Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring

A critical problem facing data collection in structural health monitoring,for instance via sensor networks,is how to extract the main components and useful features for damage detection.A structural dynamic measurement is more often a complex time-varying process and therefore,is prone to dynamic changes in time-frequency contents.To extract the signal components and capture the useful features associated with damage from such nonstationary signals,a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required.Wavelet analyses have proven to be a viable and effective tool in this regard.Wavelet transform(WT)can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale.However,the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results.This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms,using structural dynamic acceleration responses,to evaluate the effectiveness of various wavelets for damage detection in civil structures.The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage.Subsequently,damage-induced changes are tracked with time-frequency representations.Towards this aim,energy distribution and sharing information are investigated.The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure.Also,the Bump wavelet shows the best results than the others.

Point source detection performance of Hard X-ray Modulation Telescope imaging observation

The Hard X-ray Modulation Telescope（HXMT） will perform an all-sky survey in the hard X-ray band as well as deep imaging of a series of small sky regions.We expect various compact objects to be detected in these imaging observations. Point source detection performance of HXMT imaging observation depends not only on the instrument but also on the data analysis method that is applied since images are reconstructed from HXMT observed data with numerical methods. The denoising technique used plays an important part in the HXMT imaging data analysis pipeline along with demodulation and source detection. In this paper we have implemented several methods for denoising HXMT data and evaluated the point source detection performances in terms of sensitivities and location accuracies. The results show that direct demodulation with 1-fold cross-correlation should be the default reconstruction and regularization method, although both sensitivity and location accuracy could be further improved by selecting and tuning numerical methods in data analysis used for HXMT imaging observations.

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.