The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

In this paper, we present a new transmit diversity scheme with power control by using two transmit antennas in which the power control unit is added to adaptively suit the channel fading variation. Compared to the transmit diversity scheme (STD, one space time coding scheme) proposed by Alamouti and the traditional maximal ratio combining (MRC) diversity scheme employed at the receiver, simulation results indicate that the new scheme has considerable performance gain. We also discuss the effects of the imperfect channel parameter estimation on the performance of the system. Simulation results show that the new system is more robust to the estimation error of channel fading parameters than the STD. As the signal to noise ratio is relatively high, the diversity scheme with power control is more sensitive to the channel estimation error compared to the MRC. But when the channel estimation accuracy is relatively high, the diversity scheme with power control still has better performance than the ideal MRC as the BER is about 1 10-3.

In this paper, we consider the subchannel detection problem in forward links for the multicarrier DS-CDMA system when some different subchannel allocation policies are used. An optimal subchannel decision algorithm is proposed based on the maximum-likelihood (ML) criterion. Theoretical analysis and simulation results are presented. Furthermore, we discuss the parameter selection problem on the length of the training sequences in the subchannel allocation scheme in [8],[12] by using the proposed ML detection algorithm. The results show that the subchannel allocation scheme in [8],[12] is feasible since only a few symbols overhead is required.