Blind Multiridge Detection and Reconstruction Using Ultrasonic Signals

Time-frequency signal analysis has been widely applied in the modern radar, acoustic, sonar and ultrasonic signal processing techniques. Recently, the nondestructive testing (NDT) techniques via the ultrasonic instrumentation have shown the striking capability of the quality control for the material fabrication industry. In this thesis, we first provide a general mathematical model for the ultrasonic signals collected by pulse-echo sensors and then design a totally blind, novel, signal processing NDT technique relying on neither a priori signal information nor any manual effort. The signature signal can be blindly extracted by using the automatic optimal frame size selection for further modeling and characterization of the ultrasonic signal using Gabor analysis. This modeled signature signal is used for multiridge detection and for reconstruction of the signal. The detected ridge information can be used to estimate the transmission and attenuation coefficients, shear modulus, and Young’s modulus associated with any arbitrary material sample for fabrication quality control. Thus, our algorithm can be applied for ultrasonic signal characterization and ridge detection in non-destructive testing for new material fabrication. Experimental results show that the ridge detection performance by our proposed method is superior to that of the existing techniques

Blind Yield Detection in Steel Structure for Automatic Nondestructive Testing Using Ultrasonic Sensors

Detection of yield zones using nondestructive testing (NDT) technology for assessing the structural integrity of the existing steel buildings/bridges is extremely important. The average energy over the “effective echoes” (in “good” signal quality) is a robust feature for the yield detection in steel structures. Nevertheless, this average-energy feature extraction requires rigorous manual data-acquisition and human operation. Therefore, in this thesis, we make the first-ever attempt to design a totally-blind and automatic steel-structure yielddetection mechanism, which requires neither the a priori information about the signal nor the human effort in calibration, operation, or data analysis. This new scheme is built upon a robust preprocessor, which involves both blind-signature-signal-extraction and zero-crossingrate thresholding, to identify the starting and terminal time points of each ultrasonic echo. Thus, the new computer-aided system can easily estimate the signal-to-noise ratios and automatically extract the effective echoes to calculate the corresponding average energy. The performance reflected by the receiver-operating characteristic (ROC) curves of the proposed method is very close to that of the conventional human-operating technique. Hence one may save much human effort in the sacrifice of very little detection accuracy by using our proposed new system

Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications

We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive mode decomposition-based multiple harmonic ridge detection} (\textsf{SAMD-MHRD}). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \textsf{SAMD-MHRD} through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject

Characterization of signals by the ridges of their wavelet transforms

International audienceWe present a couple of new algorithmic procedures for the detection of ridges in the modulus of the (continuous) wavelet transform of one-dimensional signals. These detection procedures are shown to be robust to additive white noise. We also derive and test a new reconstruction procedure. The latter uses only information from the restriction of the wavelet transform to a sample of points from the ridge. This provides with a very efficient way to code the information contained in the signal

The Stationary Phase Approximation, Time-Frequency Decomposition and Auditory Processing

The principle of stationary phase (PSP) is re-examined in the context of linear time-frequency (TF) decomposition using Gaussian, gammatone and gammachirp filters at uniform, logarithmic and cochlear spacings in frequency. This necessitates consideration of the use the PSP on non-asymptotic integrals and leads to the introduction of a test for phase rate dominance. Regions of the TF plane that pass the test and don't contain stationary phase points contribute little or nothing to the final output. Analysis values that lie in these regions can thus be set to zero, i.e. sparsity. In regions of the TF plane that fail the test or are in the vicinity of stationary phase points, synthesis is performed in the usual way. A new interpretation of the location parameters associated with the synthesis filters leads to: (i) a new method for locating stationary phase points in the TF plane; (ii) a test for phase rate dominance in that plane. Together this is a TF stationary phase approximation (TFSFA) for both analysis and synthesis. The stationary phase regions of several elementary signals are identified theoretically and examples of reconstruction given. An analysis of the TF phase rate characteristics for the case of two simultaneous tones predicts and quantifies a form of simultaneous masking similar to that which characterizes the auditory system.Comment: Submitted to IEEE Trans Signal Processing 14th Aug 201

Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach

The special form of pipe-like structure provides the helical route for ultrasonic guided wave. Considering the pipe as a flattened plate but with periodical replications, the helical wave becomes intuitional and a corresponding imaging algorithm can be constructed. This work proposes the multihelical Lamb wave imaging method by utilizing the multiple arrival wavepackets which are denoted as different orders. The helical wave signal model is presented and the constant group velocity point is illustrated. The probabilistic reconstruction algorithm is combined with the separation and fusion of different helical routes. To verify the proposed scheme, finite element simulations and corresponding experiments are conducted. The cases of single-defect simulation and two-defect simulation indicate the successful and robust implementation of the imaging algorithm. The test on actual pipe damage is also investigated to show its capability in imaging an irregular defect. The comparison with imaging results from only first arrival demonstrates the advantage of multihelical wave imaging, including the better imaging resolution and higher localization accuracy

On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals

The extraction of oscillatory components and their properties from different time-frequency representations, such as windowed Fourier transform and wavelet transform, is an important topic in signal processing. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download.Comment: 13 pages, 7 figures, plus 4 supplementary figure

WAVOS: a MATLAB toolkit for wavelet analysis and visualization of oscillatory systems

<p>Abstract</p> <p>Background</p> <p>Wavelets have proven to be a powerful technique for the analysis of periodic data, such as those that arise in the analysis of circadian oscillators. While many implementations of both continuous and discrete wavelet transforms are available, we are aware of no software that has been designed with the nontechnical end-user in mind. By developing a toolkit that makes these analyses accessible to end users without significant programming experience, we hope to promote the more widespread use of wavelet analysis.</p> <p>Findings</p> <p>We have developed the WAVOS toolkit for wavelet analysis and visualization of oscillatory systems. WAVOS features both the continuous (Morlet) and discrete (Daubechies) wavelet transforms, with a simple, user-friendly graphical user interface within MATLAB. The interface allows for data to be imported from a number of standard file formats, visualized, processed and analyzed, and exported without use of the command line. Our work has been motivated by the challenges of circadian data, thus default settings appropriate to the analysis of such data have been pre-selected in order to minimize the need for fine-tuning. The toolkit is flexible enough to deal with a wide range of oscillatory signals, however, and may be used in more general contexts.</p> <p>Conclusions</p> <p>We have presented WAVOS: a comprehensive wavelet-based MATLAB toolkit that allows for easy visualization, exploration, and analysis of oscillatory data. WAVOS includes both the Morlet continuous wavelet transform and the Daubechies discrete wavelet transform. We have illustrated the use of WAVOS, and demonstrated its utility for the analysis of circadian data on both bioluminesence and wheel-running data. WAVOS is freely available at <url>http://sourceforge.net/projects/wavos/files/</url></p

Radon spectrogram-based approach for automatic IFs separation

The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both in time and frequency, separation is only possible through a parametric approach whenever the signal class is a priori fixed. In this work, time-frequency analysis and Radon transform are jointly used for the unsupervised separation of modes of a generic frequency modulated signal in noisy environment. The proposed method takes advantage of the ability of the Radon transform of a proper time-frequency distribution in separating overlapping modes. It consists of a blind segmentation of signal components in Radon domain by means of a near-to-optimal threshold operation. The inversion of the Radon transform on each detected region allows us to isolate the instantaneous frequency curves of each single mode in the time-frequency domain. Experimental results performed on constant amplitudes chirp signals confirm the effectiveness of the proposed method, opening the way for its extension to more complex frequency modulated signals