A-scan Data Research Articles

A novel approach for forensic identification of automotive paints using optical coherence tomography and multivariate statistical methods.

Automotive paint is one of the most important evidence in solving vehicle-related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral-domain optical coherence tomography system (SD-OCT). Two-dimensional cross-sectional OCT images and three-dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A-scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross-sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A-scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi-layer perceptron (MLP), k-nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.

Modified Immediate Sequential Bilateral Cataract Surgery (Clinical Cases)

Purpose. Analysis of two clinical cases of immediate sequential bilateral cataract surgery according to the developed modified algorithm (mаISBCS).Materials and methods. The results of two clinical cases of mаISBCS are presented. In both cases, ultrasonic cataract phacoemulsification (FEC) was performed with implantation of an intraocular lens (IOL) under local anesthesia according to the standard technique through a corneal incision of 2.2–2.4 mm. IOL calculation was performed for emmetropic refraction. The modification (compared to the traditional one) of the technology of surgical intervention was based on the inclusion in the algorithm of a long (at least 60 minutes) break between operations for the purpose of a comprehensive ophthalmological examination with a further decision to perform an operation on the second eye.Results. In the first clinical case, the patient failed to measure the anterior-posterior axis by optical biometry before surgery. In such situations, the A-scan is used, which is considered less accurate due to human error and often leads to errors in the calculation of the IOL. In addition, a certain alertness was introduced by the fact of a significant (4 diopters) difference in the preoperative refraction of both eyes. Considering these provisions, after the first operation, after 60 minutes, the correctness of the IOL calculation was monitored by two methods — by measuring (optical biometrics) the anterior-posterior axis of the eye (APA, while the differences were less than 0.3 mm, which confirmed the A-scan data), as well as autorefractometry, which showed almost no deviation from the “target”. The presented results allowed us to formulate a decision to carry out the second operation. In the second clinical case, despite the absence of risk factors (optical biometrics was taken in two eyes, similar APA values), a postoperative refraction of +1.5 diopters was determined in the operated eye, which, apparently, is associated with the identified decrease (by 0.93 diopters) corneal curvature due to hydration of the corneal stroma in the area of surgical accesses and (or) changes in the tear film. The presented results allowed us to formulate a decision to postpone the second operation. At the same time, after a week, the restoration of the corneal tissue was noted, which was proved by the required (0.37 diopters) deviation of refraction from the target values.Conclusion. The use of mаISBCS technology provides the required clinical efficacy and can be recommended taking into account the epidemiological situation, especially for people with visually strenuous work.

A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram.

Detectable Depth of Copper, Steel, and Aluminum Alloy Plates with Pulse-Echo Laser Ultrasonic Propagation Imaging System

Pulse-echo laser ultrasonic propagation imaging is a nondestructive testing technique developed for composite materials and aluminum alloys used in aerospace. Although this method has been in usage for a considerable time, information of the detectable depth and the relationship between ultrasonic frequencies and the acoustic properties of metals is not readily available. Therefore, we investigate the A-scan and C-scan ultrasonic testing data of aluminum alloy, hot rolled steel, stainless steel, and copper alloy, which are used in aircraft bodies, frameworks, and gas pipelines. Experiments are performed with the pulse-width and excitation laser power fixed at 32 ns and approximately 4 W, respectively. The metal specimens include 24 artificial cylindrical defects with a diameter of 5 mm, located at depths of 1–12 mm on the front surface. The A-scan and C-scan data obtained at room temperature indicate the detectable depth for metals via the pulse-echo laser ultrasonic propagation imaging technique.

One-class SVM based outlier detection strategy to detect thin interlayer debondings within pavement structures using Ground Penetrating Radar data

In this paper, we present a processing method to detect millimeter interlayer debondings from Ground Penetrating Radar (GPR) B-scan images. The method is matched to carry out rapid debonding detection at the operational level. A machine learning based outlier-detection strategy namely, One-class Support Vector Machines (OCSVM) is proposed to detect A-scan data vectors which differ from a reference data set collected over a known healthy pavement area.OCSVM is tested on both simulated and experimental data representing GPR data over various artificial millimetric debondings at 2.6 GHz and 4.2 GHz from respectively ground-coupled and air-coupled radar configurations. The experimental data were collected at the Accelerated Pavement Test site located in the Nantes campus of Université Gustave Eiffel. The simulated models on the other hand were generated using a numerical EM solver based on Finite Difference Time Domain (FDTD) method namely, GprMax. Simulation tests allow to conduct sensitivity analysis to determine the robustness of the detection method at various signal-to-noise ratios (10 dB to 60 dB).The proposed OCSVM method demonstrated high performance on both simulated and experimental data to detect thin interlayer debondings over various GPR configurations.

Измерение поперечного размера глазного яблока методом В-сканирования с использованием линейного датчика

Purpose. To evaluate the feasibility of measuring the transverse size of the eyeball by B-scan with a linear probe in patients with horizontal strabismus. Material and Methods. The study included 56 children (56 eyes) aged 2 to 15 years with previously not operated horizontal strabismus. Two independent operators used B-scan method to measure the axial length (for comparison with A-scan data) and the transverse size of the eyeball according to the proposed method. The accuracy of measurements was estimated by calculating the reproducibility limit, and indirectly by the forecast accuracy of the software system STRABO, which used the obtained values of the transverse size of the eyeball for individual dosing operations. Results. The B-scan technique was developed for measuring the transverse size of the eyeball using a contact linear probe. The measurement reproducibility limit was 0.22 mm (95% confidence interval is 0.18–0.25 mm), which does not exceed 1% of the average transverse size of the eyeball (22.03 mm). The results of the operations confirmed the accuracy of the prediction by the software system STRABO, since the correct eye position was obtained within 6 months in all cases, excluding 4 patients in whom the second stage of the surgery was postponed. That gave an indirect confirmation of the measurement accuracy of the transverse size of the eyeball using the proposed technique. Conclusion. The proposed ultrasound B-scan technique with a linear contact probe provides high accuracy and reproducibility of measurements of the transverse size of the eyeball and can be used in pediatric ophthalmological practice for calculating surgical procedures for strabismus using the individual dosing software system STRABO. Key words: transverse size of the eyeball, axial length, ultrasound B-scan, linear probe, horizontal strabismus, dosing.

Semi-automatic detection of buried rebar in GPR data using a genetic algorithm

This paper proposes a new method for semi-automatic detection of buried rebar in ground penetrating radar (GPR) data based on searching the reflected waveform of target by the genetic algorithm, which aims to overcome the inefficiency and instability of the manual process. Before the detection step, a binary image is generated by collecting all the local maximum points from A-scan GPR data to turn the searching for waveform image into curves. Meanwhile, the possible locations of the targets are preliminarily selected from the gray-scale frequency-wavenumber domain migration image of GPR data by setting a gray gradient threshold in the vertical direction. Then based on the genetic algorithm and time-distance analysis of the reflected signal, semi-automatic detection could be achieved by searching the best fitting hyperbola at every potential location in the binary image, and matching them with the reflected signal of targets according to several carefully chosen matching criteria. The impact of waveform distortion is also considered in the matching process to ensure a good adaptability of this method to the GPR data. Precision and efficiency of this method are analyzed lastly by four measured data of different antenna frequencies and targets, the average misjudgment and the undetected rate are 9.08% and 11.98% respectively. Meanwhile, the same GPR data is detected by four antenna central frequencies to find that the missed rate of semi-automatic detection decreases with the increase of the antenna frequency, when the misjudgment rate remains stable.

Multi-Angle Phased Array Ultrasonic Line-Scan Method for Steel Reinforced Polyethylene Electro-fusion Welded Joint

In view of the difficulties and ultrasonic blind zone problems in the ultrasonic inspection of electro-fusion welded joint of steel reinforced polyethylene composite pipes, an inspection method based on phased array inspection technology, which compounds multi-angle line scan image, is proposed in this paper. It inspects by forming a plurality of B-scan images through setting up multiple line scanning channels and quick switching in the inspection process, then forming a new B-scan image after optimizing and reconstructing by computer and retaining relevant A-scan signal data for defect evaluation, thus realizing rapid and efficient real-time imaging inspection on the ultrasonic blind zone of the electro-fusion welded joint of steel reinforced polyethylene composite pipes.

Structural and Functional Sensing of Bio-Tissues Based on Compressive Sensing Spectral Domain Optical Coherence Tomography.

In this paper, a full depth 2D CS-SDOCT approach is proposed, which combines two-dimensional (2D) compressive sensing spectral-domain optical coherence tomography (CS-SDOCT) and dispersion encoding (ED) technologies, and its applications in structural imaging and functional sensing of bio-tissues are studied. Specifically, by introducing a large dispersion mismatch between the reference arm and sample arm in SD-OCT system, the reconstruction of the under-sampled A-scan data and the removal of the conjugated images can be achieved simultaneously by only two iterations. The under-sampled B-scan data is then reconstructed using the classic CS reconstruction algorithm. For a 5 mm × 3.2 mm fish-eye image, the conjugated image was reduced by 31.4 dB using 50% × 50% sampled data (250 depth scans and 480 spectral sampling points per depth scan), and all A-scan data was reconstructed in only 1.2 s. In addition, we analyze the application performance of the CS-SDOCT in functional sensing of locally homogeneous tissue. Simulation and experimental results show that this method can correctly reconstruct the extinction coefficient spectrum under reasonable iteration times. When 8 iterations were used to reconstruct the A-scan data in the imaging experiment of fisheye, the extinction coefficient spectrum calculated using 50% × 50% data was approximately consistent with that obtained with 100% data.

Performance assessment of SVM-based classification techniques for the detection of artificial debondings within pavement structures from stepped-frequency A-scan radar data

Owing to traffic and weather conditions, pavement structures may suffer from horizontal and vertical cracks that shorten the overall lifetime of roadways. In this paper, we focus on the detection of the horizontal subsurface cracks (debondings) occurring between the first two pavement layers from Stepped-frequency Radar (SFR) data. The processing of radar data requires some refined signal analysis to detect constructive interference between overlapping echoes. It is performed from timely data by a supervised machine learning method namely, Support Vector Machines (SVM) and compared to the conventional reference method, namely, the Amplitude Ratio Test (ART), which is routinely performed at the operational level. Besides, the straightforward application of SVM on raw data is compared to the solution using physical signal features (global and local) that reduces the computational burden. Performance assessment and comparison of the processing methods are conducted on data collected on a specific test-site with three different types of artificial debondings.

A reference free ultrasonic phased array to identify surface cracks in welded steel pipes based on transmissibility

This study aims to enhance the precision of ultrasonic phased array (UPA) testing in detecting and sizing the circumferential surface cracks near the girth welds of the steel pipes, via implementing, for the first time, the transmissibility analysis on the ultrasonic scan data. The transmissibility-based approach detects the presence of cracks or crack-like flaws, solely from the relative magnitude of the A-scan data in intact materials and in materials with a crack, and hence removes the requirement for a reference block, but retains the material characteristics. This study also proposes a new approach to detect cracks by the transmissibility between measured ultrasonic A-scan data at distinct locations. Through a relative movement of the UPA sensor, the crack depths derive from the wave propagation theory. The comparison against conventional UPA tests and measurement from replica-reproduced fracture surfaces, in seven circumferentially welded steel pipes with surface cracks produced by fatigue and fracture tests, verifies the applicability of proposed crack sizing approach. The experimental study also examines the distance threshold from the crack location, within which a reasonable accurate crack sizing remains tractable.

Automated Quality Characterization for Composites Using Hybrid Ultrasonic Imaging Techniques

ABSTRACTAn enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.

Three dimensional evaluation of aluminum plates with wall-thinning by full-field pulse-echo laser ultrasound

We propose a full-field pulse-echo laser ultrasonic wave propagation imager (FF-PE-UPI) for the evaluation of structural defects. The FF-PE-UPI consists of a Q-switched laser for the generation of thermoelastic waves, a laser Doppler vibrometer (LDV) for sensing, and a two-axis translation stage for raster scanning of the combined generation and sensing laser beams. A-scan, B-scan, and C-scan data representations are used for the evaluation of structural defects. Three specimens were tested: a 4-mm aluminum plate with an area of 50% thickness reduction, a 6-mm aluminum plate with an area of 25% thickness reduction, and an 8-mm aluminum plate with engraved letters. The damages on the tested specimens were successfully visualized.

Root imaging from ground penetrating radar data by CPSO-OMP compressed sensing

As the amount of data produced by ground penetrating radar (GPR) for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal (CPSO) compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.

Determining the Optimal Frequency of Ground Penetrating Radar for Detecting Voids in Pavements

PURPOSES : The objective of this study is to determine the optimal frequency of ground penetrating radar (GPR) testing for detecting the voids under the pavement. METHODS : In order to determine the optimal frequency of GPR testing for void detection, a full-scale test section was constructed to simulate the actual size of voids under the pavement. Voids of various sizes were created by inserting styrofoam at varying depths under the pavement. Subsequently, 250-, 500-, and 800-MHz ground-coupled GPR testing was conducted in the test section and the resulting GPR signals were recorded. The change in the amplitude of these signals was evaluated by varying the GPR frequency, void size, and void depth. The optimum frequency was determined from the amplitude of the signals. RESULTS: The capacity of GPR to detect voids under the pavement was evaluated by using three different ground-coupled GPR frequencies. In the case of the B-scan GPR data, a parabolic shape occurred in the vicinity of the voids. The maximum GPR amplitude in the A-scan data was used to quantitatively determine the void-detection capacity. CONCLUSIONS: The 250-MHz GPR testing enabled the detection of 10 out of 12 simulated voids, whereas the 500-MHz testing allowed the detection of only five. Furthermore, the amplitude of GPR detection associated with 250-MHz testing is significantly higher than that of 500-MHz testing. This indicates that 250-MHz GPR testing is well-suited for the detection of voids located at depths ranging from 0.5~2.0 m. Testing at frequencies lower than 250 MHz is recommended for void detection at depths greater than 2 m.

PEGylated Aucore–Agshell Nanorods as Optical Coherence Tomography Signal Nanoamplifiers

Optimizing contrast enhancement in optical coherence tomography (OCT) is essential for producing specific image signals and realizing its potential use in various biomedical imaging fields besides ophthalmology and cardiology. Using nanoparticles as selective signal nanoamplifiers will be helpful to highlight the lesion sites and identify cancerous changes that are difficult to diagnose currently. In this study, a quantitatively detailed analysis on the improvement of contrast enhancement and penetration depth of Aucore–Agshell nanorods (Au@Ag NRs) as OCT contrast agent was performed. The average A-scan data of the tissue was extracted and investigated. The signal to noise ratio (SNR) and relative 1/e light penetration depth were also calculated. Our measurements revealed that, after intravenous delivery, the visibility of the tumor site was most obvious when treated with Au@Ag NRs. And the maximum value of 1/e penetration depth of Au@Ag NRs grew by 25 % when compared to that of gold nanorods (Au NRs).

Improved Compressive Sampling SFCW Radar by Equipartition of Energy Sampling

A Stepped-Frequency Continuous Wave (SFCW) radar performs imaging by transmitting a number of electromagnetic tones whose frequency is increased step-wisely to obtain an equivalent representation of a signal in frequency domain, then Fourier inversion is conducted to obtain a range profile. A Compressive Sampling (CS) SFCW radar reduces measurement time by randomly selecting a small number of the tones, followed by CS reconstruction. This paper shows empirically, that the knowledge of typical magnitude spectrum of the radar's signal can be used to improve the CS-SFCW radar system in term of either better reconstruction quality or less number of required samples. Instead of random selection, the knowledge of the spectrum is used to select the best set of frequencies, by applying equipartition of energy sampling (EES) principle. Three sampling schemes, i.e., Frequency Equidistant Sampling (FES), Uniform Random Sampling (URS), and the proposed EES, are used to obtain the samples of both simulated and actual A-scan data and then identical CS reconstruction methods are applied. Objective performance evaluation in term of PSNR (Peak Signal to Noise Ratio) shows that reconstructed result of URS outperforms the FES, while the EES outperforms both of them.

Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

Study on cerebral vascular image of spectral domain optical coherence tomography with compressive sensing

We propose a novel compressed sensing method to retrieve cerebral vascular image for spectral-domain optical coherence tomography (SD OCT). The compressed sensing method based on l1 norm minimization is applied to reconstruct each A-scan data. The proposed method uses about 25% of the total data as required in traditional SD OCT to reconstruct the cerebral angiograpy. Therefore this method is favorable for high speed imaging for cerebral angiograpy. It is shown that the proposed method can achieve better performance of axial resolution and higher signal-to-noise ratio (SNR) as compared with the conventional methods.

Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography

Variance processing methods in Fourier domain optical coherence tomography (FD-OCT) have enabled depth-resolved visualization of the capillary beds in the retina due to the development of imaging systems capable of acquiring A-scan data in the 100 kHz regime. However, acquisition of volumetric variance data sets still requires several seconds of acquisition time, even with high speed systems. Movement of the subject during this time span is sufficient to corrupt visualization of the vasculature. We demonstrate a method to eliminate motion artifacts in speckle variance FD-OCT images of the retinal vasculature by creating a composite image from multiple volumes of data acquired sequentially. Slight changes in the orientation of the subject’s eye relative to the optical system between acquired volumes may result in non-rigid warping of the image. Thus, we use a B-spline based free form deformation method to automatically register variance images from multiple volumes to obtain a motion-free composite image of the retinal vessels. We extend this technique to automatically mosaic individual vascular images into a widefield image of the retinal vasculature.