Filtering In Time-frequency Domain Research Articles

An efficient accuracy improvement method for cable defect location based on instantaneous filtering in time-frequency domain

Time-frequency domain reflectometry is a highly sensitive method to locate cable defect. However, locating a potential defect far from the cable head may be less accurate because of the signal amplitude attenuation and the interference parts. To solve the problem, this paper proposes an accuracy improvement method based on instantaneous filtering in time-frequency domain. The method extracts the frequency spectrum of the time-frequency signal, and then filters the spectrum and reconstructs the signal based on the difference between the signal and interference spectra, which can eliminate the interference and improve the location accuracy. In simulation, the location error of this method improves from 5.47 % to 0.57 %. In experiment, the proposed method reduces the location errors and magnifies the location peaks of the breakage defect, thermal aging defect, and ground faults. For the defects studied in this paper, the method improves the defect location accuracy and defect amplitude detectability of TFDR.

Denoising method for weak velocity test signals under laser-induced shock waves based on attention-guided multi-scale convolutional neural network

In the research of characteristics of laser-induced shock waves via the laser interference velocity detection method, noise reduction for weak echo signals still relies on the manual setting of filtering parameters, alongside the problem of the loss of useful signals caused by global filtering and excessive filtering. To solve these problems, a multi-scale network for extraction of sparse features, in combination with the ECA channel module, was established in this study to acquire the features of multi-dimensional and multi-fine-granularity noise signals in a self-adaptive manner. By integrating various channel information of the deep/shallow features through skip connection, the deep features were fully observed without ignoring the impacts of the shallow features. Finally, the attention mechanism was adopted to adjust the weight of the features at different locations in the feature space to acquire an accurate representation of stochastic noise, and noise reduction for weak signals was achieved by reconstructing the module and removing the learned noise features from the noisy data. According to the simulation and test results, the method proposed in this study helped extract the noise information from weak signals in an accurate manner; unlimited by the forms and sizes of the noise and the working conditions and independent from manual setting of parameters, it eliminated the blind-source noise from weak echo signals effectively; with this method, the initial position and the coupled peak value were extracted accurately from the weak signals. In terms of the effects of the method, the phenomena of “peak clipping” and “breakpoint” caused by excessive filtering were avoided; compared to global filtering, time-frequency domain filtering and other methods, the method proposed outperformed the conventional ones for a peak value error of only 0.19%; as for the denoising velocity, simultaneous input of multiple pieces of data as well as simultaneous output of denoising outcomes in a real time manner were also achieved under this method, with the averaged processing time for a single piece of data (0.051 s) far superior to the result under other methods.

Introducing Causality Violation for Improved DPOAE Component Unmixing.

The DPOAE response consists of the linear superposition of two components, a nonlinear distortion component generated in the overlap region, and a reflection component generated by roughness in the DP resonant region. Due to approximate scaling symmetry, the DPOAE distortion component has approximately constant phase. As the reflection component may be considered as a SFOAE generated by the forward DP traveling wave, it has rapidly rotating phase, relative to that of its source, which is also equal to the phase of the DPOAE distortion component. This different phase behavior permits effective separation of the DPOAE components (unmixing), using time-domain or time-frequency domain filtering. Departures from scaling symmetry imply fluctuations around zero delay of the distortion component, which may seriously jeopardize the accuracy of these filtering techniques. The differential phase-gradient delay of the reflection component obeys causality requirements, i.e., the delay is positive only, and the fine-structure oscillations of amplitude and phase are correlated to each other, as happens for TEOAEs and SFOAEs relative to their stimulus phase. Performing the inverse Fourier (or wavelet) transform of a modified DPOAE complex spectrum, in which a constant phase function is substituted for the measured one, the time (or time-frequency) distribution shows a peak at (exactly) zero delay and long-latency specular symmetric components, with a modified (positive and negative) delay, which is that relative to that of the distortion component in the original response. Component separation, applied to this symmetrized distribution, becomes insensitive to systematic errors associated with violation of the scaling symmetry in specific frequency ranges.

Otoacoustic Emissions as a Promising Diagnostic Tool for the Early Detection of Mild Hearing Impairment - Technical Advances in Acquisition, Analysis and Modeling

Otoacoustic emissions are a by-product of the active nonlinear amplification mechanism located in the cochlear outer hair cells, which provides high sensitivity and frequency resolution to human hearing. Being intrinsically sensitive to hearing loss at a cochlear level, they represent a promising non-invasive, fast, and objective diagnostic tool. On the other hand, the complexity of their linear and nonlinear generation mechanisms and other confounding physical phenomena (e.g., interference between different otoacoustic components, acoustical resonances in the ear canal, transmission of the middle ear) introduce a large inter-subject variability in their measured levels, which makes it difficult using them as a direct measure of the hearing threshold using commercially available devices. Nonlinear cochlear modeling has been successfully used to understand the complexity of the otoacoustic generation mechanisms, and to design new acquisition and analysis techniques that help disentangling the different components of the otoacoustic response, therefore improving the correlation between measured otoacoustic levels and audiometric thresholds. In particular, nonlinear cochlear modeling was able to effectively describe the complex (amplitude and phase) response of the basilar membrane, and the generation of otoacoustic emissions by two mechanisms, nonlinear distortion and linear reflection by cochlear roughness. Different phase-frequency relations are predicted for the otoacoustic components generated by the two mechanisms, so they can be effectively separated according to their different phase-gradient delay, using an innovative time-frequency domain filtering technique based on the wavelet transform. A brief introduction to these topics and some new theoretical and experimental results are presented and discussed in this study.

Identification of Protein-Coding Regions in DNA Sequences Using A Time-Frequency Filtering Approach

Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.