Figure 18.Plot of signal for running of single person. Electronics 2021

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Figure 19.
Spectogram of signal for running of single person.
Figures
20
and
21
represent the signal trace and the one-person jump spectrogram.
It can be seen from the chart that there is a peak when the person jumps close to the sensor cable.
Figure 20.
Plot of signal for jump of single person.

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Figure 21.
Spectogram of signal for jump of single person.
4. Algorithm for Classification
The first algorithm is based upon the table of RMS values at each signal point. The threshold has two tiers. The first threshold layer is defined as the average of the RMS
combined with a standard deviation. The lower end of the RMS is defined as the mean of the RMS valueless than SD. The second cutoff layer is defined as the mean of the RMS
values plus the X standard deviation. In this case, the lower limit is defined as the mean of the RMS value minus two times the standard deviation. The classification algorithm is based on the number of points passing through both frontiers. The steps for establishing thresholds areas follows:
a.
Calculated average of RMS per image for multi-event signal.
b.
Calculated standard deviation (Sigma) for each field for two or more event signals.
c.
Create threshold boundaries by averaging RMS values for each image, standard deviation (sigma) for each image, average sigma, average sigma, average sigma 2 sigma, average sigma * sigma.
d.
The classification of intruder is based on number of points that lie between the first boundary of average + sigma & average
−
sigma, and second boundary of average +
2 * sigma & average * sigma.
Figures
22
–
24
show thresholds for humans, tigers, and elephants. The mean RMS
values and the sample of two event signals for each class are also shown in the chart. X-axis represents the number of frames, Y-axis represents the values of average of RMS, first threshold boundary (average + sigma & average
−
Sigma), second threshold boundary
(average + 2 * sigma & average * Sigma, and RMS values of two event signals.
It can be observed from Figures
22
–
24
that most of the points of sample 1 and 2 lie between the first threshold boundaries, and few points lay between the second threshold boundaries. The algorithm was developed after analyzing and counting the number of dots that lie between the limits.

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