Data transformations like removing signal noise, compensation for environmental effects like temperature and humidity, and calibration for equipment error are needed to help turn raw data into useful data.
Raw data is not always the best way to communicate useful information. Data transformations like removing signal noise, compensation for environmental effects like temperature and humidity, and calibration for equipment error are needed to help turn raw data into useful data.
Producing useful data is a primary outcome of engineering applications, so comprehensive signal processing is a fundamental need for any analysis tool used in data acquisition. This white paper outlines five questions to consider when choosing analysis tools for your DAQ system.
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