Sampling & Nyquist Visualizer

Use sample dots, frequency folding, and IoT data trade-offs to choose a defensible sensor sample rate

animation
signal-processing
sampling
nyquist
aliasing
fundamentals
beginner
Animation Signal sampling IoT design decision

Sampling & Nyquist Visualizer

See how a smooth sensor signal becomes individual ADC samples. When the sample clock is too slow, those dots can fit a lower false frequency, so the device may record a signal that was never really there.

Aliasing risk current sampling condition
0.69x minimum sample rate / Nyquist minimum
7.0 Hz alias what the samples can appear to show
264 KB/day raw sample payload only
What A sampler that connects signal frequency, sample rate, aliasing, and IoT data volume.
Why IoT systems store dots, not the original analog wave. Sparse dots can mislead analytics and control logic.
Try First Use the aliasing default, press Play, then switch to Safe margin and compare the red apparent wave.
Notice The sample dots stay real, but the explanation your software can infer from them changes.

Controls

Signal And ADC

Presets

Sampling Workbench

The sample dots are the only evidence your digital system receives.

Aliasing risk
Learning Support

Sampling stores evidence

The ADC stores values at fixed instants. It does not store everything that happened between those instants.

Nyquist is a boundary

The ideal minimum sample rate is twice the highest signal frequency. Exact boundary cases leave no room for phase or real filters.

Aliasing cannot be repaired later

Once an out-of-band analog signal has folded into the sampled data, software cannot reliably tell which frequency was original.

Sampling has a cost

Higher sample rates can improve fidelity, but they also increase ADC work, processor time, storage, and radio traffic.

Quick Reference

Nyquist frequency

f_nyquist = sample_rate / 2

This is the highest frequency the sampled data can represent in the ideal model.

Minimum sample rate

sample_rate >= 2 x f_signal_max

Use this as the teaching minimum, then add margin for real hardware and filters.

Alias frequency

f_alias = abs(f_signal - round(f_signal / fs) x fs)

Fold the result into the 0 to fs/2 band. Example: 18 Hz at 25 samples/s appears as 7 Hz.

Guard margin

practical target = 2.5x to 5x highest useful frequency

Margin gives the anti-alias filter room to attenuate unwanted high frequencies before the ADC.

Raw data rate

bits/s = sample_rate x bits_per_sample x channels

This is only sample payload. Real network packets add headers, retries, timestamps, and metadata.

Slow sensors

temperature and humidity often need low rates

Slow environmental signals may need only a few samples per minute, but alarms may require faster polling.

Fast sensors

vibration and audio need high rates

A 1 kHz vibration feature needs at least 2 k samples/s, often more after practical margin.

Anti-alias filter

low-pass before ADC

The filter must be analog and before sampling. Digital filters are too late to prevent aliasing.

Guided Practice

Find an alias

Set the signal to 18 Hz and the sample rate to 25 /s. Notice the apparent 7 Hz result.

Add safe margin

Raise the sample rate until the condition changes from aliasing to safe. Compare the data volume metric.

Test the boundary

Use 20 Hz and 40 /s. The math reaches Nyquist, but the page marks it as thin margin.

Connect to design

Switch between Slow sensor and Vibration. The right rate depends on signal bandwidth, not just sensor type.