Ee-Luang Ang

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We present an artificial neural-network-(NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we(More)
We present a new side-intensity modulated self-organizing map (SIM-SOM) for improved visualization of multidimensional data. We have utilized DNA microarray dataset [15] for this purpose. A Gene Signature which contains a set of most informative genes was extracted using the discrimination factor method [16]. Next, the reduced dimensional microarray data(More)
This paper presents a novel method called PNLICA for image extraction from nonlinear mixtures of mutually independent images. Post Nonlinear Mixtures (PNL) [8] is used for modelling the mixing process. A modified Multilayer Perceptron (MLP) is combined with a higher order statistics linear Independent Component Analysis (ICA) model to sequentially extract(More)
A multi-layer perceptron neural network with floatingpoint number system is implemented on a field programmable gate array (FPGA). IEEE-754 32-bit single precision floatingpoint number is used to represent values in the neural network accurately. The neural network forms the core of an intelligent sensor system which has the ability to mitigate the(More)
Two computational efficient artificial neural networks (ANNs) for the prediction of major financial indices are proposed. First, we propose a single layer functional link artificial neural network (FLANN) for this purpose. FLANN has a simple structure in which the nonlinearity is introduced by the functional expansion of the input pattern using(More)
Accurate classification of DNA microarray data is vital for cancer diagnosis and treatment. For greater accuracy, a preferable strategy is to make a decision based on the result of a single classifier that is trained with various aspects of data space. It is a difficult task to create an optimal classifier for DNA analysis that deals with only a few samples(More)