This article presets a very simple method of mappig from -D to -D that is free from ay complex pre-operatio also it will work with ay graphics system where we have some primitive -D graphics fuctio. Also we discuss the iverse trasform ad how to do basic computer graphics trasformatios usig our coordiate mappig system. 1. Itroductio We have a fuctio f :R R ad our itetio is to draw the fuctio i - D plae. The fuctio z = f(x y is a -variable fuctio ad each tuple (x y f(x y R. Let s say we wat to graphically plot f oto computer scree usig a primitive graphics library (like Turbo C graphics which supports oly the basic putpixel (to draw a pixel i - D scree -like - D rederig fuctio but o - D rederig; i.e. our graphics library s putpixel s domai is R ad it s ot R. Hece i order to draw the fuctio f usig our graphics library we must desig a coordiate coversio system that will provide us with a fuctio that will take as iput -tuples (x y f(x y ad produce as output a - tuple ( x y that ca be directly passed to our graphics library to plot it oto the scree but with - D look & feel. As we discussed it s essetial that we have a simple coordiate mappig system that maps R to R ad still gives us a hypothetical feelig of drawig - D fuctios. It s very easy to fid such a map i.e. a fuctio h that maps from R to R i.e. h :R R ad i this paper we try to fid such a simple map.. Proposed approach We have a pictorial represetatio (Figure 1 of our - D to - D mappig system: Figure 1. Basic odel of a simple - D to - D mappig system But how the fuctio f should look like after mappig ad plottig? Here we simulate the rd coordiate (amely Z i our - D x-y plae. We perform the logical to physical coordiate trasform ad everythig by the map fuctio h which will basically tur out to be a matrix. The basic mappig techique is show i figure- which we are shortly goig to explai. (0 0 Figure.The basic coordiate mappig (w h If we have our Origi O at (xo yo scree coordiate we have

3 x' = xo + y - x.si( θ = yo - z + x.cos( θ i.e. we have our -D to -D trasformatio matrix: (1 Followig surfaces (figure- ad figure-4 are draw i Turbo C++ versio.0 (BGI graphics usig the above simple -D to -D mappig: si( θ cos( θ = 1 0 ( Agai we have shiftig (chage of origi by the matrix O = [xo yo] so that we have followig O + PD = P where deotes matrix multiplicatio ad + deotes matrix additio the -tuple PD = [x y z] the -tuple P = [x y] i.e. siθ cosθ [ xo yo] + [ x y z]. 1 0 = [ x' ] ( matrix matrix matrix matrix Figure. Sie fuctio draw i TurboC++ Ver.0 (BGI Graphics usig -D to -D mappig By default we keep the agle betwee X axis & Z axis = θ = π / 4 that oe ca chage if required but with the followig iequality strictly satisfied: 0 < θ < π /. Oe ca optioally use a compressio factor to cotrol the dimesio alog Z-axis by a compressio factor ρ z ad slightly modifyig the equatios: x' = xo + y - x.si( θ = yo - ρ z. z + x.cos( θ (4 Obviously 0.0 < ρ z < 1.0 By default we take ρ z =1. 0. Sample Output Surfaces draw usig the above mappig: Figure 4. Syc fuctio draw i TurboC++ Ver.0 (BGI Graphics usig the - D to - D mappig

4 4. Iverse Trasformatio - Obtaiig origial -D coordiate from the trasformed -D coordiate Here our trasformatio fuctio (matrix is: x' = xo + y - x.si( θ = yo - z + x.cos( θ (5 As we ca see it is impossible to re-covert ad obtai the origial set of coordiates amely (x y z because we have ukows ad equatios. So i order to be able to get the origial coordiates back we at least eed to store tuples as result of the trasformatio for istace (x y z (x y z the z-coordiate beig stored oly to get the iverse trasform of the form ( x y z (x y z ad the ( x y pair is used to plot the poit. So i order to get the iverse trasformatio we eed to solve the equatios for x y sice we already kow z we have -equatios ad ukow variables: y x.si( θ = x' xo x.cos( θ = - yo + z Solvig the above equatios we get x = ( - yo y = x' xo + ( + z.sec( θ - yo + z. ta( θ (6 (7 Put it i aother way our trasformatio matrix is a matrix si( θ cos( θ = 1 0 (8 Sice a o-square matrix o questio of existece of its iverse. So i order to be able to get the iverse trasform as well we eed a ivertible square matrix e.g. si( θ cos( θ 0 = (9 with si( θ cos( θ 0 Det( det X = 1 (10 si( θ cos( θ = 1. = cos( θ 1 0 Now 0 < θ < π / hece cos( θ 0 hece Det( 0 ad the iverse exists. si( θ cos( θ 0 [ xo yo 0] + [ x y z] = [ x' z] 1 1 matrix matrix matrix matrix (11 But we have Iv( Det( = ( 0 Adj( = Det( ad 0 cos( θ 0 Adj ( = si( θ 0 si( θ cos( θ (1 Hece Iv ( = = sec( θ ta( θ 0 sec( θ ta( θ 1 Here cos( θ 0 (1 So the iverse trasform is:

5 si( θ 0 cos( θ 0 1 [ x y z] = [ x' z] [ xo yo 0] [ x y z] = [ x' xo yo z]. 0 sec( θ sec( θ 1 ta( θ ta( θ [( - yo+ z.sec( θ x ' + (- yo+ z.ta( θ z] = xo (14 This exactly matches with our previous derivatio. 5. Rotatio ad affie trasformatios A poit i -D after beig mapped to -D scree followig the above mappig procedure may be required to be trasformed usig stadard computer graphics trasformatios (traslatio rotatio about a axis etc. But i order to udergo such a graphics trasformatio ad to show the poit back to the scree after the trasformatio it eeds to go through the followig steps i our previously-described coordiate mappig system: First obtai the iverse coordiate trasformatio to obtai the origial - D coordiates from the mapped - D coordiates ultiply the - D coordiate matrix by proper graphics trasformatio matrix i order to achieve graphical trasformatio. Use the same - D to - D map agai to plot the poit oto the scree. These steps ca be mathematically represeted as: P D = P ( P ' = P T D D D P ' = P ' (15 Or by a sigle-lie expressio P ' = ((P ( T (16 Here as before deotes matrix multiplicatio ad T deotes the traditioal graphics trasformatio matrix. But sice we kow the fact that matrix multiplicatio is associative we have P P ' = ((P where = P = P ' = P ( ' T T T ' = T (17 So usig this simple techique we ca escape the successive matrix multiplicatios everytime a poit o scree eeds to trasformed istead what we ca simply do is pre-compute ' = T. (18 This matrix ' is eeded to be computed oce for a give graphics trasformatio (e.g. rotatio about a axis ad applied to all poits o the scree so that usig a sigle matrix multiplicatio thereafter ay poit o the scree ca udergo graphics trasformatio by P ' = P ' where P represets the poit mapped before trasformatio T ad P is the poit re-mapped after the trasformatio as obvious. Hece usig the above tricks we are able to make the trasformatio more computatioally efficiet.

6 oreover if a trasformatio is eeded to be applied simultaeously we ca use the property (T = ( T where (T deotes ( times is a positive iteger simultaeous matrix multiplicatio of T. Let s say we have already udergoe a T trasformatio so that we have already computed = T ad let s say that we also have frequet simultaeous (T trasformatio. I order to udergo a (T trasformatio we first eed to compute the matrix (T the we eed to compute our ew matrix = (T so we eed total + matrix multiplicatios everytime we wat a (T trasform for each. But if we have computed T iitially here the trick is that we ca reuse this it to compute our ew matrix i the followig maer: = (T (19 = ( T = ( Here we eed ot compute (T ad every-time istead we eed to compute ( oly (that ca be icremetal multiplicatio to icrease efficiecy. 6. Coclusios This article preseted a very simple method of mappig from - D to - D that is free from ay complex pre-operatio. The proposed techique works with ay graphics system where we have some primitive - D graphics fuctio. We also discussed the iverse trasform ad how to do basic computer graphics trasformatios usig our coordiate mappig system. 7. Refereces [1] Rogers David F Procedural elemets for computer Graphics d editio cgraw-hill 1998 pp [] Rogers David F. Adams J. Ala athematical Elemets for Computer Graphics d editio cgraw-hill 1990 pp [] Ng C.. Bustard D. W. A New Real Time Geometric Trasformatio atrix ad its Efficiet VLSI Implemetatio Computer Graphics Forum December 1994 Vol. 1 pp [4] Shreier Dave Woo aso Neider Jackie Davis Tom OpeGL Programmig Guide Versio 1.4 Fourth Editio Pearso Educatio 004 pp [5] Turkowski Ke Fixed-Poit Trigoometry with CORDIC Iteratios Graphics Gems I Academic Press 1990 pp [6] Turkowski Ke The Use of Coordiate Frames i Computer Graphics Graphics Gems I Academic Press 1990 pp [7] Joy Ke O-Lie Computer Graphics Notes Computer Sciece Departmet Uiversity of Califoria Davis [8] Ducker ike atrix ad Vector aipulatio for Computer Graphics November [9] Howlad Joh E. Computer Graphics Departmet of Computer Sciece Triity Uiversity October [10] Keith Stepha R. A Trasformatio Structure for Aimated -D Computer Graphics AC SIGGRAPH Computer Graphics Vol. 15 Issue 1 April 1981 pp 7-91.

AVERYSIMPLEAPPROACHFOR3-DTO2-DMAPPING

AVERYSIMPLEAPPROACHFOR3-DTO2-DMAPPING Image Processing& Communications, vol. 11, no. 2, pp. 75-82 75 AVERYSIMPLEAPPROACHFOR3-DTO2-DMAPPING SANDIPANDEY (1),AJITHABRAHAM (2),SUGATASANYAL (3) (1) AnshinSoftwarePvt.Ltd. INFINITY, Tower- II, 10th

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

Intro to Scientific Computing: Solutions

Intro to Scientific Computing: Solutions Itro to Scietific Computig: Solutios Dr. David M. Goulet. How may steps does it take to separate 3 objects ito groups of 4? We start with 5 objects ad apply 3 steps of the algorithm to reduce the pile

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

Pattern Recognition Systems Lab 1 Least Mean Squares

Pattern Recognition Systems Lab 1 Least Mean Squares Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

EVALUATION OF TRIGONOMETRIC FUNCTIONS

EVALUATION OF TRIGONOMETRIC FUNCTIONS EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

Fast Fourier Transform (FFT) Algorithms

Fast Fourier Transform (FFT) Algorithms Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Parabolic Path to a Best Best-Fit Line:

Parabolic Path to a Best Best-Fit Line: Studet Activity : Fidig the Least Squares Regressio Lie By Explorig the Relatioship betwee Slope ad Residuals Objective: How does oe determie a best best-fit lie for a set of data? Eyeballig it may be

More information

top() Applications of Stacks

top() Applications of Stacks CS22 Algorithms ad Data Structures MW :00 am - 2: pm, MSEC 0 Istructor: Xiao Qi Lecture 6: Stacks ad Queues Aoucemets Quiz results Homework 2 is available Due o September 29 th, 2004 www.cs.mt.edu~xqicoursescs22

More information

LU Decomposition Method

LU Decomposition Method SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS LU Decompositio Method Jamie Traha, Autar Kaw, Kevi Marti Uiversity of South Florida Uited States of America kaw@eg.usf.edu http://umericalmethods.eg.usf.edu Itroductio

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig

More information

Numerical Methods Lecture 6 - Curve Fitting Techniques

Numerical Methods Lecture 6 - Curve Fitting Techniques Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio

More information

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig

More information

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

Research Article Kinematics Analysis and Modeling of 6 Degree of Freedom Robotic Arm from DFROBOT on Labview

Research Article Kinematics Analysis and Modeling of 6 Degree of Freedom Robotic Arm from DFROBOT on Labview Research Joural of Applied Scieces, Egieerig ad Techology 13(7): 569-575, 2016 DOI:10.19026/rjaset.13.3016 ISSN: 2040-7459; e-issn: 2040-7467 2016 Maxwell Scietific Publicatio Corp. Submitted: May 5, 2016

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 ) EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders

More information

Fuzzy Minimal Solution of Dual Fully Fuzzy Matrix Equations

Fuzzy Minimal Solution of Dual Fully Fuzzy Matrix Equations Iteratioal Coferece o Applied Mathematics, Simulatio ad Modellig (AMSM 2016) Fuzzy Miimal Solutio of Dual Fully Fuzzy Matrix Equatios Dequa Shag1 ad Xiaobi Guo2,* 1 Sciece Courses eachig Departmet, Gasu

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

Solving Fuzzy Assignment Problem Using Fourier Elimination Method

Solving Fuzzy Assignment Problem Using Fourier Elimination Method Global Joural of Pure ad Applied Mathematics. ISSN 0973-768 Volume 3, Number 2 (207), pp. 453-462 Research Idia Publicatios http://www.ripublicatio.com Solvig Fuzzy Assigmet Problem Usig Fourier Elimiatio

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045 Oe Brookigs Drive St. Louis, Missouri 63130-4899, USA jaegerg@cse.wustl.edu

More information

Assignment 5; Due Friday, February 10

Assignment 5; Due Friday, February 10 Assigmet 5; Due Friday, February 10 17.9b The set X is just two circles joied at a poit, ad the set X is a grid i the plae, without the iteriors of the small squares. The picture below shows that the iteriors

More information

BOOLEAN MATHEMATICS: GENERAL THEORY

BOOLEAN MATHEMATICS: GENERAL THEORY CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

Accuracy Improvement in Camera Calibration

Accuracy Improvement in Camera Calibration Accuracy Improvemet i Camera Calibratio FaJie L Qi Zag ad Reihard Klette CITR, Computer Sciece Departmet The Uiversity of Aucklad Tamaki Campus, Aucklad, New Zealad fli006, qza001@ec.aucklad.ac.z r.klette@aucklad.ac.z

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

CS Polygon Scan Conversion. Slide 1

CS Polygon Scan Conversion. Slide 1 CS 112 - Polygo Sca Coversio Slide 1 Polygo Classificatio Covex All iterior agles are less tha 180 degrees Cocave Iterior agles ca be greater tha 180 degrees Degeerate polygos If all vertices are colliear

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) A NEW RADIX-4 FFT ALGORITHM

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) A NEW RADIX-4 FFT ALGORITHM ITERATIOAL JOURAL OF ADVACED REEARC I EIEERI AD TECOLO (IJARET Iteratioal Joural of Advaced Research i Egieerig ad Techology (IJARET I 976 68(Prit I 976 699(Olie Volume Issue 3 April (3 IAEME I 976-68

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation Improvemet of the Orthogoal Code Covolutio Capabilities Usig FPGA Implemetatio Naima Kaabouch, Member, IEEE, Apara Dhirde, Member, IEEE, Saleh Faruque, Member, IEEE Departmet of Electrical Egieerig, Uiversity

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

Efficient Hough transform on the FPGA using DSP slices and block RAMs

Efficient Hough transform on the FPGA using DSP slices and block RAMs Efficiet Hough trasform o the FPGA usig DSP slices ad block RAMs Xi Zhou, Norihiro Tomagou, Yasuaki Ito, ad Koji Nakao Departmet of Iformatio Egieerig Hiroshima Uiversity Kagamiyama 1-4-1, Higashi Hiroshima,

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations Applied Mathematical Scieces, Vol. 1, 2007, o. 25, 1203-1215 A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045, Oe

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

Second-Order Domain Decomposition Method for Three-Dimensional Hyperbolic Problems

Second-Order Domain Decomposition Method for Three-Dimensional Hyperbolic Problems Iteratioal Mathematical Forum, Vol. 8, 013, o. 7, 311-317 Secod-Order Domai Decompositio Method for Three-Dimesioal Hyperbolic Problems Youbae Ju Departmet of Applied Mathematics Kumoh Natioal Istitute

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

Visualization of Gauss-Bonnet Theorem

Visualization of Gauss-Bonnet Theorem Visualizatio of Gauss-Boet Theorem Yoichi Maeda maeda@keyaki.cc.u-tokai.ac.jp Departmet of Mathematics Tokai Uiversity Japa Abstract: The sum of exteral agles of a polygo is always costat, π. There are

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

Neural Networks A Model of Boolean Functions

Neural Networks A Model of Boolean Functions Neural Networks A Model of Boolea Fuctios Berd Steibach, Roma Kohut Freiberg Uiversity of Miig ad Techology Istitute of Computer Sciece D-09596 Freiberg, Germay e-mails: steib@iformatik.tu-freiberg.de

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

Chapter 18: Ray Optics Questions & Problems

Chapter 18: Ray Optics Questions & Problems Chapter 18: Ray Optics Questios & Problems c -1 2 1 1 1 h s θr= θi 1siθ 1 = 2si θ 2 = θ c = si ( ) + = m = = v s s f h s 1 Example 18.1 At high oo, the su is almost directly above (about 2.0 o from the

More information

A Note on Least-norm Solution of Global WireWarping

A Note on Least-norm Solution of Global WireWarping A Note o Least-orm Solutio of Global WireWarpig Charlie C. L. Wag Departmet of Mechaical ad Automatio Egieerig The Chiese Uiversity of Hog Kog Shati, N.T., Hog Kog E-mail: cwag@mae.cuhk.edu.hk Abstract

More information

Consider the following population data for the state of California. Year Population

Consider the following population data for the state of California. Year Population Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB

More information

The following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels:

The following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels: DBE Memo#1 MARK 5 MEMO #18 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 1886 November 19, 24 Telephoe: 978-692-4764 Fax: 781-981-59 To: From: Mark 5 Developmet Group

More information

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION 397 AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jag, S~ Youg Lee, ad Sag-Yug Shi Korea Advaced Istitute of Sciece ad Techology, P.O. Box 150, Cheogryag, Seoul, Korea ABSTRACT Iverse matrix calculatio

More information

Section 7.2: Direction Fields and Euler s Methods

Section 7.2: Direction Fields and Euler s Methods Sectio 7.: Directio ields ad Euler s Methods Practice HW from Stewart Tetbook ot to had i p. 5 # -3 9-3 odd or a give differetial equatio we wat to look at was to fid its solutio. I this chapter we will

More information

ANN WHICH COVERS MLP AND RBF

ANN WHICH COVERS MLP AND RBF ANN WHICH COVERS MLP AND RBF Josef Boští, Jaromír Kual Faculty of Nuclear Scieces ad Physical Egieerig, CTU i Prague Departmet of Software Egieerig Abstract Two basic types of artificial eural etwors Multi

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Lecture 18. Optimization in n dimensions

Lecture 18. Optimization in n dimensions Lecture 8 Optimizatio i dimesios Itroductio We ow cosider the problem of miimizig a sigle scalar fuctio of variables, f x, where x=[ x, x,, x ]T. The D case ca be visualized as fidig the lowest poit of

More information

Math 10C Long Range Plans

Math 10C Long Range Plans Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

Project 2.5 Improved Euler Implementation

Project 2.5 Improved Euler Implementation Project 2.5 Improved Euler Implemetatio Figure 2.5.10 i the text lists TI-85 ad BASIC programs implemetig the improved Euler method to approximate the solutio of the iitial value problem dy dx = x+ y,

More information

Redundancy Allocation for Series Parallel Systems with Multiple Constraints and Sensitivity Analysis

Redundancy Allocation for Series Parallel Systems with Multiple Constraints and Sensitivity Analysis IOSR Joural of Egieerig Redudacy Allocatio for Series Parallel Systems with Multiple Costraits ad Sesitivity Aalysis S. V. Suresh Babu, D.Maheswar 2, G. Ragaath 3 Y.Viaya Kumar d G.Sakaraiah e (Mechaical

More information

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH J. Appl. Math. & Computig Vol. 21(2006), No. 1-2, pp. 233-238 Website: http://jamc.et A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH YEON SOO YOON AND JU KYUNG KIM Abstract.

More information

Extended Space Mapping with Bézier Patches and Volumes

Extended Space Mapping with Bézier Patches and Volumes Eteded Space Mappig with Béier Patches ad Volumes Bejami Schmitt 1, 2 Aleader Pasko 1 Vladimir Savcheko 1 Abstract We eplore a geeral eteded space mappig as a framework for trasformig a hypersurface i

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order

More information

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful

More information

One advantage that SONAR has over any other music-sequencing product I ve worked

One advantage that SONAR has over any other music-sequencing product I ve worked *gajedra* D:/Thomso_Learig_Projects/Garrigus_163132/z_productio/z_3B2_3D_files/Garrigus_163132_ch17.3d, 14/11/08/16:26:39, 16:26, page: 647 17 CAL 101 Oe advatage that SONAR has over ay other music-sequecig

More information

PLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com)

PLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com) wwwtheopguptacom wwwimathematiciacom For all the Math-Gya Buy books by OP Gupta A Compilatio By : OP Gupta (WhatsApp @ +9-9650 350 0) For more stuffs o Maths, please visit : wwwtheopguptacom Time Allowed

More information

The Magma Database file formats

The Magma Database file formats The Magma Database file formats Adrew Gaylard, Bret Pikey, ad Mart-Mari Breedt Johaesburg, South Africa 15th May 2006 1 Summary Magma is a ope-source object database created by Chris Muller, of Kasas City,

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions:

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions: CS 604 Data Structures Midterm Sprig, 00 VIRG INIA POLYTECHNIC INSTITUTE AND STATE U T PROSI M UNI VERSI TY Istructios: Prit your ame i the space provided below. This examiatio is closed book ad closed

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Load balanced Parallel Prime Number Generator with Sieve of Eratosthenes on Cluster Computers *

Load balanced Parallel Prime Number Generator with Sieve of Eratosthenes on Cluster Computers * Load balaced Parallel Prime umber Geerator with Sieve of Eratosthees o luster omputers * Soowook Hwag*, Kyusik hug**, ad Dogseug Kim* *Departmet of Electrical Egieerig Korea Uiversity Seoul, -, Rep. of

More information

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10 Lecture 24: Bezier Curves ad Surfaces thou shalt be ear uto me Geesis 45:0. Iterpolatio ad Approximatio Freeform curves ad surfaces are smooth shapes ofte describig ma-made objects. The hood of a car,

More information

Wavelet Transform. CSE 490 G Introduction to Data Compression Winter Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual)

Wavelet Transform. CSE 490 G Introduction to Data Compression Winter Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual) Wavelet Trasform CSE 49 G Itroductio to Data Compressio Witer 6 Wavelet Trasform Codig PACW Wavelet Trasform A family of atios that filters the data ito low resolutio data plus detail data high pass filter

More information

COARSE ANGLE ROTATION MODE CORDIC BASED SINGLE PROCESSING ELEMENT QR-RLS PROCESSOR

COARSE ANGLE ROTATION MODE CORDIC BASED SINGLE PROCESSING ELEMENT QR-RLS PROCESSOR 7th Europea Sigal Processig Coferece (EUSIPCO 9 Glasgow, Scotlad, August 4-8, 9 COARSE ANGLE ROTATION MODE CORDIC BASED SINGLE PROCESSING ELEMENT QR-RLS PROCESSOR Qiag Gao, Louise Crockett ad Robert Stewart

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

South Slave Divisional Education Council. Math 10C

South Slave Divisional Education Council. Math 10C South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

27 Refraction, Dispersion, Internal Reflection

27 Refraction, Dispersion, Internal Reflection Chapter 7 Refractio, Dispersio, Iteral Reflectio 7 Refractio, Dispersio, Iteral Reflectio Whe we talked about thi film iterferece, we said that whe light ecouters a smooth iterface betwee two trasparet

More information

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations Chapter 14 Graph class desig Bjare Stroustrup Abstract We have discussed classes i previous lectures Here, we discuss desig of classes Library desig cosideratios Class hierarchies (object-orieted programmig)

More information

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS) Mathematics Revisio Guides More Trigoometric ad Log Itegrals Page of 7 MK HOME TUITION Mathematics Revisio Guides Level: AS / A Level AQA : C Edexcel: C OCR: C OCR MEI: C FURTHER INTEGRATION TECHNIQUES

More information

Mobile terminal 3D image reconstruction program development based on Android Lin Qinhua

Mobile terminal 3D image reconstruction program development based on Android Lin Qinhua Iteratioal Coferece o Automatio, Mechaical Cotrol ad Computatioal Egieerig (AMCCE 05) Mobile termial 3D image recostructio program developmet based o Adroid Li Qihua Sichua Iformatio Techology College

More information

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction.

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction. Texture Mappig Jia Huag This set of slides refereces the oes used at Ohio State for istructio. Ca you do this What Dreams May Come Texture Mappig Of course, oe ca model the exact micro-geometry + material

More information

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite

More information

Cubic Polynomial Curves with a Shape Parameter

Cubic Polynomial Curves with a Shape Parameter roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad

More information

A Polynomial Interval Shortest-Route Algorithm for Acyclic Network

A Polynomial Interval Shortest-Route Algorithm for Acyclic Network A Polyomial Iterval Shortest-Route Algorithm for Acyclic Network Hossai M Akter Key words: Iterval; iterval shortest-route problem; iterval algorithm; ucertaity Abstract A method ad algorithm is preseted

More information

The golden search method: Question 1

The golden search method: Question 1 1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())

More information

Integration: Reduction Formulas Any positive integer power of sin x can be integrated by using a reduction formula.

Integration: Reduction Formulas Any positive integer power of sin x can be integrated by using a reduction formula. Itegratio: Reductio Formulas Ay positive iteger power of si x ca be itegrated by usig a reductio formula. Prove that for ay iteger 2, si xdx = 1 si 1 x cos x + 1 si Solutio. Weuseitegratiobyparts. Let

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Improved Random Graph Isomorphism

Improved Random Graph Isomorphism Improved Radom Graph Isomorphism Tomek Czajka Gopal Paduraga Abstract Caoical labelig of a graph cosists of assigig a uique label to each vertex such that the labels are ivariat uder isomorphism. Such

More information

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters. SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that

More information