Signal Sampling

Delta Compression

The delta compression reduces the data of a data set by removing all values for which the absolute deviation from the preceding value is smaller than a given Y interval. An increase in the number of predecessors Improves the reproduction of slopes in the compressed data set.

Extracting every nth value

The reduction is achieved by copying only every nth value into the result data set. The Factor specifies the sampling interval and must be an integer greater than or equal to 1. The Starting position specifies the position of the first value to be taken.

Taking one value per X interval

The reduction is performed by splitting the data set into segments of the same X length and taking the first value from each segment. The data set must have an X component, but this does not have to be equidistant. The X interval specifies the size of the segments and must be specified in the unit of the X component.

Forming an average value per X interval

The reduction is achieved by splitting the data set into segments of the same X length and calculating the mean value from each segment. The data set must have an X component, but this does not have to be equidistant. The X interval specifies the size of the segments and must be specified in the unit of the X component.

Resampling using Exact-n FFT

For resampling using FFT, the time signal is first transformed into the frequency domain. To reduce the sampling rate, a part of the spectrum is cut off. It is then transformed back into the time domain. The data has to be sampled at a constant rate. The Factor is the ratio of the previous to the new number of values. It does not have to be an integer, but it must be greater than or equal to 1.

Resampling using Linear Interpolation

Resampling occurs using linear interpolation. The data has to be sampled at a constant rate. The Factor is the ratio of the previous to the new number of values. It does not have to be an integer, but it must be greater than or equal to 1.

Linear Interpolation of Neighboring Values

The expansion is achieved through linear interpolation, i.e. neighboring values are joined up by a straight line, which is evaluated at the respective number of points. The Factor must be an integer greater than or equal to 1.

Resampling using Exact-n FFT

For resampling using FFT, the time signal is first transformed into the frequency domain. To increase the sampling rate, zeros are appended to the spectrum. It is then transformed back into the time domain. The data has to be sampled at a constant rate. The Factor is the ratio of the new to the previous number of values. It does not have to be an integer, but it must be greater than or equal to 1.

Resampling using Linear Interpolation

Resampling occurs using linear interpolation. The Factor is the ratio of the new to the previous number of values. It does not have to be an integer, but it must be greater than or equal to 1.

Linear interpolation without extrapolation at the edges

Samples a signal using linear interpolation. X values to sample that lie before the first or after the last X value of the signal are set to void.

Linear interpolation with extrapolation at the edges

Samples a signal using linear interpolation. X values to sample that lie before the first or after the last X value of the signal are linearly extrapolated.

Assignment of a new X component with a specified sampling interval

Assigns an X component to the data or replaces the existing X component. The X values are calculated using the specified sampling interval. The number of samples of the output signal is equal to that of the input signal.