The interaction between entities in complex systems is vividly described by multiplex networks， and motifs frequently appear in networks as a higher-order structure. Compared with single-layer motifs， multiplex motifs have the characteristics of large quantity， diverse types， and complicated structure. Given the current lack of complete detection algorithm for multiplex motifs， a Fast Algorithm for Multiplex Motif Detection （FAMMD） suitable for multiplex networks was proposed. Firstly， an improved ESU （Enumerate SUbgraphs） algorithm was used to enumerate multiplex subgraphs. Then a method combining layer markers and binary strings was used for accelerating the process of isomorphism detection， and a null model that preserved degree sequences and inter-layer dependencies was constructed for multiplex subgraph testing. Finally， motif detection was performed on two-layer real networks. Multiplex motifs exhibited a closely connected triple mode， and they were more homogeneous in social networks while more complementary in transportation networks. Experimental results show that the proposed method can accurately and quickly detect multiplex motifs that reflect the structure characteristics of the network and conform the actual situation.

Nested entities pose a challenge to the task of entity-relation joint extraction. The existing joint extraction models have the problems of generating a large number of negative examples and high complexity when dealing with nested entities. In addition， the interference of nested entities on triplet prediction is not considered by these models. To solve these problems， a forest-based entity-relation joint extraction method was proposed， named EF2LTF （Entity Forest to Layering Triple Forest）. In EF2LTF， a two-stage joint training framework was adopted. Firstly， through the generation of an entity forest， different entities within specific nested entities were identified flexibly. Then， the identified nested entities and their hierarchical structures were combined to generate a hierarchical triplet forest. Experimental results on four benchmark datasets show that EF2LTF outperforms methods such as joint entity and relation extraction with Set Prediction Network （SPN） model， joint extraction model for entities and relations based on Span — SpERT （Span-based Entity and Relation Transformer） and Dynamic Graph Information Extraction ++ （DyGIE++）^{on F1 score. It is verified that the proposed method not only enhances the recognition ability of nested entities， but also enhances the ability to distinguish nested entities when constructing triples， thereby improving the joint extraction performance of entities and relations.}

In the circumstance of the real business process changing constantly， the original business process model needs to be repaired to better represent the real business process. The key step of model repair is to analyze the deviation between the real log and the model. However， the current methods to find the deviation mainly use the alignment repetition technique， and do not quantitatively analyze the abstract structure from the perspective of behavior. Therefore， a method of analyzing deviation between log and model by behavioral profile was proposed， and based on the above， a model repair method was further proposed on the basis of logical Petri nets. Firstly， based on the behavioral profile， the compliance between the log and the model was calculated to identify the deviation trace. Secondly， the logic transitions were selected from deviant activities through the deviant triple set in the deviation trace. Finally， the logic function was set based on the logic transitions， and the original model was repaired by adding new branches or reconstructing new structures. The fitness and precision of the repair models were verified. Simulation results show that when the all finesses are 1， the repair model obtained by the proposed repair method has higher precision compared with Fahland method and Goldratt method， on the basis of maintaining the similarity between the repair model and original model as much as possible.

The Multi-Object Tracking （MOT） task needs to track multiple objects at the same time and ensures the continuity of object identities. To solve the problems in the current MOT process， such as object occlusion， object ID Switch （IDSW） and object loss， the Transformer-based MOT model was improved， and a multi-object tracking method based on dual-decoder Transformer was proposed. Firstly， a set of trajectories was generated by model initialization in the first frame， and in each frame after the first one， attention was used to establish the association between frames. Secondly， the dual-decoder was used to correct the tracked object information. One decoder was used to detect the objects， and the other one was used to track the objects. Thirdly， the histogram template matching was applied to find the lost objects after completing the tracking. Finally， the Kalman filter was utilized to track and predict the occluded objects， and the occluded results were associated with the newly detected objects to ensure the continuity of the tracking results. In addition， on the basis of TrackFormer， the modeling of apparent statistical characteristics and motion features was added to realize the fusion between different structures. Experimental results on MOT17 dataset show that compared with TrackFormer， the proposed algorithm has the IDentity F1 Score （IDF1） increased by 0.87 percentage points， the Multiple Object Tracking Accuracy （MOTA） increased by 0.41 percentage points， and the IDSW number reduced by 16.3%. The proposed method also achieves good results on MOT16 and MOT20 datasets. Consequently， the proposed method can effectively deal with the object occlusion problem， maintain object identity information， and reduce object identity loss.

Aiming at the problems of low accuracy and poor real-time performance of Noctiluca scintillans red tide extraction in the field of satellite remote sensing， a Noctiluca scintillans red tide extraction method from Unmanned Aerial Vehicle （UAV） images based on deep learning was proposed. Firstly， the high-resolution RGB （Red-Green-Blue） videos collected by UAV were used as the monitoring data， the backbone network was modified to VGG-16 （Visual Geometry Group-16） and the spatial dropout strategy was introduced on the basis of the original UNet++ network to enhance the feature extraction ability and prevent the overfitting respectively. Then， the VGG-16 network pre-trained by using ImageNet dataset was applied to perform transfer learning to increase the network convergence speed. Finally， in order to evaluate the performance of the proposed method， experiments were conducted on the self-built red tide dataset Redtide-DB. The Overall Accuracy （OA）， F1 score， and Kappa of the Noctiluca scintillans red tide extraction of the proposed method are up to 94.63%， 0.955 2， 0.949 6 respectively， which are better than those of three traditional machine learning methods — K-Nearest Neighbors （KNN）， Support Vector Machine （SVM） and Random Forest （RF） as well as three typical semantic segmentation networks （PSPNet （Pyramid Scene Parsing Network）， SegNet and U-Net）. Meanwhile， the red tide images of different shooting equipment and shooting environments were used to test the generalization ability of the proposed method， and the corresponding OA， F1 score and Kappa are 97.41%， 0.965 9 and 0.938 2， respectively， proving that the proposed method has a certain generalization ability. Experimental results show that the proposed method can realize the automatic accurate Noctiluca scintillans red tide extraction in complex environments， and provides a reference for Noctiluca scintillans red tide monitoring and research work.

Aiming at the problem that the existing microblog event extraction methods are based on the content characteristics of events and ignore the relationship between the social attributes and time characteristics of events， so that they cannot identify the key events in the propagation process of microblog hot spots， a key event extraction method from microblog by integrating social influence and temporal distribution was proposed. Firstly， the social influence was modeled to present importance of microblog events. Secondly， the temporal characteristics of microblog events during evolution were considered to capture the differences of events under different temporal distributions. Finally， the key microblog events were extracted under different temporal distributions. Experimental results on real datasets show that the proposed method can effectively extract key events in microblog hot spots. Compared with four methods of random selection， Term Frequency-Inverse Document Frequency （TF-IDF）， minimum-weight connected dominating set and degree and clustering coefficient information， the proposed method has the event set integrity index improved by 21%， 18%， 26% and 30% on dataset 1 respectively， and 14%， 2%， 21% and 23% on dataset 2 respectively. The extraction effect of the proposed method is better than those of the traditional methods.

Because there are many differences in real life scenes， human emotions are various in different scenes， which leads to an uneven distribution of labels in the emotion dataset. Furthermore， most traditional methods utilize model pre-training and feature engineering to enhance the expression ability of expression-related features， but do not consider the complementarity between different feature representations， which limits the generalization and robustness of the model. To address these issues， EE-GAN， an end-to-end deep learning framework including the network integration model Ens-Net was proposed. It took the characteristics of different depths and regions into consideration，the fusion of different semantic and different level features was implemented， and network integration was used to improve the learning ability of the model. Besides， facial images with specific expression labels were generated by generative adversarial network， which aimed to balance the distribution of expression labels in data augmentation. The qualitative and quantitative evaluations on CK+， FER2013 and JAFFE datasets demonstrate the effectiveness of proposed method. Compared with existing view learning methods， including Locality Preserving Projections （LPP）， EE-GAN achieves the facial expression accuracies of 82.1%， 84.8% and 91.5% on the three datasets respectively. Compared with traditional CNN models such as AlexNet， VGG， and ResNet， EE-GAN achieves the accuracy increased by at least 9 percentage points.

Concerning the problem of insufficient image feature extraction and ignorance of single-modal internal relations and the interactions between single-modal and multi-modal， a text and image information based Multi-Modal Deep Fusion （MMDF） model was proposed. Firstly， the Bi-Gated Recurrent Unit （Bi-GRU） was used to extract the rich semantic features of the text， and the multi-branch Convolutional-Recurrent Neural Network （CNN-RNN） was used to extract the multi-level features of the image. Then the inter-modal and intra-modal attention mechanisms were established to capture the high-level interaction between the fields of language and vision， and the multi-modal joint representation was obtained. Finally， the original representation of each modal and the fused multi-modal joint representation were re-fused according to their attention weights to strengthen the role of the original information. Compared with the Multimodal Variational AutoEncoder （MVAE） model， the proposed model has the accuracy improved by 1.9 percentage points and 2.4 percentage points on the China Computer Federation （CCF） competition and the Weibo datasets respectively. Experimental results show that the proposed model can fully fuse multi-modal information and effectively improve the accuracy of false information detection.

Mobile Edge Computing （MEC） can reduce the energy consumption of mobile devices and the delay of users’ acquisition to services by deploying resources in users’ neighborhood； however， most relevant caching studies ignore the regional differences of the services requested by users. A cache cooperation strategy for maximizing revenue was proposed by considering the features of requested content in different regions and the dynamic characteristic of content. Firstly， considering the regional features of user preferences， the base stations were partitioned into several collaborative domains， and the base stations in each collaboration domain was able to serve users with the same preferences. Then， the content popularity in each region was predicted by the Auto?Regressive Integrated Moving Average （ARIMA） model and the similarity of the content. Finally， the cache cooperation problem was transformed into a revenue maximization problem， and the greedy algorithm was used to solve the content placement and replacement problems according to the revenue obtained by content storage. Simulation results showed that compared with the Grouping?based and Hierarchical Collaborative Caching （GHCC） algorithm based on MEC， the proposed algorithm improved the cache hit rate by 28% with lower average transmission delay. It can be seen that the proposed algorithm can effectively improve the cache hit rate and reduce the average transmission delay at the same time.

At present， social media platforms have become the main ways for people to publish and obtain information， but the convenience of information publish may lead to the rapid spread of rumors， so verifying whether information is a rumor and stoping the spread of rumors has become an urgent problem to be solved. Previous studies have shown that people's stance on information can help determining whether the information is a rumor or not. Aiming at the problem of rumor spread， a Joint Stance Process Multi?Task Rumor Verification Model （JSP?MRVM） was proposed on the basis of the above result. Firstly， three propagation processes of information were represented by using topology map， feature map and common Graph Convolutional Network （GCN） respectively. Then， the attention mechanism was used to obtain the stance features of the information and fuse the stance features with the tweet features. Finally， a multi?task objective function was designed to make the stance classification task better assist in verifying rumors. Experimental results prove that the accuracy and Macro?F1 of the proposed model on RumorEval dataset are improved by 10.7 percentage points and 11.2 percentage points respectively compared to those of the baseline model RV?ML （Rumor Verification scheme based on Multitask Learning model）， verifying that the proposed model is effective and can reduce the spread of rumors.

Communication is an important way to achieve effective cooperation among multiple agents in a non? omniscient environment. When there are a large number of agents， redundant messages may be generated in the communication process. To handle the communication messages effectively， a multi?agent reinforcement learning algorithm based on attentional message sharing was proposed， called AMSAC （Attentional Message Sharing multi?agent Actor?Critic）. Firstly， a message sharing network was built for effective communication among agents， and information sharing was achieved through message reading and writing by the agents， thus solving the problem of lack of communication among agents in non?omniscient environment with complex tasks. Then， in the message sharing network， the communication messages were processed adaptively by the attentional message sharing mechanism， and the messages from different agents were processed with importance order to solve the problem that large?scale multi?agent system cannot effectively identify and utilize the messages during the communication process. Moreover， in the centralized Critic network， the Native Critic was used to update the Actor network parameters according to Temporal Difference （TD） advantage policy gradient， so that the action values of agents were evaluated effectively. Finally， during the execution period， the decision was made by the agent distributed Actor network based on its own observations and messages from message sharing network. Experimental results in the StarCraft Multi?Agent Challenge （SMAC） environment show that compared with Native Actor?Critic （Native AC）， Game Abstraction Communication （GA?Comm） and other multi?agent reinforcement learning methods， AMSAC has an average win rate improvement of 4 - 32 percentage points in four different scenarios. AMSAC’s attentional message sharing mechanism provides a reasonable solution for processing communication messages among agents in a multi?agent system， and has broad application prospects in both transportation hub control and unmanned aerial vehicle collaboration.

The different importance of the activities in the business process in real world is not taken into account by the existing cost functions， so that in the alignment process of model and log， alignment cost may deviates from perceived cost significantly. To solve this problem， a concept of important synchronization cost function was proposed based on the typical flow characteristic of the behaviors in business processes， and an alignment method that can improve efficiency was proposed under this function. Firstly， the important synchronization cost function was defined based on the concept of perceived cost. Then， the important matching sub-sequence to segment the process model and the log trace was determined according to the log trace and the typical flow characteristic of the behaviors in the process model. Finally， based on the important synchronization cost function， the segmented sub-process and the corresponding log trace subsequence were aligned， and the segmented alignment results were combined to obtain the final alignment result. The experiments were carried out to verify the proposed method from the perspectives of accuracy and efficiency. In terms of accuracy， compared with the existing standard cost function and maximum synchronous cost function， the proposed cost function improved the alignment accuracy by up to 17.44 percentage points， and when the event log contained mixed noise， the proposed cost function had the highest average alignment accuracy of 88.67%. The efficiency of alignment was verified by comparing the time consumed by alignment. The average time of the existing two functions were 1.58 s and 2.21 s respectively， while that of the proposed method was 0.63 s， which was improved by 150.79% and 250.79% respectively. Experimental results show that the proposed method can satisfy the accuracy demand and improve the efficiency of alignment at the same time.

Under the constrains of very short message content on social media platforms， a large number of empty forwards in the transmission structure， and the mismatch between user roles and contents， a rumor detection model based on user attribute information and message content in the propagation network， namely GMB_GMU， was proposed. Firstly， user propagation network was constructed with user attributes as nodes and propagation chains as edges， and Graph Attention neTwork （GAT） was introduced to obtain an enhanced representation of user attributes； meanwhile， based on this user propagation network， the structural representation of users was obtained by using node2vec， and it was enhanced by using mutual attention mechanism. In addition， BERT （Bidirectional Encoder Representations from Transformers） was introduced to establish the source post content representation of the source post. Finally， to obtain the final message representation， Gated Multimodal Unit （GMU） was used to integrate the user attribute representation， structural representation and source post content representation. Experimental results show that the GMB_GMU model achieves an accuracy of 0.952 on publicly available Weibo data and can effectively identify rumor events， which is significantly better than the propagation algorithms based on Recurrent Neural Network （RNN） and other neural network benchmark models.

In order to solve the problems of high computational cost of Attribute-Based Encryption （ABE） scheme and low efficiency of data search in cloud servers simultaneously， an Outsourced Attribute-Based Encryption scheme with Keyword Search （OABE-KS） was proposed. Firstly， the outsourced computation technology was used for reducing the local computing cost of encryption and decryption users to the constant level. Then， the indexes and trapdoors of the corresponding keywords were generated by the encryption user and the decryption user respectively， and the cloud server was used to match them. After that， the successful matching results would be returned to the decryption user by the cloud server. The adaptive security of the proposed scheme was proved under the composite order group. According to the experimental analysis， when the number of attributes changes from 10 to 100， the running time of each stage of the proposed scheme is basically unchanged， showing that the running time of the proposed scheme in each stage does not vary with the number change of attributes. Experimental results show that， the proposed scheme is suitable for the application on resource-limited devices and is not affected by the number of attributes in practical applications.

For redundancy chaos, and the lack of representation of group observers' scanpath data in natural scenes, by mining the potential characteristics of individual scanpaths, a method for group scanpath generation based on fixation Regions of Interest (ROI) spatial temporal clustering and transferring was proposed. Firstly, multiple observers' scanpaths under the same stimulus sample were analyzed, and multiple fixation regions of interest were generated by utilizing affinity propagation clustering algorithm to cluster the fixation points. Then, the statistics and analysis of the information related to fixation intensity such as the number of observers, fixation frequency and lasting time were carried out and the regions of interest were filtered. Afterwards, the subregions of interest with different types were extracted via defining fixation behaviors in the regions of interest. Finally, the transformation mode of regions and subregions of interest was proposed on the basis of fixation priority, so as to generate the group scanpath in natural scenes. The group scanpath generation experiments were conducted on two public datasets MIT1003 and OSIE. The results show that compared with the state-of-the-art methods, such as eMine, Scanpath Trend Analysis (STA), Sequential Pattern Mining Algorithm (SPAM), Candidate-constrained Dynamic time warping Barycenter Averaging method (CDBA) and Heuristic, the proposed method has the group scanpath generated of higher entirety similarity indexes with ScanMatch (w/o duration) reached 0.426 and 0.467 respectively, and ScanMatch (w/duration) reached 0.404 and 0.439 respectively. It can be seen that the scanpath generated by the proposed method has high overall similarity to the real scanpath, and has a certain function of representation.

The objective of Blind Source Separation (BSS) is to restore the unobservable source signals from their mixtures without knowing the prior knowledge of the mixing process. It is considered that the potential source signals are spatially uncorrelated but temporally correlated, i.e. they have non-vanishing temporal structure. A second-order statistics based BSS method was proposed for such sources. The robust prewhitening was firstly performed on the observed mixing signals, where the dimension of the sources was estimated based on the Minimum Description Length (MDL) criterion. Then, the blind separation was realized by implementing the Singular Value Decomposition (SVD) on the time-delayed covariance matrix of the whitened signals. The simulation on separation of a group of speech signals proves the effectiveness of the algorithm, and the performance of the algorithm was measured by Signal-to-Interference Ratio (SIR) and Performance Index (PI).

Aiming at the limitations of easily falling into local minimum and poor stability in simple Monkey-King Genetic Algorithm (MKGA), a MKGA by Immune Evolutionary Hybridized (MKGAIEH) was proposed. MKGAIEH divided the total population into several sub-groups. In order to make full use of the best individual (monkey-king) information of total population, the Immune Evolutionary Algorithm (IEA) was introduced to iterative calculation. In addition, for the other individuals in the sub-groups, the crossover and mutation operations were performed on the monkey-kings of sub-groups and total population. As local searches of all sub-groups were completed, the solutions of sub-groups were mixed again. As the iteration proceeds, this strategy combined the global information exchange with local search is not only to avoid the premature convergence, but also to approximate the global optimal solution with a higher accuracy. Comparison experiments on 6 test functions using MKGAIEH, MKGA, Improved MKGA (IMKGA), Bee Evolutionary Genetic Algorithm (BEGA), Algorithm of Shuffled Frog Leaping based on Immune Evolutionary Particle Swarm Optimization (IEPSOSFLA), and Common climbing Operator Genetic Algorithm (COGA) were given. The results show that the MKGAIEH can find the global optimal solutions for all 6 test functions, and the mean values and standard deviation accuracy of 5 test functions achieve the minimums with improving several orders of magnitude than those of the comparison algorithms. Therefore, MKGAIEH has the optimal searching ability and the stability all the better.

In order to improve the real-time response ability of massive data processing, Storm distributed real-time platform was introduced to process data mining, and the Density-Based Spatial Clustering of Application with Noise (DBSCAN) clustering algorithm based on Storm was designed to deal with massive data. The algorithm was divided into three main steps: data collection, clustering analysis and result output. All procedures were realized under the pre-defined component of Storm and submitted to the Storm cluster for execution. Through comparative analysis and performance monitoring, the system shows the advantages of low latency and high throughput capacity. It proves that Storm suits for real-time processing of massive data.

The exchangeable key solutions and homomorphic encryption solutions were analyzed. Meanwhile, the deficiencies of these two solutions on the computational complexity were pointed out. On the basis of that, two new solutions were put forward: one was based on chaotic encryption solution, and the other was asymmetric encryption solution which introduced the incredible third party. The correctness, security and complexity of them were analyzed and verified. The comparison between the proposed solutions and the existing ones was given. The experimental results show that the new solutions can reduce the complexity of the algorithm, and greatly improve the execution efficiency of algorithm.

Node scheduling scheme is an effective approach to solve problems of energy constraints and high coverage redundancy in Wireless Sensor Network (WSN). However, it should satisfy the requirements of coverage rate as well as energy saving. To solve the problems of unbalanced and inefficient energy consumption of nodes in random node scheduling schemes, a node scheduling scheme based on spatial resolution was proposed. This scheme maintained coverage rate by controlling the number of active nodes in a deployed area, and balanced the remaining energy of every node. Meanwhile, a neighbor nodes protection mechanism was adopted to ensure that the dormant nodes closed real-time listening to reduce energy consumption, and the demand of coverage rate was fulfilled effectively due to relieving the situation of coverage holes which may occur when nodes take turns to rest. The simulation results indicate the performance of this scheme is superior to other similar schemes in coverage rate, lifetime and balance of energy consumption among nodes.

The existing algorithms for trajectory prediction have very low prediction accuracy when there are a limited number of available trajectories. To address this problem, the Modified Bayesian Inference (MBI) approach was proposed, which constructed the Markov model to quantify the correlation between adjacent locations. MBI decomposed historical trajectories into sub-trajectories to get more precise Markov model and the probability formula of Bayesian inference was obtained. The experimental results based on real datasets show that MBI approach is two to three times faster than the existing algorithm, and it has higher prediction accuracy and stability. MBI makes full use of the available trajectories and improves the efficiency and accuracy for the prediction of trajectory.

For the typical steganographic algorithms in JPEG images, this paper firstly analyzed the correlation between neighboring coefficients of intra- and inter-block in Discrete Cosiine Transform (DCT) domain, and then extracted the conditional distribution probability matrix of the bilateral coefficients as the sensitive steganalytic features by taking the middle coefficient of three neighboring coefficients as the condition. At last, this paper proposed a JPEG image steganalytic algorithm on a basis of bilateral transition probability distribution of DCT coefficients. The experimental results show that, for different embedding ratios, the algorithm proposed in this paper outperforms the existing algorithms.

In order to provide a logical basis and mathematical model for directly processing natural language, the Lukasiewicz implication algebra based on the graded linguistic values chain and the linguistic truth-valued concept lattice were established. The natural language used to depict certain values in practical problems was analyzed and equivalently expressed as the graded linguistic values set, on which the definitions of partial order relations and binary operators were given. The bijective relation was established between the graded linguistic values chain and the natural language set, and the specific linguistic truth-valued concept lattice was constructed based on the linguistic truth-valued lattice implication algebra. And then, the linguistic truth-valued concept lattice was applied into the analytical system with natural language for vehicle transport safety performance, which verified the feasibility of the model to directly deal with the natural language and the readability of the structure graph.

In order to solve the problems of long handover delay and high packet loss rate existing in FMIPv6, an improved scheme named PI-FMIPv6 was designed. Information learning, proxy binding and the tunnel timer were introduced into it so as to complete the configuration of the New Care-of Address (NCoA), Duplicate Address Detection (DAD), Binding Update (BU) by advancing and managing the tunnel. π-calculus was used to define and deduce the mathematical model about PI-FMIPv6. It is proved that the optimized scheme PI-FMIPv6 is standard and precise. Furthermore, the simulation results from the NS-2 show that PI-FMIPv6 can reduce the handover delay by 60.7% and packet loss rate by 61.5% at least compared to FMIPv6, which verifies that the PI-FMIPv6 is superior to FMIPv6 and can better meet the real-time requirement.

The shortened LDPC codes based on the current IEEE802.16e Standard, has plenty of small girth. In order to resolve this problem, this paper presents a new scheme of designing the shortened LDPC codes. In the proposed scheme, the design has modified the sub-parity check matrix under the frame of the IEEE802.16e Standard. The proposed check matrix with the spreading factor (zf=48) is constructed with quasi-cyclic matrix method and finite geometry method. Moreover, parity check points was searched the node degree in the way of synchronous sequential, the proposed sub- parity check matrix has minor girth-6 and no girth-4. The results of simulation in the AWGN channels show that the improved short code with the code rate approaching to 0.5, can fast encode as excellent as the IEEE802.16e protocol contains，moreover, a good BER performance of the proposed code is 1.1dB more than the Shannon limit of this channel.

A closed-loop scheduling algorithm was proposed for multi-user Multi-Input Multi-Output (MIMO) system to improve wireless spectral performance. Using greedy scheduling technology from multi-user diversity, the algorithm combined the adaptive modulation in the physical layer with automatic repetitive request in the data link layer, and the system's spectral efficiency was enhanced under the interaction of the multi-antenna diversity and multi-user diversity. Considering the practical case of delayed feedback environment, the closed-form expressions of system spectral efficiency under delayed channel were derived. And the experimental results show that this algorithm can hardly be influenced by delay time, which is more applicable to multi-user MIMO system.

Grade classification of seed cotton is a major issue that has a significant impact on the agricultural economy. According to the characteristics such as impurities, yellowness and brightness extracted from images of seed cotton, fuzzy pattern recognition was used to improve the classification of cotton grade. A classification model of seed cotton was constructed based on the fuzzy nearness. Fuzzy mathematics was combined with artificial neural network to build up a well improved model and algorithm. Statistical distribution was used to calculate and select the model parameter method. Eventually, the numbers of impurities of different sizes were worked out by using the Euler's numbers of the image. Based on the method of selecting model parameters, the proposed algorithm could be optimized step by step. After full learning, seed cotton classification accuracy rate reached 92%. The experimental results show that the presented algorithm satisfies the actual application needs.

It has been a new approach that flash memory is used as storage media to improve database system performance. In the current flash database system, to solve the problems of low search performance, improper log region allocation, and high index update cost in storage management, a forecasting algorithm based on the latest version of Bloom Filter was proposed, record locator and log summary structure were introduced, and a self-adaptive mechanism based on flash search and update cost estimate model were given. The experimental results prove that it can make a proper log region allocation, efficiently improve search performance, and reduce non-clustered index update cost.

License plate location plays an important role in the License Plate Recognition (LPR) system. A license plate location algorithm was presented, which was based on the quaternion specific color-pair edge detection technique. The original color image was first represented by using quaternion Same-Hue-Full-Saturation (SHFS) form. Then, four pairs of masks were used to detect specific color-pair edges. Next, mathematical morphology dilation operation was applied to extract potential license plate regions. Finally, several shape constraint conditions were employed to locate true license plate regions. The proposed method combined the color feature, edge feature and shape feature of license plates, and offered high robustness. The experimental results on 485 car images that are taken under various conditions show that the recall rate is 96.8% and the precision rate is more than 93.2%.

In action classification, methods of feature extraction were either simple with low accuracy, or complicated with poor real-time quality. To resolve this problem, firstly, Enhanced Gait Energy Image (EGEI) was derived from Gait Energy Image (GEI); secondly, high dimensional feature space of the action was reduced to lower dimensional space by Two-Dimensional Locality Preserving Projection (2DLPP); then Nearest-Neighbor (NN) classifier was adopted to distinguish different actions. EGEI could extract more obvious feature information than GEI; 2DLPP outperformed principal component analysis and locality preserving projections in dimensional reduction. It was tested on the Weizmann human action dataset. The experimental results show that the proposed algorithm is simple, achieves higher classification accuracy, and the average recognition ratio reaches 91.22%.