Communication Theory II

Transcription

1 Communication Theory II Lecture 4: Review on Fourier analysis and probabilty theory Ahmed Elnakib, PhD Assistant Professor, Mansoura University, Egypt Febraury 19 th,

2 Course Website o o The site contains the lectures, quizzes, homework, and open forums for feedback and questions o Log in using your name and password o Password for quizzes: third o One page quiz: for less download time 2

3 Lecture Outlines o o Review on Fourier analysis of signals and systems The Dirac delta function Fourier transform of periodic signals Transmission of signals through LTI systems Hilbert transform Review on probability theory Deterministic vs. probabilistic mathematical models Probability theory, random variables, and the distribution functions The concept of expectation and second order statistics Characteristic function, the center limit theory and the Bayesian interface 3

4 The Dirac Delta Function (Unit Impulse) δ(t) 0 t o An even function of time t, centered at the origin t = 0 o Sifting property: sifts out the value g(t0) of the function g(t) at time t = t0, where o Replication property: convolution of any function with the delta function leaves that function unchanged 4

5 Amplitude Amplitude Amplitude The Dirac Delta Function (cont d) W=1 W=2 W=5 f(t)=δ(t) 1 F(f)=1 t 0 0 f 5

6 The Dirac Delta Function (cont d) The delta function may be viewed as the limiting form of a pulse of unit area (symmetric with respect to the origin) as the duration of the pulse approaches zero T 0 Rectangular impulse Sinc impulse T=1/2W 6

7 Existence of Fourier Transform o Physical realizability is a sufficient condition for the existence of a Fourier transform (e.g., all energy signals are Fourier transformable ). o Condition for energy signal: What about power and periodic signals? Do they have a Fourier transform? 7

8 Fourier Transform of Periodic Signals o Can Fourier transform works for periodic or power signals? Periodic signal (power).. 1 g T0 (t )? - T 0 /2 T 0 /2.. t 3T 0 /2 Aperiodic signal with computable G(f) g(t)=δ(t/ τ ) G(f) 1 t - T 0 /2 T 0 /2 Time shift property 8

9 Fourier Transform for Analyzing Signals and Systems o Provides the mathematical link between the time domain of a signal (waveform) and its frequency domain (spectrum) o Time and frequency response of a linear time-invariant (LTI) system defined in terms of its impulse response and frequency response, respectively x(t) y(t) = h(t)*x(t) F F F -1 X(f) H(f) Y(f) = H(f).G(f) 9

10 LTI system If and x 1 (n) h(n) y 1 (n) x 2 (n) h(n) y 2 (n) If x 1 (n) h(n) y 1 (n) Then x 1 (n-k) h(n) y 1 (n-k) Then a 1 y 1 (n)+a 2 y 2 (n)=h{a 1 x 1 (n)+a 2 x 2 (n)} Where a1, a2 are scalars (a) (b) A (a) linear and (b) a time invariant system 10

11 Hilbert Transform o A quadrature filter: phase angles of all components of a given signal are shifted by ±90 Hilbert transform pair o Example: g(t)= δ(t) δ = Using replication property of the delta function 11

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13 Hilbert transform (cont d) othe function may be interpreted as g(t)* G(f). g(t) = h(t)*g(t) F F F -1 G(f) H(f) = H(f).G(f) 13

14 Hilbert Transform properties A Hilbert pair is orthogonal over the entire time interval 14

15 Lecture Outlines o o Review on Fourier analysis of signals and systems The Dirac delta function Fourier transform of periodic signals Transmission of signals through LTI systems Hilbert transform Review on probability theory* Deterministic vs. probabilistic mathematical models Probability theory, random variables, and the distribution functions The concept of expectation and second order statistics Characteristic function, the center limit theory and the Bayesian interface *Note that soft materials are from COMPE Pattern Recognition, 15

16 Deterministic vs. Probabilistic Models o Deterministic mathematical model: if there is no uncertainty about its time-dependent behavior at any instant of time o Example: linear time-invariant systems o Problem: underlying physical phenomenon involves too many unknown factors (e.g., noise, fading, interference) o Probabilistic model accounts uncertainty in mathematical terms o Probabilistic models are intended to assign probabilities to the collections (sets) of possible outcomes of random experiments 16

17 Probability Theory: Sets o Probability makes extensive use of set operations o A set is a collection of objects, which are the elements of the set o If S is a set and x is an element of S, we write x S, otherwise x S o A set can have no elements, in which case it is called the empty set, denoted by Ø o The set of all possible outcomes of an experiment is the sample space or universe, denoted Ω o An event A is a (set of) possible outcomes of the experiment, and corresponds to a subset of Ω 17

18 Probability Theory: Sets (cont d) o Sets can be specified as o For example, The set of possible outcomes of a die roll is {1, 2, 3, 4, 5, 6}, The set of possible outcomes of a coin toss is {H, T}, where H stands for heads and T stands for tails 18

19 Probability Theory: Sets (cont d) o Alternatively, we can consider the set of all x that have a certain property P, and denote it by (The symbol is to be read as such that. ) o For example, the set of even integers can be written as {k k/2 is integer}. 19

20 Probability Theory: Sets Operations o Complement: The complement of a set S, with respect to the universe Ω, is the set {x Ω x S} of all elements of Ω that do not belong to S, and is denoted by S c. o Union: o Intersection: T is a subset of S T S 20

21 Probability Theory: Sets Operations odisjoint sets: two sets are said to be disjoint if their intersection is empty opartition of a set A: A collection of disjoint subsets of A, where their union is A Partition of Ω 21

22 Probability measure o A probability law / measure is a function p(a) that assigns a nonnegative value to event A based on the expected proportion of number of times that event A is actually likely to happen: encodes our belief in the likelihood event A occurring when the experiment is conducted 22

23 experiment 1 experiment experiment 0 23

24 Probability Theory: Axioms of Probability 24

25 othe probability function P(A) must satisfy the following: Probability Theory: Axioms of Probability (cont d) ), ( ) ( ) ( ) ( ), ( ) ( ) ( ) ( ) ( 1, ) ( 0 j i j i j i j i j i j i j i i i A A P A P A P A A P A A A P A P A A P A A A P P A P A i A j 25

26 Example 1 26

27 Example 1 (cont d) 27

28 Example 2 28

29 Example 2 (cont d) 29

30 Random Variables o Real experiments involve using one or more real valued quantities called random variables o The outcomes are associated with some numerical values of interest (random variable). o Example: if the experiment is the selection of students from a given population, we may wish to consider their grade point average. experiment experiment experiment The random variable is a function whose domain is a sample space and whose range is some set of real numbers 30

31 Discrete and Continuous Random Variables Random variables can discrete, e.g., the number of heads in three consecutive coin tosses, or continuous, the weight of a class member. 31

32 Questions 32