Undersampling terminology: Sampling a signal at a rate below the Nyquist minimum produces a spurious component known as a(n) ________.

Introduction / Context:
The Nyquist–Shannon sampling theorem states that to capture all information in a bandlimited signal, the sampling frequency must be strictly greater than twice the highest frequency component. Violating this condition leads to spectral overlap in the sampled domain, creating deceptive components in the reconstructed or analyzed signal.

Given Data / Assumptions:

• Sampling at f_s < 2 * f_max.
• Real-world signals may not be perfectly bandlimited; anti-alias filters are used.
• We seek the term for the resulting artifact.

Concept / Approach:
When sampling below Nyquist, higher-frequency content folds back (mirrors) into lower frequencies within the baseband, producing components that were not present in the original baseband. These folded components are called aliases. Anti-aliasing filters attenuate frequencies above f_s/2 to prevent this phenomenon.

Step-by-Step Solution:

Verification / Alternative check:
Spectrum plots show mirrored images about integer multiples of f_s; the overlapping areas in 0..f_s/2 are the aliases perceived after sampling.

Why Other Options Are Wrong:

• Nyquist: A theorem/rate, not the artifact.
• Sampling frequency: A parameter, not the artifact.
• Basis: Unrelated; term from linear algebra or transforms.

Common Pitfalls:
Confusing undersampling used deliberately with bandpass signals and stringent filtering with careless undersampling that causes distortion; assuming DSP can “fix” aliasing after the fact (it cannot).

Final Answer:
alias