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The low signal-to-carrier ratio and frequency deviation play an important role in the bit synchronization algorithm. In this paper, an intersect differential bit synchronization algorithm is putted forward, which uses the short coherent time to improve the signal gain and utilizes the long distance differential coherent method to enhance the discrimination between bit edge and others. The results show that the proposed algorithm maintains a good frequency deviation tolerance, and achieves good synchronization performance under 60Hz frequency deviation. Compared with previous methods, the proposed algorithm improves the bit synchronization sensitivity by 2~8dB.

Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.