Algorithm required in R

About Data:

My acquired data is composed to a number of traces (T) and No. of samples per Trace (S) and it makes a matrix of dimension TxS. ‘T’ in our case is 10,000 traces and ‘S’ represents the number of approximately 25,000 samples per trace. A perturbation occurs at a certain location along the length ‘S’, and all the traces represent this perturbation, to be different from the rest of the samples. In my case this perturbed location ranges from 21,940 to 21,960 (specified as DV_min and DV_max in M-File). I have applied the ‘Corr’ algorithm to show this perturbation. The Raw data also show the perturbation location to be different from rest of the samples if viewed closely. The M-file is attached to show a plot representing this perturbed location. Two CSV files (Vector form and Matrix form) from processed data have been generated for the expert to load the lighter version of data to R-program. The expert may use any of these 02 CSV files in R-program, whatever feasible for him/her, to differentiate perturbed region from that of non-perturbed one.

Expert Task:

The expert is required to apply some algorithm in R that should distinguish the data of perturbed region from that of non-perturbed region. He/She may find any specific pattern in perturbed region that is different from that of non-perturbed region. He/She can notice that by increasing the R_Samples value (~150-200), the perturbed region should be seen quite differentiable than that of non-perturbed region. In the current situation, by increasing the value of R_Samples, the perturbed region can easily be differentiated from non-perturbed region by taking a simple mean of parts of data, but this logic is not good if value of R_Samples is too low. The algorithm should be so powerful that the perturbed region should be differentiated from non-perturbed region for small values of R_Samples (R_Samples < 40). The algorithm should be valid for all the Data-Files, the links of which are provided here-with.

Data Files are attached in following links:

D_SIN_100H_02V: ([login to view URL] )

D_Sq_100H_02V: ([login to view URL] )

D_SIN_100H_1V: ([login to view URL] )

D_SIN_100H_r10V: ([login to view URL] )

D_SIN_100H_01V: ([login to view URL] )

Taidot: Algoritmi, Matematiikka, Matlab ja Mathematica, Neural Networks, R-ohjelmointikieli

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