On a Generalization of the GCD for Intervals in R + Stan BaggenJune 4, 2014 or how can a camera see at least 1 tone for unkown T exp.

Slides:



Advertisements
Similar presentations
Discrete Mathematics Lecture 3
Advertisements

Section 11 Direct Products and Finitely Generated Abelian Groups One purpose of this section is to show a way to use known groups as building blocks to.
Study Guides Quantitative - Arithmetic - Numbers, Divisibility Test, HCF and LCM Mycatstudy.com.
Chapter Primes and Greatest Common Divisors ‒Primes ‒Greatest common divisors and least common multiples 1.
February 19, 2015Applied Discrete Mathematics Week 4: Number Theory 1 The Growth of Functions Question: If f(x) is O(x 2 ), is it also O(x 3 )? Yes. x.
Quotient-Remainder Theory, Div and Mod
Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects by Paul A. Viola Presented By: Emrah Ceyhan Divin Proothi Sherwin Shaidee.
Thinking Mathematically
CPSC 411, Fall 2008: Set 12 1 CPSC 411 Design and Analysis of Algorithms Set 12: Undecidability Prof. Jennifer Welch Fall 2008.
1 Lecture 8: Genetic Algorithms Contents : Miming nature The steps of the algorithm –Coosing parents –Reproduction –Mutation Deeper in GA –Stochastic Universal.
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
Chapter 4 Probability.
Image Analysis Preprocessing Image Quantization Binary Image Analysis
Copyright © Cengage Learning. All rights reserved.
Mathematics of Cryptography Part I: Modular Arithmetic, Congruence,
Digital Communication Symbol Modulated Carrier RX Symbol Decision Binary Bytes D/A Recovered Analog Binary Bytes Symbol State Modulation A/D Analog Source.
Chapter 12 Fast Fourier Transform. 1.Metropolis algorithm for Monte Carlo 2.Simplex method for linear programming 3.Krylov subspace iteration (CG) 4.Decomposition.
Fall 2002CMSC Discrete Structures1 Let us get into… Number Theory.
BY MISS FARAH ADIBAH ADNAN IMK
The Integers and Division
Mathematics of Cryptography Part I: Modular Arithmetic, Congruence,
The Fundamentals: Algorithms, Integers, and Matrices CSC-2259 Discrete Structures Konstantin Busch - LSU1.
© by Kenneth H. Rosen, Discrete Mathematics & its Applications, Sixth Edition, Mc Graw-Hill, 2007 Chapter 3 (Part 2): The Fundamentals: Algorithms, the.
Chapter 2 The Fundamentals: Algorithms, the Integers, and Matrices
9/2/2015Discrete Structures1 Let us get into… Number Theory.
CSE 504 Discrete Mathematics & Foundations of Computer Science
(CSC 102) Discrete Structures Lecture 10.
Lecture 1 Signals in the Time and Frequency Domains
Data Communications & Computer Networks, Second Edition1 Chapter 2 Fundamentals of Data and Signals.
MATH 224 – Discrete Mathematics
Random Sampling, Point Estimation and Maximum Likelihood.
Discrete Mathematics Math Review. Math Review: Exponents, logarithms, polynomials, limits, floors and ceilings* * This background review is useful for.
Basic Concepts in Number Theory Background for Random Number Generation 1.For any pair of integers n and m, m  0, there exists a unique pair of integers.
Unsolvability and Infeasibility. Computability (Solvable) A problem is computable if it is possible to write a computer program to solve it. Can all problems.
11 -1 Chapter 11 Randomized Algorithms Randomized Algorithms In a randomized algorithm (probabilistic algorithm), we make some random choices.
The Integers. The Division Algorithms A high-school question: Compute 58/17. We can write 58 as 58 = 3 (17) + 7 This forms illustrates the answer: “3.
 2004 SDU Lecture 7- Minimum Spanning Tree-- Extension 1.Properties of Minimum Spanning Tree 2.Secondary Minimum Spanning Tree 3.Bottleneck.
Point Operations – Chapter 5. Definition Some of the things we do to an image involve performing the same operation on each and every pixel (point) –We.
CS 103 Discrete Structures Lecture 10 Basic Structures: Sets (1)
Copyright © Cengage Learning. All rights reserved. CHAPTER 7 FUNCTIONS.
Chapter 4 Probability ©. Sample Space sample space.S The possible outcomes of a random experiment are called the basic outcomes, and the set of all basic.
Copyright © 2009 Pearson Education, Inc. Chapter 5 Section 1 - Slide 1 Chapter 1 Number Theory and the Real Number System.
1 COMS 161 Introduction to Computing Title: The Digital Domain Date: September 6, 2004 Lecture Number: 6.
Chapter 2 (Part 1): The Fundamentals: Algorithms, the Integers & Matrices The Integers and Division (Section 2.4)
Algorithms 1.Notion of an algorithm 2.Properties of an algorithm 3.The GCD algorithm 4.Correctness of the GCD algorithm 5.Termination of the GCD algorithm.
Basic Principles (continuation) 1. A Quantitative Measure of Information As we already have realized, when a statistical experiment has n eqiuprobable.
Elements of Coding and Encryption 1. Encryption In the modern word, it is crucial that the information is transmitted safely. For example, Internet purchases,
The Fundamentals. Algorithms What is an algorithm? An algorithm is “a finite set of precise instructions for performing a computation or for solving.
Slide Copyright © 2009 Pearson Education, Inc. Unit 1 Number Theory MM-150 SURVEY OF MATHEMATICS – Jody Harris.
Application: Algorithms Lecture 20 Section 3.8 Wed, Feb 21, 2007.
Greatest Common Divisors & Least Common Multiples  Definition 4 Let a and b be integers, not both zero. The largest integer d such that d|a and d|b is.
Clock Driven Scheduling
Section 2.5. Cardinality Definition: A set that is either finite or has the same cardinality as the set of positive integers (Z + ) is called countable.
The Fundamentals: Algorithms, Integers, and Matrices CSC-2259 Discrete Structures Konstantin Busch - LSU1.
Chapter 4 With Question/Answer Animations 1. Chapter Summary Divisibility and Modular Arithmetic - Sec 4.1 – Lecture 16 Integer Representations and Algorithms.
Ch04-Number Theory and Cryptography 1. Introduction to Number Theory Number theory is about integers and their properties. We will start with the basic.
Number Theory Lecture 1 Text book: Discrete Mathematics and its Applications, 7 th Edition.
Discrete Mathematics Chapter 2 The Fundamentals : Algorithms, the Integers, and Matrices. 大葉大學 資訊工程系 黃鈴玲.
Chapter 3 The Fundamentals: Algorithms, the integers, and matrices Section 3.4: The integers and division Number theory: the part of mathematics involving.
Agenda Review:  Relation Properties Lecture Content:  Divisor and Prime Number  Binary, Octal, Hexadecimal Review & Exercise.
1-1 Copyright © 2013, 2005, 2001 Pearson Education, Inc. Section 2.4, Slide 1 Chapter 2 Sets and Functions.
Number Theory. Introduction to Number Theory Number theory is about integers and their properties. We will start with the basic principles of divisibility,
CS 210 Discrete Mathematics The Integers and Division (Section 3.4)
Chapter 6: Discrete Probability
CMSC Discrete Structures
Number Theory (Chapter 7)
Discrete Math for CS CMPSC 360 LECTURE 12 Last time: Stable matching
CSC 381/481 Quarter: Fall 03/04 Daniela Stan Raicu
Copyright © 2013, 2010, and 2007, Pearson Education, Inc.
Fourier Transform of Boundaries
Presentation transcript:

On a Generalization of the GCD for Intervals in R + Stan BaggenJune 4, 2014 or how can a camera see at least 1 tone for unkown T exp

Philips Research Stan Baggen Contents Introduction Cameras, exposure times and problem definition Introduction to Solution using GCD for Integer Frequencies Extension of GCD to intervals over R + Application to the Original Problem Discussion Yet another generalization 2

Philips Research Stan Baggen Introduction Transmit digital information from a luminaire to a smartphone or tablet using Visible Light Communication (VLC) –Bits are encoded in small intensity variations of the emitted light –Detect bits using the camera of a smartphone We consider an FSK-based system –Symbols correspond to frequencies (tones) –Emitted light variations are sinusoidal Problem: camera may be “blind” for certain frequencies 3

Philips Research Stan Baggen Camera Image divided into lines and pixels 4 lines covering source lines per frame hidden lines active lines Each line consists of a row of pixels

Philips Research Stan Baggen A camera can set its exposure time T exp typically, T exp ranges from 1/30 to 1/2500 [s] Each pixel “sees” the average light during T exp seconds before read-out –smearing of intensity variations of received light If an integer number of periods of a sinusoid fit into T exp, the camera cannot detect such a sinusoid Exposure Time time T exp ISI filter (moving average) f1f1 f2f2

Philips Research Stan Baggen Due to the exposure time T exp of a camera, certain frequencies cannot be detected by it (multiples of f exp = 1/T exp ) Can we have sets of 2 frequencies each, such that not both can be blocked for any f exp ≥ 30 Hz Each set then forms an f exp -independent detection set for a light source that emits both frequencies Exposure Time 6 f1f1 f2f2 time T exp ISI filter

Philips Research Stan Baggen Discrete Solution If the involved frequencies can only take on integer values, we can find solutions using the GCD (Greatest Common Divisor) from number theory We would like to have 2 frequencies f 1 and f 2, such that not both can be integer multiples of any f exp ≥ 30 Suppose that both f 1 and f 2 are integer multiples of f exp If GCD(f 1,f 2 ) < 30  no solution possible for f exp ≥ 30  pair (f 1,f 2 ) is a good choice 7

Philips Research Stan Baggen Discrete Solution: Example f 1 = 290; f 2 = 319 Largest integer that divides both f 1 and f 2 equals GCD(f 1,f 2 ) = 29 No integer f exp ≥ 30 exists for which multiples are simultaneously equal to f 1 and f 2 8

Philips Research Stan Baggen Problem with Discrete Solution GCD(300,301) = 1; GCD(300,300) = 300 Physically: due to the nature of the T exp -filter and detection algorithms, if a pair of frequencies (f 1,f 2 ) is bad for detection, then a real interval (f 1 ±ε,f 2 ±ε) is also bad We need a method that allows us to eliminate bad intervals over R + 9 f1f1 f2f2

Philips Research Stan Baggen GCD for intervals in R + Consider 2 half-open intervals I 1 and I 2 in R + Definition: Note that the concept I 1,I 2 : GCD(I 1,I 2 ) < 30 solves our original problem: There can be no real f exp ≥30 such that integer multiples are simultaneously close to F 1 and F ( ] I1I1 I2I2 0 30

Philips Research Stan Baggen GCD for intervals in R + How to find GCD(I 1,I 2 )? Define divisor sets D 1,D 2 in R: Theorem 1: Proof: □ 11 0 ( ] I1I1 I2I2

Philips Research Stan Baggen Example 12

Philips Research Stan Baggen 13 Enlargement of Example

Philips Research Stan Baggen Overlap of Intervals in Divisor Sets Consider divisor set Let where Theorem 2: for w>0, D consists of a finite number n 0 of disjunct intervals, where Proof: overlap of consecutive intervals happens if Corollary: 14 0 ( ] I

Philips Research Stan Baggen Another Theorem Suppose that we have 2 intervals I 1 =(f 1 -w 1,f 1 ] and I 2 =(f 2 -w 2,f 2 ] Theorem 3: For w 1,w 2 > 0, GCD(f 1,f 2 ; w 1,w 2 ) equals an integer sub-multiple of either f 1, f 2 or both Proof: equals a right limit point of for some i and j. Each is the intersection of 2 half-open intervals (...], where the right limit point of each half-open interval is an integer sub-multiple of either f 1 or f 2 or both. □ Note: f 1 and f 2 are real numbers 15

Philips Research Stan Baggen Some Interesting Examples Numbers in N + –For w sufficiently small, we find the classical solutions for f 1, f 2 in N + –GCD(15,21; w≤1) = 3 –GCD(15,21; w=1.1) = 7 w too large for finding the classical solution Numbers in Q + –GCD(0.9,1.2; w=0.1) = 0.3 Numbers in R + (computed with finite precision) –GCD(7π,8π; w=0.1) = –GCD(6π,8π; w=0.1) =

Philips Research Stan Baggen Application to the Original Problem Suppose that we find that for a certain (f 1, f 2 ; w 1,w 2 ) : GCD (f 1, f 2 ; w 1,w 2 ) < 30 Then there exists no real number f exp ≥30 such that integer multiples of f exp fall simultaneously in (f 1 -w 1, f 1 ] and (f 2 -w 2, f 2 ] By picking F 1 = f 1 -w 1 /2 and F 2 = f 2 -w 2 /2, we can insure that if one multiple of f exp ≥30 falls within a range of w i /2 of F i for some i, then the other interval is free from any multiple of f exp 17 0 ( ] 30 f

Philips Research Stan Baggen 18 Numerical Examples (1) acceptable_frequencies_2012_10_20_1

Philips Research Stan Baggen 19 acceptable_frequencies_2012_10_20_1 typical solutions: (f 1,f 2 ) = (f 1, f 1 +15) Numerical Examples (1) detail

Philips Research Stan Baggen 20 Numerical Examples (2) acceptable_frequencies_2012_10_18_2

Philips Research Stan Baggen 21 Numerical Examples (2) detail acceptable_frequencies_2012_10_18_2

Philips Research Stan Baggen 22 Numerical Examples (3) acceptable_frequencies_2012_10_18_3

Philips Research Stan Baggen 23 Numerical Examples (4) acceptable_frequencies_2012_10_18_4

Philips Research Stan Baggen 24 Numerical Examples (4) detail acceptable_frequencies_2012_10_18_4 typical solutions: (f 1,f 2 ) = (f 1, f 1 +15), (f 1, 2f 1 -20), ), (f 1, 2f 1 +15)

Philips Research Stan Baggen Discussion (1) It is convenient to use half open intervals (…] and have the right limit point as a characterizing number, since then –We can reproduce the familiar results from number theory –The maximum in the definition of GCD exists –We do not obtain subsets in having measure 0 The concept of GCD can be generalized to an arbitrary number of K intervals over R + Theorem 2 shows that the complexity of the computation of a GCD is reasonable Can we have an efficient algorithm like Euclid’s algorithm for computing the GCD of real intervals? 25

Philips Research Stan Baggen Discussion (2) It can be shown that GCD(f 1, f 2 ;w) is non-decreasing as w increases For rational numbers a/b and p/q, where a,b,p,q are in N +, we find for sufficiently small w: where LCM(.) is the Least Common Multiple. How small must w be as a function of a,b,p and q to find this solution? Conjecture: for incommensurable numbers a and b Effects of finite precision computations 26

Philips Research Stan Baggen Yet Another Generalization GCD(f 1,f 2 ;w) on intervals still makes hard decisions on frequencies being in or out of intervals Can we make some sensible reasoning that leads to “smooth” decisions concerning acceptable frequency pairs We have to use a more friendly measure on the intervals We start by re-phrasing the previous approach in a different manner 27

Philips Research Stan Baggen GCD(f 1,f 2 ;w) on intervals as discussed previously, effectively uses indicator functions as a measure of membership: Divisor Measure DM 1,DM 2 in R ( ] I1I1 I2I2 1 f

Philips Research Stan Baggen 29 Example f 1 = 9; f 2 = 12 w = 0.5 GCD(f 1,f 1 ;w) =

Philips Research Stan Baggen Using a Different Measure Suppose that we change the definition of the measure of membership for the fundamental interval Divisor Measure: Common Divisor Measure: 30 example

Philips Research Stan Baggen 31 Example Multiples of frequencies in the neighborhood of 3 (and 3/n) also end up both near 9 and 12 For frequencies f>3.2, no multiples end up both near 9 and 12 according to the measure Multiples of 1.1, 1.3 and 1.7 come somewhat close to both 9 and 12 (c.f. other measure)

Philips Research Stan Baggen 32 Example If we increase σ, it becomes more difficult to “avoid” the intervals around 9 and 12 for integer multiples of f For σ=0.5, some multiples of 4.16 also come close to both 9 and 12 according to the measure

Philips Research Stan Baggen 33 f 1 = 9; f 2 = 12 σ = 0.5 f exp = 4.16 Example samples taken at integer multiples of 4.16 CDM(4.16;.) equals product of largest “red” sample (n=3) and largest “blue” sample (n=2)

Philips Research Stan Baggen 34