In this paper, we propose an efficient and robust method for the protection of patient data (ID) when transmitting biomedical signals (bio-signals) from one storage device to another one. The proposed zero-watermarkin...
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ISBN:
(数字)9783031298608
ISBN:
(纸本)9783031298592;9783031298608
In this paper, we propose an efficient and robust method for the protection of patient data (ID) when transmitting biomedical signals (bio-signals) from one storage device to another one. The proposed zero-watermarking method is naturally imperceptible because it doesn't involve modifying the original signal. The features of the latter are extracted based on Tchebichef moments, and then these features are used in combination with the ID image (watermark) to produce the zero-watermark binary image that hides the patient ID. Then, the zero-watermark binary image is transmitted with the patient bio-signal to a different storage device. To recover the patient ID image, the transmitted bio-signal and the zero-watermark are used. As a platform, we use labview to implement the proposed application. The results of the performed simulations show the high efficiency of the proposed method in terms of simplicity of implementation, high security, and good noise robustness.
This paper presents a systematic tuning approach for Model Predictive Control (MPC) parameters' using an original labview-implementation of advanced metaheuristics algorithms. Perturbed Particle Swarm Optimization...
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This paper presents a systematic tuning approach for Model Predictive Control (MPC) parameters' using an original labview-implementation of advanced metaheuristics algorithms. Perturbed Particle Swarm Optimization (pPSO), Gravitational Search Algorithm (GSA), Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimizer (GWO) metaheuristics are proposed to solve the formulated MPC tuning problem under operational constraints. The MPC tuning strategy is done offline for the selection of both prediction and control horizons as well as the weightings matrices. All proposed algorithms are firstly evaluated and validated on a benchmark of standard test functions. The same algorithms were then used to solve the formulated MPC tuning problem for two dynamical systems such as the magnetic levitation system MAGLEV 33-006, and the three-tank DTS200 process. Demonstrative results, in terms of statistical metrics and closed-loop systems responses, are presented and discussed in order to show the effectiveness and superiority of the proposed metaheuristics-tuned approach. The developed CAD interface for the labview implementation of the proposed metaheuristics is given and freely accessible for extended optimization puposes.
In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a Lab VIEW-based perturbed Particle Swarm Optimization (pPSO) approach. This original labview implementation of this ...
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In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a Lab VIEW-based perturbed Particle Swarm Optimization (pPSO) approach. This original labview implementation of this metaheuristic algorithm is firstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed labview pPSO algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSO-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic Algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSO-based MPC tuning methodology. (C) 2016, IFAC (International Federation of Automatic. Control) Hosting, by Elsevier Ltd. All rights reserved.
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