Harnessing the Power of Sensors and Machine Learning to Design Smart Fence to Protect Farmlands
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- Figure 3. Threshold setting for noise removal (threshold level = 0.25). Electronics 2021
- Figure 4. Noise level.Figure4shows the signal for the noise.Figure 5.
| 3. Signal Analysis The signal classification is based on ground vibration and ground stress. The experimental configuration of the fiber optic sensor produces a frequency signal of approximately Hz when the human or animal crosses it. This frequency is much lower compared to other ground vibrations such as vehicular passage and soil excavation, etc. The signals were recorded in various locations (such as the zoo, forests, open lands, etc. Each signal has different features that are affected by the state of the ground, the depth of the optical fiber sensor in the ground, the gait of the animals, etc. The following signal retrieval techniques are used for signal analysis: 1. First, signals were recorded without any motion on an optical fiber sensor (Figure 3 ). 2. The threshold value was calculated from the previous signal. 3. Signal information about the events was recorded (Figure 4 ). 4. Discard signals with an amplitude below the threshold and retrieve signals with an amplitude greater than the threshold. Figure 5 shows the de-noised signal. The de-noised signals are the events signals. Figure 3. Threshold setting for noise removal (threshold level = 0.25). Electronics 2021, 10, 3094 6 of 20 Figure 3 represents the signal that is registered with few movement on the fiber sensor to find out the level of noise. The recorded signals shows the default vibrations on the ground with a few spikes of footsteps. The amplitude of the signal is found to be less than v. In that case, the threshold for the noise is set at 0.13 v. Figure 4. Noise level. Figure 4 shows the signal for the noise. Figure 5. De-noised (Event) signal. Figure 5 shows the signal given after removing the low-level noise. All values lower than the threshold value (0.13) turn to zero. Now, only occurrence information is present in the signal. The occurrence signals were separated and analyzed for signal characteristics. Following an investigation of different signs, it was seen that no decent model or envelope was found. This is the justification for why, as per different works, 15 boundaries were distinguished, which are valuable to perceive the type of animals introduced in Table 1 . These components were stored in the essential dataset for each sign and utilized in executing the acknowledgment calculation. Other than the above-described features, MFCC was also used for classification. The qualities of the mel frequency cepstral coefficients (MFCC) stand apart among the attributes most commonly utilized in acoustic signal processing. The process of MFCC (represented in Figure) from the signal is as follows: 1. Input Digital Signal Windowing Handling an enormous dataset is troublesome. To comprehend the sign model and examine it, an enormous informational collection is separated into various pictures, known as windowing. 2. Carry out a discrete Fourier (DFT) change of every window to ascertain the recurrence range of a sign. 3. Rewind the yield of the second stage with mel-recurrence channel bank. 4. Apply the logarithm toward the end of the third stage. 5. Calculate the Inverse discrete Fourier change (IDFT) of log yield. 6. Calculate the careful cosine change (DCT) of the IDFT yield to pack the sign. 7. When delivered, an incentive for every window will be produced. |