快速分形图象编码

提出了一种新的快速分形图象编码方法。该方法通过自适应的图象分块、相似块集的矩分类、相似块和图象块的正交分解以及象素递增的动态搜索等４ 个步骤来加快分形图象编码的过程。实验结果表明，提出的方法在缩短编码时间方面确有其效，特别是对于细节丰富的图象，效果尤为明显。

一种基于彩色图像的道路交通标志检测新方法

提出了一种改进的彩色图像分割方法,并将该方法与不变矩理论相结合用于检测彩色图像中的交通禁令标志。首先对采集图像进行预处理,包括对图像进行RGB到HSI或改进HSI颜色空间的转换和图像形态学的运算,然后对图像中不同的封闭子区域进行标记,并去除不满足面积阈值的子区域。分别计算剩下子区域的hu矩组得到每个子区域的7个图像特征值。将相应子区域的特征值与事先准备好的环形和三角形路标特征值用欧式距离分类器进行比较判别。实验结果表明,此方法能准确并较为快速地实现警告标志检测。

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.

Case study on the extraction of land cover information from the SAR image of a coal mining area

In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Detection of Fabric Defects with Fuzzy Label Co-occurrence Matrix Set

Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.

STUDY ON A NOVEL ELLIPSOIDAL HELICAL ANTENNA

A novel ellipsoidal helical antenna is proposed and studied in this letter. As a special in-stance,the hemispherical helical antennas are analyzed firstly,which indicates that the characteristics of a two-arm unit are better than that of a single-arm unit. Based on this,the ellipsoidal helical antenna,formed by changing the axial direction’s dimension of the two-arm hemispherical helical antenna,is analyzed by the moment method with curved basic and testing function. The effects to VSWR (Voltage Standing Wave Ratio),gain,polarization and patterns by the axial direction’s dimensions are inves-tigated. The study results provide dependable gist to the choice of antenna format according to the practical requirements.

Effect of Heteroscedastic Variance Covariance Matrices on Two Groups Linear Classification Techniques

The authors investigate the comparative classification performance of the two groups linear classification techniques. They compared the Fisher linear classification analysis, its robust version based on the minimum covariance determinant with the Filter linear classification rule and the linear combination linear classification technique. These procedures are investigated using laboratory reared aedes albopictus mosquito data set and simulated data set generated based on heteroscedastic covariance matrices with various proportion of contamination. The evaluation procedure is based on the effect of contamination on the mean probabilities of correct classification obtain for each technique. The comparative analysis revealed that the robust Fisher linear classification rule and the linear combination linear classification rule are robust and comparable than the other procedures.

A Roller Bearing Fault Diagnosis Method Based on Improved LMD and SVM

Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is proposed. In this paper, firstly, the wavelet analysis is introduced to the signal decomposition and reconstruction; secondly, the LMD method is used to decompose the recomtnion signal obtained by the wavelet analysis into a ntmaber of Product Ftmctions （PFs） that include main fault characteristics, thus, the initial feattwe vector matrixes could be formed automatically; Thirdly, by applying the Singular Valueition （SVD） techniques to the initial feature vector matrixes, the singular values of the matrixes can be obtained, which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier; Finally, the recognition results can be obtained from the SVM output. The results of analysis show that the propsed method can be applied to roller beating fault diagnosis effectively.

Matched Field Localization Based on CS-MUSIC Algorithm

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.

Maximal subalgebras of the general linear Lie algebra containing Cartan subalgebras

Let gl,,（R） be the general linear Lie algebra of all n×n matrices over a unital commutative ring R with 2 invertible, dn（R） be the Cartan subalgebra of gln（R） of all diagonal matrices. The maximal subalgebras of gln（R） that contain dn（F：） are classified completely.

A new application of multiwavelets:Discrimination of marine algae taxonomic groups with emission-excitation matrix fluorescence

The 3D fluorescence discrimination of phytoplankton classes was investigated by SA4 multiwavelet,GHM multiwavelet,and coifman-2(coif2) wavelet analysis.Belonging to 35 genera of 7 major phytoplankton divisions in the inshore area of China Sea,Single species cultures of 51 phytoplankton species were employed.The second scale vector (Ca2) of SA4,Ca2 of GHM and the third scale vector (Ca3) of coif2 were selected as feature spectra by Bayesian discriminate analysis (BDA).The reference spectra were obtained via hierarchical cluster analysis (HCA).With average high correct discrimination ratios (CDRs),reference spectra were representative to phytoplankton species.For one-algae samples,the average CDRs were 95.6% at genus level and 86.7% at division level.For the laboratory mixed samples,the average CDRs (one division accounted for 25%,75% or 100% of the total biomass) were 86.6%,91.4% and 100% at division level.Moreover,the average CDRs of the dominant species (accounted for 75%) was 79.8% at genus level.Results for the in situ samples were coincided with the microscopic ones at division level with the relative contents of 54.3%-96.5%.The fluorometric discriminating technique was further tested during the cruise in Bohai Sea recently.

Source number estimation and separation algorithms of underdetermined blind separation

Recently,sparse component analysis (SCA) has become a hot spot in BSS re-search. Instead of independent component analysis (ICA),SCA can be used to solve underdetermined mixture efficiently. Two-step approach (TSA) is one of the typical methods to solve SCA based BSS problems. It estimates the mixing matrix before the separation of the sources. K-means clustering is often used to estimate the mixing matrix. It relies on the prior knowledge of the source number strongly. However,the estimation of the source number is an obstacle. In this paper,a fuzzy clustering method is proposed to estimate the source number and mixing matrix simultaneously. After that,the sources are recovered by the shortest path method (SPM). Simulations show the availability and robustness of the proposed method.