Aiming at the photo spoofing problem that often occurs in identity verification， a face liveness detection model based on InceptionV3 and feature fusion， called InceptionV3 and Feature Fusion （InceptionV3_FF）， was proposed. Firstly， the InceptionV3 model was pretrained on ImageNet dataset. Secondly， the shallow， middle， and deep features of the image were obtained from different layers of the InceptionV3 model. Thirdly， different features were fused to obtain the final features. Finally， the fully connected layer was used to classify the features to achieve end-to-end training. The InceptionV3_FF model was simulated on NUAA dataset and self-made STAR dataset. Experimental results show that the proposed InceptionV3_FF model achieves the accuracy of 99.96% and 98.85% on NUAA dataset and STAR dataset respectively， which are higher than those of the InceptionV3 transfer learning and transfer fine-tuning models. Compared with Nonlinear Diffusion-CNN （ND-CNN）， Diffusion Kernel （DK）， Heterogeneous Kernel-Convolutional Neural Network （HK-CNN） and other models， the InceptionV3_FF model has higher accuracy on NUAA dataset and has certain advantages. When the InceptionV3_FF model recognizes a single image randomly selected from the dataset， it only takes 4 ms. The face liveness detection system consisted of the InceptionV3_FF model and OpenCV can identify real and fake faces.

Aiming at the universal No-Reference Image Quality Assessment （NR-IQA） algorithms， a new NR-IQA algorithm based on the saliency deep features of the pseudo reference image was proposed. Firstly， based on the distorted image， the corresponding pseudo reference image of the distorted image generated by ConSinGAN model was used as compensation information of the distorted image， thereby making up for the weakness of NR-IQA methods： lacking real reference information. Secondly， the saliency information of the pseudo reference image was extracted， and the pseudo saliency map and the distorted image were input into VGG16 netwok to extract deep features. Finally， the obtained deep features were merged and mapped into the regression network composed of fully connected layers to obtain a quality prediction consistent with human vision.Experiments were conducted on four large public image datasets TID2013， TID2008， CSIQ and LIVE to prove the effectiveness of the proposed algorithm. The results show that the Spearman Rank-Order Correlation Coefficient （SROCC） of the proposed algorithm on the TID2013 dataset is 5 percentage points higher than that of H-IQA （Hallucinated-IQA） algorithm and 14 percentage points higher than that of RankIQA （learning from Rankings for no-reference IQA） algorithm. The proposed algorithm also has stable performance for the single distortion types. Experimental results indicate that the proposed algorithm is superior to the existing mainstream Full-Reference Image Quality Assessment （FR-IQA） and NR-IQA algorithms， and is consistent with human subjective perception performance.

With the development of communication technology， communication terminals gradually adopt software to be compatible with multiple communication modes and protocols. As in the traditional software radio architecture with a Central Processing Unit （CPU） of computer as an arithmetic unit， the wideband data throughput of high-speed wireless communication systems such as Multiple-Input Multiple-Output （MIMO） is not be satisfied， an acceleration method of Low Density Parity Check （LDPC） code decoder based on Graphics Processing Unit （GPU） was proposed. Firstly， according to the theoretical analysis of the acceleration performance of GPU parallelly accelerated heterogeneous computing in GNU Radio 4G/5G physical layer signal processing module， a more parallelly efficient Layered Normalized Min-Sum （LNMS） algorithm was adopted. Then， the decoding delay of the decoder was reduced by using the methods such as global synchronization strategy， reasonably allocation of GPU memory space and stream parallelism mechanism. At the same time， the LDPC code decoding process was optimized in parallel with the multi-threaded parallel technology in GPU. Finally， the GPU accelerated decoder was implemented and verified on the software radio platform， and the bit error rate performance and acceleration performance bottlenecks of the parallel decoder were analyzed. Experimental results show that compared with the traditional CPU serial code processing method， CPU+GPU heterogeneous platform has the decoding rate for LDPC codes increased to about 200 times， and the throughput of decoder can reach more than 1 Gb/s， especially in the case of large-scale data， the decoding performance is greatly improved compared with traditional decoder.

In the production of printing industry， using You Only Look Once version 4 （YOLOv4） directly to detect printing defect targets has low accuracy and requires a large number of training samples. In order to solve the problems， a defect target detection method for printed matter based on Siamese-YOLOv4 was proposed. Firstly， a strategy of image segmentation and random parameter change was used to enhance the dataset. Then， the Siamese similarity detection network was added to the backbone network， and the Mish activation function was introduced into the similarity detection network to calculate the similarity of image blocks. After that， the regions with similarity below the threshold were regarded as the defect candidate regions. Finally， the candidate region images were trained to achieve the precise positioning and classification of defect targets. Experimental results show that， the detection precision of the proposed Siamese-YOLOv4 model is better than those of the mainstream target detection models. On the printing defect dataset， the Siamese-YOLOv4 network has the detection precision for satellite ink droplet defect of 98.6%， the detection precision for dirty spot of 97.8%， the detection precision for print lack of 93.9%； and the mean Average Precision （mAP） reaches 96.8%， which is 6.5 percentage points，6.4 percentage points， 14.9 percentage points and 10.6 percentage points higher respectively than the YOLOv4 algorithm， the Faster Regional Convolutional Neural Network （Faster R-CNN） algorithm， the Single Shot multibox Detector （SSD） algorithm and the EfficientDet algorithm. The proposed Siamese-YOLOv4 model has low false positive rate and miss rate in the defect detection of printed matter， and improves the detection precision by calculating similarity of the image blocks through the similarity detection network， proving that the proposed defect detection method can be applied to the printing quality inspection and therefore improve the defect detection level of printing enterprises.

In order to improve the performance of continuous obstacle avoidance ability of Unmanned Surface Vessel (USV) in unknown and complex environment, a fuzzy algorithm of obstacle avoidance with speed feedback was proposed. The USV utilized laser scanning radar and multi-channel ultrasonic sensors to perceive the surroundings and performed multi-sensor data fusion by grouping and setting the weight of the obstacle information, and the speed of USV was automatically adjusted according to the environmental situation based on fuzzy control. Then a more comprehensive fuzzy control rule table considering all the distribution of obstacles was proposed to enhance the adaptability of USV to complex environments. The experimental results show that the algorithm can make the USV successfully avoid obstacles and optimize the obstacle avoidance path by adjusting the speed through interaction with the environment, and has good feasibility and effectiveness.

The research about concept similarity is very important in knowledge representation and information retrieval. After studying the current classic distance-based semantic similarity algorithm, a more standardized similarity algorithm was proposed by analyzing the other key factors of semantic concept and increasing the impact of the node density and attributes of the concept for the semantic similarity. Through the experimental analysis, the similarity value of the improved algorithm is more reasonable; and compared with human subjective judgements under certain condition of the mediation parameter, the compatibility of the improved algorithm increases about 15% than that of the original algorithm.

After analyzing Retinex theory and its typical algorithm for color image enhancement, a new hue preserving algorithm for color image enhancement was presented. Firstly, it utilized a nonlinear sigmoid transfer function to intensify the image brightness component at multi-scale, and then brightness gain curved surface was acquired by reinforcing the image local contrast, finally the original color image RGB three components were enhanced at the same proportion by brightness gain curved surface, which ensured the hue was constant and undistorted. Experimental results of the proposed algorithm and other algorithms were compared and analyzed to illustrate the effectiveness of the proposed method.