RMBC (version 0.1.0)

phytoplankton_acoustic_data: Phytoplankton_acoustic_data

Description

Data obtained by taking laboratory measurements of ultrasonic acoustic signals: a pulse is emitted by a transducer, this pulse interacts with phytoplankton suspended in the water and produces an acoustic dispersion (scattering), which is recorded by an electronic acquisition device. A filtering process of the signal is performed in a first stage. Portions of the signal belong o one of the two main cases:

• (a) Signals corresponding to the acoustic response of phytoplankton

• (b) Signals corresponding to spurious dispersers, such as bubbles or particles in suspension, whose intensity is greater than in case (a).

To classify a signal in one of these two groups biologists create a vector (X1, X2) defined as follows:

• X1 = ratio of filtered to non-filtered signal power

• X2 = filtered signal power expressed in dB.

The available data consists of 375 such measurements. These data is particularly useful to compare robust procedures because 20 to be outliers produced by a communication failure between the electronic device (digital oscilloscope) and the software for acquiring the acoustic signal. This failure occurs once every 5 microseconds, which allows the scientists to identify the outliers. The outliers appear as a separated group in the region X1 < 0.5 and X2 > 20.

Usage

phytoplankton_acoustic_data

Format

a list of length 2, where its elements are

• Y: A matrix of dimension 375 x 2, each row contains X1 and X2 values

• outliers_index: An array with the outliers index-locations

References

• [1] Cinquini, M., Bos, P., Prario, I and Blanc, S. (2016), <U+201C>Advances on modelling, simulation and signal processing of ultrasonic scattering responses from phytoplankton cultures,<U+201D> in Proceedings of Meetings on Acoustics 22ICA, 28, American Society of Acoustics.

• [2] Gonzalez J.D, Maronna R., Yohai V., & and Zamar . (2021). Robust Model-Based Clustering. arXiv preprint <https://arxiv.org/abs/2102.06851>

Examples

# NOT RUN {
################################
# upload matrix ################
################################

Y <- phytoplankton_acoustic_data$Y

outliers_index <- phytoplankton_acoustic_data$outliers_index

Yclean=Y[-outliers_index,]

trueOutliers=Y[outliers_index,]

################################
# plot results ################
################################

plot(Y, main = "Phytoplankton acoustic data", cex.main = 3, lwd = 1,pch = 19, cex = 1, 
     type = "n", xlab = "x1", ylab = "x2",  xlim = c(0,1.1), ylim = c(0,43)
     )
     
points(trueOutliers,lwd=2,cex=1,pch=4)

points(Yclean,col=1,lwd=1.5,pch=21, bg=4, cex=1)

# }