• The Plant Pathology Journal
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• v.34 no.6
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• pp.544-554
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• 2018

Tomato (Solanum lycopersicum) production in Ghana is limited by the root-knot nematode (Meloidogyne incognita, and yield losses over 70% have been experienced in farmer fields. Major management strategies of the root-knot nematode (RKN), such as rotation and nematicide application, and crop rotation are either little efficient and harmful to environments, with high control cost, respectively. Therefore, this study aims to examine morphometric variations of RKN populations in Ghana, using principal component analysis (PCA), of which the information can be utilized for the development of tomato cultivars resistant to RKN. Ninety (90) second-stage juveniles (J2) and 16 adult males of M. incognita were morphometrically characterized. Six and five morphometric variables were measured for adult males and second-stage juveniles (J2) respectively. Morphological measurements showed differences among the adult males and second-stage juveniles (J2). A plot of PC1 and PC2 for M. incognita male populations showed clustering into three main groups. Populations from Asuosu and Afrancho (Group I) were more closely related compared to populations from Tuobodom and Vea (Group II). There was however a single nematode from Afrancho (AF4) that fell into Group III. Biplots for male populations indicate, body length, DEGO, greatest body width, and gubernaculum length serving as variables distinguishing Group 1 and Group 2 populations. These same groupings from the PCA were reflected in the dendogram generated using Agglomerative Hierarchical Clustering (AHC). This study provides the first report on morphometric characterisation of M. incognita male and juvenile populations in Ghana showing significant morphological variation.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2179-2188
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• 2015

Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the “Two-step” method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.