In large-scale MIMO system, as the number of antennas increases, the huge computational complexity makes traditional antenna selection algorithms impossible to effectively apply. This paper propose a joint transmitreceive antenna selection model based on ResNet. We utilize the optimal antenna selection algorithm to create labels for all channel matrices, which based on maximizing channel capacity criterion. Then using large-scale channel data to train a powerful residual neural network classifier. Consequently the trained model can classify the corresponding label for each channel matrix in the test set and select the optimal antenna subset. Experimental results show that the method can effectively decrease the number of antenna selection and its communication performance outperforms compared methods
Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.
Recently, human action recognition in videos has attracted much attention. This paper proposed a framework for human action recognition based on procrustes analysis and Fisher vector encoding. First, we apply a pose based feature extracted from silhouette image by employing Procrustes analysis and local preserving projection. It can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation, rotation and scaling. After the pose feature is extracted, a recognition framework based on Fisher vector encoding and multi-class supporting vector machine is employed for classifying the human action. Experimental results on benchmarks demonstrate the effectiveness of the proposed method.
In order to analysis the fibrosis stage and inflammatory activity grade of chronic hepatitis C, a novel classification method based on collaborative representation (CR) with smoothly clipped absolute deviation penalty (SCAD) penalty term, called CR-SCAD classifier, is proposed for pattern recognition. After that, an auto-grading system based on CR-SCAD classifier is introduced for the prediction of fibrosis stage and inflammatory activity grade of chronic hepatitis C. The proposed method has been tested on 123 clinical cases of chronic hepatitis C based on serological indexes. Experimental results show that the performance of the proposed method outperforms the state-of-the-art baselines for the classification of fibrosis stage and inflammatory activity grade of chronic hepatitis C.
Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.
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