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This paper examines the suitability of the discretizing approach of Roy and Dasgupta, based on the discrete concentration method of Roy, for determination of reliability of complex systems under Weibull set‐up. A system is said to be complex if its distribution is intractable and analytical evaluation of probability is not available. Numerical analyses on a hollow cylinder, solid shaft, hollow rectangular tube, and the power dissipated by a resistor have been worked out to examine the closeness between the discretized reliability estimators and the simulated values based on Monte Carlo method. Also, it may be observed that the proposed approach has superior performance in comparison to the discretizing approach of English et al., based on the moment equalization method of Taguchi. The mean absolute deviation under the discrete concentration approach is much less than the same under the moment equalization approach for the Weibull set‐up.

This paper demonstrates a colour image encryption algorithm that incorporates multiple fractional order chaotic systems. Here, the fractional order extensions of four well known chaotic systems are tactfully combined together to generate the cyphering key. The encrypted image shows uniform histogram, zero autocorrelation and very high entropy for all the three colour-channels. Moreover, the algorithm possesses an extremely huge key-space. Thus, the algorithm becomes invulnerable against brute-force attacks. However, due to the fact that the fractional order derivatives are not 'local operations' like their integer order counterparts, solving fractional order differential equations become a difficult and time consuming task for computers. To overcome this, some efficient computing techniques have been adopted to run the encryption-decryption process reasonably fast.

Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUNet dataset after being trained on just 8 percent of the data used in previous methods.