Time-rearrangement compression transformation-based time-frequency analysis and reconstruction method of impact-type signal

Abstract

The invention discloses a time-rearrangement compression transformation-based time-frequency analysis and reconstruction method of an impact-type signal. The method includes: 1) calculating short-timeFourier transform (STFT) of a to-be-analyzed discrete signal to obtain a corresponding time-frequency complex-matrix S<x>[n,k]; 2) carrying out short-time Fourier transform on the product of a time variable and the signal to obtain a time-frequency complex-matrix S<tx>[n,k]; 3) calculating a group delay estimation operator; 4) only rearranging the time-frequency complex-matrix S<x>[n,k], which isobtained in the step 1, along a time direction to obtain a time-frequency matrix V<x>[m,k] after time-rearrangement compression transformation; and 5) adding all rows of elements of the time-frequency matrix V<x>[m,k], which is obtained after time-rearrangement compression transformation, together to obtain a one-dimensional column vector, and then dividing the column vector by a mean value of awindow function, which is used by short-time Fourier transform, to obtain a frequency spectrum of a reconstructed signal. For analyzing the impact-type signal, the time-frequency graph aggregation obtained by time-rearrangement compression transformation to which the invention relates is higher than that of synchronous compression transformation, and good anti-noise performance is realized. Compared with traditional time-frequency rearrangement, the method has the advantages of reconstructability and a fast calculation speed.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows researchers studying how pressure waves affect human health by measuring their changes over time. By comparing different types of waveforms (pressures) they are exposed to during an event or examination, we found that there were significant differences between these measurements compared to previous methods used beforehand. These improvements led us towards better understanding stress patterns related diseases such as heart disease and stroke risk factors.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the performance of current techniques like Time Frequency Analysis (TFA) and Linear Temporary Factor Compression Transform (LTF)). While these techniques aim at capturing specific features within certain types of data without compromising their ability to recover complex patterns, they lack flexibility due to requiring different mathematical models depending upon factors like timing resolution and computational efficiency requirements.

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