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This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

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This paper presents a smart-routing mechanism of a control system to track Low Earth Orbit (LEO) satellites. Satellite tracking mainly relies on the antenna pointing database generated by SGP4 orbit forecasting model and follow the point coordinates to command the rotation of the axes. Gears rotation gaps will affect the strength of the received signal; the Proportional Integral (PI) controller is used to adjust the error values caused by the drive shaft mechanism. Particle swarm optimization (PSO) algorithm has fewer parameter settings and the advantages of fast convergence, which is adopted for variable selection and optimization for the parameters kp and ki of PI controller. The resolver feedback mechanism of actual angle indicator is using as a basis for performance adjustment in the search process. The experimental results of a three axes tracking system demonstrate the reliability and better performance of the proposed PSO-PI satellite tracking system.