Abstract-A large number of gray shades can be displayed in rms responding displays by using integer wavelets. The technique is demonstrated by displaying 64 gray shades in a twisted nematic liquid crystal display. We have reduced the hardware complexity of the display drivers by adding a few analog multiplexers that are common to a large number of stages (one for each output) in the drivers. A simple controller was implemented in a low cost complex programmable logic device.Index Terms-Gray shades, liquid crystal displays (LCDs), matrix addressing, multi-line addressing (MLA), wavelets.

Abstract-A multi-line addressing technique that is based on sparse orthogonal matrices is proposed to drive the liquid crystal displays (LCDs). Hardware in the column drivers is utilized fully and yet the hardware complexity of the controller is reduced by using sparse matrices to scan the passive matrix LCDs.

-The design and implementation of a wavelet-based addressing technique capable of displaying gray shades is presented. The hardware complexity of the display drivers has been reduced by adding a few analog multiplexers that are common to the drivers. The controller was implemented by using a low-cost complex programmable logic device (CPLD) and it was demonstrated by displaying 16 gray shades in a liquid-crystal display.

Facial recognition algorithm should be able to work even when the similar looking people are found i.e. also in the extreme case of identical looking twins. An experimental data set which contains 40 images of 20 pairs of twins collected randomly from the internet. The training is done with the selected images of the twins using different training algorithms and inbuilt functions available. The extracted features are stored over the Amazon public cloud. As a part of testing phase random images from the dataset trained are selected and upon running it over the system we get the features of those images which then will be compared by extracting the features already stored in Amazon cloud. The stored values and the current image features are compared and result will be displayed on the GUI. Identical twin’s facial recognition system uses the machine learning, image processing algorithms and deep learning algorithms. Regardless of the conditions of the images acquired, distinguishing identical twins is significantly harder than distinguishing faces that are not identical twins for all the algorithms.

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