The rapid development of quantum technology and the continuous improvement of quantum computing efficiency， especially the emergence of Shor algorithm and Grover algorithm， greatly threaten the security of traditional public key cipher and symmetric cipher. The block cipher PFP algorithm designed based on Feistel structure was analyzed. First， the linear transformation P of the round function was fused into the periodic functions in the Feistel structure， then four 5-round periodic functions of PFP were obtained， two rounds more than periodic functions in general Feistel structure， which was verified through experiments. Furthermore， by using quantum Grover and Simon algorithms， with a 5-round periodic function as the distinguisher， the security of 9， 10-round PFP was evaluated by analyzing the characteristics of PFP key arrangement algorithm. The time complexity required for key recovery is 2²⁶， 2^38.5， the quantum resource required is 193， 212 qubits， and the 58， 77 bits key can be restored， which are superior to the existing impossible differential analysis results.

Since most of the spoofing detection methods working in the satellite navigation receiver tracking phase can only detect one spoofing signal transmitted from single source, a novel spoofing detection method based on the statistical property analysis of carrier tracking loop was proposed. Firstly, the shortcomings of the existing spoofing detection methods were analyzed. Secondly, the received signal model under the spoofing attack environment was established and the statistical character of the complex signal which composites the real and spoofing signals was analyzed. Theoretical analysis shows that when the frequencies of the spoof signal and real signal are different, the novel algorithm can detect the spoofing signal since the I-branch signal's amplitude is not stable. The simulation results show that the proposed method can achieve 100% detection probability under 2% false alarm probability when the carrier-to-noise ratio of the received signal in the normal range (28 dB·Hz to 50 dB·Hz). By using the proposed algorithm, the spoofing signals transmitted from different sources can be detected, and the performance is greatly improved compared with the existing methods (about 1 dB under the same carrier-to-noise ratio, and about 4 dB under the same interference-to-signal ratio).

Unlike block cipher, stream ciphers are relatively simple and widely use linear operation, so there is often a strong correlation between the power of attack point and other power components, making it difficult to implement power analysis attacks. For the aforementioned situation, a chosen-Initial Vector (IV) correlation power analysis attack on synchronous stream cipher Grain-128 was proposed. First, the attack point and its power consumption model were gotten by analyzing the property of Grain-128's output function h(x). Then the correlation between the power of attack point and other power components was eliminated by choosing specific initial vectors, and the key problem facing the energy attacks was solved. Finally, a verification experiment was conducted based on power analysis tool PrimeTimePX. The results show that the scheme can implement 23 rounds attack and recover 46 bits key with only 736 initial vectors.

While Twitter and Sina micro-blogs abundant registered users formed a social network of focusing relationship, by using the degree of symmetry its change regulation with the scale of the social circle was studied. Firstly, based on the collection of 1000000 focusing relationships among the Sina micro-blog users and 236 Twitter users as well as their focusing relationships, the initial social network was established. Here focus lied on the connected sub-networks which had obvious symmetrical connects, then the elimination method was applied to obtain these conclusions: The major factors that affect the symmetry of the maximum connected sub-networks are those who are called big V users and negligible users. After that, comparative analysis method was used to find out that the sub-network consisted of the big V users in Twitter has a stronger symmetry. Finally, the difference between these two kinds of micro-blogs was figured out in terms of functional localization. Through the researches on the symmetry of all connected sub-networks within the initial network, the result shows that when the scale of a public social circle decreases, the corresponding symmetry becomes stronger.

To solve the problem that software implementation cannot meet real-time requirements, a hardware scheme based on Field-Programmable Gate Array (FPGA) was presented. By analyzing the digital watermark extraction system, a watermark-embedding algorithm suitable for FPGA implementation was designed and its structure is applicable to 5/3 wavelet transform. Moreover, a new watermark extraction structure that corresponded to the embedding algorithm was also proposed. The pipeline and highly parallel structure has the features of high computation efficiency, small size, low-power and real-time process. The simulation results demonstrate the system's correctness and the algorithm's abroad applicability.

The recognition of frontal facial appearance with illumination is a difficult task for face recognition. In this paper, a novel illumination invariant extraction method was proposed to deal with the illumination problem based on wavelet transform and denoising model. The illumination invariant was extracted in wavelet domain by using wavelet-based denoising techniques. Through manipulating the high frequency wavelet coefficient combined with denoising model, the edge features of the illumination invariants were enhanced and more useful information was restored in illumination invariants, which could lead to an excellent face recognition performance. The experimental results on Yale face database B and CMU PIE face database show that satisfactory recognition rate can be achieved by the proposed method.

A new text hierarchical clustering algorithm based on K-Means was presented,which combined features from both K-Means and agglomerative approach that allowed them to reduce the early-stage errors made by agglomerative method and hence improved the quality of clustering solutions.The experimental evaluation shows that,our algorithm leads to better solutions than agglomerative methods.

A kind of method of web data mining on association rule,authoritative Web pages and Weblog items with quantum Grover search algorithm and probability were given in this paper,and then the algorithm to be optimal in mining a large volumes of unsorted and unstructured Web data was dicussed.

A new method of time series data mining using discrete wavelet transform was proposed. The main property of this method lied in its feature extraction strategy, which was different from other methods before. Instead of using only the first k coefficients or the largest k coefficient, which emphasized on energy preservation, this new method decided extracted coefficients according to their classification ability on real time sequences in the training set. Generally speaking, this method tried to select coefficients from all wavelet coefficients to form a feature coefficient set, which best enlarged the distance between different classes and reduced the distance within same class. With this feature coefficient set, we can make prediction on test sequences set by calculating pertaining degree of each sequence to each class. The prediction result is the pertaining relation with largest pertaining degree. For real time series data used in our research, the efficiency and accuracy of this method is satisfying.