Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

Ground-roll separation of seismic data based on morphological component analysis in twodimensional domain

Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation.In this study,according to differences in morphological characteristics between ground roll and reflected waves,we use morphological component analysis based on two-dimensional dictionaries to separate ground roll and reflected waves.Because ground roll is characterized by lowfrequency,low-velocity,and dispersion,we select two-dimensional undecimated discrete wavelet transform as a sparse representation dictionary of ground roll.Because of a strong local correlation of the reflected wave,we select two-dimensional local discrete cosine transform as the sparse representation dictionary of reflected waves.A sparse representation model of seismic data is constructed based on a two-dimensional joint dictionary then a block coordinate relaxation algorithm is used to solve the model and decompose seismic record into reflected wave part and ground roll part.The good effects for the synthetic seismic data and application of real seismic data indicate that when using the model,strong-energy ground roll is considerably suppressed and the waveform of the reflected wave is effectively protected.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Application of Decomposition and Denoising of Gearbox Signal Based on Morphological Component Analysis

Morphological component analysis( MCA) is a signal separation method based on signal morphological diversity and sparse representation. MCA can extract the signal components of different morphologies by different dictionary combinations. Firstly,the theory of MCA was analyzed with sparse representation principle and relaxation criterion. Then detailed steps of block coordinate relaxation( BCR) were given. Finally,algorithm performance was verified by simulation signals analysis, MCA was applied to decomposing and denoising gearbox signals, and the fault parameters were extracted by energy operator demodulation envelop of morphological component.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.

Seismic data denoising under the morphological component analysis framework combined with adaptive K-SVD and wave atoms dictionary

Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data.Traditional single or fixed transform is not suit for exploiting their complicated characteristics and attenuating the noise.Recent years,a novel method so-called morphological component analysis(MCA)is put forward to separate different geometrical components by amalgamating several irrelevance transforms.According to study the local singular and smooth linear components characteristics of seismic data,we propose a method of suppressing noise by integrating with the advantages of adaptive K-singular value decomposition(K-SVD)and wave atom dictionaries to depict the morphological features diversity of seismic signals.Numerical results indicate that our method can dramatically suppress the undesired noises,preserve the information of geologic body and geological structure and improve the signal-to-noise ratio of the data.We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transform(DCT),undecimated discrete wavelet transform(UDWT),or curvelet transform,etc.This algorithm provides new ideas for data processing to advance quality and signal-to-noise ratio of seismic data.

Morphological Diversity Analysis of Red-seed Watermelon (Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao) Germplasm Resources

[Objective] This study aimed to analyze the morphological diversity of red- seed watermelon （Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao） germplasm resources. [Method] Multiple cluster analysis and principal components analysis on the morphological traits of 51 red-seed watermelon germplasm resources were carried out. [Result] The coefficient of variations （CVs） of 39 morphological traits in 51 red-seed watermelon idioplasm resources ranged from 5.37% to 66.95%, with an average of 22.87%. The average of Shannon diversity information indices was 1.55. Among them, the Shannon diversity information index of seed length was the highest （2.16） and that of seed shell figure pattern was the lowest （0.32）. In ad- dition, the morphological diversity information indices of quantity characters were higher than that of quality characters. The principal components analysis revealed that the variance contribution rates of the first, second and third principal compo- nents were 19.49%, 15.32% and 9.55%, respectively. Cluster analysis divided the 51 materials into three broad branches based on the morphological traits. There was only one material in the fist branch and two in the second branch, and all the three materials were wild. The other 48 materials were divided into the third branch and all of them were cultivars. [Conclusion] This study provided a theoretical basis for the protection and utilization of red-seed watermelon resources.

Comparative Analysis of Morphologic Traits of 50 Large-flowered Chrysanthemum Varieties

With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized block design. The morphological differences were observed by uniformity analysis, variability analysis, principal component analysis and cluster analysis. The result showed that the vari- able coefficients of 59 traits were greater than 15%; the contribution rate of first seven principal components reached 81.45%; and it was found by clustering analy- sis that the 50 germplasm resources could be divided into four clusters with obvious morphological differences, and plant type could be used as an index for classifica- tion.

Capability of discrete element method to investigate the macro-micro mechanical behaviours of granular soils considering different stress conditions and morphological gene mutation

Discrete element method(DEM)has been widely utilised to model the mechanical behaviours of granular materials.However,with simplified particle morphology or rheology-based rolling resistance models,DEM failed to describe some responses,such as the particle kinematics at the grain-scale and the principal stress ratio against axial strain at the macro-scale.This paper adopts a computed tomography(CT)-based DEM technique,including particle morphology data acquisition from micro-CT(mCT),spherical harmonic-based principal component analysis(SH-PCA)-based particle morphology reconstruction and DEM simulations,to investigate the capability of DEM with realistic particle morphology for modelling granular soils’micro-macro mechanical responses with a consideration of the initial packing state,the morphological gene mutation degree,and the confining stress condition.It is found that DEM with realistic particle morphology can reasonably reproduce granular materials’micro-macro mechanical behaviours,including the deviatoric stressevolumetric straineaxial strain response,critical state behaviour,particle kinematics,and shear band evolution.Meanwhile,the role of multiscale particle morphology in granular soils depends on the initial packing state and the confining stress condition.For the same granular soils,rougher particle surfaces with a denser initial packing state and a higher confining stress condition result in a higher degree of shear strain localisation.

Removal of White Noise from ECG Signal Based on Morphological Component Analysis

To effectively suppress white noise and preserve more useful components of electrocardiogram(ECG) signal, a novel de-noising method based on morphological component analysis(MCA) is proposed. MCA is a method which allows us to separate features contained in an original signal when these features present different morphological aspects. According to the features of ECG, we used the UWT dictionary to sparsely represent mutated component, and used the DCT dictionary to sparsely represent smooth component. The experimental results of the samples choosing from MIT-BIH databases show that the MCA-based method is effective for white noise removal.

A proposal for the morphological classification and nomenclature of neurons

The morphological and functional characteristics of neurons are quite varied and complex. There is a need for a comprehensive approach for distinguishing and classifying neurons. Similar to the biological species classification system, this study proposes a morphological classification system for neurons based on principal component analysis. Based on four principal components of neuronal morphology derived from principal component analysis, a nomenclature system for neurons was obtained. This system can accurately distinguish between the same type of neuron from different species.

Algorithm Based on Morphological Component Analysis and Scale-Invariant Feature Transform for Image Registration

In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.

Barnyard millet global core collection evaluation in the submontane Himalayan region of India using multivariate analysis

Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.

Morphological Study of Ficus deltoidea Jack in Malaysia

Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis （PCA） and cluster analysis （CA） using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component （PC） with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base （BL）, shape of leaf apex （SA）, ratio of lamina width to lamina length （R）, dots color at the lower midrib （DLM）, color of young syconium （CYS）, color of mature syconium （CMS） and the number of syconium on trees （DST）. This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised.

Evaluation of Sorghum (Sorghum bicolor) Landraces for Drought Tolerance Using Morphological and Yield Characters under Rainfed Conditions of Sub Region Hagaz, Eritrea

Sorghum is an important food crop in Eritrea where it is widely grown in the mid and low lands,of semi-arid regions.Eritrea being the center of ori­gin of sorghum,a large variability exist in its landraces being grown by the farmers since generations.In order to improve the productivity of sorghum under moisture stress conditions,it is imperative to evaluate these landraces for drought tolerant characteristics and their use for further crop improve­ment programmes.Therefore,a field study was conducted in a randomized complete block design with three replications to estimate the extent of genetic variability of 20 sorghum genotypes for moisture stress tolerance using various morphological,phenological,yield and yield related parame­ters under rainfed conditions at Hagaz Research Station.Significant differ­ence was observed for almost all the characters in the individual analysis of variance suggesting that these sorghum accessions were highly variable.Accessions EG 537,EG 1257,EG 849,EG 791,EG 783 and EG 813 showed promising results for post flowering drought tolerance,grain yield and stay green traits.Higher PCV and GCV were also obtained in parame­ters like plant height,leaf area,biomass,peduncle exertion,panicle length,and grain yield and panicle weight.The genotypes also exhibited varying degrees of heritability estimates.Characters such as plant height,panicle length,days to flowering and maturity showed higher heritability.Cluster analysis revealed that sorghum landraces were grouped on the basis of their morphological traits and geographical sites.77.3%of the total variation of sorghum landraces was contributed by the first four principal components analysis having Eigen value>1.Overall,the current study confirmed that EG 537,EG 849,EG 1257,EG 791,and EG 813 are drought tolerant sor­ghum landraces during post flowering stage.

基于多形态学成分分析的图像融合

将多尺度分解与稀疏表示相结合,提出了一种基于多形态学成分分析(MCA)的图像融合算法。采用基于联合稀疏表示(JSR)的方法融合卡通子图像中的冗余和互补信息,并利用基于方向特征的方法融合具有更多细节信息和噪声的纹理子图像。结果表明,提出的图像融合算法在主观视觉效果和客观评价指标上均优于先进的图像融合算法。

基于改进MCA的干涉高光谱图像分解

干涉高光谱图像特殊的成像原理,使其帧内存在着大幅值且位置固定的干涉条纹,而帧间存在着水平移位的背景图像,这种特点会严重的破坏原始图像的固有结构,从而导致新兴的压缩感知理论与传统压缩算法的直接应用无法得到理想的效果。由于干涉条纹信息与背景图像信息的特征不同,能够对干涉条纹与背景图像进行稀疏表示的正交基也是不同的。基于这种思想,使用MCA(morphological component analysis)算法对干涉高光谱图像中干涉条纹信息与背景图像信息进行分离处理。由于干涉高光谱图像数据量庞大,传统的MCA算法对干涉高光谱数据的图像分解,迭代收敛速度慢,运算效率较低,故而针对干涉高光谱数据特点对传统MCA算法进行改进,改变其迭代收敛条件,当分离后的图像信号与原始图像信号的误差已经基本保持不变时,即终止迭代;并根据对应正交基能且仅能稀疏表示对应信号的思想,对阈值采用自适应的方式进行更新,在新的阈值更新模式中,图像信号在不同正交基下的映射系数被计算与比较。大量实验结果表明,对于LASIS数据与LAMIS数据,MCA算法都能够较完美的将干涉高光谱图像分解,改进的MCA算法更能在保持完美分解输出结果的同时,相对于传统MCA方法显著的减小迭代次数,更快的达到迭代收敛条件,从而有效的提高了算法的运算效率与实时性需求,也为新兴的压缩感知理论在干涉高光谱图像中的进一步应用提供了一种很好的解决方案。

一种用于PCA与MCA的神经网络学习算法

主元分析（ＰＣＡ）和次元分析（ＭＣＡ）是用于特征提取、数据压缩、频率估计、曲线拟合等信号处理的基本技术．以神经网络来实现ＰＣＡ和ＭＣＡ是当今研究的一大热点，相关矩阵Ｒ的特征值重数不为１时的主、次元分析则是其中一大难题．本文提出了一种新的学习算法，使得在输入数据的相关矩阵含多重特征值时。

39个传统秋菊品种扦插生根能力综合评价

为了筛选出扦插生根能力较强的传统秋菊(Chrysanthemum morifolium Ramat.)品种,对39个传统秋菊品种扦插12 d的8个根系形态指标进行差异分析、相关性分析和主成分分析,并对这些传统秋菊品种的扦插生根能力进行隶属函数分析和分级。结果显示:供试传统秋菊品种的总根数为3.3~44.0,总根长、平均根长和最长根长分别为2.463~129.174、0.632~4.470和1.610~7.964 cm,根直径为0.200~0.405 mm,根投影面积和根表面积分别为0.102~3.062和0.319~9.619 cm 2,总根体积为0.002~0.079 cm 3,且多数指标以‘火凤凰’(‘Huofenghuang’)最小。这些根系形态指标的变异系数为15.789%~51.605%,其中,总根长的变异系数最大,根直径的变异系数最小,其余指标的变异系数均大于32%。供试传统秋菊品种的多数根系形态指标间存在显著(P<0.05)或极显著(P<0.01)正相关。主成分分析结果显示:前3个主成分的累计贡献率达97.976%。隶属函数分析和分级结果表明:供试传统秋菊品种的扦插生根能力分为优秀、良好、中等和偏差4个等级,‘龙都春丽’(‘Longdu Chunli’)、‘玉楼人醉’(‘Yulou Renzui’)、‘龙都月华’(‘Longdu Yuehua’)、‘圣光华宝’(‘Shengguang Huabao’)、‘龙都秋枫’(‘Longdu Qiufeng’)、‘龙都春晓’(‘Longdu Chunxiao’)的扦插生根能力优秀,综合得分均大于0.70。根投影面积、根表面积和总根体积在不同等级间差异显著。研究结果显示:不同传统秋菊品种间扦插苗的根系形态指标变异较大,其中,‘龙都春丽’等6个品种的扦插生根能力较强,可作为传统秋菊品种遗传改良的亲本。