• Estimator for the temporal autocorrelation and for the power spectral density from laser Doppler data sets using arrival-time quantization (recommended, because from the signal theoretical point of view it is as efficient as the slotting technique and the direct spectral estimation, but the fastest among these three) including normalization with the correlation function of the sampling function and individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, shorten of the correlation function, Bessel's correction and, optionally, local normalization

  aquant.py - Python source code

  aquant.sci - Scilab application

  libLDV.c - C source code (ported from aquant.py, thanks Stephan Weiss)

  aquant.pdf - A detailed description of the algorithm

• Estimator for the temporal cross-correlation and for the power spectral density from laser Doppler data sets using the slotting technique (recommended, because this is the only estimation technique that can correctly handle mixed independent and dependent measurements) including individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, a variable exponent of the product of the individual weights taking into account the fraction of dependent measurements among the data in each slot, Bessel's correction and, optionally, local normalization and fuzzy slotting

  cslotuniv.py - Python source code

  cslot.pdf - A detailed description of the algorithm

  cquantcoinc.py - A crosscorrelation and power spectral density estimator using arrival-time quantization, for coincident data only!

  cquantindep.py - A crosscorrelation and power spectral density estimator using arrival-time quantization, for independent data only!

• Estimator for the temporal autocorrelation and for the power spectral density from laser Doppler data sets using the direct spectral estimation (not recommended, arrival-time quantization is faster) including individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, normalization with the correlation function of the sampling function, shorten of the correlation function, Bessel's correction and, optionally, local normalization and fuzzy time quantization

  adir.py - Python source code

  adir.pdf - A detailed description of the algorithm

• Estimator for the temporal autocorrelation and for the power spectral density from laser Doppler data sets using the interpolation method (not recommended, insufficient bias correction at low data rates), namely the sample-and-hold interpolation with refinement, Bessel's correction, with additional weights suppressing intervals between samples of more than five times the mean inter-arrival time and the according errors due to possible long gaps in the data stream and with a new model-free noise reduction

  aint.py - Python source code

  aint.pdf - A detailed description of the algorithm

• Estimator for the temporal autocorrelation and for the power spectral density from laser Doppler data sets using the slotting technique (not recommended, arrival-time quantization is faster) incliding individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, Bessel's correction and, optionally, local normalization and fuzzy slotting

  aslot.py - Python source code

  aslot.pdf - A detailed description of the algorithm

• Estimator for the temporal autocorrelation and for the power spectral density from laser Doppler data sets using the arrival-time quantization (recommended, because from the signal theoretical point of view it is as efficient as the slotting technique and the direct spectral estimation, but the fastest among these three) including normalization with the correlation function of the sampling function and individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, shorten of the correlation function, Bessel's correction and, optionally, local normalization

  aquant.py - Python source code

  libLDV.c - C source code (ported from aquant.py, thanks Stephan Weiss)

  aquant.pdf - A detailed description of the algorithm

• Estimator for the temporal cross-correlation and for the power spectral density from laser Doppler data sets using the direct spectral estimation including individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, Bessel's correction and, optionally, local normalization and fuzzy time quantization

  	cdircoinc.py - Python source code for an estimator for coincident two-channel data, eg. from two-component measurements (not recommended, arrival-time quantization is faster)
  	cdirindep.py - Python source code for an estimator for independent two-channel data, eg. from transversal two-point measurements (not recommended, arrival-time quantization is faster)
  	For data with mixed independent and dependent measurements with a certain delay between the channels this estimation principle doesn't work. In this case, the arrival-time quantization is necessary.
  	cdir.pdf - A detailed description of the algorithm

• Estimator for the temporal cross-correlation and for the power spectral density from laser Doppler data sets using the interpolation method including Bessel's correction

  	cintcoinc.py - Python source code for an estimator for coincident two-channel data, eg. from two-component measurements (not recommended, insufficient bias correction at low data rates)
  	cintindep.py - Python source code for an estimator for independent two-channel data, eg. from transversal two-point measurements (not recommended, insufficient bias correction at low data rates)
  	cintuniv.py - Python source code for an universal estimator, which can be used with all kinds of two-channel data, eg. coincident two-component measurements, independent measurements, eg. from transversal two-point measurements or mixed independent and shifted dependent non-coincident measurements, eg. from longitudinal two-point measurements incl. variations of the preferred delay of the dependent measurements (not recommended, insufficient bias correction at low data rates, high computational costs)
  	cint.pdf - A detailed description of the algorithm

• Estimator for the temporal cross-correlation and for the power spectral density from laser Doppler data sets using the slotting technique including individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, a variable exponent of the product of the individual weights taking into account the fraction of dependent measurements among the data in each slot, Bessel's correction and, optionally, local normalization and fuzzy slotting

  	cslotcoinc.py - Python source code for an estimator for coincident two-channel data, eg. from two-component measurements (not recommended, arrival-time quantization is faster)
  	cslotindep.py - Python source code for an estimator for independent two-channel data, eg. from transversal two-point measurements (not recommended, arrival-time quantization is faster)
  	cslotuniv.py - Python source code for an universal estimator, which can be used with all kinds of two-channel data, eg. coincident two-component measurements, independent measurements, eg. from transversal two-point measurements or mixed independent and shifted dependent non-coincident measurements, eg. from longitudinal two-point measurements incl. variations of the preferred delay of the dependent measurements (recommended, because this is the only estimation technique that can correctly handle mixed independent and dependent measurements)
  	cslot.pdf - A detailed description of the algorithm

• Estimator for the temporal cross-correlation and for the power spectral density from laser Doppler data sets using arrival-time quantization including individual weighting or forward-backward arrival-time weighting with a maximum of five times the mean inter-arrival time suppressing errors due to possible long gaps in the data stream, Bessel's correction and, optionally, local normalization

  	cquantcoinc.py - Python source code for an estimator for coincident two-channel data, eg. from two-component measurements (recommended, because from the signal theoretical point of view it is as efficient as the slotting technique and the direct spectral estimation, but the fastest among these three)
  	cquantindep.py - Python source code for an estimator for independent two-channel data, eg. from transversal two-point measurements (recommended, because from the signal theoretical point of view it is as efficient as the slotting technique and the direct spectral estimation, but the fastest among these three)
  	cquantuniv.py - Python source code for an universal estimator, which can be used with all kinds of two-channel data, eg. coincident two-component measurements, independent measurements, eg. from transversal two-point measurements or mixed independent and shifted dependent non-coincident measurements, eg. from longitudinal two-point measurements incl. variations of the preferred delay of the dependent measurements (not recommended, because this estimation technique can't correctly handle mixed independent and dependent measurements)
  	This procedure is similar to the direct estimation (cdir.pdf) with quantized arrival times.

• Nobach H (2015): Corrections to the direct spectral estimation for laser Doppler data. Experiments in Fluids 56:109
• Nobach H (2015): A model-free noise removal for the interpolation method of correlation and spectral estimation from laser Doppler data. Experiments in Fluids 56:100
• Nobach H (2015): LDA-Korrelations- und Spektralschätzung — Ein Zwischenstand. 23. Fachtagung "Lasermethoden in der Strömungsmesstechnik", 8.-10. Sept. 2015, Dresden
• Nobach H (2015): Fuzzy time quantization and local normalization for the direct spectral estimation from laser Doppler velocimetry data. Experiments in Fluids 56:182
• Nobach H (2016): Present methods to estimate the cross-correlation and cross-spectral density for two-channel laser Doppler anemometry. Proc. of the 18th International Symposium on Applications of Laser Techniques to Fluid Mechanics, July 04-07, 2016, Lisbon, Portugal
• Nobach H (2016): Methoden zur Schätzung der Kreuzkorrelationsfunktion und des Kreuzleistungsdichtespektrums aus zufällig abgetasteten LDA-Daten. 24. Fachtagung "Lasermethoden in der Strömungsmesstechnik", 6.-8. Sept. 2016, Cottbus
• Nobach H, Damaschke N, Kühn V (2016): Methoden zur Schätzung der Autokorrelationsfunktion und des Leistungsdichtespektrums aus zufällig abgetasteten Laser-Doppler-Daten. XXX. Messtechnisches Symposium des Arbeitskreises der Hochschullehrer für Messtechnik, 15.-16. Sept. 2016, Hannover
• Damaschke N, Kühn V, Nobach H (2018): A Fair Review of Non-parametric Bias-free Autocorrelation and Spectral Methods for Randomly Sampled Data in Laser Doppler Velocimetry Digital Signal Processing 76, pp. 22-33