This paper studies the physical-layer security problem for mixed single-input multiple-output (SIMO) simultaneous wireless information and power transfer (SWIPT) based radio frequency (RF) and free-space optical (FSO) communication systems. The FSO link experiences Málaga turbulence and each RF link suffers from Nakagami- $m$ fading and path loss. We consider one energy harvesting receiver in our system model that may act as a potential eavesdropper. More precisely, to investigate the secrecy performance of considered mixed SIMO SWIPT based RF and FSO communication system, we derive closed-form expressions for the average secrecy capacity and the lower bound of secrecy outage probability by considering the fixed-gain relaying scheme, the multiple-antenna technique, the energy harvesting, the intensity modulation with direct detection, and the heterodyne detection techniques in the presence of pointing error.

Power systems include multiple units linked together to produce constantly moving electric power flux. Stability is very important in power systems, so controller systems should be implemented in power plants to ensure power system stability either in normal conditions or after the events of unwanted inputs and disorder. Frequency and active power control are more important regarding stability. Our effort focused on designing and implementing robust PID and PI controllers based on genetic algorithm by changing the reference of generating units for faster damping of frequency oscillations. Implementation results are examined on two-area power system in the ideally state and in the case of parameter deviation. According to the results, the proposed controllers are resistant to deviation of power system parameters and governor uncertainties.

Image denoising approaches based on partial differential modeling have attracted a lot of attention in image processing due to their high performance. The nonlinear anisotropic diffusion equations, specially Perona–Malik model, are powerful tools that improve the quality of the image by removing noise while preserving details and edges. In this paper, we propose a powerful and accurate local meshless algorithm to solve the time-fractional Perona–Malik model which has an adjustable fractional derivative making the control of the diffusion process more convenient than the classical one. In order to overcome the complexities of the problem, a suitable combination of the compactly supported radial basis function method and operator splitting technique is proposed to convert a complex time-fractional partial differential equation into sparse linear algebraic systems that standard solvers can solve. The numerical results of classical and fractional models are explored in different metrics to demonstrate the proposed scheme’s effectiveness. The numerical experiments confirm that the method is suitable to denoise digital images and show that the fractional derivative increases the model’s ability to remove noise in images.