Chapter 8 · MCQ Read Mode

Signal System and Digital Signal Processing

The sifting property of the unit impulse function states that:

The sinc function is the Fourier transform pair of:

The impulse response of an LTI system is defined as its output when the input is:

The output of a continuous-time LTI system for any input is found via:

The Discrete-Time Fourier Series (DTFS), unlike the CTFS, represents a periodic signal using:

The Discrete-Time Fourier Transform (DTFT) of a sequence is always:

Energy Spectral Density (ESD) of a signal is given by:

Power Spectral Density (PSD) applies most naturally to:

The Hilbert transform of a signal changes its:

The signum function sgn(t) is defined as:

The sampling theorem states that perfect reconstruction requires the sampling rate fₛ to satisfy:

The linearity property of an LTI system means it obeys:

A causal system's output at any given time depends on:

The frequency response H(ω) of an LTI system is obtained by:

The impulse response of an ideal low pass filter is a:

The step response of an ideal low pass filter exhibits:

BIBO stability of an LTI system requires that its impulse response be:

For a causal discrete-time system described by a transfer function, BIBO stability requires all poles to lie:

Implementation of a discrete-time LTI system is typically based on:

A memoryless system's output at any time depends on:

The z-transform of a sequence x[n] is defined as:

The DTFT of a sequence can be obtained from its z-transform by:

The Region of Convergence (ROC) of a causal sequence is typically:

Convolution in the time domain corresponds, in the z-domain, to:

The system function H(z), evaluated on the unit circle, gives the system's:

For a causal LTI system, BIBO stability requires all poles of H(z) to lie:

The Discrete Fourier Transform (DFT) can be understood as:

Multiplying the DFTs of two sequences and taking the inverse DFT yields:

Sampling the DTFT in the frequency domain corresponds, in the time domain, to:

The inverse z-transform can be computed using:

A direct-form realization of a digital filter implements the difference equation using:

A key advantage of the lattice structure over direct form is:

A lattice-ladder structure is used specifically to realize:

Fixed-point number representation is characterized by:

Floating-point representation offers which advantage over fixed-point?

Rounding error, compared to truncation error, is generally:

Truncation, as a finite-precision effect, produces an error that is:

Quantizing filter coefficients to finite precision primarily causes:

Coefficient quantization effects are especially critical for filters whose poles are located:

The conversion between direct-form coefficients and lattice reflection coefficients is typically performed using:

Classical IIR filter design typically starts from:

The impulse-invariant method for IIR design obtains the digital filter by:

FIR filter design by Fourier approximation computes coefficients as:

The window function method for FIR design is used primarily to:

Which window is NOT commonly used in FIR filter design by the window method?

The frequency sampling method for FIR design computes coefficients by:

The Remez exchange algorithm (Parks-McClellan) designs FIR filters by:

An equi-ripple FIR filter design distributes approximation error:

Compared to window-based FIR design, equi-ripple (Remez) design generally achieves:

A key drawback of the impulse-invariant method for IIR design is:

A digital filter processes its input according to:

The FFT algorithm reduces the computational complexity of computing a DFT from:

Fast convolution using the FFT relies on the principle that:

The FFT is commonly used in spectral analysis to:

Frequency-domain filtering using the FFT involves:

OFDM (Orthogonal Frequency Division Multiplexing), used in modern communications, relies heavily on:

Digital filters can be implemented in which of the following ways?

Cascade (second-order sections) implementation of a digital filter is chosen mainly to improve:

The FFT enables practical frequency-domain processing mainly because it:

2-D FFT is an application area relevant to: