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Abstract: In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an efficient simulation-based two-stage scheduling methodology by integrating vector ordinal optimization (VOO) and response surface methodology (RSM) to make a good scheduling policy for fab operation. The suggested method combines local and global rules into a single rule, with the objective of simultaneously optimizing multiple performance indices. Our approach consists of 2 stages: rule combination selection, and parameter optimization. In the first stage, we apply the VOO techniques to effectively selecting good rule combination. Results show that 1 orders of computation time reduction over traditional simulations can be achieved. In the second stage, we adopt RSM and desirability function to tune the parameters associated to scheduling algorithm. The proposed approach is validated by means of a comparison with other scheduling policies. The results show that the proposed scheduling method is effective. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]