The paper presented the wavelet transform method for de-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25 Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals is varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.
The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.