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Articles you may be interested inOn the derivation of second order variable step variable order block backward differentiation formulae for solving stiff ODEs AIP Conf. Abstract.A new block method that generates two values simultaneously is developed for the integration of stiff initial value problems. The method is proven to be A -stable and is a super class of the 2 -point block backward differentiation formula (BBDF). A comparison is made between the method, 1 point backward differentiation formula (BDF) and the 2 point BBDF methods. The numerical results indicate that the new method outperformed the 1 point BDF and the 2 point BBDF methods in terms of accuracy and stability. The total number of steps to complete the integration by the 1 point BDF method is reduced to half. Computation time for the method is also competitive.

Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also leads to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset.  The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet.

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This research investigates the application of texture features for leaf recognition for herbal plant identification.  Malaysia is rich with herbal plants but not many people can identify them and know about their uses.   Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary.  Leaf image is chosen for plant recognition since it is available and visible all the time.   Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections.  A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier.  A new leaf dataset has been constructed from ten different herb plants.  Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.