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The optimal multilayer, multiobjective resource allocation model for multiuser and multitraffic orthogonal frequencydivision multiplexing (OFDM)-based power-line communication (PLC) systems is analyzed with the restrictions of maximal total power, constant rates for every real time (RT) user, minimal rates for every non-real time (NRT) user, maximal upper limit of power and number of bits for every subcarrier in each OFDM symbol. A bit-loading lookup table resource allocation algorithm with rateand margin-adaptation is proposed based on resource factor, which first assigns fairly Pareto non-dominated resources for all RT users according to resource factor so as to attain their constant rates, second computes the relevant remaining power and non-used subcarriers for every Pareto solution and assigns fairly them for all NRT users so as to attain their minimal rates, third also computes the relevant remaining resources and assigns them so as to achieve the maximal sum of the allocated rates for all NRT users, lastly selects out the globally optimal solution according to the order of partial information. Based on the typical power-line channel, the simulation results illustrate that the proposed algorithm outperforms the existed multiuser bit-loading greedy algorithm and it realizes better the multiple aims of resource allocation in OFDM-based PLC systems.Index Terms-Optimal resource allocation, Pareto non-dominated solution, power-line communication (PLC), resource factor.

Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively.