Underexposed and overexposed colour image enhancement using information set theory

This paper presents a new approach for improving the contrast of digital colour images in extreme lighting environment. The concept of information sets that are derived from fuzzy sets is applied for this purpose. The S and V components of the HSV (hue, saturation, value) colour model are fuzzified using Gaussian and triangular membership functions but H is preserved. Objective measures like contrast information, quality and visual factor (V_f) are defined to represent the image characteristics. A new entropy function and V_f are used to form the objective function which is optimised using particle swarm optimisation to learn the image-specific parameters required for the enhancement. These new measures defined here serve to evaluate the performance of the proposed algorithm. In addition to preserving the colour and specific image features, the subjective and objective evaluations clearly depict the improvement in the quality of both the underexposed and overexposed images. On comparison, it is found that the proposed technique outperforms the existing techniques in terms of entropy, information measure and mean opinion score.     

Modelling and quantification of industry 4.0 manufacturing complexity based on information theory: a robotics case study

The new industrial revolution called Industry 4.0 imposes new challenges to the research community. One of the main issues in Industry 4.0 is the management of the huge amount of information exchanged among its different integrated systems. The present work demonstrates how the traditional manufacturing system is transformed to the Industry 4.0 manufacturing system and proposes a modelling and quantification approach, which includes metrics from the Information Theory estimating the complexity and the capacity of the Industry 4.0, from the perspective of the communication among the systems. The application impact of the proposed Industry 4.0 system and the comparison of these metrics before and after shifting to Industry 4.0 is also analysed and validated in a case study from the Robotics Industry.