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The Publisher's final version can be found by following the DOI link. Open access article. System monitors are applications used to monitor other systems (often mission critical) and take corrective actions upon a system failure. Rather than reactively take action after a failure, the potential of fuzzy logic to anticipate and proactively take corrective actions is explored here. Failures adversely affect a system’s non-functional qualities (e.g., availability, reliability, and usability) and may result in a variety of losses such as data, productivity, or safety losses. The detection and prevention of failures necessarily improves a critical system’s non-functional qualities and avoids losses. The paper is self-contained and reviews set and logic theory, fuzzy inference systems (FIS), explores parameterization, and tests the neighborhood of rule thresholds to evaluate the potential for anticipating failures. Results demonstrate detectable gradients in FIS state spaces and means fuzzy logic based system monitors can anticipate rule violations or system failures.

The standard approach to the estimation of homographies consists in the application of the RANSAC algorithm to a set of tentative matches. More recent strategies based on deep learning, namely convolutional architectures, have become available. In this work, a new algorithm for the estimation of homographies is developed. It is rooted in a convolutional neural network for homography estimation, which is provided with a range of versions of the input pair of pictures. Such versions are generated by perturbation of the color levels of the input images. Each generated pair of images yields a distinct estimation of the homography, and then the estimations are combined together to obtain a final, more robust estimation. Experiments have been designed and carried out to test the validity of our approach, including qualitative and quantitative performance measures. In particular, it is demonstrated that our approach consistently outperforms the baseline approach consisting of using the output of the homography estimation deep network for the original input pair of images.