Signal Processing
The analysis, filtering, and transformation of signals to extract information or improve quality.
Definition
Signal processing is the set of techniques for analyzing and manipulating signals, ordered measurements such as sound, images, or sensor readings. It includes filtering out noise, extracting features, compressing, and detecting patterns.
The Nyquist-Shannon sampling theorem sets the floor: a signal must be sampled at more than twice its highest frequency to be reconstructed faithfully, or aliasing folds high frequencies into false low ones. This theorem underlies every analog-to-digital conversion.
The sampling theorem is the field's foundation: a signal must be sampled at more than twice its highest frequency to be reconstructed faithfully, or aliasing folds high frequencies into false low ones, an error no later processing can undo. This principle governs every analog-to-digital conversion. Filtering, spectral analysis, and convolution build on it to extract meaningful information from noisy measurements across communications, audio, imaging, and instrumentation.
Core operations
- Filtering: remove noise or isolate frequency bands.
- Sampling and reconstruction (governed by the Nyquist limit).
- Spectral analysis via the Fourier transform.
- Convolution for filtering and feature detection.
Why it matters
Every sensor and communication system relies on signal processing to turn raw measurements into usable information. Its principles, especially the sampling theorem and frequency-domain analysis, are foundational across engineering and underpin much of modern data analysis.
Fusion connection
Diagnostic signals from a fusion machine are filtered and analyzed with signal-processing methods to extract physical quantities cleanly from noisy, high-rate raw measurements.