NDN (Named Data Networking) is clean-slate architecture for future networking, in which packets carry data names rather than source or destination addresses, being a sharp contrast to today IP architecture. The default routing strategy for NDN is flooding the interest packets to all the possible interfaces which could reach for the data provider or data cache container. Obviously, the default routing strategy is not best way in the actual networking for the heavy load of routing items. This article proposes an interface rank-based routing strategy (RBRS), in which the PIT and FIB structures are detailed and expanded to satisfy the Interest and Data packets transmission, and the interfaces are ranked through a cumulative algorithm for route selection. Through the simulation, RBRS has been proved an adaptive routing mechanism with better performance of network load balance, while reducing the average routing hops and the centric nodes load.

The data traffic needed to be transferred by APs is much more than the other STAs in the IEEE802.11 DCF networks. However, APs access the wireless link with the same priority as STAs, and thus the throughput between uplink and downlink is of great injustice. In this paper, an adaptive optimization scheme is proposed to achieve fairness between uplink and downlink flows. APs monitor the growing real-time data traffic, and when the network system is in heavy traffic, APs could adaptively adjust the contention window to achieve fairness. Detailed simulation results show that the scheme can effectively adapt to various networks different in data flow numbers and packet size, and consequently achieve fairness between uplink and downlink flows with the total throughput increased.

An auto-stereoscopic 3D video conversation system is demonstrated with an improved eye-tracking method based on a lenticular sheet and two cameras. The two cameras are used to get stereoscopic picture pairs and addressed the viewers position by an Improved Eye Tracking Method. The computer combines the stereoscopic picture pairs with different masks graphic processing unit. Low crosstalk correct stereoscopic video pairs for the end-to-end commutation are achieved.

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Lung cancer has become the world's human cancer disease in the "first killer." In this paper, three aspects of lung CT images were treated. Firstly, based on the CT image preprocessing, the lung parenchyma was segmented by random walk algorithm and the ROI was extracted from the pulmonary parenchyma; Secondly, the 10-dimensional feature vectors of pulmonary nodule ROI were extracted by the gray level co-occurrence matrix algorithm; Finally, support vector machine as a classifier is to identify the pulmonary nodules and the accuracy rate is more than 94%. The experimental results show that the study of automatic CT image recognition can provide some data reference for doctors and play a supporting role in the course of treatment.