Nonconductive materials are extensively used in industrial applications, particularly as coatings for metal structures like oil pipelines. However, these nonmetallic coatings are prone to damage from factors, such as corrosion and scratches, leading to widespread failures. This increases the demand for nondestructive evaluation techniques capable of accurately quantifying defect parameters in such materials. Capacitive Imaging (CI) technique is an emerging electromagnetic nondestructive testing method with promising application prospects in defect evaluation in nonconducting materials. However, the CI technique is commonly used as a screening technique to detect the presence of possible defects, and its defect sizing ability, which is crucial in some engineering applications, has yet to be explored. This article proposes a high precision defect sizing method for the CI technique based on a physics informed neural network. First, the physical model of the CI technique for the detection of defects in nonconducting material is analyzed. A physical formula, which was later used as physical information, for the quantification of defect length and width was then obtained. Finite-element simulations were then conducted to visualize the sensitivity distribution of the CI sensor and analyze the characteristics of defect signals the physical information was integrated into a neural network, enabling it to quantify defect parameters from the CI detection data. Experimental results demonstrate that this method can accurately determine defect length, width, depth, and buried depth. Compared to other neural network structures and traditional algorithms, the proposed approach achieves superior precision in defect quantification.

Skewed histogram shifting (SHS) is an efficient scheme in reversible data hiding (RDH) research. By employing a pair of symmetric predictors which averages part of sorted pixels around the to-be-predicted pixel, two skewed histograms are generated. With the embedding and shifting directions toward the short tail of the two histograms, SHS reduces many invalid modifications. However, the design of the symmetric predictors pair is strictly constrained, which seriously degrades the performance on both embedding capacity and distortion of this SHS scheme. In this work, we propose a generalized SHS model to remove the weight and symmetry constraints. With the help of differential evolution algorithm, the optimized parameters are obtained in a short period of time, avoiding wasting time using exhaustive search. What is more, adaptive pairwise mapping and embedding bin selection are also realized by adding parameters into the evolutionary process, which greatly improve the embedding performance without increasing too much computational complexity. Experiments demonstrate the superiority of our method by comparing it with state-of-the-art RDH schemes.

Pixel-value-ordering (PVO) is a widely used reversible data hiding (RDH) framework which aims to achieve the high quality of stego-image under low capacity. In this letter, we propose a general location-based adaptive complexity for PVO. Different from the block-based complexity in the previous PVO-based methods, our proposed method adaptively selects context pixels from the perspective of the relative locations of predicted pixel and prediction pixel. Consequently, different number of high correlation context pixels can be adaptively selected and the context pixels can break the limitation of the current block. Moreover, instead of sharing the same block complexity by two predicted pixels in the current block, each predicted pixel can be utilized independently according to its own corresponding complexity. Our proposed adaptive complexity can combine with any PVO-based methods and the experimental results show that our proposed method achieves a significant improvement in prediction accuracy and embedding performance.