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.
The identical MOSFETs \( M_1 \) and \( M_2 \) in the circuit given below are ideal and biased in the saturation region. \( M_1 \) and \( M_2 \) have a transconductance \( g_m \) of 5 mS. The input signals (in Volts) are: \[ V_1 = 2.5 + 0.01 \sin \omega t, \quad V_2 = 2.5 - 0.01 \sin \omega t. \] The output signal \( V_3 \) (in Volts) is _________.
