To solve the problems of multiple network parameters and high computational complexity in image super-resolution reconstruction of deep network architecture, an image super-resolution reconstruction method based on accelerated residual network was proposed. Firstly, a residual network was constructed to reconstruct the high-frequency residual information between low-resolution image and high-resolution image, so as to reduce the deep network transmission process of redundant information and improve the reconstruction efficiency. Secondly, the dimensionality of the extracted low-resolution feature map was reduced by the feature shrinking layer to realize fast mapping with fewer network parameters. Thirdly, the dimensionality of the high-resolution feature map was increased by the feature expanding layer to reconstruct the high-frequency residual information with the rich information. Finally, the residual and low-resolution images were summed to obtain the reconstructed high-resolution image. Experimental results show that the Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) mean results obtained by the proposed method are 0.57 dB and 0.013 3 higher than those obtained by Super-Resolution using Convolutional Neural Network (SRCNN) respectively, and 0.45 dB and 0.006 7 higher than those obtained by Intermediate Supervision Convolutional Neural Network (ISCNN). In terms of reconstruction speed, using dataset Urban100 as example, the proposed method is 1.5 to 42 times faster than the existing methods. In addition, when this method is applied to the super-resolution reconstruction of motion blur images, it has the performance better than image Super-Resolution using Very Deep convolutional network (VDSR). The proposed method achieves better reconstruction quality with fewer network parameters and provides a new idea for image super-resolution reconstruction.

Aiming at the problem that the audio steganography has low utilization of carriers, poor imperceptibility and small embedding capacity, an adaptive audio steganography based on the wavelet packet decomposition and matrix code was proposed. Comparing the differences between the wavelet-packet decomposed coefficients before and after the audio's MP3 compression, the algorithm took the unchanged bits' position of wavelet-packet decomposed coefficients as embedding carriers, which effectively increased the embedding capability. And the algorithm improved the matrix code by using chaotic model to generate random triple-groups, which promoted the safety and efficiency. As for capacity, the proposed algorithm promoted about 30%, compared with the algorithm that directly uses the medium-frequency sub-bands as the carriers. On the aspect of Signal-to-Noise Ratio (SNR), the proposed algorithm promoted about 9%, compared with the matrix steganography that fixes the triple-groups. The experimental results show that the algorithm is correct and can basically satisfies large-capacity and secure communications.

A new security localization algorithm based on trust mechanism was proposed to recognize the malicious beacon nodes timely in UnderWater Sensor Network (UWSN). According to the location information offered by the beacon nodes and combining cluster structure with trust mechanism, this algorithm used Beta distribution to form the initial trust value and the trust update weight could be set as required. In order to reduce the influence of the instability of underwater acoustic channel on the trust evaluation process, meanwhile, recognize the trust cheating of malicious beacon nodes, this algorithm proposed a mechanism named TFM (Trust Filter Mechanism), which calculated and quantized the trust value, and let the cluster head node decide whether each beacon node was credible or not. The results of simulation prove that the proposed algorithm is suitable for UWSNs and it can recognize malicious beacon nodes timely, and the accuracy and security of localization system are greatly improved.

To study the effect of meteorological conditions on Unmanned Aerial Vehicle (UAV), an algorithm of multi-level fuzzy comprehensive evaluation method based on threat value was proposed. This algorithm improved a two-level weight value determination and the comprehensive evaluation model, which can get the comprehensive threat index after being calculated. The simulation results show that this algorithm can assess the degree of weather threat accurately and have faster operation speed, smaller error and lower complexity. The efficiency and validity have also been improved.