Construction of S-box based on chaotic Boolean functions and its application in image encryption

In numerous encryption frameworks, the first information is changed into encoded form by applying nonlinear substitutions and affecting diffusion. The goal of the nonlinear change is to accomplish high level of randomness in the image content. The choice of the source of randomness is critical because the success in cryptanalysis is demarked by the characteristics identified in the encrypted data. The chaotic frameworks show random conduct that is suitable for encryption applications where nonlinear transformations are needed in the middle of plaintext and the scrambled information. The application of nonlinear functional chaos-based system with embedded chaotic systems and binary chaotic sequences can prompt randomness and diffusion in the information. In addition to the high state of randomness, the requirement for various round keys is needed in a run of the mill substitution–permutation process. The proposed strategy kills the requirement for different round keys, which is suitable for high-speed communication frameworks. The measurable analyses performed on the proposed nonlinear algorithm which show improvement in encryption quality and safety against numerous brute-force and statistical attacks. Also, the proposed framework demonstrates high safety against differential and linear cryptanalysis.     

Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images

In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-scanned images using spatial Fuzzy C-Mean and morphological techniques known as Fuzzy Entropy and Morphology based Segmentation. To determine dynamic and adaptive optimal threshold, we have incorporated Fuzzy Entropy. We have proposed a novel histogram-based background removal operator. The proposed system is capable to perform fully automatic segmentation of CT Scan Lung images, based solely on information contained by the image itself. We have used different cluster validity functions to find out optimal number of clusters. The proposed system can be used as a basic building block for Computer-Aided Diagnosis. The technique was tested against the 25 datasets of different patients received from Aga Khan Medical University, Pakistan. The results confirm the validity of technique as well as enhanced performance.     

Colour tonality inspection using eigenspace features

In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth. Xianghua Xie is currently a Ph.D. student and a research assistant in the Department of Computer Science, University of Bristol, U.K. Prior to this, he received an M.Sc. degree in advanced computing with commendation from the University of Bristol in 2002 and a B.Sc. degree in environmental engineering from the Tongji University, Shanghai, P.R. China, in 2000. His current research interests are texture analysis, image segmentation, surface inspection, deformable models and historical document analysis. He is a student member of the BMVA, the IEE and the IEEE. Majid Mirmehdi received the B.Sc. (Hons.) and Ph.D. degrees in computer science in 1985 and 1991 respectively, from the City University, London. He has worked both in industry and in academia. He is currently a Reader in the Department of Computer Science at the University of Bristol, UK. His research interests include texture analysis, colour image analysis, medical imaging and document recognition. He has over 100 refereed conference and journal publications in these areas. He is an associate editor of the Pattern Analysis and Applications Journal. He is a member of the IEE, IEEE and a member and the Chairman of the British Machine Vision Association.     

Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion

An efficient method for face recognition which is robust under illumination variations is proposed. The proposed method achieves the illumination invariants based on the illumination-reflection model employing local matching for best classification. Different filters have been tested to achieve the reflectance part of the image, which is illumination invariant, and maximum filter is suggested as the best method for this purpose. A set of adaptively weighted classifiers vote on different sub-images of each input image and a decision is made based on their votes. Image entropy and mutual information are used as weight factors. The proposed method does not need any prior information about the face shape or illumination and can be applied on each image separately. Unlike most available methods, our method does not need multiple images in training stage to get the illumination invariants. Support vector machines and k-nearest neighbors methods are used as classifier. Several experiments are performed on Yale B, Extended Yale B and CMU-PIE databases. Recognition results show that the proposed method is suitable for efficient face recognition under illumination variations.     

Collaborative and Reconfigurable Object Tracking

Many Applications perceive visual information through networks of embedded sensors. Intensive image processing computations have to be performed in order to process the perceived information. Such computations usually demand hardware implementations in order to exhibit real time performance. Furthermore, many of such applications are hard to be characterized a priori, since they take different paths according to events happening in the scene at runtime. Hence, reconfigurable hardware devices are the only viable platform for implementing such applications, providing both real time performance and dynamic adaptability for the system.In this paper, we present a collaborative and dynamically adaptive object tracking system that has been built in our lab. We exploit reconfigurable hardware devices embedded in a number of networked cameras in order to achieve our goal. We justify the need for dynamic adaptation of the system through scenarios and applications. Experimental results on a set of scenes advocate the fact that our system works effectively for different scenario of events through reconfiguration. Comparing results with non-adaptive implementations verify the fact that our approach improves system's robustness to scene variations and outperforms the traditional implementations.     

A novel analytical solution of a fractional diffusion problem by homotopy analysis transform method

In this study, we present the homotopy analysis transform method for finding solution of fractional diffusion-type equations. We can attain these equations by substituting a first-order time derivative by a fractional-order derivative in regular diffusion equation. We add some examples in order to illustrate the usefulness and efficiency of our novel proposed technique for fractional diffusion equations.     

3D garment positioning using Hermite radial basis functions

Virtual Reality - Fitting an elegant 3D garment model onto a target 3D human model is crucial for garment design industry and virtual try-on systems. The alignment of the garment onto virtual...     

Retracted Article: Analysis of critical ricochet angle using two space discretization methods

Ricochet of a tungsten long-rod projectile from oblique steel plates with a finite thickness was investigated numerically using two explicit finite element methods. These two methods are Lagrange and smooth particle hydrodynamic (SPH). Three distinctive regimes resulting from oblique impact depending on the obliquity, namely simple ricochet, critical ricochet and target perforation, were investigated in detail. Critical ricochet angles were calculated for various impact velocities and strengths of the target plates in Lagrange and SPH methods. It was predicted that in every two methods, critical ricochet angle increases with decreasing impact velocities and that higher ricochet angles were expected if higher strength target materials are employed. The experimental results are discussed and compared with results predicted by the simulations and existing two-dimensional analytical model Through Investigation of the angles in which projectile only ricochets, both SPH and Lagrange methods represent approximate alike results. But in the cases that projectile begins to crack in head region out of high impact angles, the SPH method yields better results. One other advantage of the SPH against the Lagrange method is that no erosion happens though the method and therefore all the particles caused by impact are clearly seen. This means better satisfaction of the principle of conservation of mass. Therefore the correlation between the numerical results and the available experimental and observed data demonstrates that the SPH approach is an accurate and effective analysis technique for long rod ricochet phenomena in ricochet of tungsten rod with RHA target. This article has been retracted due to copyright issues that cannot be resolved. An erratum to this article can be found at     

Analyzing the solution of a system of fuzzy linear equations by a fuzzy distance

We introduce a fuzzy norm on a subset of all fuzzy numbers by a distance and we use it to analyze an approximate solution of a system of fuzzy linear equations. By a theorem we give two error bounds for error of an approximate solution of a system of fuzzy linear equations.     

Optimization of circular antenna arrays using the cuckoo search algorithm

This article presents a study of circular antenna array design and optimization using the cuckoo search (CS) algorithm. The goal of optimization is to minimize the maximum sidelobe level with and without null steering. The CS algorithm is used to determine the parameters of the array elements that produce the desired radiation pattern. We illustrated the effectiveness of the CS in the design and optimization of circular antenna arrays by means of extensive numerical simulations. We compared our results with other methods from the literature whenever possible. We presented numerous examples that show the excellent performance and robustness of the CS algorithm and the results reveal that the design of circular antenna arrays using the CS algorithm provides acceptable enhancement compared with the uniform array or the design obtained using other optimization methods.