Biblio

In this paper, fuzzy logic was implemented as a proposed approach for modelling of Thickness as an output response of thin film layer in Titanium Nitrite (TiN). The layer was deposited using Physical Vapor Deposition (PVD) process that uses a sputtering technique to coat insert cutting tools with TiN. Central cubic design (CCD) was used for designing the optimal points of the experiment. In order to develop the fuzzy rules, the experimental data that collected by PVD was used. Triangular membership functions (Trimf) were used to develop the fuzzy prediction model. Residual error (e) and prediction accuracy (A) were used for validating the result of the proposed fuzzy model. The result of the developed fuzzy model with triangular membership function revealed that the average residual error of 0.2 is low and acceptable. Furthermore, the model obtained high prediction accuracy with 90.04%. The result revealed that the rule-based model of fuzzy logic could be an efficient approach to predict coatings layer thickness in the TiN.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.