A New HFRT Algorithm Based on Maximal Overlap Discrete Wavelet Packet Transformation

The high frequency resonant technique （HFRT） algorithm is a popular technique for fault-detection and is widely applied in mechanism systems and industrial constructions. In this paper, a new HFRT algorithm based on maximal overlap discrete wavelet packet transformation （MODWPT） is developed. By the simulation test for soil embedded pipes fault-detection, it is shown that the performance of newly proposed HFRT algorithms is more sensitive to early defects than the traditional HFRT methods based on the Hilbert transform.

Stock profiling using time–frequency‑varying systematic risk measure

This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.

Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset

In this paper,complexity analysis and dynamic characteristics of electroencephalogram(EEG) signal based on maximal overlap discrete wavelet transform(MODWT) has been exploited for the identification of seizure onset.Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG,we have applied MODWT which is an improved version of discrete wavelet transform(DWT).The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients.Therefore,we have investigated MODWT using four different wavelets,namely Haar,Coif4,Dmey,and Sym4 sub-bands until seven levels.Further,we have explored the potentials of six entropies,namely sigmoid,Shannon,wavelet,Renyi,Tsallis,and Steins unbiased risk estimator(SURE) entropies in each sub-band.The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals(RMCH).Further,the highest accuracy of 100% and 94.51% was achieved for the University of Bonn(UBonn) and CHB-MIT databases respectively.The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively.Besides,in terms of dynamic characteristics,MODWT results revealed that the highest energy present in sub-bands D2,D3,and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database.Similarly,using all the entropies,sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively.In conclusion,the comparison results of MODWT outperformed DWT.