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Solar energy has become one of the popular choices among all renewable energy resources. In order to harvest solar energy, a photovoltaic (PV) system is required. Nowadays, researchers are increasingly paying more attention to PV system since it is affordable and easy to install and maintain. However, unpredictable weather and operating conditions of a PV system may reduce power generation. Therefore, a global maximum power point tracking (GMPPT) controller needs to be installed in the PV system to improve the power generation capability. However, the conventional GMPPT algorithm is less effective because of unsteady oscillations. In this paper, we propose a hybrid method of gravitational search particle swarm optimization (GSPSO) algorithm to track GMPP faster and more efficiently. The proposed algorithm utilizes the exploitation ability of the particle swarm optimization (PSO) algorithm and the exploration ability of the gravitational search algorithm (GSA). Simulation results show that the proposed algorithm has the fastest tracking speed and the highest generated power compared with the other competitive algorithms. [ABSTRACT FROM AUTHOR]

Numerical modeling plays an important role in the prediction of received signal strength inside the human body, in case of on-body to in-body radio wave propagation in a body area network (BAN). Since the structure of a human body is heterogeneous in nature, the Fresnel scattering coefficients could not be used to incorporate the attenuation properties of the multilayer tissue structure of the human body. This paper proposes the use of ABCD matrix method, which has been used in the analysis of multilayer dielectric slab structure, to determine the attenuation of the electro magnetic (EM) signal due to various in-body structures. In addition, variation of reflection and transmission coefficients of a four-layer body tissue structure with respect to the incident angle is presented and recommends suitable operating frequency in ultra-wide band (UWB) for BAN applications. The authors conducted measurements in body mimicking liquid phantom in UWB and included in support of their research outcome. [ABSTRACT FROM AUTHOR]

Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment. [ABSTRACT FROM AUTHOR]