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The purpose of this paper was to develop an efficient and accurate classification for flesh aromatic coconuts in daylight by using image processing technique. In actual implementation , the brightness of daylight was not constant as a major problem that is affecting for the classification flesh aromatic coconuts. So we need to adjust the brightness of image to have the same brightness in all images before to be classify flesh aromatic coconut. The color of the coconut's rind around the bottom of aromatic coconuts are correlated with coconuts age. Convert the region of interested in RGB color to HSV color then find the appropriately threshold and then draw 4 circular ring area into the region of interested. After that find the percentage of the white pixels of each ring and find relationship between the ring order and the percentage of the white pixels of each rings by using polynomial regression equation. The results showed that the system could classify flesh aromatic coconuts at 92.8% with single layer, 71.43% with one and half layer and 72.2% with double layer.

Abnormal gait leads to falling which can cause of human's injury. Normally human has resembled gait cycle between walking. But if human has falling or abnormal walking that gait cycle is not resemble the normal walking. The walking gait can calculate the locus of the Zero Moment Point (ZMP) and the ZMP can be estimated by the signal from low-cost Force Sensitive Resistors (FSRs) . Four FSRs were installed in the sole of a shoe. This paper presents the detection of human's abnormal gait by using the FSRs signal. Artificial Neural Networks were applied to recognize ZMP locus of the normal gait cycle and use the trained neuron to classify the normal gait. Experimental data were recorded from 10 volunteers of age between 18-25 years, height 150-175 cm., and weight 40 -75 kg, The results show that the neural network can detect abnormal gait cycles.