To solve the problem that the direct use of high-dimensional， high-frequency， noise-containing real-world data to perform data processing leads to unreliable estimators， a data uncertainty quantification method based on Generative Adversarial Network （GAN） was proposed. Firstly， the original data distribution was reconstructed by GAN to construct a mapping distribution from the noise space to the space of the original data. Secondly， the samples were extracted by Markov Chain Monte Carlo （MCMC） method to obtain new samples based on the original data distribution. Thirdly， confidence intervals for the uncertainty of the samples were defined based on the specified functions. Finally， the confidence intervals were used to estimate the uncertainty of the original data， and within the data the confidence intervals was selected as the data used by the estimator. Experimental results show that 50% fewer samples are required to train the estimator to reach the upper limit by using the data within the confidence intervals compared to the samples required by using the original data. At the same time， compared to the original data， the data within the confidence intervals requires 30% fewer samples on average to achieve the same test accuracy.

Aiming at the problem that the scale of integrated circuits and the number of on-chip registers are increasing, which makes the verification more difficult, a lightweight register model was proposed. Firstly, a concise underlying structure was designed, and parameterized settings were combined to reduce the memory consumption of the register model at runtime. Then the register verification requirements at different levels such as module level and system level were analyzed, and SystemVerilog language was used to implement various functions required for verification. Finally, the built-in test cases and register model automatic generation tools were developed to reduce the setup time of the verification environment in which the register model was located. The experimental results show that the proposed register model is 21.65% of the Universal Verification Methodology (UVM) register model in term of memory consumption at runtime; in term of function, the proposed register model can be applied to traditional UVM verification environments and non-UVM verification environments, and the functions such as read-write property, reset value and backdoor access path of 25 types of registers are checked. This lightweight register model has good universality and flexibility in engineering practice, meets the needs of register verification, and can effectively improve the efficiency of register verification.

Aimed at the problem that existing RFID (Radio Frequency Identification) group proof protocols are inefficient and easily encounter many attacks like replay, tracking and so on, this paper proposed a new group proof protocol based on secret key-sharing tree. This protocol designed a new secret group-proofing key construction based on secret key sharing scheme. The group-proofing key was divided many times into many sub-keys to creat a key tree. This method increased the complexity of the construction of the secret key, increased the difficulty of that attackers attempt to recover the group key and increased the security of tag's group proof. The reader interacts with each tag only once to authenticate its validity and collect the group-proof information. This protocol enormously increases the proof efficiency. Compared to the existing protocols such as Yoking-Proofs, ECC-based and Tree-based, this protocol has better security and higher efficiency.

In current multi-hop unidirectional identity-based proxy re-encryption schemes, the ciphertext length increases with the number of hops, which leads to the reduction of efficiency. To solve this issue, a new multi-hop unidirectional identity-based proxy re-encryption scheme was designed by changing the re-encryption key generation side. The re-encryption keys were generated by the sender. In the scheme, the first-level and second-level ciphertexts were of the same pattern, and the length of the re-encrypted ciphertext remained unchanged. The efficiency analysis shows that the proposed scheme reduces the numbers of exponent, multiplication, and bilinear pairing computations. The new scheme has been proved to be chosen-ciphertext attack secure in the random oracle model based on the Decisional Bilinear Diffie-Hellman (DBDH) assumption.

Concerning the high bandwidth occupation problem caused by deploying the P2P (Peer-to-Peer) streaming media server cluster on the Data Center Network (DCN) in cloud, a P2P streaming media server cluster deployment algorithm based on cloud computing was proposed. This algorithm modeled the P2P streaming media server cluster deployment as a quadratic assignment problem, and sought the mapping relationship between each virtual streaming media server and each deployment point to realize the P2P streaming media server cluster deployment based on cloud computing. The simulation experiment demonstrates that the P2P streaming media server cluster deployment algorithm based on cloud computing can effectively reduce the bandwidth usage of DCN in cloud.

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.

The authentication efficiency of tags is always an important factor that restricts the extensive application of Radio Frequency IDentification (RFID). But there is not a good method to solve the problem till now. On the basis of tree-based RFID protocol, this article shared the secret of each path with many portions using the secret-sharing scheme. A new secret tree was created and a secret-sharing-based protocol was proposed while still keeping the searching efficiency. After being analyzed, the protocol is proved to be secure and efficient, and it also solves the key-updating problem which has slowed the study of RFID system for a long time.

As it is known that the underwater environment is quite complicated and changeable, as a result, targets and pseudo targets always have a high degree of mixing, and one single segmentation method usually could not abstract ideal target regions. Therefore, this paper proposed a new segmentation method based on Blob analysis and Bayesian design-making. Firstly, the optimistic thresholds were calculated by the improved OTSU algorithm, and then the image was segmented according to this threshold. Through analyzing the connectivity characters, closed contours of regions were achieved. Secondly, the connected regions were described using 7 dimensions of Blob operators and pseudo-target regions were eliminated based on Bayesian decision-making rules. Finally, burrs and disturbances were wiped off through the usage of mathematical morphology operators and ideal target regions were achieved. Through dealing with the images grabbed during the pool experiments using the above method, accuracy and efficiency of the method were verified and the real target regions were acquired.

Isometric Feature Mapping (ISOMAP) requires that the data belong to a single well-sampled manifold; however, when the data are sampled from an imperfect manifold, ISOMAP tends to overcluster the data. To alleviate this problem, this paper presented a new variant of ISOMAP called Weighted ISOMAP (WISOMAP), which used Weighted Multidimensional Scaling (WMDS) instead of Classical Multidimensional Scaling (CMDS) to map the data into the low-dimensional embedding space. As a new variant of MDS, WMDS gave smaller weight to the distances with more edges, which were generally worse approximated and then less trustworthy than those with fewer edges, and thus could limit the effects of the generally worse-approximated distances with many edges and preserved the more trustworthy distances with few edges in the low-dimensional embedding space more precisely, by which the data relying on an imperfect manifold could be visualized better. The efficiency of WISOMAP is verified by experimental results well.

To segment moving crowd with different dynamics in complex video surveillance scenes, this paper proposed a crowd motion segmentation algorithm which was based on video particle flow and Finite Time Lyapunov Exponent (FTLE) field. Firstly, video particle flow was used to represent the long-range particle motion estimation. To optimize these particles trajectories, an energy function containing point-based appearance matching and distortion between the particles was minimized. Then the spatial gradient of the particle flow map was solved and the FTLE field was constructed. Finally, the Lagrangian Coherent Structure (LCS) in the FTLE field was used to divide flow into regions of qualitatively different dynamics. The experimental results show that the proposed algorithm can effectively segment crowd flow with different dynamics in complex video surveillance scenes, and it has strong robustness.

Aggregate nearest neighbor query involves many query points, so it is more complicated than traditional nearest neighbor query, and the distribution characteristic of query set implies the region where its aggregate nearest neighbor exists. Taking full account of the distribution characteristic of query set, a method by utilizing distribution characteristic to direct the way of aggregate nearest neighbor searching was given. Based on the method, a new algorithm named AM was presented for aggregate nearest neighbor query. AM algorithm can dynamically capture and use the distribution characteristic of query set, which enables it to search data points in a right order, and avoid unnecessary searching to data points. The experimental results show the efficiency of the algorithm.

An octree based on the rapid k nearest search was presented for scattered points from 3D scanner. Through the establishment of bounding box on point sets, octree was used to record segmentation process, so that the search of point neighbors was limited to the bounding box of sample points and its neighbor, and through pruning strategies to further narrow the scope of the search.The experiments on a great deal of real data show that the search speed can be well improved.

There are few researches on fuzzy queries of relational databases involving the fuzzy conditions or relative linguistic quantifiers in "having" clause. Methods based on fuzzy set theory to these queries were presented here. The fuzzy conditions in "having" clause were translated to general SQL queries on the basis of-cut of membership function. Therefore, the tuples can be filtered by the RDBMS, which improves the efficiency of fuzzy queries. Nonfuzzy cardinality of fuzzy set was used to evaluate the quantified "having" clause, so the computation and the result became simple.

To improve classific precision of network intrusion detection model and reduce the number of training data set and learning time, a new supervisal algorithm based on ε Support Vector Regression machines (ε-SVR) machine was proposed. Firstly, normalization was used on training data set, and then a new coefficience of sparse penalty function was adjusted. Finally, the experimental results using KDD CUP 1999 data set show that this approach can detect intrusion behavior, increase its veracity and validity, and reduce its distortion.

On the basis of contour extraction of 3D fragments, an algorithm for rapid fragment matching in the 3D fragement reassembly was presented. In the algorithm, the type of feature point was calculated according to its neighbor surface type, and then higher similarity contour segments belonging to different contours were found, thus the normal vector was utilized to verify the matching probability of similar space curves. The similarity between feature segments of different contours was measured according to feature point type, feature point curvature and the hausdorff distance of feature segments. This algorithm is suitable for point-based surface.