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Melt-quenched Ge x Te 100−x glasses and Si x Te 100−x glasses (15 ≤ x ≤ 25) have been found to exhibit memory switching, with threshold fields of the order of 4-11 kV/cm and 6-25 kV/cm, respectively. It is found that the switching voltages of Ge x Te 100−x samples increase linearly with Ge content and the composition dependence of threshold voltage V t shows a marked slope change at x = 20, which has been earlier identified as the rigidity percolation threshold (RPT) of the system. Above the RPT, V t of Ge-Te glasses continues to increase with composition until the boundary of bulk glass formation (x = 28). On the other hand, the switching voltages of Si x Te 100−x glasses increase with x, exhibiting a broad maximum around x = 20 (RPT). The difference in the composition dependence of Si x Te 100−x and Ge x Te 100−x glasses has been understood on the basis of separation between the rigidity percolation threshold and the stoichiometric threshold (CT ST) in these samples. The present results also indicate that the turnaround in the composition dependence of V t and the subsequent minimum observed in the switching voltages of chalcogenide glasses is likely to be due to CT ST and not to the chemical ordering threshold (CT COCRN).

We present a system for recognizing human faces from a database consisting of multiple images per test subject, which spans the normal variations in a human face. The faces are represented based on a Gabor wavelet transform. The features are extracted as a vector of values using a carefully chosen symmetrical Gabor wavelet matrix. This feature extraction is biologically motivated and models systems based on human vision. The extracted features are fed into an Artificial Neural Network, in dual phases. The training and testing phases of the neural network work on the features extracted by the same method. Excellent pattern-recognition-specific neural network like a multi layer perceptron with back propagation provides the necessary classification once the feature extraction is complete.

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