Name of the Student: Problems on Discrete & Continuous R.Vs

Transcription

1 Engineering Mathematics 03 SUBJECT NAME : Probability & Random Process SUBJECT CODE : MA 6 MATERIAL NAME : Problem Material MATERIAL CODE : JM08AM008 (Scan the above QR code for the direct download of this material) Name of the Student: Branch: Unit I (Random Variables) Problems on Discrete & Continuous RVs ) A random variable X has the following probability function: 7 X K + K P(X) 0 K K K 3K K K a) Find K b) Evaluate P X 6, P X 6 c) Find P X, P X 3, P X 5 d) If P X C, find the minimum value of C e) P 5 X 45 / X ) The probability function of an infinite discrete distribution is given by P X j, j,,3 Find the mean and variance of the distribution j Also find P X is even, P X 5 and X is divisible by 3 P 3) Suppose that X is a continuous random variable whose probability density function is C 4x x, 0 x given by f( x) 0, otherwise 4) A continuous random variable X has the density function (a) findc (b) find P X K f ( x), x Find the value of K,the distribution function and x P X 0 Prepared by CGanesan, MSc, MPhil, (Ph: ) Page

2 Engineering Mathematics 03 5) A random variable X has the pdf x, 0 x f( x) Find (i) 0, otherwise 3 P X 4 (iii) 3 P X / X 4 (iv) 3 P X / X 4 6) If a random variable X has the pdf (b) P X (c) PX 3 5, x f( x) 4 0, otherwise P X (ii) Find (a) P X 7) The amount of time, in hours that a computer functions before breaking down is a continuous random variable with probability density function given by x 00 f( x) e, x 0 What is the probability that (a) a computer will function 0, x 0 between 50 and 50 hrs before breaking down (b) it will function less than 500 hrs 8) A random variable X has the probability density function f( x) xe x, x 0 Find, c d f, P X 5, P X 7 0, otherwise 9) If the random variable X takes the values,,3 and 4 such that P X 3P X P X 3 5P X 4 Find the probability distribution 0) The distribution function of a random variable X is given by x F( x) x e ; x 0 Find the density function, mean and variance of X ) A continuous random variable X has the distribution function 0, x F x k x x 0, x 30 4 ( ) ( ), 3 Find k, probability density function ( ) f x, P X ) A test engineer discovered that the cumulative distribution function of the lifetime x 5 of an equipment in years is given by F( x) e, x 0 0, x 0 i) What is the expected life time of the equipment? ii) What is the variance of the life time of the equipment? Moments and Moment Generating Function Prepared by CGanesan, MSc, MPhil, (Ph: ) Page

3 Engineering Mathematics 03 ) Find the moment generating function of RV X whose probability function P( X x), x,, Hence find its mean and variance x ) The density function of random variable X is given by f ( x) Kx( x), 0 x Find K, mean, variance and rth moment x 3 e, x 0 3) Let X be a RV with pdf f( x) 3 Find the following 0, Otherwise a) P(X > 3) b) Moment generating function of X c) E(X) and Var(X) 4) Find the MGF of a RV X having the density function x, 0 x f( x) Using 0, otherwise the generating function find the first four moments about the origin 5) Define Binomial distribution and find the MGF, Mean and Variance of the Binomial distribution 6) Define Poisson distribution and find the MGF, Mean and Variance of the Poisson distribution 7) Define Geometric distribution and find the MGF, Mean and Variance of the Geometric distribution 8) Write the pdf of Uniform distribution and find the MGF, Mean and Variance 9) Define Exponential distribution and find the MGF, Mean and Variance of the Exponential distribution 0) Define Gamma distribution and find the MGF, Mean and Variance of the Gamma distribution ) Define Normal distribution and find the MGF, Mean and Variance of the Normal distribution Problems on distributions ) The mean of a Binomial distribution is 0 and standard deviation is 4 Determine the parameters of the distribution ) If 0% of the screws produced by an automatic machine are defective, find the probability that of 0 screws selected at random, there are (i) exactly two defectives (ii) atmost three defectives (iii) atleast two defectives and (iv) between one and three defectives (inclusive) 3) In a certain factory furning razar blades there is a small chance of /500 for any blade to be defective The blades are in packets of 0 Use Poisson distribution to Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 3

4 Engineering Mathematics 03 calculate the approximate number of packets containing (i) no defective (ii) one defective (iii) two defective blades respectively in a consignment of 0,000 packets 4) The number of monthly breakdown of a computer is a random variable having a Poisson distribution with mean equally to 8 Find the probability that this computer will function for a month a) Without a breakdown b) With only one breakdown and c) With atleast one breakdown 5) Prove that the Poisson distribution is a limiting case of binomial distribution t 8 6) If the mgf of a random variable X is of the form (04e 06), what is the mgf of 3 E X X Evaluate 3 t 7) A discrete RV X has moment generating function M X () t e 4 4 Find E X, Var X and P X 8) If X is a binomially distributed RV with E( X) and 4 Var( X ), find P X 5 3 9) If X is a Poisson variate such that P X 9P X 4 90P X 6, find the mean and variance 0) The number of personal computer (PC) sold daily at a CompuWorld is uniformly distributed with a minimum of 000 PC and a maximum of 5000 PC Find the following (i) The probability that daily sales will fall between,500 PC and 3,000 PC (ii) What is the probability that the CompuWorld will sell at least 4,000 PC s? (iii) What is the probability that the CompuWorld will exactly sell,500 PC s? ) Suppose that a trainee soldier shoots a target in an independent fashion If the probability that the target is shot on any one shot is 08 (i) What is the probability that the target would be hit on 6 th attempt? (ii) What is the probability that it takes him less than 5 shots? (iii) What is the probability that it takes him an even number of shots? ) A die is cast until 6 appears What is the probability that it must be cast more than 5 times? 3) The length of time (in minutes) that a certain lady speaks on the telephone is found to be random phenomenon, with a probability function specified by the function 5 Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 4

5 Engineering Mathematics 03 x 5 f( x) Ae, x 0 (i) Find the value of A that makes f(x) a probability 0, otherwise density function (ii) What is the probability that the number of minutes that she will talk over the phone is (a) more than 0 minutes (b) less than 5 minutes and (c) between 5 and 0 minutes 4) If the number of kilometers that a car can run before its battery wears out is exponentially distributed with an average value of 0,000 km and if the owner desires to take a 5000 km trip, what is the probability that he will be able to complete his trip without having to replace the car battery? Assume that the car has been used for same time 5) The mileage which car owners get with a certain kind of radial tyre is a random variable having an exponential distribution with mean 40,000 km Find the probabilities that one of these tyres will last (i) atleast 0,000 km and (ii) atmost 30,000 km 6) If a continuous random variable X follows uniform distribution in the interval 0, and a continuous random variable Y follows exponential distribution with parameter, find such that P X P Y 7) If X is exponantially distributed with parameter, find the value of K there exists P X k P X k a 8) State and prove memoryless property of Geometric distribution 9) State and prove memoryless property of Exponential distribution 0) The time required to repair a machine is exponentially distributed with parameter ½ What is the probability that the repair times exceeds hours and also find what is the conditional probability that a repair takes at least 0 hours given that its duration exceeds 9 hours? ) The weekly wages of 000 workmen are normall distributed around a mean of Rs70 with a SD of Rs5 Estimate the number of workers whose weekly wages will be (i) between Rs 69 and Rs 7, (ii) less than Rs 69 and (iii) more than Rs 7 ) In a test on 000 electric bulbs, it was found that the life of a particular make, was normally distributed with an average life of 040 hours and SD of 60 hours Estimate the number of bulbs lilkely to burn for (i) more than 50 hours, (ii) less than 950 hours and (iii) more than 90 hours but less than 60 hours Function of random variable Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 5

6 Engineering Mathematics 03 x, x 5 ) Let X be a continuous random variable with pdf f( x), find the 0, otherwise probability density function of X 3 ) If X is a uniformly distributed RV in,, find the pdf of tan Y X 3) If X has an exponential distribution with parameter, find the pdf of Y X 4) If X is uniformly distributed in, X, find the pdf of Y sin x 5) If the pdf of X is f ( x) e, x 0, find the pdf of Y X 6) If X is uniformly distributed in 0, find the pdf of Y X Unit II (Two Dimensional Random Variables) Joint distributions Marginal & Conditional ) The two dimensional random variable (X,Y) has the joint density function x y f ( x, y), x 0,,; y 0,, Find the marginal distribution of X and Y 7 and the conditional distribution of Y given X = x Also find the conditional distribution of X given Y = ) The joint probability mass function of (X,Y) is given by P( x, y) K x 3 y, x 0,, ; y,, 3 Find all the marginal and conditional probability distributions Also find the probability distribution of X P X Y 3 3) If the joint pdf of a two dimensional random variable (X,Y) is given by K(6 x y),0 x, y 4 f ( x, y) Find the following (i) the value of K; 0,otherwise (ii) P x, y 3 ; (iii) P x y 3 ; (iv) P x / y 3 4) If the joint pdf of a two dimensional random variable (X,Y) is given by xy x,0 x, 0 y f ( x, y) 3 Find (i) 0,otherwise P X Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 6 Y and ; (ii) P Y X P Y / X Check whether the conditional density functions are valid ; (iii)

7 Engineering Mathematics 03 5) The joint pdf of the random variable (X,Y) is given by x y f ( x, y) Kxye, 0 x, y Find the value of K and Prove that X and Y are independent 6) If the joint distribution function of X and Y is given by x y F( x, y) e e, x 0, y 0 and "0" otherwise (i) Are X and Y independent? (ii) Find P X 3, Y Covariance, Correlation and Regression ) Define correlation and explain varies type with example ) Find the coefficient of correlation between industrial production and export using the following data: Production (X) Export (Y) ) Let X and Y be discrete random variables with probability function x y f ( x, y), x,,3; y, Find (i) Cov X, Y (ii) Correlation co efficient 4) Two random variables X and Y have the following joint probability density function f ( x, y) x y, 0 x, 0 y Find Var X, 0, otherwise Var Y and the covariance between X and Y Also find Correlation between X and Y ( ( XY, ) ) 5) Let X and Y be random variables having joint density function 3 x y, 0 x, y f ( x, y) Find the correlation coefficient ( XY, ) 0, otherwise 6) The independent variables X and Y have the probability density functions given by f X 4 ax, 0 x 4 by, 0 y ( x) fy ( y) Find the correlation 0, otherwise 0, otherwise coefficient between X and Y (or) Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 7

8 Engineering Mathematics 03 The independent variables X and Y have the probability density functions given by 4 ax, 0 x 4 by, 0 y fx ( x) fy ( y) Find the correlation 0, otherwise 0, otherwise coefficient between X Y and X Y 7) Let X,Y and Z be uncorrelated random variables with zero means and standard deviations 5, and 9 respectively If U X Y and V Y Z, find the correlation coefficient between U and V 8) If the independent random variables X and Y have the variances 36 and 6 respectively, find the correlation coefficient between X Y and X Y 9) From the data, find (i) The two regression equations (ii) The coefficient of correlation between the marks in Economics and Statistics (iii) The most likely marks in statistics when a mark in Economics is 30 Marks in Economics Marks in Statistics ) The two lines of regression are 8x 0y + 66 = 0, 40x 8y 4 = 0 The variance of X is 9 Find (i) the mean values of X and Y (ii) correlation coefficient between X and Y (iii) Variance of Y ) The joint pdf of a two dimensional random variable is given by f ( x, y) ( x y); 0 x, 0 y Find the following 3 (i) The correlation co efficient (ii) The equation of the two lines of regression (iii) The two regression curves for mean Transformation of the random variables ) If X is a uniformly distributed RV in,, find the pdf of tan Y X ) Let (X,Y) be a two dimensional non negative continuous random variables having the joint probability density function density function of U X Y x y f ( x, y) 4 xye, x 0, y 0 Find the 0, elsewhere Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 8

9 Engineering Mathematics 03 3) X and Y be independent exponential RVs with parameter Find the jpdf of X U X Y and V X Y (Or) (The above problem may be ask as follows) The waiting times X and Y of two customers entering a bank at different times are assumed to be independent random variables with respective probability density x y e, x 0 e, y 0 functions f( x) and f( y) 0, otherwise 0, otherwise Find the joined pdf of the sum of their waiting times, U X Y and the fraction of X this time that the first customer spreads waiting, ie V Find the marginal X Y pdf s of U and V and show that they are independent (Or) x y If X and Y are independent random variable with pdf e, x 0and e, y 0, find the X density function of U and V X Y Are they independent? X Y 4) If X and Y are independent exponential random variables each with parameter, find the pdf of U = X Y 5) Let X and Y be independent random variables both uniformly distributed on (0,) Calculate the probability density of X + Y 6) Let X and Y are positive independent random variable with the identical probability x density function f ( x) e, x 0 Find the joint probability density function of X U X Y andv Are U and V independent? Y 7) If the joint probability density of Xand X is given by xx e x x, 0, 0 X f ( x, x), find the probability of Y 0, elsewhere X X x, 0 x 8) If X is any continuous RV having the pdf f( x) 0, otherwise the pdf of the RV Y 9) If the joint pdf of the RVs X and Y is given by X pdf of the RV U Y Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 9, andy e X, find, 0 x y f ( x, y) find the 0, otherwise

10 Engineering Mathematics 03 0) Let X be a continuous random variable with pdf probability density function of X 3 x, x 5 f( x), find the 0, otherwise Central Limit Theorem ) If X, X, X n are Poisson variables with parameter, use the Central Limit Theorem to estimate P(0 S n 60) where Sn X X X n and n 75 ) The resistors r, r, r 3 and r 4 are independent random variables and is uniform in the interval (450, 550) Using the central limit theorem, find P(900 r r r3 r4 00) 3) Let X, X, X 00 be independent identically distributed random variables with and Find P(9 X X X00 0) 4 4) Suppose that orders at a restaurant are iid random variables with mean Rs8 and standard deviation Rs Estimate (i) the probability that first 00 customers spend a total of more than Rs840 (ii) P(780 X X X00 80) 5) The life time of a certain brand of a Tube light may be considered as a random variable with mean 00 h and standard deviation 50 h Find the probability, using central limit theorem, that the average life time of 60 light exceeds 50 h 6) A random sample of size 00 is taken from a population whose mean is 60 and variance is 400 Using Central limit theorem, with what probability can we assert that the mean of the sample will not differ from 60 by more than 4 7) A distribution with unknown mean has variance equal to 5 Use central limit theorem to determine how large a sample should be taken from the distribution in order that the probability will be at least 095 that the sample mean will be within 05 of the population mean Unit III (Classification of Random Processes) Verification of SSS and WSS process Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 0

11 Engineering Mathematics 03 ) Define the following: a) Markov process b) Independent increment random process c) Strict sense stationary process d) Second order stationary process ) Classify the random process and give example to each 3) Let X Acos( n) Bsin( n) where A and B are uncorrelated random variables n with E A E B 0 andvar A Var B Show that X n is covariance stationary 4) A stochastic process is described by X ( t) Asin t B cos t where A and B are independent random variables with zero means and equal standard deviations show that the process is stationary of the second order 5) If X ( t) Y cost Z sint, where Y and Z are two independent random variables with E( Y ) E( Z) 0, E( Y ) E( Z ) and is a constants Prove that Xt () is a strict sense stationary process of order (WSS) 6) At the receiver of an AM radio, the received signal contains a cosine carrier signal at the carrier frequency 0 with a random phase that is uniformly distributed over 0, The received carrier signal is X( t) Acos t process is second order stationary 7) The process X( t) : t T is given by P X () t n Show that the 0 whose probability distribution, under certain conditions, n ( at), n, n at Show that it is not stationary at, n 0 at Ergodic Processes, Mean ergodic and Correlation ergodic ) Consider the process X ( t) Acost B sint where A and B are random variables with E( A) E( B) 0 and E( AB) 0 Prove that Xt () ) Prove that the random processes X( t) Acos t is mean ergodic where A and are constants and is uniformly distributed random variable in 0, is correlation ergodic Prepared by CGanesan, MSc, MPhil, (Ph: ) Page

12 Engineering Mathematics 03 3) Consider the random process () uniformly distributed random variable in Xt with X( t) Acos A t, where is a, Prove that Xt () ergodic Note: The same problem they may ask by putting A 0 is correlation 4) Let Xt () be a WSS process with zero mean and auto correlation function R ( ), wheret is a constant Find the mean and variance of the time T average of () 0,T Is Xt () mean ergodic? Xt over Note: The same problem they may ask by puttingt 5) Given that the autocorrelation function for a stationary ergodic process with no 4 periodic components is R ( ) 5 Find the mean and variance of the 6 process Xt () Problems on Markov Chain 6) Consider a Markov chain n ; X n with state space S, 09 0 transition probability matrix P 0 08 i) Is chain irreducible? ii) Find the mean recurrence time of states and iii) Find the invariant probabilities and one step 7) A raining process is considered as two state Markov chain If it rains, it is considered to be state 0 and if it does not rain, the chain is in state The transitions probability of the Markov chain is defined as P Find the probability that it will 0 08 rain for 3 days Assume the initial probabilities of state 0 and state as 04 and 06 respectively 8) A person owning a scooter has the option to switch over to scooter, bike or a car next time with the probability of (03, 05, 0) If the transition probability matrix is What are the probabilities vehicles related to his fourth purchase? Prepared by CGanesan, MSc, MPhil, (Ph: ) Page

13 Engineering Mathematics 03 9) Assume that a computer system is in any one of the three states: busy, idle and under repair respectively denoted by 0,, Observing its state at pm each day, we get the transition probability matrix as P Find out the 3 rd step transition probability matrix Determine the limiting probabilities 0) Two boys B and B and two girls G and G are throwing a ball from one to the other Each boys throws the ball to the other boy with probability / and to each girl with probability /4 On the other hand each girl throws the ball to each boy with probability / and never to the other girl In the long run, how often does each receive the ball? ) A housewife buys 3 kinds of cereals A, B, C She never buys the same cereal in successive weeks If she buys cereal A, the next week she buys cereal B However if she buys B or C the next week she is 3 times as likely to buy A as the other cereal How often she buys each of the 3 cereals? ) Three boys A, B, C are throwing a ball each other A always throws the ball to B and B always throws the ball to C, but C is just as likely to throw the ball to B as to A Find the transition matrix and classify the states 3) The transition probability matrix of a Markov chain n n,, and 3 is P P X 3 and P X3, X 3, X 3, X0 4) The tpm of a Markov chain with three states 0,, is X having 3 states, (0) and the initial distribution is 07, 0, 0 the initial state distribution of the chain is P X and (ii) P X, X, X, X Poisson process 3 0 P Find 3 / 4 / 4 0 P / 4 / / 4 and 0 3 / 4 / 4 P X0 i / 3, i 0,, Find (i) ) Define Poisson process and obtain its probability distribution ) Prove that the Poisson process is Covariance stationary 3) Show that the sum of two independent Poisson process is a Poisson process 4) Suppose that customers arrive at a bank according to a Poisson process with a mean rate of 3 per minute; find the probability that during a time interval of mins Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 3

14 Engineering Mathematics 03 (i) Exactly 4 customers arrive and (ii) More than 4 customers arrive 5) If customers arrive at a counter in accordance with a Poisson process with a mean rate of 3 per minute, find the probability that the interval between consecutive arrivals is (i) more than minute (ii) between minute and minutes (iii) 4 minutes or less 6) A radar emits particles at the rate of 5 per minute according to Poisson distribution Each particles emitted has probability 06 Find the probability that 0 particles are emitted in a 4 minutes period 7) Queries presented in a computer data base are following a Poisson process of rate 6 queries per minute An experiment consists of monitoring the data base for m minutes and recording N( m ) the number of queries presented i) What is the probability that no queries in a one minute interval? ii) What is the probability that exactly 6 queries arriving in one minute interval? iii) What is the probability of less than 3 queries arriving in a half minute interval? Normal (Gaussian) & Random telegraph Process ) Let Xt () is a Gaussian random process with Xt ( ) 0 and C ( t, t ) 6e t t Find the probability that (i) (0) 8 X (ii) X(0) X(6) 4 ) Prove that a random telegraph signal process Y ( t) X ( t) is a wide sense stationary process when is a random variable which is independent of X() t, ( t t) assume values and with equal probability and R ( t, t ) e Unit IV (Correlation and Spectral densities) Section I ) Determine the mean and variance of process given that the auto correlation 4 function R 5 6 ) A stationary random process has an auto correlation function and is given by R Find the mean and variance of the process Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 4

15 Engineering Mathematics 03 3) If Xt () and () Yt are two random processes then R ( ) R (0) R (0) XY YY where R ( ) and RYY ( ) are their respective auto correlation function are two random processes then RXY ( ) R (0) RYY (0) R and R ( ) are their respective auto correlation function 4) If Xt () and Yt () where ( ) Section II YY 5) State and Prove Wiener Khinchine theorem 6) The auto correlation of a stationary random process is given by b R ( ) ae, b 0 Find the spectral density function 7) The auto correlation of the random binary transmission is given by R, ( ) T 0, for T Find the power spectrum for T Note: By putting T =, the above problem can be ask R, for ( ) 0, for 8) Show that the power spectrum of the auto correlation function e is 3 4 9) Find the power spectral density of a WSS process with auto correlation function ( ) R, 0 e 0) Find the power spectral density of the random process, if its auto correlation function is given by R ( ) e cos ) Find the power spectral density function whose auto correlation function is given by A R ( ) cos( 0 ) Section III ) If the power spectral density of a WSS process is given by S b a ( ) a 0,, a, find the auto correlation function of the process a Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 5

16 Engineering Mathematics 03 3) The power spectral density of a zero mean WSS process Xt () is given by S, a ( ) Find R ( ) and show that X() t and Xt 0, elsewhere a are uncorrelated 4) Find the autocorrelation function of the process Xt (), for which the spectral, density is given by S( ) 0, 5) The cross power spectrum of real random processes Xt () and Yt () S XY a jb, ( ) Find the cross correlation function 0, elsewhere Section IV 6) If Y ( t) X ( t a) X ( t a),prove that R ( ) R ( ) R ( a) R ( t a) Hence prove that YY S a S YY ( ) 4sin ( ) ( ) Xt and Yt () 7) () is given by are zero mean and stochastically independent random process having autocorrelation function R ( ) e, R ( ) cos respectively Find (i) the auto correlation function of W ( t) X ( t) Y ( t) and Z( t) X ( t) Y ( t) (ii) The cross correlation function of W() t and Z() t 8) If Xt () and Yt () YY are independent with zero means Find the auto correlation function of Zt () where Z( t) a bx ( t) cy ( t) and Y ( t) cos t are two random processes where is a random variable uniformly distributed in 0, Prove that 9) If X( t) 3cos t 0 0 R R R YY XY 0) Two random process Xt () and Yt () are given by X( t) Acos t ; Y( t) Asint where A and are constants and " " is a uniform random variable over 0 to Find the cross correlation function ) If () Xt is a process with mean ( t) 3 and auto correlation R t, t 9 4e 0 Determine the mean, variance of the random variable Z X(5) and W X(8) Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 6

17 Engineering Mathematics 03 Unit V (Linear systems with Random inputs) ) Prove that if the input X() t is WSS then the output Y() t is also WSS ) If X() t is the input voltage to a circuit and Y() t is the output voltage, stationary random process with x 0 S ( ) YY, if the system function is given by 3) If () () Xt is a and ( ) R e Find y, S ( ) and H( ) i Xt is a band limited process such that S ( ) 0,, prove that R (0) R ( ) R (0) 4) Let Xt () be a random process which is given as input to a system with the system transfer function H( ), 0 0 If the autocorrelation function of the input process is 5) If N 0 ( ), find the auto correlation of the output process Y( t) Acos t N( t) where A is a constant, is a random variable with a uniform distribution in with a power spectral density 0, and Nt () N is a band limited Gaussian white noise 0 SNN ( ) for 0 B and S ( ) 0 NN,elsewhere Find the power spectral density of Y() t, assuming that () independent 6) Consider a white Gaussian noise of zero mean and power spectral density N t and are applied to a low pass RC filter whose transfer function is H( f) i frc Find the autocorrelation function of the output random process 7) A WSS random process X() t with auto correlation R ( ) Ae N 0 where A and are real positive constants, is applied to the input of an linear time invariant (LTI) bt system with impulse response h( t) e u( t) where b is a real positive constant Find the auto correlation of the output Y() t of the system t 8) An linear time invariant (LIT) system has an impulse response h( t) e u( t) Find the output auto correlation function RYY ( ) corresponding to an input X() t Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 7

18 Engineering Mathematics All the Best---- Prepared by CGanesan, MSc, MPhil, (Ph: ) Page 8