Noise-assisted data processing in measurement science: Part one part 40 in a series of tutorials on instrumentation and measurement

Noise is generally referred to as something undesirable. For example, in measurement science noise is considered a disturbance that makes measured data unclear. When an engineer tries to understand the data, data processing techniques - which are focused on minimizing the effects of noise - are often applied to the data to extract the true signal. The basic idea behind such a process is that noise is a nuisance which should be filtered out or removed. However, useful information may be influenced or destroyed with the weakening of noise. On the other hand, researchers have found that adding an appropriate amount of noise can facilitate signal detection in a noisy environment [1], [2]. In this two part tutorial, we introduce two noise-assisted data processing techniques: stochastic resonance (SR) [1] in Part One and ensemble empirical mode decomposition (EEMD) [2] in Part Two. We show their real-world applications in signal detection.Noise is generally referred to as something undesirable. For example, in measurement science noise is considered a disturbance that makes measured data unclear. When an engineer tries to understand the data, data processing techniques - which are focused on minimizing the effects of noise - are often applied to the data to extract the true signal. The basic idea behind such a process is that noise is a nuisance which should be filtered out or removed. However, useful information may be influenced or destroyed with the weakening of noise. On the other hand, researchers have found that adding an appropriate amount of noise can facilitate signal detection in a noisy environment [1], [2]. In this two part tutorial, we introduce two noise-assisted data processing techniques: stochastic resonance (SR) [1] in Part One and ensemble empirical mode decomposition (EEMD) [2] in Part Two. We show their real-world applications in signal detection.