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The successful implementation of high-level decision algorithm on quadrotor depends on the accurate trajectory tracking performance. In this paper attitude estimation and trajectory tracking control problem of quadrotor unmanned aerial vehicle (UAV) with endogenous and exogenous disturbance are considered, where the lumped disturbance characteristic does not have a probabilistic illustration but instead the dynamics are known to have a bound. The problem is handled by developing disturbance estimator and control strategy. In order to estimate lumped disturbance precisely, a globally finite time stable extended state observer is proposed based on super-twisting algorithm. Stability analysis and observer’s parameters selection rule are discussed by using Lyapunov’s stability theory. The proposed observer strategy achieves accurate observing performance of disturbance without increasing observer’s order, and chattering effect is also reduced by applying super-twisting algorithm. Furthermore, a super-twisting sliding mode control law is proposed to guarantee the asymptotic convergence of the drone’s orientation with respect to the reference. Finally, a numerical study based on simulations is presented to analyze the performance of proposed approach.

In this article, a novel ultrasound computed tomography (USCT) reconstruction algorithm for breast imaging is proposed. This algorithm is based on an ultrasound propagation model, the refract-ray model (RRM). In this model, the field of imaging is assumed as piecewise homogenous and is divided into several regions. The ultrasound propagation paths are considered polylines that only refract at the borders of the regions. The edge information is provided by B-mode imaging. Both simulations and experiments are implemented to validate the proposed algorithm. Compared with the traditional bent-ray model (BRM), the time of reconstructions using RRM decreases by over 90 %. In simulations, the imaging qualities for RRM and BRM are comparable, in terms of the root mean square error, the Tenengrad value, and the deformation of digital phantom. In the experiments, a cylindrical agar phantom is imaged using a customized imaging system. When imaging using RRM, the estimate of the phantom radius is about 0.1 mm in error, while it is about 0.3 mm in error using BRM. Moreover, the Tenengrad value of the result using RRM is much higher than that using BRM (9.76 compared to 0.79). The results show that the proposed algorithm can better delineate the phantom within a water bath. In future work, further experimental work is required to validate the method for improving imaging quality under breast-mimicking imaging conditions.

Breast cancer has become one of the most common and lethal cancers in women worldwide. Ultrasound tomography (UST) is supposed to be an imaging method for early diagnosis of breast cancer. In the implementation of UST, operators usually need to compromise between the image quality and the complexity of scanning. In order to explore the relationship between them, a preliminary UST imaging system is designed and introduced in this paper. It uses two co-planar 128-element piezoelectric ultrasonic transducers as transmitter and receiver respectively, each of which can rotate freely in the horizontal plane. Based on this prototype, a series of experiments with different scanning patterns are designed, the corresponding signal completeness are calculated, and the imaging results are compared.

In order to classify the acoustic emission (AE) signals of rail cracks more accurately and quickly, a significant problem to be solved is how to select the optimal feature set of AE signals. Two aspects are included in this problem, one is feature sets dimensionality reduction, the other is feature selection. In this paper, the method is proposed to find effective features and the best dimension of feature set, which based on an adaptive weight binary particle swarm optimization algorithm combined with support vector machine (BPSO-SVM) method. The optimization ability in the later stage of the algorithm has been enhanced to avoid falling into local minima. The error rate is taken as the optimization objective which is generated by SVM to obtain the weight of features. These weights are counted in several experiments to get the number of feature votes. Effective features are filtered by an adaptive threshold and form the optimal feature set. Experimental results show that the improved method is able to effectively reduce the feature set dimension, improve the classification accuracy, and boost the real-time performance in rail AE signal detection. The stability and validity of this method are verified by several experiments, which provide guidance for the AE signal classification and feature selection problems.

Single tracking location shear wave elasticity imaging (STL-SWEI) has been proven to have the capability to eliminate the speckle bias due to its unique push-tracking mode. However, compared to multiple tracking location shear wave elasticity imaging (MTL-SWEI), STL-SWEI requires more acoustic excitation and push-track sequences, and thus has a lower frame rate. In this work, a new approach named fast single tracking location shear wave elasticity imaging (FSTL-SWEI) is presented. FSTL utilizes spatially modulated ultrasound radiation force (SMURF) applying a special acoustic intensity pattern to generate lateral sinusoidal distributed (LSD) shear waves. It infers shear wave speed (SWS) distribution using the instantaneous phase of the shear wave motion signals obtained by the Hilbert-Huang Transform. Able to map the elasticity of the region of interest (ROI) in only one push-track sequence, it can achieve a frame rate comparative to MTL-SWEI. To validate the performance of this method, a finite element model (FEM) was established to simulate breast tissue with an inclusion. The simulation results show that FSTL can well distinguish the inclusion from the background.

Despite the promising clinical application of linear-array photoacoustic tomography, it has been shown the variable speed of sound would severely affect the photoacoustic imaging quality, resulting in the target deterioration and inaccurate depth positioning in the conventional constant speed assumed delay and sum (DAS) reconstruction. By contrast, multi-stencils fast marching (MSFM) method is able to produce accurate time delay map for the tissue with inhomogeneous acoustic speed. This study explored the combination of DAS reconstruction algorithm with the MSFM approach to reduce the imaging distortions due to the speed spatial variation, where the target structure and target position in depth could be measured more precisely. To validate the performance of the proposed method, numerical, phantom, and in vivo photoacoustic studies were conducted with the qualitative and quantitative analysis, especially in the detection of mouse deep brain tumor with an intact skull.

Photoacoustic (PA) tomography (PAT) is a cutting-edge imaging modality for visualizing the internal structure and light-absorption distribution in tissue. However, reconstruction of the absorption distribution has been limited by nonuniform light fluence. This paper introduces a novel method for quantitative reconstruction of the distribution of optical absorption coefficients in tissue. In this method, we implement an iterative algorithm for recovering absorption coefficients from optical absorbed energy maps based on a 3D Monte Carlo simulation of light transport and integrated with fluence compensation to obtain the initialization parameters. In the iteration algorithm, we calculate the deviation between the detected and the computed absorbed energy distribution at each iteration. By minimizing the deviation in the absorbed energy, the recovered values converge to the true absorption distribution. The results of numerical simulation and phantom experiment theoretically and experimentally demonstrate that the proposed method performs an accurately quantitative estimate of the distribution of optical absorption coefficients. This work expects to provide accurate quantitative information for absorbers within tissues or organs, and thereby broaden the clinical applications of PAT.

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.

As a high-resulotion biological imaging technology, photoacoustic microscopy (PAM) is difficult to use in real-time imaging due to the long data acquisition time. Herein, a fast data acquisition and image recovery method named sparse PAM based on a low-rank matrix approximation is proposed. Specifically, the process to recover the final image from incomplete data is formulated into a low-rank matrix completion framework, and the “Go Decomposition” algorithm is utilized to solve the problem. Finally, both simulated and real PAM experiments are conducted to verify the performance of the proposed method and demonstrate clinical potential for many biological diseases.

As a high-resolution deep tissue imaging technology, photoacoustic microscopy (PAM) is attracting extensive attention in biomedical studies. PAM has trouble in achieving real-time imaging with the long data acquisition time caused by point-to-point sample mode. In this paper, we propose a sparse photoacoustic microscopy reconstruction method based on matrix nuclear norm minimization. We use random sparse sampling instead of traditional full sampling and regard the sparse PAM reconstruction problem as a nuclear norm minimization problem, which is efficiently solved under alternating direction method of multiplier (ADMM) framework. Results from PAM experiments indicate the proposed method could work well in fast imaging. The proposed method is also be expected to promote the achievement of PAM real-time imaging.

In the field of image processing and machine vision, it is sometimes necessary to obtain common tangent line equations and tangent point coordinates from ellipses. A rolling tangent line method was proposed to obtain the 4 common tangent line equations and 8 tangent point coordinates from two ellipses in this paper. The principle of this method is simple and it is easy to program on a computer. Use this method to process two ellipse targets in an image and the experiment results show that the 4 common tangent equations and 8 tangent point coordinates can be obtained in high precision and the maximum execution time is less than 0.1 s.

In order to detect cracks in railroad tracks, various experiments have been examined by Acoustic Emission (AE) method. However, little work has been done on studying rail defect detection at high speed. This paper presents a study on AE detection of rail defect at high speed based on rail–wheel test rig. Meanwhile, Wavelet Transform and Shannon entropy are employed to detect defects. Signals with and without defects are acquired, and characteristic frequencies from them at different speeds are analyzed. Based on appropriate decomposition level and Energy-to-Shannon entropy ratio, the optimal wavelet is selected. In order to suppress noise effects and ensure appropriate time resolution, the length of time window is investigated. Further, the characteristic frequency of time window is employed to detect defect. The results clearly illustrate that the proposed method can detect rail defect at high speed effectively.

Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO) optimized support vector machine (SVM) interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

The appearance of blood vessels is an important biomarker to distinguish diseased from healthy tissues in several fields of medical applications. Photoacoustic microangiography has the advantage of directly visualizing blood vessel networks within microcirculatory tissue. Usually these images are interpreted qualitatively. However, a quantitative analysis is needed to better describe the characteristics of the blood vessels. This Letter addresses this problem by leveraging an efficient multiscale Hessian filter-based segmentation method, and four measurement parameters are acquired. The feasibility of our approach is demonstrated on experimental data and we expect the proposed method to be beneficial for several microcirculatory disease studies.

Acoustic Emission (AE) technique is an effective nondestructive detecting method, and has a promising application for rail defect detection. So far, little attention has been paid to propagation distances, types, and depths of AE sources, which are important for rail defect detection accurately. This paper presents an experimental study on the simulated AE sources with different propagation distances, types and depths for rail defect detection. Three simulated AE sources with different frequencies are seeded on the cross section of rail, and the depths of AE sources are changed in the vertical direction. After receiving AE signals, wavelet transform and Rayleigh–Lamb equations are utilized to extract time–frequency features and modes. Based on the wavelet transform with corresponding group-velocity curves, the influences of different propagation distances, the features of different source types and the rules of different source depths are examined. It is concluded that the features of AE sources with different propagation distances, types and depths can be obtained by AE technique for rail defect detection. It is very useful to analyze and detect defects in rail defect detection.

This paper investigates the H∞ output tracking control system design of an autopilot applying Takagi-Sugeno (T-S) fuzzy model. The nonlinear dynamics of the autopilot are modeled as a two-rule fuzzy model. Based on the T-S fuzzy model, an observer-based controller is designed to achieve the required tracking performance. A compensator is proposed by using backstepping method to compensate for the influence of the actuator dynamics. Performance and stability analysis for the designed control system are deduced in this work. Finally, simulation results show the effectiveness of the proposed design.

As an innovative optical absorption microscopy technology, Photoacoustic Microscopy (PAM) can provide high-sensitivity and high-resolution image of tissue samples in vivo. For the conventional scanning PAM system, intensive spatial sampling is required to obtain higher-resolution images result in a long acquisition time and high-cost system. Thus, a new PAM data acquisition and image recovery method, called non-uniform sampling PAM based on low rank matrix completion, is proposed in this paper to make low sampling rate and high-resolution PAM image possible. In the system, a non-uniform sampling scheme is utilized to dramatically decrease the total sampling points while keep a relatively high resolution. Besides, the corresponding image recovery problem is perfectly solved by low rank matrix completion. Both numerical and experimental results from real PAM system have shown the effectiveness of the proposed method.

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.

Empirical mode decomposition is useful in nonparametric signal denoising problem. In this paper, a signal denoising method based on intrinsic mode functions is proposed. A block energy criterion based on the signal samples between two adjacent zero-crossings is proposed to distinguish the useful signal from noise. To verify the effectiveness of the method presented in this paper, two different synthetic signals corrupted by additive white Gaussian noise are simulated under different sampling frequencies settings, and the results are compared with the existing EMD based denoising methods. The simulation results illustrated that the presented block energy based technique can improve signalto-noise ratio of the synthetic signals obviously.