Naizhang Feng

Photoacoustic imaging method based on arc-direction compressed sensing and multi-angle observation

In photoacoustic imaging (PAI), the photoacoustic (PA) signal can be observed only from limit-view angles due to some structure limitations. As a result, data incompleteness artifacts appear and some image details lose. An arc-direction mask in PA data acquisition and arc-direction compressed sensing (CS) reconstruction algorithm are proposed instead of the conventional rectangle CS methods for PAI. The proposed method can effectively realize the compression of the PA data along the arc line and exactly recover the PA images from multi-angle observation. Simulation results demonstrate that it has the potential of application in high-resolution PAI for obtaining highly resolution and artifact-free PA images.

Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.

Mitigating end effects of EMD using non-equidistance grey model

Aiming at mitigating end effects of empirical mode decomposition (EMD), a new approach motivated by the non-equidistance grey model (NGM) termed as NGM(1,1) is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation (AGO) series. Hopefully, it is worth stressing that the proposed NGM(1,1) model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.

Feature selection and classification algorithm for non-destructive detecting of high-speed rail defects based on vibration signals

Many rail accidents were caused by rail defects, therefore the detection of the rail defects is of vital importance. Using simulated and experimental measurements, the rail defect detection was carried out. The feature parameters were extracted both from time domain and time-frequency domain. Then the sequential backward selection method was applied to select the important feature parameters. After optimizing of the feature parameter set, support vector machine method was applied to recognize and classify the rail defects. It has been proved that the proposed algorithm of analyzing and processing the rail defect vibration signals is an effective and non-destructive detecting method of the rail defects.

PHOTOACOUSTIC SIGNALS DENOISING BASED ON EMPIRICAL MODE DECOMPOSITION AND ENERGY-WINDOW METHOD

In the process of photoacoustic imaging (PAI), the photoacoustic signals are polluted by a strong background white noise, which is caused by many factors such as the system thermal noise or short noise, the tissue reflecting or scattering interference, and the impedance match lack between the transducer and tissue. The inevitable noise can degrade the contrast sensitivity of photoacoustic images seriously. In this paper, based on the energy window, a CMSE-EMD denoising method is employed to photoacoustic image reconstruction. Results of the simulation demonstrate that it can eliminate the image artifacts more effectively and achieve great improvement in image quality.

An investigation on rail health monitoring using acoustic emission technique by tensile test

In order to detect the health status of high-speed railway, various studies have been examined by Acoustic Emission (AE) method. However, little work has been done on studying the relationship between rail status and features of AE signals, and this relationship can be used to establish a detection criterion for rail health monitoring. This paper presents a methodology on rail health monitoring by AE signals and establishing a detection criterion. AE signals in different safe status are obtained by tensile testing machine and AE data acquisition system. The safe and unsafe region of rail steel is analyzed by stress-strain curve. Based on the Chebyshevs inequality and the variation rate of AE hits, a detection criteria is established to detect the safe status of rail, and the corresponding detection procedure are provided. The results clearly illustrate that the proposed method can detect the safe status of rail effectively.

Photoacoustic microscopy image resolution enhancement via directional total variation regularization

Photoacoustic microscopy (PAM) is recognized as a powerful tool for various microcirculation system studies. To improve the spatial resolution for the PAM images, the requirements of the system will always be increased correspondingly. Without additional cost of the system, we address the problem of improving the resolution of PAM images by integrating a deconvolution model with a directional total variation regularization. Additionally, we present a primal-dual-based algorithm to solve the associated optimization problem efficiently. Results from both test images and some PAM images studies validate the effectiveness of the proposed method in enhancing the spatial resolution. We expect the proposed technique to be an alternativeresolution enhancement tool for some important biomedical applications.

Compressive sampling photoacoustic tomography based on edge expander codes and TV regularization

A new photoacoustic (PA) signal sampling and image reconstruction method, called compressive sampling PA tomography (CSPAT), is recently proposed to make low sampling rate and high-resolution PA tomography possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation (TV) regularization-based model to formulate the associated problem. The edge expander codes-based masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.

Compressive Sampling Photoacoustic Microscope System based on Low Rank Matrix Completion

Abstract Photoacoustic Microscopy (PAM) has developed into a powerful tool for deep tissue imaging with a better spatial resolution. But the data acquisition time in PAM is so long that it is a great challenge for real time imaging. In this paper, a new PAM data acquisition and image recovery method, called Compressive Sampling PAM System based on Low Rank Matrix Completion (CSLRM-PAM) is proposed to obtain a high-resolution PAM image with relatively low sampling rates. In order to successfully set up a CSLRM-PAM system, the two key problems which we need to keep focus on are design of the compressive sampling scheme and the corresponding image recovery algorithm. In this paper, two compressive sampling schemes based on expander graphs are proposed to replace the conventional point-by-point scanning scheme to implement fast data acquisition. Then, the low rank matrix completion is utilized to obtain high-resolution PAM image directly from the compressive sampling data. The effectiveness of the proposed scheme is validated using both numerical analysis and PAM experiments. In contrast with the conventional system, the proposed CSLRM-PAM system is able to dramatically decrease the total sampling points for a relatively high-resolution PAM image and to implement accelerated data acquisition.

Rail health monitoring using acoustic emission technique based on NMF and RVM

In order to detect the health status of high-speed railway, this paper proposes a detection method based on non-negative matrix factorization (NMF) and relevance vector machine (RVM) by acoustic emission (AE) signals. AE signals are obtained by tensile testing machine and AE data acquisition system. According to the stress-time curve, AE signals with safe state and unsafe state are obtained. Based on the frequency spectrum analysis of AE signals, the ratio of each frequency component relative to maximum frequency component is used as a feature vector to distinguish safe and unsafe states. Vectors with compressed and optimized features are obtained based on NMF, and these vectors are used to train and test the classifier by RVM. The classification accuracy of 10-folds cross validation on the whole dataset is up to 96%. The results illustrate that the proposed method can detect the safe status of rail effectively.