In Fixed Word-Aligned (FWA) inverted index compression algorithms of Web search engines, due to the "greedy" block partition strategy of the inverted list and the interleaved storage of the codeword information, it is difficult for the algorithm to achieve the optimal compression performance. To solve the above problem, an FWA partition compression algorithm based on Directed Acyclic Graph (DAG) was proposed. Firstly, considering the small integer information in the inverted list brought by the clustering characteristics of Web pages, a novel FWA compression format with data area of 64-bit blocks was designed. In this compression format, the data area was divided into 16 storage modes suitable for continuous small integer compression through 4-bit selector area, and the selector area and data area in each block of the inverted list were stored separately, so as to ensure good batch decompression performance. Secondly, a DAG described Word-Aligned Partition (WAP) algorithm was proposed on the basis of the new compression format. In the algorithm, the inverted list block partitioning problem was regarded as a Single-Source Shortest-Path (SSSP) problem by DAG, and by considering the constraints of various storage modes of data area in FWA compression format, the structure and recursive definition of the SSSP problem were determined. Thirdly, the dynamic programming technique was used to solve the problem of SSSP and generate the pseudo-code and algorithm complexity of the optimal partition vector. The original storage modes of traditional FWA algorithms such as S9, S16 and S8b were partitioned and optimized based on DAG, and the computational complexities of the algorithms before and after optimization were compared and analyzed. Finally, the compression performance experiments were conducted with simulation integer sequence data and Text REtrieval Conference (TREC) GOV2 Web page index data. Experimental results show that, compared with the traditional FWA algorithms, the DAG based FWA partition algorithm can improve the compression ratio and decompression speed by batch decompression and partition optimization technology. At the same time, it can obtain a higher compression ratio than the traditional Frame Of Reference (FOR) algorithms for the compression of continuous small integer sequence.

As a clue with physiological basis, the center-surround contrast theory has been widely used in traditional saliency detection models. However, this theory is rarely applied to models based on deep Convolutional Neural Network (CNN) explicitly. In order to introduce the classic center-surround contrast theory into deep CNN, a salient object detection model based on Difference of Gaussian (DoG) feature network was proposed. Firstly, a Difference of Gaussian Pyramid (DGP) structure was constructed on the deep features of multiple scales to perceive the local prominent features of salient object in an image. Then, the obtained differential feature were used to perform weighted selection to the deep features with rich semantic information. Finally, the accurate extraction of the salient object was realized. In addition, the Gaussian smoothing process was implemented by using standard one-dimensional convolution in the proposed network design, so as to reduce the computational complexity and realize the end-to-end training of the network at the same time. Through comparison of the proposed model and six salient object detection algorithms on four public datasets, it can be seen that the results obtained by the proposed model achieve the best performance in the quantitative evaluation of Mean Absolute Error (MAE) and maximum F-measure. Especially on the DUTS-TE dataset the maximum F-measure and the mean absolute error of the results of the proposed model reach 0.885 and 0.039 respectively. Experimental results show that the proposed model has good detection performance for salient objects in complex natural scenes.

In order to solve the problem that it is difficult to leverage the performances of effect and efficiency, an image matching algorithm based on the Histogram of Gradient Angle (HGA) was proposed. After obtaining the key points by Features from Accelerated Segment Test (FAST), the block gradient and the new structure as dartboards were introduced to descript the local structure feature. The image matching algorithm based on HGA can work against the rotation, blur and luminance and overcome the affine partly. The experimental results, compared with Speeded Up Robust Feature (SURF), Scale Invariant Feature Transform (SIFT) and ORB (Oriented FAST and Rotated Binary Robust Independent Elementary Features (BRIEF)) in the complex scenes, demonstrate that the performance of HGA is better than other descriptors. Additionally, HGA achieves an accuracy of over 94.5% with only 1/3 of the time consumption of SIFT.

As an important component of the surface meteorological observation, the daily observation of surface frost still relies on manual labor. Therefore, a new method for detecting frost based on computer vision was proposed. First, a k-nearest neighbor graph model was constructed by incorporating the manually labeled frosty image samples and the test samples which were acquired during the real-time detection. Second, the candidate frosty regions were extracted by rating those test samples using a graph-based manifold learning procedure which took the aforementioned frosty samples as the query nodes. Finally, those candidate frosty regions were identified by an on-line trained classifier based on Support Vector Machine (SVM). Some experiments were conducted in a standardized weather station and the manual observation was taken as the baseline. The experimental results demonstrate that the proposed method achieves an accuracy of 87% in frost detection and has a potential applicability in the operational surface observation.

Consultative Committee for Space Data System (CCSDS) protocol is the mainstream of international space-ground link standard for space communication. The reversing of unknown CCSDS protocol can be used in at least two areas: one is to analyze the unknown communication traffics; the other is to detect and analyze the network attack aiming at space station as well as other space entities which are networked for international space co-operation. Thus, a computer aided analytical system was designed to reverse unknown protocol based on CCSDS protocol standard framework, and the system included the architecture design and the workflow design. Moreover, to solve the problem of telegram clustering efficiency of iterative phylogenetic tree of unknown protocol in the workflow, an improved algorithm, called Feedback Dynamic Relaxation Factor-Affinity Propagation (FDRF-AP), was given to solve the unknown communication protocol reversing problem. The simulation results indicate that the algorithm enhances the efficiency of protocol reversing engineering.

In order to make up for the limitation of research scenario of call admission control in heterogeneous wireless networks and reduce blindness of access network selection, the scenario was extended from integrated system with two networks to integrated system with multiple networks, and a joint call admission control algorithm based on reputation model was proposed. The reputation model was applied in the network selection and feedback mechanism. On the user side, the terminals chose the access network according to the networks' reputation; on the network side, the networks made decisions by adaptive bandwidth management and buffer queuing policy to enhance the probability of successful acceptation. Simulation results show that by using the proposed algorithm, new call blocking probability and handoff call dropping probability can be reduced effectively.

It is very important to ascertain rationally the number and positions of split points for discretization of continuous variables. To improve the efficiency of unsupervised discretization, an entropy-based algorithm was proposed for discretization of continuous variables. It made use of the characteristics of the information content(entropy) of a continuous variable, and partitioned the continuous variable by itself for minimizing both the loss of entropy and the number of partitions, in order to find the best balance between the information loss and a low number of partitions, so then obtained an optimal discretization result. The experiments show this approach effective.