| Feature | Description |
|---|---|
| Dominant Frequencies | Identifies the most prominent frequency components within a signal's spectrum through frequency-domain analysis. These frequencies are critical for interpreting physical phenomena, system behaviors, or material properties across diverse applications. Typical applications are as follows: • Biomedical signals: in atrial fibrillation studies, dominant frequencies help map arrhythmia drivers (for example, 4–9 Hz frequencies indicating rapid atrial activity). HRV analysis uses LF (0.04–0.15 Hz) and HF bands to assess autonomic nervous system balance. • Geophysical sensing: ground-penetrating radar (GPR) identifies soil moisture or material boundaries by linking dominant frequencies to subsurface reflections. • Fluid dynamics: dominant frequencies in particle image velocimetry (PIV) data reveal vortex shedding patterns around airfoils. |
| Dominant Frequency Amplitudes | Represents the amplitudes of the most prominent frequency components in a signal's spectrum, enabling efficient representation and analysis of complex waveforms. |
| Dominant Frequency Mean Distance | Quantifies the average separation between dominant frequency components in a signal or across signal segments. It is particularly useful for analyzing temporal or spatial variations in frequency characteristics. Typical applications are as follows: • In rotating machinery, a high value indicates irregular vibration patterns (for example, bearing wear). • Tracking dominant frequency variations in EEG/ECG to detect arrhythmias or seizures. • Monitoring frequency drift in wireless signals. |
| Dominant Frequency SNR | Calculates the ratio of the power of the dominant frequency component to the power of the noise in a signal. Useful for vibration analysis and anomaly detection. |
| Dominant Frequency THD | Calculates the total harmonic distortion by comparing the energy in harmonic frequencies to the fundamental (dominant) frequency, while the dominant frequency represents the primary oscillation component in a signal. Typical applications are as follows: • System characterization: quantifies spurious emissions in RF sensors and distortion in pressure and acceleration sensors. • Condition monitoring: high THD in vibration sensors indicates bearing wear or mechanical faults. • Signal filtering: helps design notch filters to suppress harmonics in ECG and biomedical sensors. |