Aiming at the problems that the high-capacity steganography model based on encoding-decoding network has weak robustness and can not resist noise attack and channel compression， a high-capacity robust image steganography scheme based on encoding-decoding network was proposed. In the proposed scheme， encoder， decoder and discriminator based on Densely connected convolutional Network （DenseNet） were designed. The secret information and the carrier image were jointly encoded into a steganographic image by the encoder， the secret information was extracted by the decoder， and the discriminator was used to distinguish between carrier images and steganographic images. A noise layer was added between the encoder and the decoder； Dropout， JPEG compression， Gaussian blur， Gaussian noise and salt and pepper noise were used to simulate a real environment with various kinds of noise attacks. The steganographic image output by the encoder was processed by different kinds of noise and decoded by the decoder. Through training the model， the secret information could be extracted from the noise-processed steganographic image by the decoder， so that the noise attacks could be resisted. Experiment results show that the steganographic capacity of the proposed scheme reaches 0.45 - 0.95 bpp on 360×360 pixel images， and the relative embedding capacity is improved by 2.04 times compared to the suboptimal robust steganographic scheme； the decoding accuracy reaches 0.72 - 0.97， and compared with the steganography without noise layer， the average decoding accuracy is improved by 44 percentage points. The proposed scheme not only guarantees high embedding quantity and high coding image quality， but also has stronger anti-noise capability.

Using random projection acceleration technology to project the Singular Value Decomposition (SVD) of higher dimensional matrices onto a lower subspace can reduce the time consumption of SVD. The singular value random projection compression operator was defined to replace the singular value compression operator, then it was used to improve the Fixed Point Continuation (FPC) algorithm and got FPCrp algorithm. Lots of experiments were conducted on the original algorithm and the improved one. The results show that the random projection technology can reduce more than 50% time consumption of the FPC algorithm, while maintaining its robustness and precision. The modified matrix completion algorithm based on random projection technology is effective in solving large scale problems.

In this paper, a new monocular vision system was proposed to improve obstacle detection capability of indoor mobile robot. In this system, firstly, the Hue, Saturation, Intensity (HSI) color space conversion of images was performed. Secondly, a small target threshold selection method was proposed to segment the images, which enhanced the precision of the image segmentation. Thirdly, the target scene matching method and target projection matching method were used to calculate the change of the target pixel and projection so as to judge whether the target is obstacles or ground graphs. The experimental results show that the monocular vision system is effective and feasible, and this system can be applied to the navigation for small indoor mobile robots.

To improve the performance of a text-independent speaker recognition system, the authors proposed a speaker recognition system based on linear log-likelihood kernel function. The linear log-likelihood kernel compressed the input cepstrum feature sequence of a speaker model by a Gaussian mixture model. The log-likelihood between two utterances was simplified to the distance between the parameters of Gaussian mixture model. Polarization identity was applied to obtain the mapping from a cepstrum feature sequence to a high dimension vector. Support Vector Machine (SVM) was used to train speaker models. The experimental results on National Institute of Standard and Technology show that the proposed kernel has excellent performance.