Contents
What are the inputs to peak signal detection?
The algorithm takes 3 inputs: lag = the lag of the moving window, threshold = the z-score at which the algorithm signals and influence = the influence (between 0 and 1) of new signals on the mean and standard deviation. For example, a lag of 5 will use the last 5 observations to smooth the data.
Which is the best algorithm for peak detection?
Robust peak detection algorithm (using z-scores) I have constructed an algorithm that works very well for these types of datasets. It is based on the principle of dispersion: if a new datapoint is a given x number of standard deviations away from some moving mean, the algorithm signals (also called z-score).
How are peak detectors used in AM demodulation?
Peak detectors employing p-n junction diodes are well-known means for AM demodulation (Carlson, 2002 ). However, because of the temperature dependence of the I–V characteristic of the p-n junctions, such peak detectors are associated with significant signal-dependent thermal drifts ( Nabavi and Nihtianov, 2009; Meyer, 1995 ).
Why do I need a fast peak detector?
Fast peak detectors place unusual demands on amplifiers. The output stage must have a high slew rate in order to keep up with the intermediate stages of the amplifier. This condition causes either a long overload or DC accuracy errors.
Fit a very simple 2-gaussian mixture model to your data (for example, Numerical Recipes has a nice ready-made chunk of code). Take the earlier peak. This will deal correctly with overlapping peaks. Find the best match in the data to a simple Gaussian, Cauchy, Poisson, or what-have-you curve.
Why are future signals identified with the same accuracy as past signals?
The algorithm is very robust because it constructs a separate moving mean and deviation, such that signals do not corrupt the threshold. Future signals are therefore identified with approximately the same accuracy, regardless of the amount of previous signals.
When to use T / are > 1 for peak detection?
T/R > 1 indicates a peak. This works OK if large travel due to noise is unlikely or if noise distributes symmetrically around a base curve shape. For your application, accept the earliest peak with a score above a given threshold, or analyze the curve of travel per rise values for more interesting properties.