The safety of rail is very important for the development of high speed railway,and it is necessary to investigate the features of inner cracks in rail.In order to obtain the features of Acoustic Emission(AE)sources of inner cracks in rail,AE sources with different types,depths and propagation distances are examined for crack in rail.The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model.Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources.The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail.There are uniform AE mode features existing in the AE signals from AE sources in rail web,however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes.Different AE source types have the different propagation features in rail.It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.
As a powerful tool for image processing,bi-dimensional empirical mode decomposition (BEMD) covers a wide range of applications. In this paper,we explore a novel hyperspectral classification algorithm which integrates BEMD and support vector machine (SVM) . By virtue of BEMD,the selected hyperspectral bands are decomposed into several bi-dimensional intrinsic mode functions (BIMFs) ,which reflect the essential properties of hyperspectral image. We further make full use of SVM,which is a supervised classification tool widely accepted,to classify the suitable sum of BIMFs. Experimental results indicate that though the proposed method has no advantage in computing time,it exhibits higher classification accuracy and stability than the classical SVM.
In this paper,three distributed and scalable nonuniform deployment algorithms in order to enhance the quality of monitoring(QoM).Mobile sensors are to be deployed around a target of interest which can be stationary or moving,and to approximate a given weight function which is a measure of information or event density.The first two algorithms generate nonuniform deployments by inverse-transformations from a uniform deployment.They handle the situations of global coordinate system which is available and not with appropriate assumptions,respectively.The third algorithm,which relocates sensors to adjust inter-node distances based on the local measurements only,is suitable for general cases.The simulation results demonstrate the proposed algorithms can achieve reliable and satisfactory deployments.
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.
