Nodes in the Content-Centric Network (CCN) cache all the passed content by default, without selective caching and optimally placing of the content. In order to solve the problems, a new Cooperative Caching strategy based on User Preference (CCUP) was proposed. Firstly, user's preference for content type and content popularity were considered as user's local preference indexes to realize the selection of cached content. Then, the differentiated caching strategy was executed on the content that needed to be cached, the globally active content was cached at the important central node, and the inactive content was cached according to the matching of the local preference and the distance level between node and user. Finally, the near access of user to local preference content and the quick distribution of global active content were both achieved. The simulation results show that, compared with typical caching strategies, such as LCE (Leave Copy Everywhere)、Prob(0.6) (Probabilistic caching with 0.6)、Betw (cache "less for more"), the proposed CCUP has obvious advantages in average cache hit rate and average request delay.

In functional programming language compilation, closure conversion and defunctionalization are two widely used higher-order code eliminating methods. To improve the operational efficiency of functional programming languages, focusing on the higher-order code eliminating phase, a compiler frame to compare the performance of code generated by closure conversion and defunctionalization was proposed. Both closure conversion and defunctionalization were used in parallel in the comparing frame with a diamond structure. A functional programming language named FUN and a compiling system for FUN based on the comparing frame was proposed. Comparison experiments of closure conversion and defunctionalization were conducted on the proposed system by using typical use cases, and the experimental results were compared in code quantity and operation efficiency. The result suggests that compared with closure conversion, defunctionalization can produce shorter and faster target code; the amount of code can be decreased by up to 33.76% and performance can be improved by up to 69.51%.

In order to solve the problem of multi-source multi-sink multicast network coding, an algorithm for computing achievable information rate region and an approach for constructing linear network coding scheme were proposed. Based on the previous studies, the multi-source multi-sink multicast network coding problem was transformed into a specific single-source multicast network coding scenario with a constraint at the source node. By theoretical analyses and formula derivation, the constraint relationship among the multicast rate of source nodes was found out. Then a multi-objective optimization model was constructed to describe the boundary of achievable information rate region. Two methods were presented for solving this model. One was the enumeration method, the other was multi-objective optimization method based on genetic algorithm. The achievable information rate region could be derived from Pareto boundary of the multi-objective optimization model. After assigning the multicast rate of source nodes, the linear network coding scheme could be constructed by figuring out the single-source multicast network coding scenario with a constraint. The simulation results show that the proposed methods can find out the boundary of achievable information rate region including integral points and construct linear network coding scheme.

In order to reduce the noise of signal, a new denoising approach was proposed based on Singular Value Decomposition (SVD) and Savitzky-Golay filter. The change law of negentropy with Signal-to-Noise Ratio (SNR) was analyzed, then the negentropy was treated as an evaluating parameter of noise suppression, and the optimal dimension of the Hankel matrix of the signal was obtained. Then the Savitzky-Golay filter was used to process the singular values which are used to reconstruct the denoised signal, and the effect of Savitzky-Golay filter configuration on denoising result was studied, then the optimal configuration of Savitzky-Golay filter was determined by defining error function. The proposed approach was applied to multi-component periodic signal and linear frequency modulation signal denoising. The result shows that the proposed approach can reduce the noise effectively, and it is an effective denoising approach.

The accurate estimation of the Point Spread Function (PSF) is the key point in image restoration. For the unknown PSF parameter of defocus blur, an estimation method was proposed based on blur spectrum characteristic of image edge. Specifically, the blur spectrum feature of basic edge was analyzed, and then the edge model of natural image was treated as reference image. Furthermore, the max spectrum similarity was analyzed to obtain the right parameter between the image to be restored and the blurred reference image with defocus parameter in a continuous range. The experimental results show that the proposed algorithm suits large scale defocus blur images and has strong anti-noise ability.

To address the challenge of Android kernel hook detection, a new approach was proposed to detect Android kernel hooks by combining static technique based on characteristic pattern and dynamic technique based on behavioural analysis. The attacks including modifying system call tables and inline hook could be detected by the proposed approach. Software prototypes and test experiments were given. The experimental results show that the proposed method is effective and efficient in detecting Android kernel hooks, for most of the test cases, the runtime overhead is below 7%; and it is suitable to detect Android kernel hooks.

Concerning the problem of modulation identification in the Rayleigh channel, a new algorithm based on cumulants was proposed. The method was efficient and could easily classify seven kinds of signals of BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 4ASK (4-ary Amplitude Shift Keying), 16QAM (16-ary Quadrature Amplitude Modulation), 32QAM (32-ary Quadrature Amplitude Modulation), 64QAM (64-ary Quadrature Amplitude Modulation) and OFDM (Orthogonal Frequency Division Multiplexing) by using the decision tree classifier and the feature parameters that were extracted from combination of four-order cumulant and six-order cumulant. Through theoretical derivation and analysis, the algorithm is insensitive to Rayleigh fading and AWGN (Additive White Gaussian Noise). The computer simulation results show that the successful rates are over 90% when SNR (Signal-to-Noise Ratio) is higher than 4dB in Rayleigh channel, which demonstrates the feasibility and effectiveness of the proposed algorithm.

To solve the problem of the Orthogonal Frequency Division Multiplexing (OFDM) broadband signal processing, an algorithm for the Direction of Arrival (DOA) estimation of OFDM signal based on the broadband focused matrix and higher-order cumulant was introduced. In the former algorithm, broadband array data was broken down into several narrowband signals by Fourier transform, the direction matrices under different frequence bands were transformed to the same reference frequency by a focused matrix, and then with the Multiple Signal Classification (MUSIC) algorithm DOA was estimated. In the higher-order cumulant algorithm, through the focus operation, array output vectors at different frequency bins were transformed to focusing frequency and individual cumulant matrix was got. Each cumulant matrix was made weighted average and eigen-decomposition, and then the MUSIC algorithm was applied to estimate DOA. Theoretical analysis and simulation results show that the two methods are able to accurately estimate DOA of OFDM signal, the spatial resolution of four-order cumulant method is better than the focusing matrix method. The four-order cumulant expanded the array aperture, and it also has good adaptability when the Signal-to-Noise Ratio (SNR) is low.

Fast and accurate Point Spread Function (PSF) estimation method is the premise to obtain good results on the blur image restoration. To solve the deficiency of the discrete PSF of defocus-blurred and motion-blurred images, a discretization method was proposed based on the combination of geometric property of degradation model and sub-pixel estimation. Specifically, the principle of weight allocation was defined, which was related to the distance with the neighboring pixels. Thus, the discretization of PSF was realized. Finally, the experimental results illustrate that the proposed method improves result precision and outperforms the traditional one on visual quality, sharpness evaluation function, Peak Signal-to-Noise Ratio (PSNR) and Improved Signal-to-Noise Ratio (ISNR).

Based on the current research on improving realtime PPM algorithms in IP traceback, the relations among marking probability, traffic volume for constructing an attack path and the distance of the attacking path were analysed. And an approach of realtime IP tracebacking which deploys larger traffic first probabilistic packet marking scheme (LTFMS) was proposed. According to the statistics on the present traffic of routers, a victim could construct a major attacking path in minimum time. For large-scale DDoS attacks, by establishing a simulated test environment and experiment analysis, LTFMS can construct more attacking paths than the existing schemes within the same time.