Step 1: Mean of the process \( f(t) \)
The mean of \( f(t) \) is computed as: \[ E[f(t)] = E\left[\sum_{n=1}^{N} a_n p(t - nT)\right]. \] Since \( a_n \) are zero-mean independent random variables, the expectation of each \( a_n \) is 0. Thus: \[ E[f(t)] = 0 \quad {for all } t. \] Therefore, the mean of the process \( f(t) \) is indeed independent of time \( t \), and (i) is TRUE.
Step 2: Autocorrelation function
The autocorrelation function is: \[ R_f(\tau) = E[f(t)f(t+\tau)] = E\left[\sum_{n=1}^{N} a_n p(t - nT) \sum_{m=1}^{N} a_m p(t + \tau - mT)\right]. \] Since \( a_n \) are independent, the autocorrelation will depend on \( t \) because the function \( p(t) \) is not constant over time (it is non-zero only in the range \( [0, 0.5T] \)). Thus, the autocorrelation function is not independent of time. So, (ii) is FALSE.
Conclusion:
Statement (i) is TRUE because the mean is constant and independent of time.
Statement (ii) is FALSE because the autocorrelation function depends on time \( t \). Thus, the correct answer is (A): (i) is TRUE and (ii) is FALSE.
A positive-edge-triggered sequential circuit is shown below. There are no timing violations in the circuit. Input \( P_0 \) is set to logic ‘0’ and \( P_1 \) is set to logic ‘1’ at all times. The timing diagram of the inputs \( SEL \) and \( S \) are also shown below. The sequence of output \( Y \) from time \( T_0 \) to \( T_3 \) is _________.
