Spectral histogram using the minimization algorithm-theory and applications to flaw detection

Measuring predictability in ultrasonic signals: an application to scattering material characterization

In this paper, we present a novel and completely different approach to the problem of scattering material characterization: measuring the degree of predictability of the time series. Measuring predictability can provide information of the signal strength of the deterministic component of the time series in relation to the whole time series acquired. This relationship can provide information about coherent reflections in material grains with respect to the rest of incoherent noises that typically appear in non-destructive testing using ultrasonics. This is a non-parametric technique commonly used in chaos theory that does not require making any kind of assumptions about attenuation profiles. In highly scattering media (low SNR), it has been shown theoretically that the degree of predictability allows material characterization. The experimental results obtained in this work with 32 cement probes of 4 different porosities demonstrate the ability of this technique to do classification. It has also been shown that, in this particular application, the measurement of predictability can be used as an indicator of the percentages of porosity of the test samples with great accuracy.

The feasibility of pulse compression by nonlinear effective bandwidth extension

Chirp-encoded excitation has been utilized for increased signal-to-noise ratio (SNR) in both linear and harmonic imaging. In either case, it is necessary to isolate the relevant frequency band to avoid artifacts. In contrast, the present study isolates and then combines the fundamental and the higher harmonics, treating them as a single, extended bandwidth. Pulse-inverted sum and difference signals are first used to isolate even and odd harmonics. Matched filters specific to the source geometry and the transmit signal are then separately applied to each harmonic band. Verification experiments are performed using up to the third harmonic resulting from an underwater chirp excitation. Analysis of signal peaks after scattering from a series of steel and nylon wires indicates increased compression using the extended bandwidth, as compared to well-established methods for fundamental and second harmonic chirp compression. Using third harmonic bands, a mean pulse width of 56% relative to fundamental compression and 48% relative to second harmonic compression was observed. Further optimization of the compression by altering the transmission indicated 17% additional reduction in the pulse width and a 47% increase in peak-to-sidelobe ratio. Overall, results establish the feasibility of extended bandwidth signal compression for simultaneously increasing SNR and signal resolution.

Signal denoising and ultrasonic flaw detection via overcomplete and sparse representations

Sparse signal representations from overcomplete dictionaries are the most recent technique in the signal processing community. Applications of this technique extend into many fields. In this paper, this technique is utilized to cope with ultrasonic flaw detection and noise suppression problem. In particular, a noisy ultrasonic signal is decomposed into sparse representations using a sparse Bayesian learning algorithm and an overcomplete dictionary customized from a Gabor dictionary by incorporating some a priori information of the transducer used. Nonlinear postprocessing including thresholding and pruning is then applied to the decomposed coefficients to reduce the noise contribution and extract the flaw information. Because of the high compact essence of sparse representations, flaw echoes are packed into a few significant coefficients, and noise energy is likely scattered all over the dictionary atoms, generating insignificant coefficients. This property greatly increases the efficiency of the pruning and thresholding operations and is extremely useful for detecting flaw echoes embedded in background noise. The performance of the proposed approach is verified experimentally and compared with the wavelet transform signal processor. Experimental results to detect ultrasonic flaw echoes contaminated by white Gaussian additive noise or correlated noise are presented in the paper.

Measurements of acoustic dispersion on calcaneus using spilt spectrum processing technique

The speed of sound (SOS) has become a useful tool in osteoporosis assessment, since it represents a combination of density and compressibility of bone tissue and should provide better information on bone quality and an estimate the fracture risk. In general, the speed of sound on dispersive material, such as bone tissue, depends strongly on frequency. Therefore, a measurement of velocity dispersion magnitude (VDM) might provide more important bone structure information than measurements of bone mineral density (BMD), SOS or broadband ultrasound attenuation (BUA). To obtain the velocity dispersion magnitude requires a sequence of pulses that have a frequency that is different from that used in conventional approaches. The measurement is complicated by the fact that pulse waveform will distort as the pulses propagate through the frequency-dependent medium. Alternatively, the phase velocity and velocity dispersion measurements also can be obtained on frequency-domain processing. However, the accuracy of those techniques is affected by the 2mpi ambiguity in the phase unwrapping process in frequency domain. And the spectrum approach is highly dependent on the gating window selection in time domain signals. The time-domain split spectrum processing (SSP) technique is proposed here to measure the phase velocity and the VDM. The SSP technique is also used to measure the SOS and VDM of two commercial calcaneus phantoms. Simulation results are in good agreement with the preset parameters of a model-based signal obtained using the SSP technique. In addition, in vitro SSP measurements agree with the manufacturer's specifications for two commercial calcaneus phantoms. The negative dispersion is also found in in vivo measurements on human heel. Finally, an approach based on the time domain SSP technique has potential clinical applications for osteoporosis diagnosis.

Combining phase and energy detection with mathematical morphology in dual time-frequency representation leads to improved SSP noise robustness

In this paper a fusion algorithm for the detection of specular echoes blurred by coloured Gaussian noise is proposed. The phase and energy criteria, characteristic of these echoes, are handled separately. First, the detection capabilities of split spectrum (phase) and short time Fourier transform spectrogram (energy) are compared theoretically and experimentally. Second, the new algorithm based on mathematical morphology operators using both time-frequency representations is proposed. In the last part our method is shown to be more robust than absolute minimisation split spectrum processing.

Two approaches to multiple specular echo detection using split spectrum processing: moving bandwidth minimization and mathematical morphology

A split spectrum processing technique using a novel moving bandwidth minimization (MBM) method was developed to detect multiple specular targets having different spectral characteristics. Mathematical morphology (MM) algorithms were also implemented in order to compare the results. An experimental approach to optimal parameter determination is described. These non-linear filtering methods are applied to medical in vivo imaging to illustrate specular detection and signal to noise ratio (SNR) enhancement.

Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection

The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.