Informatics Publishing Limited

P. Surekha ¹, S. Sumathi ¹

Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore – 641 004, IN

Abstract

The increasing effect of illegal exploitation and imitation of digital images in the field of image processing has led to the urgent development in the growth of copyright protection methods. Digital watermarking has proved best in protecting illegal authentication of data. In this paper, we propose a hybrid digital image watermarking scheme based on computational intelligence paradigms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The watermark image is embedded into the host image using Discrete Wavelet Transform (DWT). During the extraction process, GA, and PSO are applied to improve the robustness, and fidelity of the watermarked image by evaluating the fitness function. The perceptual transparency and the robustness of the watermarked and the extracted images are evaluated by applying filtering attacks, additive noise, rotation, scaling and  JPEG compression attacks to the watermarked image. From the simulation results the performance of the Particle Swarm Optimization technique is proved best based on the computed robustness and transparency measures along with the evaluated parameters like elapsed time, computation time and fitness value. The performance of proposed scheme was evaluated with a set of 50 textures images taken from online resources of Tampere University of Technology, Finland and the entire algorithm for different stages was simulated using MATLAB R2008b.

Keywords

DWT, Genetic Algorithm, Particle Swarm Optimization, Robustness and Transparency.

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P. Surekha ¹, S. Sumathi ¹

Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN

Abstract

The Multi-Depot Vehicle Routing Problem (MDVRP), an extension of classical VRP, is a NP-hard problem for simultaneously determining the routes for several vehicles from multiple depots to a set of customers and then return to the same depot. The objective of the problem is to find routes for vehicles to service all the customers at a minimal cost in terms of number of routes and total travel distance, without violating the capacity and travel time constraints of the vehicles. The solution to the MDVRP, in this paper, is obtained through Particle Swarm Optimization (PSO). The customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method. Further the routes are scheduled and optimized using POS. A set of five different Cordeau’s benchmark instances (p01, p02, p03, p04, p06) from the online resource of University of Malaga, Spain were experimented using MATLAB R2008b software. The results were evaluated in terms of depot’s route length, optimal route, optimal distance, computational time, average distance, and number of vehicles. Comparison of the experimental results with state-of-the-art techniques shows that the performance of PSO is feasible and effective for solving the multi-depot vehicle routing problem.

Keywords

MDVRP, Particle Swarm Optimization, Optimal Route, Scheduling, Clarke and Wright Saving Method.

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P. Surekha ¹, P. R. A. Mohana Raajan ², S. Sumathi ¹

Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
2 Cognizant Technology Solutions, Chennai, IN

Abstract

Evolutionary algorithms are having a leading focus in solving several optimization problems. Job-shop scheduling problem (JSSP) is one among the common NP-hard combinatorial optimization problems used to allocate machines for a set of jobs over time and hence optimizing the processing time, waiting time, completion time, and makespan. In this paper an eminent approach based on the paradigm of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, the jobs are scheduled, in which the machines and jobs with respect to levels are planned. Scheduling is optimized using Genetic Algorithm (GA), which is a powerful search technique, built on a model of the biological evolution. Like natural evolution GA deal with a population of individuals rather than a single solution and fuzzy interface is applied for planning and scheduling of jobs. The Fisher and Thompson 10×10 instance (FT10) problem is selected as the experiment problem and the algorithm is simulated using the MATLAB R2008B software.

Keywords

Job Shop Scheduling Problem, Genetic Algorithm, Fuzzy Logic, FT10, Makespan.

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P. Surekha ¹, S. Sumathi ¹

Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN

Abstract

In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm generates the initial population, selects the individuals for reproduction creating new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimized using GA and ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the genetic algorithm and the ant colony algorithm are evaluated and compared. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems.

Keywords

Job Shop Scheduling Problem, Makespan, Planning, Scheduling, Genetic Algorithm, Ant Colony Optimization.

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P. Surekha ¹, S. Sumathi ¹

Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN

Abstract

This paper proposes a new optimization method for digital images in the Discrete Wavelet Transform (DWT) domain. Digital image watermarking has proved its efficiency in protecting illegal authentication of data. The amplification factor of the watermark is the significant parameter that helps in improving the perceptual transparency and robustness against attacks. The tradeoff between the transparency and robustness is considered as an optimization problem and is solved by applying Genetic Algorithm. The experimental results of this approach prove to be secure and robust to filtering attacks, additive noise, rotation, scaling, cropping and JPEG compression. The Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and computational time are evaluated for a set of images obtained from the Tampere University of Technology, Finland using the MATLAB R2008b software.

Keywords

DWT, Genetic Algorithm, Robustness, Transparency, PSNR, MSE, Computational Time.

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P. Surekha ¹, Pavan Gurudath ¹, R. Prithvi ¹, V. G. Ritesh Ananth ¹

Affiliations
1 Department of Electrical and Electronics Engineering, PES Institute of Technology, IN

Abstract

In recent times, the number of vehicles on road has exponentially risen due to which traffic congestion and violations are a menace on roads. Automatic License Plate Recognition system can be used to automate the process of traffic management thereby easing out the flow of traffic and strengthening the access control systems. In this paper, we compare the efficiency achieved by morphological processing and edge processing algorithms. A detailed analysis and optimization of neural network parameters such as regularization parameter, number of hidden layer units and number of iterations is done. Here, a scheme is designed for implementation in real time and controlled using a graphical user interface suitable for the application of parking security in offices, institutions, malls, etc. The system utilizes image processing techniques and machine learning algorithms running on matlab and Raspberry Pi 2B to obtain the results with an efficiency of 97%.

Keywords

License Plate Recognition, Edge Processing, Vertical Projection, Horizontal Projection, Neural Network, Back Propagation Algorithm.

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