The development of functional materials with nanometer-scale architectures and the effect of these architectures on their chemical and physical properties are currently of great interest in materials design. Polymerizable lyotropic liquid crystal (LLC) assemblies provide a facile entry into this area by allowing one to fix the inherent order in these systems using covalent bonds to create robust, nanostructured materials. The use of the cross-linked inverted hexagonal phase in templated nanocomposite formation and heterogeneous catalysis has been demonstrated. Additionally, the polymerization of LLC mesogens in the regular hexagonal and bicontinuous cubic phases is being targeted for future developments in functional materials. Future directions for new applications of these materials are also discussed.

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Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest.

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