Signal Averaging
Signal averaging is a signal processing technique used to improve the signal-to-noise ratio (SNR) of a measured signal by averaging multiple measurements. It works by summing or averaging repeated signal acquisitions, where random noise tends to cancel out over time while the underlying signal reinforces. This method is widely applied in fields like electronics, biomedical engineering, and scientific instrumentation to extract weak signals from noisy environments.
Developers should learn signal averaging when working on applications involving data acquisition, sensor processing, or scientific computing where measurements are corrupted by noise. It is essential in scenarios like EEG/ECG analysis in healthcare, audio processing for noise reduction, or improving accuracy in low-signal experiments in physics and chemistry. Understanding this technique helps in designing robust systems for real-time or post-processing data enhancement.