Digital reproduction accuracy of an analog waveform In sampled-data systems, which action increases the accuracy of a digital reproduction of an analog curve, assuming all other factors remain the same?

Introduction / Context:
Digital representation of analog signals involves two dimensions: time sampling and amplitude quantization. Accuracy improves when we better capture temporal changes and when each sample has finer amplitude resolution. This question isolates the time-sampling dimension.

Given Data / Assumptions:

• Sampling theorem applies: sampling frequency should exceed twice the highest frequency component in the signal (practical margin recommended).
• Quantization resolution (bits per sample) remains unchanged here.
• Anti-aliasing filtering is correctly applied prior to sampling.

Concept / Approach:
Increasing the sampling rate places sample points closer together in time, better tracing rapid changes in the analog signal and reducing interpolation error. While increasing bit depth also improves accuracy, among the provided choices, “sampling more often” is the only action that unambiguously increases accuracy.

Step-by-Step Solution:
Hold quantization constant.Raise sampling frequency → more temporal detail.Conclude → accuracy improves with more frequent sampling.

Verification / Alternative check:
Simulation of a fast-changing waveform shows reduced reconstruction error as the sampling interval decreases, given adequate anti-aliasing.

Why Other Options Are Wrong:

• Sampling less often: increases temporal error and risks aliasing.
• Decreasing bits: increases quantization error.
• All of the above: cannot be correct as two options reduce accuracy.

Common Pitfalls:
Ignoring the need to also manage quantization noise and anti-alias filters; higher sampling alone does not fix insufficient bit depth.

Final Answer:
sampling the curve more often