There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.

Edge Adaptive Image Steganography Based on LSB Matching Revisited

A review of the methods used in the design of interval type-2 fuzzy controllers has been considered in this work. The fundamental focus of the work is based on the basic reasons for optimizing type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy controllers.

A review on the design and optimization of interval type-2 fuzzy controllers

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

https://isiarticles.com/bundles/Article/pre/pdf/46311.pdf

A review on interval type-2 fuzzy logic applications in intelligent control

Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground Thresholding plays a major in binarization of images Thresholding can be categorized into global thresholding and local thresholding In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background In such cases, binarization with local thresholding is more appropriate This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation Normally the local mean computational time depends on the window size Our technique uses integral sum image as a prior processing to calculate local mean It does not involve calculations of standard deviations as in other local adaptive techniques This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques

/pdf/a-new-local-adaptive-thresholding-technique-in-binarization-31sla466r5.pdf

A New Local Adaptive Thresholding Technique in Binarization

The paper describes a short survey on different types of steganography techniques for image in spatial and transform domains and steganalysis techniques for the detection of secret message in the image. The strong and weak points of these techniques are mentioned briefly so that researchers who work in steganography and steganalysis gain prior knowledge in designing these techniques and their variants. One can develop a better steganography technique by analyzing the contemporary steganalysis techniques.

A short survey on image steganography and steganalysis techniques

There is wide interest in multimedia security and copyright protection due the explosion of data exchange in the Internet and the extensive use of digital media. We propose a novel video watermarking scheme based on visual cryptography and scene change detection in discrete wavelet transform domain. We start with a complete survey of the current image and video watermarking technologies, and have noticed that majority of the existing schemes are not capable of resisting all attacks. We propose the idea to use different parts of a single watermark into different scenes of a video for generation of the owner's share from the original video based on the frame mean in same scene and the binary watermark, and generation of the identification share based on the frame mean of probably attacked video. These two shares after stacking can reveal the copyright ownership. Experiments are conducted to verify the robustness through a series of experiments. The security requirement of the proposed algorithm is achieved with the visual cryptography.

Video watermarking scheme based on visual cryptography and scene change detection

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

/pdf/a-genetic-algorithm-and-fuzzy-logic-approach-for-video-shot-1l2b5i3117.pdf

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

The purpose of steganography is covert communication - to hide the very existence of a message from a third party. The paper proposes a new least significant bit embedding algorithm for hiding secret messages in nonadjacent pixel locations of edges of images. It ensures a better security against eavesdroppers.

Kh. Manglem Singh

Papers

A New Local Adaptive Thresholding Technique in Binarization

A short survey on image steganography and steganalysis techniques

Video watermarking scheme based on visual cryptography and scene change detection

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

Hiding Secret Message in Edges of the Image