We consider a SU(3) spin–orbit coupled Bose–Einstein condensate confined in a harmonic plus quartic trap. The ground-state wave functions of such a system are obtained by minimizing the Gross–Pitaevskii energy functional, and the effects of the spin-dependent interaction and spin–orbit coupling are investigated in detail. For the case of ferromagnetic spin interaction, the SU(3) spin–orbit coupling induces a threefold-degenerate plane wave ground state with nontrivial spin texture. For the case of antiferromagnetic spin interaction, the system shows phase separation for weak SU(3) spin–orbit coupling, where three discrete minima with unequal weights in momentum space are selected, while hexagonal honeycomb lattice structure for strong SU(3) SOC, where three discrete minima with equal weights are selected.

In the traditional three-dimensional CT reconstruction algorithms, high-frequency characteristics are difficult to obtain, and the noise is difficult to filter. In addition to, the reconstruction purpose is of diversification, and the three-dimensional filter to extract feature is more difficult to design and implement. Based on the characteristics of three-dimensional reconstruction algorithm and the RADON transform, a 3D CT reconstruction method to directly reconstruct multi-characteristic based on EMD was proposed and derived from the theoretical possibility and feasibility. EMD algorithm could firstly be used to decompose projection image before the reconstruction, and we could combine different IMF components to achieve a variety of feature, then use three-dimensional reconstruction algorithm to directly reconstruct the characteristics. The experimental results showed that the algorithm can better highlight a variety of useful information, simplify the filter design, and is of a certain practicability.

When the thickness changes of the workpiece in practical engineering are wider than the range of digital imaging,the digital image will have dark gray or gray-scale saturation region with details information loss in thin or thick of the workpiece.An image enhancement technology based on variable doses of X-ray image was proposed,in which different X-ray doses were applied to the workpiece for obtaining a sequence of images,and the most informative image blocks were selected from a sequence image splicing and used for fusion in order to get the final enhanced images.Experiments showed that this method was simple and effective,the thin and thick parts details could be clearly presented in an image after enhancement,thus having good control performance of the noise and achieving the purpose of expanding the dynamic range of X-ray images.

This paper presents an intelligent approach to recognize 3D objects using line structure correspondences. The proposed approach simultaneous recognizes an object and estimates the pose of the object. In order to achieve this goal, three challenges should be solved. First of all, line structures that human usually used to describe an object is used to represent the object. A set of such feature representation that shares the same properties with corresponding model line structures are first generated from images. Secondly, the structure correspondences are evaluated and ranked by additional features in the image. Only the most meaningful correspondences are selected. Each correspondence contributes a pose hypothesis with a transformation matrix. Finally, the approximate model pose hypotheses are estimated and refined based on the selected correspondences.

Instead of the subjective assessment method, the objective assessment method is used for adjusting the tube voltages and currents to achieve the closed-loop control of X-ray imaging system. Combined with the characteristics of X-ray image, an image quality evaluation method is introduced in this paper based on the weighted entropy of region of interest(ROI). Compared with the traditional image quality assessment method, The experiments results show that the method proposed in this paper can evaluate ray image quality effectively and has better single peak, robustness and sharpness. Copyright © 2011 Binary Information Press.

The Zernike moment edge detection algorithm is a detection algorithm which is based on sub-pixel level. However, the algorithm requires manual tuning on selecting threshold, so it does not have intelligence and unable to meet the detectable technical requirements of the modern efficient industry. In order to overcome this weakness, this paper improves the algorithm to derive an optimizing calculation of threshold from the selection of threshold. Experimental results show that the improved algorithm can effectively detect the edge, it has extremely high inspection accuracy and detection efficiency, it can enhance the practicality of the algorithm.

Welch method has better performance and is widely used in the classic spectral estimation.Through studying the factors how to affect Fourier transform of finite-length sequen￣ce,the factors that influence the performance of spectral estimation using Welch method is analyz￣ed in this paper,and its general principles of parameter selection can be obtained,which will provide a theoretical basis for parameter selection in practice.These factors include the length of discrete Fourier transform,window function and its length.Results of the simulation signal and the actual speech signal show that the more satisfactory spectral estimation can be obtained from the Welch method according to the parameter selection method proposed in this paper.

A computer code was developed to simulate the operation of radioscopic or tomographic devices. The simulation is based on ray-tracing techniques and on the X-ray attenuation law. However, in the process of projection simulation, we can’t use an expression to describe the distribution of the consecutive X-ray spectrum which is simulated by the Monte Carlo method. We only use numerical integration to realize the projection simulation. So in order to enhance the fidelity of the projection simulation, we research X-ray spectrum sampling technology, and propose projection simulation algorithm based on self-adaptive Simpson integration. The algorithm mainly uses self-adaptive Simpson integration to sampling, based on the distribution of the X-ray spectrum. Then realize the integration of the projection process. With simulation experiment and practical experiment, we have demonstrated that the new sampling method is not only more accurate but also very efficient. This new sampling method is expected to be very useful in X-ray projection simulation, as well as in computing similar sampling or integrals in other area.

Non-destructive testing of polymer materials meets specific problems caused by strongly scattering of X-ray detection. When the expected flaws are very small, the information reflected by the flaws may be hidden in the gray-level oscillation caused by strongly scattered structures of polymer materials. For improved detection of defects in polymer materials, a new automatic detection approach based on the EMD (Empirical Modal Decomposition) method, including a new algorithm that using IE (Information Entropy) and PSNR (Peak Signal to Noise Ratio) to control EMD optimal decomposition is proposed. The experimental investigations demonstrated a good performance of the proposed technique in the case of strongly scattering polymer sample. At the same time, it is suitable for all types of defect detection.

In the industrial CT detection systems, because of the restrictions of noises such as scattering noise, the traditional methods for CT reconstruction have high noise and low resolution, and cannot detect smaller flaws. So this paper has proposed using the algorithm of characteristic reconstruction to reconstruct the characteristic of object. About this algorithm, relying on the inner structure character of detected object, according to the character of wavelet function before and after RADON transform, the characteristic part of the image is reconstructed by prefiltering the projected data. Then some experiment show that the method can protrude the inner small flaw of detected object with the less sacrifice of the resolution of image background.

Instead of the subjective evaluation method, the objective method is used for adjusting the tube voltages and currents to achieve the closed-loop control of X-ray imaging system. Combined with the characteristics of ray images, an objective image quality evaluation method is introduced in this paper based on the weighted entropy of region of interest(ROI). Compared with the traditional image quality evaluation method, the results show that the method proposed in this paper can evaluate ray image quality effectively and has better single peak, robustness and sharpness.

In order to improve the detection precise of weld,an image segmentation method based on Support Vector Machine(SVM)used for X ray image of weld was proposed.The image gray-scale and morphological gradient were used as training vector to train the SVM,after the SVM segmentation model was obtained,the test samples would be inputted in the segmentation model for segmentation processing.Taking porosity defect as an example,it is proved that this method can achieve the accurate segmentation.Comparing with other segmentation method,the precision of defect detection has been improved.

In the process of testing axisymmetric workpiece based on the digital radiographic system,caused by several factors,the center image is more bright than the margin's.Some trend is produced.However,the flaw need to be distinguished superimposes on the trend,which increase the flaw testing difficulty.A extracting method for trend of signal based on empirical mode decomposition(EMD) was investigated.Through the analyse and experiment,it proved that the method could extract the trend of images more precisely and enhance the recognition rate of flaw comparing with the traditional trend analysis methods(such as least square approximation).

A multi-resolution CT reconstruction method based on EMD(Empirical Modal Decomposition) was presented. This method took advantage of the character of that EMD as a self-adaptive filter and the linear nature in the reconstruction process. Firstly,using EMD method decomposed the projection data. Afterward,the IMF(Intrinsic Mode Function) which contained the feature information needed was reconstructed. Since each IMF represented the signal characteristics of the different scales,it was able to directly reconstruct different image feature. Thus it could achieve the multi-resolution CT reconstruction. The experimental results showed that,this method could directly obtain multiple features of reconstructed image in reconstruction process.

In the non-restraint environment,because of the impact of environmental factor and target attitude,it is difficult to high-precision target matching.Based on this,this paper uses the arithmetic of SIFT feature extracting to match target in the non-restraint environment.This can effectively enhance the precision of target tracking in the non-restraint environment.At the same time,this paper verifies the method through the interrelated experiment.This experiment proves the arithmetic of SIFT feature extracting can reach a higher target detection requirements,overcome the difficult of feature extracting,and enhance the accuracy of target matching.

A method based on EMD(Empirical Modal Decomposition) for X-ray detection and extraction for tiny flaws in composites was presented.This method took advantage of information entropy and PSNR(peak signal to noise ratio) to control EMD decomposition order and realize the optimum extraction of defect characteristics.By using experiment and image segmentation,it could segment tiny flaws effectively.Comparison with traditional methods,it could extract tiny flaws from material structure and image noise,detect all types of tiny flaws in any direction and extract other material characteristic which lack of commonality and had specific applications.