Debashis Nandi

An Image Encryption Process based on Chaotic Logistic Map

Abstract This paper proposes a high security image encryption technique using logistic map. The proposed image encryption algorithm is described in detail along with its security analysis such as key space analysis, statistical analysis and differential analysis. A comparison in terms of correlation between the initial and transformed images, Number of pixels change rate and unified average changing intensity is also done. The present algorithm has been tested using different images to prove that the encryption method has a great potential and has a good ability to achieve the high confidential security.

Bit-plane Encrypted Image Cryptosystem Using Chaotic, Quadratic, and Cubic Maps

ABSTRACT Recently, in chaotic cryptography several new techniques have been suggested to develop efficient and secure image encryption algorithm for communication of image over insecure/public channels. Some of these techniques are computationally less complex but fails to provide sufficient security, and some techniques are though highly secured but increases computational overhead for encryption and decryption. This paper presents an efficient bit-plane encryption algorithm using chaotic, quadratic, and cubic maps. The proposed algorithm is based on the confusion, diffusion, and pixel randomisation processes. The encryption security of this technique for different bit-plane has been tested through key space analysis, statistical analysis, entropy analysis, and sensitivity analysis for real time image encryption and transmission. The presented algorithm shows the strength of security in terms of information entropy, peak signal-to-noise ratio (PSNR), number of pixels change rate (NPCR), unified average changing intensity (UACI), and correlation coefficient with the number of encrypted bit planes. It is clear that the strength of security increases with the number of encrypted bit planes, but the presented results demand that the level of security reached to the requirements (expected theoretically according to [C. Fu, et al., Optic Express, 20, 2363–78, Jan. 2012]) after encryption of four to six bit planes with reasonable small encryption/decryption time. This features proved the novelty of the proposed work. The algorithm is tested by the random and pseudo-random sequence of test standard SP 8000-22 released by National Institute of Standard and Technology Commission (NIST).

Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

This paper illustrates, for the first time, the use of artificial bee colony optimization ABC technique to study optimal reactive power dispatch ORPD in power system with the use of flexible AC transmission systems FACTS. FACTS controller cannot only increase the power transmission capacity without installing new transmission lines, but they can also enhance voltage profile and reduce transmission loss in power system. Two types of FACTS devices namely, thyristor-controlled series capacitor TCSC and thyristor-controlled phase shifter TCPS are considered in this paper. A standard IEEE 30-bus test system with multiple TCSC and TCPS devices is used for two different objective functions to validate the performance of the proposed method. The simulation results demonstrates the ability of the ABC to produce better optimal solutions compared to particle swarm optimization with inertia weight approach PSOIWA and real coded genetic algorithm RGA.

Multi-robot Assembling Along a Boundary of a Given Region in Presence of Opaque Line Obstacles

This paper presents a distributed algorithm for assembling a swarm of autonomous mobile robots on a common boundary of a given polygonal region in presence of opaque horizontal line obstacles. Robots and obstacles are initially scattered in an unknown environment and they do not have direct communication among themselves. The algorithm guarantees successful assembling of all the robots on the left boundary of the given region within finite amount of time and without facing any collision during their movement. The intermediate distances among the assembled robots are not fixed. In this proposed algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the Full-Compass and Synchronous/Semi-synchronous timing models.

Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch

Static synchronous series compensator (SSSC) is one of the most effective flexible AC transmission systems (FACTS) devices used for enhancing power system security. In this paper, optimal location and sizing of SSSC are investigated for solving the optimal reactive power dispatch (ORPD) problem in order to minimize the active power loss in the transmission networks. A new and efficient chemical reaction optimization (CRO) is proposed to find the feasible optimal solution of the SSSC based optimal reactive power dispatch (ORPD) problem. The proposed approach is carried out on the standard IEEE 30 bus and IEEE 57 bus test systems. The optimization results obtained by the proposed CRO are analyzed and compared with the same obtained from genetic algorithm (GA), teaching learning based optimization (TLBO), quasi-oppositional TLBO (QOTLBO) and strength pareto evolutionary algorithm (SPEA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solutions.

Hybrid biogeography–based optimisation for optimal power flow incorporating FACTS devices

This paper presents hybrid biogeography–based optimisation (BBO) technique to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). The proposed hybrid method combines the BBO algorithm with the differential evolution (DE) to improve the performance of the conventional BBO and DE algorithms. Here, BBO is the main optimiser, while the DE is used to fine tune the solution of the BBO algorithm. Two types of FACTS devices namely, thyristor–controlled series capacitor (TCSC) and thyristor–controlled phase shifter (TCPS) are considered in this paper. The proposed method is implemented with MATLAB and tested on modified IEEE 30–bus system in five different cases. The simulation results show that the proposed HDE–BBO algorithm is effective, fast and accurate in finding the optimal parameter settings for FACTS devices to solve OPF problems. The proposed HDE–BBO method gives better solution quality compared to particle swarm optimisation with inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), differential evolution (DE) and conventional BBO. The simulation study also shows that FACTS devices are capable of providing an economically attractive solution to power system problems.

Low cost distance estimation system using low resolution single camera and high radius convex mirrors

This paper proposes a single camera based stereo vision system to determine disparity map and the distance of the object from the camera system. The proposed system uses two convex mirrors with a sufficiently long radius of curvature. These two mirrors help to capture a pair of images of the same scene from two different viewpoints just like a conventional two camera based stereo vision system. The use of single camera makes the calibration and rectification process easier. The identical intensity response of the stereo images improves the accuracy of the disparity map for computing the object distance. Graph cut based algorithm is used to compute the disparity and the object distance from the camera system. The experimental results justify the claims.

Image registration in noisy environment using particle swarm optimization

Image registration is a process of overlaying multiple images of the same scene taken from different viewpoints or by different sensors or at different times. The registration geometrically aligns multiple images with respect to a reference image. It has wide application in remote sensing, superresolution, medicine, cartography (map updating), and in computer vision (target localization, automatic quality control) etc. Traditional methods for image registration are sensitive to image degradations such as variations in noise, blur or illumination. In this paper an accurate rigid image registration technique based on Particle Swarm Optimization (PSO) is proposed which is robust to additive noise. It is observed that proposed algorithm shows same performance at different level of noise power. Experimental results confirm the claims of the algorithm.

Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

In this paper, quasi-oppositional teaching-learning based optimization QOTLBO is introduced and successfully applied for solving an optimal power flow OPF problem in power system incorporating flexible AC transmission systems FACTS. The main drawback of the original teaching-learning based optimization TLBO is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning OBL concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization HDE-BBO. This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.

Image registration in presence of multiplicative noise by Particle Swarm Optimization

Image registration is a really challenging task especially when multiplicative noise is present in the images. The presence of multiplicative noise degrades the image content severely. Derivative based methods like gradient descent and Levenberg Marquardt Algorithm fails to perform image registration in such cases. In this paper we propose a Particle Swarm Optimization (PSO) based image registration technique which is well capable to perform image registration between multiple images that are corrupted with multiplicative noise. The specialty of this method is that its accuracy is high. It is faster and it successfully works when the number of registration parameter is higher. The supremacy of this technique compared to other differentiation based techniques is that, it assures robustness to both the additive and multiplicative noise. It is an iterative approach of optimization where the mean square error between one pair of noisy images is reduced. It deals with four different registration parameters namely the shift along horizontal and vertical direction, rotation and scaling. It can equally perform for large scale as well as small scale disparities between pair of images.