/ˈsɪɡnəl ˈprɑːsɛsɪŋ/
noun … “Analyzing, modifying, and interpreting signals.”
Signal Processing is the field of engineering and computer science concerned with the analysis, transformation, and manipulation of signals to extract information, improve quality, or enable transmission and storage. Signals can be analog (continuous) or digital (discrete), representing phenomena such as sound, images, temperature, or electromagnetic waves.
Key characteristics of Signal Processing include:
- Transformation: converting signals from one form to another (e.g., Fourier transform).
- Filtering: removing unwanted noise or emphasizing desired components.
- Compression: reducing data size for efficient storage or transmission.
- Analysis: detecting patterns, extracting features, or measuring parameters.
- Applications: audio and video processing, communications, control systems, radar, medical imaging, and machine learning.
Workflow example: Digital filtering of a noisy signal:
noisy_signal = adc.read_samples()
filtered_signal = digital_filter.apply(noisy_signal)
dac.write(filtered_signal)
Here, the analog signal is converted to digital, processed to remove noise, and converted back to analog for output.
Conceptually, Signal Processing is like refining a photograph: the raw data is transformed, cleaned, and enhanced to reveal meaningful information.
See ADC, DAC, Analog, Digital, Communication.