Concerning the problem that face recognition is affected by the factors such as attitude, occlusion, resolution and so on, a method for image super-resolution and face frontalization in unconstrained image was proposed, which could generate high-quality and standard front images. The projection matrix between the input image and 3D model was estimated to generate the standard front image. Also, through the characteristics of face symmetry, the missing pixels by occlusion and attitude could be filled. In order to avoid the loss of pixel information during the process of generating standard front image and improve the image quality, a deeply-recursive convolutional network which had 16 layers was introduced for image super-resolution. To ease the difficulty of training, two extensions were proposed:recursive-supervision and skip-connection. The experimental results on the processed LFW datasets show that it is surprisingly effective when used for face recognition and gender estimation.

To guarantee the QoS of WLAN(Wireless Local Area Network) after the network load increased,the technology of reserved contention window maintenance was applied to EDCA mechanism.When network was in low load,the performance was the same as the original access mechanism.As the load increased,it could greatly reduce the collision probability under stable condition.Therefore it can not only support QoS of the high priority traffic,but also improve the throughput of the whole network.

The hyper-granularity immingling technology is inefficent with small decrypted programs,then instruction set alternation technology based on a thin virtual machine was proposed.This technology employed an automata to record encrypt and decrypt ways.Furthermore,the thin virtual machine was responsible for interpretation and execution of these encrypted programs.Test results show that t
he technology can improve encrypted programs’ efficiency without weaken the security.