Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home General Processing Steps Data Preprocessing Signal and Noise  
See also: Types of Noise, origin of noise, time averaging, Coefficient of Variation  
Signal and Noise
Any value obtained by a measurement contains two components: one carries the information of interest, the signal, the other consists of random errors, or noise, that is superimposed on the first component. These random errors are, of course, unwanted because they diminish the accuracy and precision of the measurement. The term "signal" is sometimes used for the pure, noisefree signal but sometimes also for the noisy "raw" data. The term noise originates from radio engineering, where it describes the unwanted signal that we hear when we do not exactly tune our radio to a radio station. Noise may be present in many different types of signals. Click on the image on the left see a few examples. Noise free data can never be realized in practice since some types of noise are the result of thermodynamic and quantum effects that cannot be avoided during a measurement. But measurements produced from nonelectronic devices are also contaminated with random errors.
There are two methods of calculating the SNR. The first one defines the SNR as the ratio of the mean and the standard deviation of the measured signal. SNR = / s When the signal is a transient one (e.g. a chromatographic peak), then we use the ratio of the mean around the maximum and the standard deviation of the measured signal: SNR = _{max} / s The second method, which is mainly used in the field of electronics, the SNR is calculated as the ratio of the power of the signal P_{signal} to the power of the noise P_{noise}: SNR = P_{signal} / P_{noise} = (V_{signal} / V_{noise})^{2}, where the voltage V is the RMS voltage (root mean square voltage). The signal to noise ratio can be improved by repeating a measurement several times and summing up the results. The SNR improves with the square root of the number of repetitions (see section on time averaging for more details).


Home General Processing Steps Data Preprocessing Signal and Noise 
Last Update: 20121008