Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.

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Skin detection is a key aspect of many computer vision applications including face detection, person identification, illicit content detection and other related applications. In this paper, a skin detection method is proposed combining two color spaces HSV (Hue, Saturation, Value) and YCgCr (luminance, chrominance in green, chrominance in red). The S, Cg and Cr components are used to form a hybrid SCgCr color space. Skin detection results show that, the proposed method can respond well to different skin color tones with less sensitivity to skin-like background pixels. It is also shown that higher face detection rate can be achieved when applied to face detection problem.