In this paper, we address a real-time object tracking algorithm considering local binary pattern (LBP) as a feature descriptor. In addition to texture feature, Ohta color features are included in the feature vector of the covariance tracking algorithm. The performance of the proposed algorithm is compared with some other competitive object tracking algorithms such as the RGB feature-based covariance method and color histogram method. The comparisons of the performance among these algorithms include detection rate and computational time. These methods have been applied to four different challenging situations, and the resulting experimental results show the robustness of the proposed technique against occlusion, camera motion, appearance, and change in illumination condition.
Fair power allocation to the users in a power domain nonorthogonal multiple access (PD-NOMA) networks is an essential aspect. In this paper, we have proposed a coefficient-scaling-based fair power allocation scheme, for a single cell multi-user PD-NOMA network. First, we have developed a model for two users with a fixed power allocation scheme, and proposed a modified strong user fair-power allocation scheme. Furthermore, this two-user scheme is extended for an ad-hoc multi-user PD-NOMA network and a coefficient-scaling-based approach for fair power allocation is proposed to address the outage issue. The performance of the proposed schemes is evaluated through bit-error-rate, sum-rate, outage, and fairness index. The simulation results reveal a remarkable increase in fairness performance of the proposed scheme, in comparison with a state-of-the-art method.
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