Signal conditioning is the process of modifying a signal to make it suitable for further processing, measurement, or analysis. It is a crucial step in data acquisition systems, ensuring that signals are accurate, noise-free, and compatible with the receiving system. Signal conditioning is widely used in industrial automation, medical devices, aerospace, and other applications requiring precise measurements.

Why Signal Conditioning is Important
Raw signals from sensors or transducers often contain noise, interference, or distortions that can affect the accuracy of measurements. Signal conditioning improves signal quality by amplifying, filtering, or converting signals into a readable format for processing systems.

Types of Signal Conditioning
1. Amplification
Boosts weak signals to a detectable level.

Used in applications where signals from sensors are too small for processing.

2. Filtering
Removes unwanted noise or interference.

Common filter types include low-pass, high-pass, band-pass, and notch filters.

3. Isolation
Prevents unwanted electrical interference and protects equipment.

Ensures safe signal transmission in high-voltage environments.

4. Linearization
Corrects non-linear sensor outputs to ensure accurate readings.

Used in thermocouples, pressure sensors, and other measurement devices.