Anis Salwa Mohd Khairuddin

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Classifying tropical wood species poses a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. The problem of wood recognition is compounded with the nonlinearities of the features among the similar wood species. Besides that, large wood databases presented a problem of large(More)
A human-decision based classification wood recognition system is designed to classify 52 tropical wood species. The system is designed based on visual inspection of the wood anatomy textures, which can actually be presented as image data processing using statistical parameters representing the texture and the grey values. There are thousands of wood images(More)
A cascaded wood species recognition system using simple statistical properties of the wood texture is presented where a total of 24 statistical features are extracted from each wood sample. They are mainly vessel features that allow a broad initial grouping of wood texture using fuzzy logic. Then, a neural network classifier is used to refine the broad(More)
Automatic classification of tropical wood species is becoming more important especially for timber exporting countries due to the considerable economic challenge as a result of fraudulent labelling of timber species at the custom checkpoints. Hence, a reliable automated wood species recognition system is needed to inspect the wood species labelling at the(More)
Light-emitting diode (LED) applications consist of various materials. One of the materials used is polymer. In this study, MEH-PPV known as poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1, 4-phenylenevinylene] was used. The objective of this paper is to characterize the electrical and optical properties of MEH-PPV thin film by using sol-gel method. The scope is to(More)
An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is used to complement a set of support vector machines (SVM) to(More)
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