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Network utility maximization (NUM) is studied for multihop wireless networks employing an efficient random linear network coding scheme called BATS codes. Compared with the classical random linear network coding scheme, BATS codes have lower computational and storage costs at the intermediate network nodes, and can achieve close-to-optimal end-to-end throughput and latency for multihop networks with packet loss. We formulate a NUM problem that optimizes the total utility of multiple communication flows under certain link scheduling constraints. Our problem employs a practical throughput measure induced by BATS codes and hence can provide realistic guidelines about network protocol designs for multihop wireless networks. Our problem in general has a non-convex objective function with integer variables, so that the algorithms of solving existing network utility maximization problems cannot be directly applied to our problem. We discuss a modified dual-based algorithm for solving our problem and evaluate its performance numerically.

The communication capacity of a network of line topology is studied, where only two adjacent nodes are connected by communication channels, and the intermediate network nodes have a buffer size constraint. Let L be the number of hops from the source node to the destination node. For general channels, we provide schemes to achieve Ω(1/ ln L) rates using a buffer of size B 1 + B 2 bits, where B 1 does not change with L and B 2 = O(ln ln L). In particular, B 1 bits of the buffer are used to store the data generated from the communication messages, and the other B 2 bits of the buffer are used to store the status of counters with the maximum value O(ln L).

Cataract is a dulling or clouding of the lens inside the eye. Which is one of the most common diseases that might cause blindness. Considering the damage impact of cataract, we propose to use computer science for automatic cataract detection, which is based on the classification of retinal image. This method focuses on the feature extraction step of retinal image. Firstly, the maximum entropy method is used to preprocess the fundus images. Next, we use deep learning network which is based on Caffe to automatically extract more distinctive features of fundus images. Last, several representative classification algorithms are used to identify automatically extracted features. Comparing to features extracted by deep learning and wavelet feature extracted from retinal vascular, SVM(support vector machines) and Softmax are usedfor cataract classification. Finally, cataract images are classified into normal, slight, medium or severe four-class. Through comparing with the results of classification, the feature extracted from deep learning which is classified by Softmax get better accuracy. The results demonstrate that our research on deep learning is effective and has practical value.

Cataract is one of the most common diseases that might cause blindness. Previous research shows that cataract occupies almost 50% in severe visually impairments. Fundus image is a significant reference for the diagnosis of the cataract disease. The classification of fundus images mainly consists of four parts: pre-processing of fundus images, features extraction, features weighting and classification. In this paper, firstly a whole fundus image is divided into 17 images evenly, secondly features are extracted features from each sub-image, then the feature vectors are weighted with the result of genetic algorithm and finally support vector machine is used to train and classify the fundus images. The experimental result shows that the accuracy of four-class classification can reach 87.52%.

As an important area of modern management research, psychological management is a new development of management science, which has attracted wide attention all over the world. In no case one's psychology management should be neglected. As of scientific psychology management, this paper put forwards that to manage one's psychology is to manage one's behavior and that to operate psychological management is a higher request to managers. When improvement of the capacities of psychological managers becomes available, final achievement of the harmonious development and common progress of the individuals and the organization will come true.