The pink hibiscus mealybug, Maconellicoccus hirsutus (Green), is a major pest of mulberry (Morus alba L.), the sole host of the mulberry silkworm, Bombyx mori (L.), which is a source of livelihood to millions of sericulture farmers in India. Several predators, mainly Coccinellidae (Coleoptera), have been reported to feed on M. hirsutus on mulberry. Coccinellid predators of M. hirsutus collected on mulberry from different parts of India are illustrated here with brief diagnostic notes to facilitate their identification. An account of mycophagous species of coccinellids commonly found on mulberry and misreported as predators of mulberry pests is also given with illustrations. Scymnus (Pullus) latifolius sp. nov., a promising predator of M. hirsutus, hitherto misidentified and reported as Scymnus pallidicollis Mulsant, is described and illustrated from West Bengal, India, with detailed biological notes. Keiscymnus taiwanensis Yang Wu, 1972 is reduced to a new junior synonym of Scymnus pallidicollis Mulsant, 1853 (syn. nov.). Illeis bielawskii Ghorpade, 1976 is found to be a valid species and removed from synonymy with I. bistigmosa Mulsant, 1850 (stat. rev.).

No abstract

Abstract-Face recognition using eigen faces is an approach to the detection and identification of human faces and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space `face space' that best encodes the variation among known face images. The face space is defined by the `eigen faces', which are the eigenvectors of the set of faces. They do not necessarily correspond to isolated features such as eyes, ears, and noses. Eigen faces are obtained from eigenvectors of an image which is a principle component of analysis. The principal component analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of facial images. Face recognition has many applicable areas. Moreover, it can be categorized into face identification, face classification, or sex determination. The most useful applications contain crowd surveillance, video content indexing, personal identification (ex. driver's license), and mug Shots matching, entrance security, etc.

No abstract

No abstract