Consider the following discrete-time systems: T[x(n)] = 2x(n) T[x(n)] = 3x(n) + 4 T[x(n)] = x(n)...
Consider the following discrete-time systems:
- T[x(n)] = 2x(n)
- T[x(n)] = 3x(n) + 4
- T[x(n)] = x(n) +2x(n − 1) – x(n − 2)
- Use (2.12) to determine analytically to see whether these systems are time-invariant?
- Let x1(n) be a uniform distributed random sequence and x2(n) be a Gaussian distributed random sequence with mean 0 and variance 10 over 0 ≤ n ≤ 100. Test time-invariant of 3rd system only. Choose any values for a1 and a2.
Homework Answers
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Consider the following discrete-time systems:
T[x(n)] = 2x(n)
T[x(n)] = 3x(n) + 4
T[x(n)] = x(n)...
solve using MatLab please. HW - 1 Use a cover page Word file attached for the...
solve using MatLab please.
HW - 1 Use a cover page Word file attached for the homework solution. Solve all questions using Word and the Matlab output should be copied into the Word file. All questions and answers should be numbered HW1.1 Generate the following sequences using the basic MATLAB signal functions and the basic MATLAB signal operations discussed in this chapter. Plot signal samples using the stem function. 1- x(n) = n°[u(n+5] – u(n-6) + 105(n) + 20(0.5)* [u(n-4)...
1) A causal discrete-time system is described by the difference equation, y(n) = x(n)+3x(n-1)+ 2x(n-4) a)...
1) A causal discrete-time system is described by the difference equation, y(n) = x(n)+3x(n-1)+ 2x(n-4) a) What is the transfer function of the system? b) Sketch the impulse response of the system
13. Let X1, X2, ...,Xy be a sequence of independent and identically distributed discrete random variables,...
13. Let X1, X2, ...,Xy be a sequence of independent and identically distributed discrete random variables, each with probability mass function P(X = k)=,, for k = 0,1,2,3,.... emak (a) Find the expected value and the variance of the sample mean as = N&i=1X,. (b) Find the probability mass function of X. (c) Find an approximate pdf of X when N is very large (N −0).
5. A stationary random process X[n] is input to a discrete time LTI system with frequency...
5. A stationary random process X[n] is input to a discrete time LTI system with frequency response j“)-10 zero mean given as A(e nmay be expressed as where Wnlis a zero mea a-HS1 unit variancei.i.d. (independent identically distributed) Gaussian sequence and c, d are constants. Let Yl be the output random a)Determine the mean function for the output random sequence Yn in terms ofa, c and d b) Determine S7 (e), the power spectral density ofthe output random sequence Yn]...
12. Let X(e") be the DTFT of the discrete-time signal z[n] = (0.5)"u[n]. Let gin] be the length-5...
12. Let X(e") be the DTFT of the discrete-time signal z[n] = (0.5)"u[n]. Let gin] be the length-5 sequence whose 5-point DFT, Gk], is made from uniform samples from X(eu): g[n] CH 0 for n<0and n > 4 = x(e,2 ) for k = 0, 1, 2,3,4 = Find g(0] and gl1].
12. Let X(e") be the DTFT of the discrete-time signal z[n] = (0.5)"u[n]. Let gin] be the length-5 sequence whose 5-point DFT, Gk], is made from uniform samples...
How do you show this? 1.2.12. Accept the following definition. Discrete random variables X1, X2,.. ,...
How do you show this?
1.2.12. Accept the following definition. Discrete random variables X1, X2,.. , Xn, taking values in Ai, A2,..., An, are said to be independent if (1) P(Xi = ai , . . . ,x, = an) =11P(X, = a.) 仁1 for all ai E A1,., an E An. Then prove that random variables in any subsequence of a finite sequence of independent random variables are independent.
In x[n] = sin(w*T*n), 'w' and 'T' are both constant, is the discrete-time sequence x[n] =...
In x[n] = sin(w*T*n), 'w' and 'T' are both constant, is the discrete-time sequence x[n] = sin(w*T*n) peroid?
7. Consider the discrete time systems G : y[n] = x[n]-x[n-1] For each of the combined...
7. Consider the discrete time systems G : y[n] = x[n]-x[n-1] For each of the combined systems below, determine the output y[n]. Then determine which systems are equivalent, ie., produce the same output y[n] GHy FHy
Q8) Consider the following causal linear time-invariant (LTI) discrete-time filter with input x[n...
Q8) Consider the following causal linear time-invariant (LTI) discrete-time filter with input x[n] and output y[n] described by bx[n-21- ax[n-3 for n 2 0, where a and b are real-valued positive coefficients. A) Is this a finite impulse response (FIR) or infinite impulse response (IIR) filter? Why? B) What are the initial conditions and their values? Why? C) Draw the block diagram of the filter relating input x[n] and output y[n] D) Derive a formula for the transfer function in...
Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability...
Recall that a discrete random variable X has Poisson
distribution with parameter λ if the probability mass function of
X
Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X is r E 0,1,2,...) This distribution is often used to model the number of events which will occur in a given time span, given that λ such events occur on average a) Prove by direct computation that the mean of...
solve using MatLab please.
HW - 1 Use a cover page Word file attached for the homework solution. Solve all questions using Word and the Matlab output should be copied into the Word file. All questions and answers should be numbered HW1.1 Generate the following sequences using the basic MATLAB signal functions and the basic MATLAB signal operations discussed in this chapter. Plot signal samples using the stem function. 1- x(n) = n°[u(n+5] – u(n-6) + 105(n) + 20(0.5)* [u(n-4)...
1) A causal discrete-time system is described by the difference equation, y(n) = x(n)+3x(n-1)+ 2x(n-4) a) What is the transfer function of the system? b) Sketch the impulse response of the system
13. Let X1, X2, ...,Xy be a sequence of independent and identically distributed discrete random variables, each with probability mass function P(X = k)=,, for k = 0,1,2,3,.... emak (a) Find the expected value and the variance of the sample mean as = N&i=1X,. (b) Find the probability mass function of X. (c) Find an approximate pdf of X when N is very large (N −0).
5. A stationary random process X[n] is input to a discrete time LTI system with frequency response j“)-10 zero mean given as A(e nmay be expressed as where Wnlis a zero mea a-HS1 unit variancei.i.d. (independent identically distributed) Gaussian sequence and c, d are constants. Let Yl be the output random a)Determine the mean function for the output random sequence Yn in terms ofa, c and d b) Determine S7 (e), the power spectral density ofthe output random sequence Yn]...
12. Let X(e") be the DTFT of the discrete-time signal z[n] = (0.5)"u[n]. Let gin] be the length-5 sequence whose 5-point DFT, Gk], is made from uniform samples from X(eu): g[n] CH 0 for n<0and n > 4 = x(e,2 ) for k = 0, 1, 2,3,4 = Find g(0] and gl1].
12. Let X(e") be the DTFT of the discrete-time signal z[n] = (0.5)"u[n]. Let gin] be the length-5 sequence whose 5-point DFT, Gk], is made from uniform samples...
How do you show this?
1.2.12. Accept the following definition. Discrete random variables X1, X2,.. , Xn, taking values in Ai, A2,..., An, are said to be independent if (1) P(Xi = ai , . . . ,x, = an) =11P(X, = a.) 仁1 for all ai E A1,., an E An. Then prove that random variables in any subsequence of a finite sequence of independent random variables are independent.
7. Consider the discrete time systems G : y[n] = x[n]-x[n-1] For each of the combined systems below, determine the output y[n]. Then determine which systems are equivalent, ie., produce the same output y[n] GHy FHy
Q8) Consider the following causal linear time-invariant (LTI) discrete-time filter with input x[n] and output y[n] described by bx[n-21- ax[n-3 for n 2 0, where a and b are real-valued positive coefficients. A) Is this a finite impulse response (FIR) or infinite impulse response (IIR) filter? Why? B) What are the initial conditions and their values? Why? C) Draw the block diagram of the filter relating input x[n] and output y[n] D) Derive a formula for the transfer function in...
Recall that a discrete random variable X has Poisson
distribution with parameter λ if the probability mass function of
X
Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X is r E 0,1,2,...) This distribution is often used to model the number of events which will occur in a given time span, given that λ such events occur on average a) Prove by direct computation that the mean of...
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