Since the existing adaptive neighborhood based multi-view clustering algorithms do not consider the noise and the loss of consensus graph information, a Robust Multi-View Graph Clustering(RMVGC) algorithm based on adaptive neighborhood was proposed. Firstly, to avoid the influence of noise and outliers on the data, the Robust Principal Component Analysis(RPCA) model was used to learn multiple clean low-rank data from the original data. Secondly, the adaptive neighborhood learning was employed to directly fuse multiple clean low-rank data to obtain a clean consensus affinity graph, thus reducing the information loss in the process of graph fusion. Experimental results demonstrate that the Normalized Mutual Informations(NMI) of the proposed algorithm RMVGC is improved by 5.2, 1.36, 27.2, 4.66 and 5.85 percentage points, respectively, compared to the current popular multi-view clustering algorithms on MRSCV1, BBCSport, COIL20, ORL and UCI digits datasets. Meanwhile, in the proposed algorithm, the local structure of data is maintained, the robustness against the original data is enhanced, the quality of affinity graph is improved, and such that the proposed algorithm has great clustering performance on multi-view datasets.

Object tracking based on Siamese network has made some progresses, that is it overcomes the limitation of the spatial invariance of Siamese network in the deep network. However, there are still factors such as appearance changes, scale changes, and occlusions that affect tracking performance. Focusing on the problems of large changes in object scale, object motion blur and small scale of object in the specified object tracking of Unmanned Aerial Vehicles (UAV), a new tracking algorithm was proposed based on the Siamese region proposal attention mechanism network, namely Attention-SiamRPN+. Firstly, an improved deep residual network ResNet-50 was employed as a feature extractor to extract feature maps. Secondly, the channel attention mechanism module was used to filter the semantic information of different channel feature maps extracted by the residual network, and the corresponding weights to different channel features were reassigned. Thirdly, a hierarchical fusion of two Region Proposal Networks (RPN) was applied. The RPN module was consisted of channel-by-channel deep cross-correlation of feature maps, classification of positive and negative samples and bounding box regression. Finally, the box of the object position was selected. In the test on the VOT2018 platform, the proposed algorithm had the accuracy of 59.4% and the Expected Average Overlap (EAO) of 39.5%. In the experiment with one-pass evaluation mode on the OTB2015 platform, the algorithm had the success rate and precision of 68.7% and 89.4% respectively. Experimental results show that the evaluation results of the proposed algorithm are better than the results of three excellent correlation filtering tracking and Siamese network tracking algorithms in recent years, and the proposed algorithm has good robustness and real-time processing speed when applying to the tracking of specified objects of UAV.

Due to the instability of network training, the image super-resolution reconstruction based on Generative Adversarial Network (GAN) has a mode collapse phenomenon. To solve this problem, a Spherical double Discriminator Super-Resolution Generative Adversarial Network (SDSRGAN) based on spherical geometric moment matching and feature discrimination was proposed, and the stability of network training was improved by adopting geometric moment matching and discrimination of high-frequency features. First of all, the generator was used to produce a reconstructed image through feature extraction and upsampling. Second, the spherical discriminator was used to map image features to high-dimensional spherical space, so as to make full use of higher-order statistics of feature data. Third, a feature discriminator was added to the traditional discriminator to extract high-frequency features of the image, so as to reconstruct both the characteristic high-frequency component and the structural component. Finally, game training between the generator and double discriminator was carried out to improve the quality of the image reconstructed by the generator. Experimental results show that the proposed algorithm can effectively converge, its network can be stably trained, and has Peak Signal-to-Noise Ratio (PSNR) of 31.28 dB, Structural SIMilarity (SSIM) of 0.872. Compared with Bicubic, Super-Resolution Residual Network (SRResNet), Fast Super-Resolution Convolutional Neural Network (FSRCNN), Super-Resolution using a Generative Adversarial Network (SRGAN), and Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) algorithms, the reconstructed image of the proposed algorithm has more precise structural texture characteristics. The proposed algorithm provides a double discriminant method for spherical moment matching and feature discrimination for the research of image super-resolution based on GAN, which is feasible and effective in practical applications.

Since the methods of multiple kernel subspace spectral clustering do not consider the problem of noise and relation graph structure, a novel Joint Low-rank and Sparse Multiple Kernel Subspace Clustering algorithm (JLSMKC) was proposed. Firstly, with combination of low-rank and sparse representation for subspace learning, the relation graph obtained the attribute of low-rank and sparse structure. Secondly, a robust multiple kernel low-rank and sparsity constraint model was constructed to reduce the influence of noise on the relation graph and handle the nonlinear structure of data. Finally, the quality of relation graph was enhanced by making full use of the consensus kernel matrix by multiple kernel approach. The experimental results on seven datasets show that the proposed JLSMKC is better than five popular multiple kernel clustering algorithms in ACCuracy (ACC), Normalized Mutual Information (NMI) and Purity. Meanwhile, the clustering time is reduced and the block diagonal quality of relation graph is improved. JLSMKC has great advantages in clustering performance.

Because the air quality in different regions of the city are correlated with each other in both time and space, the traditional deep learning model structure is relatively simple, and it is difficult to model from the perspectives of time and space. Aiming at this problem, a Spatio Temporal Air Quality Index (STAQI) model that can simultaneously extract the complex spatial and temporal relationships between air qualities was proposed for air quality prediction. The model was composed of local components and global components, which were used to describe the influences of local pollutant concentration and air quality states of adjacent sites on the air quality prediction of target site, and the prediction results were obtained by using the weighted fusion component output. In the global component, the graph convolutional network was used to improve the input part of the gated recurrent unit network, so as to extract the spatial characteristics of the input data. Finally, STAQI model was compared with various baseline models and variant models. Among them, the Root Mean Square Error (RMSE) of STAQI model is decreased by about 19% and 16% respectively compared with those of the gated recurrent unit model and the global component variant model. The results show that STAQI model has the best prediction performance for any time window, and the prediction results of different target sites verify the strong generalization ability of the model.

The traditional opinion sentence recognition tasks mainly realize the classification by emotional features inside the sentence. In the task of cross-lingual multi-document opinion sentence recognition, the certain supporting function for opinion sentence recognition was provided by the association between sentences in different languages and documents. Therefore, a Chinese-Vietnamese bilingual multi-document news opinion sentence recognition method was proposed by combining Bi-directional Long Short Term Memory (Bi-LSTM) network framework and sentence association features. Firstly, emotional elements and event elements were extracted from the Chinese-Vietnamese bilingual sentences to construct the sentence association diagram, and the sentence association features were obtained by using TextRank algorithm. Secondly, the Chinese and Vietnamese news texts were encoded in the same semantic space based on the bilingual word embedding and Bi-LSTM. Finally, the opinion sentence recognition was realized by jointly considering the sentence coding features and semantic features. The theoretical analysis and simulation results show that integrating sentence association diagram can effectively improve the precision of multi-document opinion sentence recognition.

Segmenting Left Atrial Appendage (LAA) from ultrasound image is an essential step for obtaining the clinical indicators, and the prerequisite and difficulty for automatic and accurate segmentation is locating the target accurately. Therefore, a method combining with automatic location based on deep learning and segmenting algorithm based on model was proposed to accomplish the automatic segmentation of LAA from ultrasound images. Firstly, You Only Look Once (YOLO) model was trained as the network structure for the automatic location of LAA. Secondly, the optimal weight files were determined by the validation set and the bounding box of LAA was predicted. Finally, based on the correct location, the bounding box was magnified 1.5 times as the initial contour, and C-V (Chan-Vese) model was utilized to realize the automatic segmentation of LAA. The performance of automatic segmentation was evaluated by 5 metrics, including accuracy, sensitivity, specificity, positive, and negative. The experimental results show that the proposed method can achieve a good automatic segmentation in different resolutions and visual modes, small samples data achieve the optimal location performance at 1000 iterations with a correct position rate of 72.25%, and C-V model can reach the accuracy of 98.09% based on the correct location. Therefore, deep learning is a rather promising technique in the automatic segmentation of LAA from ultrasound images, and it can provide a good initial contour for the segmentation algorithm based on contour.

Traditional instance selection algorithms may remove non-noise samples by mistake and have low algorithm efficiency. For this issue, a genetic instance selection algorithm for K-Nearest Neighbor ( KNN) classifier was proposed. A two-stage selection mechanism based on decision tree and genetic algorithm was used in the algorithm. Firstly, the decision tree was used to determine the range of noise samples. Then, the genetic algorithm was used to remove the noise samples in this range precisely, which could reduce the risk of mistaken remove effectively and improve the algorithm efficiency. Secondly, the 1NN-based selection strategy of validation set was proposed to improve the instance selection accuracy of the genetic algorithm. Finally, the MSE (Mean Squared Error)-based objective function was used as the fitness function, which could improve the effectiveness and stability of the algorithm. Compared with PRe-classification based KNN (PR KNN), Instance and Feature Selection based on Cooperative Coevolution (IFS-CoCo), K-Nearest Neighbors ( KNN), the improvement in classification accuracy is 0.07 to 26.9 percentage points, 0.03 to 11.8 percentage points and 0.2 to 12.64 percentage points respectively, the improvement in AUC (Area Under Curve) and Kappa is 0.25 to 18.32 percentage points, 1.27 to 23.29 percentage points, and 0.04 to 12.82 percentage points respectively. The experimental results show that the proposed method has advantages in terms of classification accuracy and classification efficiency.

As an important and challenging problem in the multimedia area, common semantic topic has different expression across different modalities, and exploring the intrinsic semantic information from different modalities in a collaborative manner was usually neglected by traditional cross-media retrieval methods. To address this problem, a Latent Semantic Topic Reinforce cross-media retrieval (LSTR) method was proposed. Firstly, the text semantic was represented based on Latent Dirichlet Allocation (LDA) and the corresponding images were represented with Bag of Words (BoW) model. Secondly, multiclass logistic regression was used to classify both texts and images, and the posterior probability under the learned classifiers was exploited to indicate the latent semantic topic of images and texts. Finally, the learned posterior probability was used to regularize their image counterparts to reinforce the image semantic topics, which greatly improved the semantic similarity between them. In the Wikipedia data set, the mean Average Precision (mAP) of retrieving text with image and retrieving image with text is 57.0%, which is 35.1%, 34.8% and 32.1% higher than that of the Canonical Correlation Analysis (CCA), Semantic Matching (SM) and Semantic Correlation Matching (SCM) method respectively. Experimental results show that the proposed method can effectively improve the average precision of cross-media retrieval.

Duing to the complicated pore structure of low porosity and low permeability reservoirs, the prediction accuracy of the existing Nuclear Magnetic Resonance (NMR) logging permeability model for low porosity and low permeability reservoirs is not high. In order to solve the problem, a permeability prediction method based on Deep Belief Network (DBN) algorithm and Kernel Extreme Learning Machine (KELM) algorithm was proposed. The pre-training of DBN model was first carried out, and then the KELM model was placed as a predictor in the trained DBN model. Finally, the Deep Belief Kernel Extreme Learning Machine Network (DBKELMN) model was formed with supervised training by using the training data. Considering that the proposed model should make full use of the information of the transverse relaxation time spectrum which reflected the pore structure, the transverse relaxation time spectrum of NMR logging after discretization was taken as the input, and the permeability was taken as the output. The functional relationship between the transverse relaxation time spectrum of NMR logging and permeability was determined, and the reservoir permeability was predicted based on the functional relationship. The applications of the example show that the permeability prediction method based on DBN algorithm and KELM algorithm is effective and the Mean Absolute Error (MAE) of the prediction sample is 0.34 lower than that of Schlumberger Doll Researchcenter (SDR) model. The experimental results show that the combination of DBN algorithm and KELM algorithm can improve the prediction accuracy of low porosity and low permeability reservoir, and can be used to the exploration and development of oil and gas fields.

Since the hyponymy between symptoms has strong structural features, an automatic hyponymy extracting method based on symptom components was proposed. Firstly, it was found that symptoms can be divided into eight parts: atomic symptoms, adjunct words, and so on, and the composition of these parts satisfied certain constructed rules. Then, the lexical analysis system and Conditional Random Field (CRF) model were used to segment symptoms and label the parts of speech. Finally, the hyponymy extraction was considered as a classification problem. Symptom constitution features, dictionary features and general features were selected as the features of different classification algorithms to train the models. The relationship between symptoms were divided into hyponymy and non-hyponymy. The experimental results show that when these features are selected simultaneously, precision, recall and F1-measure of Support Vector Machine (SVM) are up to 82.68%, 82.13% and 82.40%, respectively. On this basis, by using the above hyponymy extracting algorithm, 20619 hyponymies were extracted, and the knowledge base of symptom hyponymy was built.

In Wireless Sensor Network (WSN), disjoint multipath routing can lead to the long-path problem, and braided multipath routing can lead to the weakening of fault-tolerant performance. To address these issues, a multipath braided model and a fault-tolerant routing scheme based upon the model were proposed. Firstly, the intersection of multiple paths were quantified from the source to the destination by establishing corresponding multipath braided model, and then a probability model of fault tolerance was proposed to build the relationship between path interactivity and fault tolerance. Secondly, a fault-tolerant routing scheme was designed based on local intersection adjustment. Experimental results show that, when using the proposed model and its scheme in typical multipath routing schemes—Sequential Assignment Routing (SAR) and Energy Efficient Fault-tolerant Multipath Routing (EEFTMR), the data transfer success rate can be improved effectively. In addition, it also has good performance in the network throughput and energy consumption.

In order to solve the problem of fixed target size in Kernel Correlation Filtering (KCF) tracker, a scale adaptive tracking method was proposed. Firstly, the Lucas-Kanade optical flow method was used to track the movement of keypoints in the neighbor frames, and the reliable points were obtained by introducing the forward-backward method. Secondly, the reliable points were used to estimate the target changing in scale. Thirdly, the scale estimation was applied to the adjustable Gaussian window. Finally, the forward-backward tracking method was used to determine whether the target was occluded or not, the template updating strategy was revised. The fixed target size limitation in the KCF was solved, the tracker was more accurate and robust. The object tracking datasets were used to test the algorithm. The experimental results show that the proposed method ranks over the original KCF, Tracking-Learning-Detection (TLD), Structured output tracking with kernel (Struck) algorithms both in success plot and precision plot. Compared with the original method, the proposed tracker can be better applied in target tracking with scale variation and occlusion.

Based on open source softwares of Computer Haptics, visualizAtion and Interactive in 3D (CHAI 3D) and Open Graphic Library (OpenGL), a virtual surgical system was designed for reduction of maxillary fracture. The virtual simulation scenario was constructed with real patients' CT data. A geomagic force feedback device was used to manipulate the virtual 3D models and output haptic feedback. On the basis of the original single finger-proxy algorithm, a multi-proxy collision algorithm was proposed to solve the problem that the tools might stab into the virtual organs during the simulation. In the virtual surgical system, the operator could use the force feedback device to choose, move and rotate the virtual skull model to simulate the movement and placement in real operation. The proposed system can be used to train medical students and for preoperative planning of complicated surgeries.

Applications such as information retrieval need a precise representation of text content while the representations using traditional topic model can only extract topic background and have no ability for a precise description. A new low-rank and sparse model was proposed to decompose text into a low-rank component which represents topic background and a sparse component which represents keywords. To implement this model, the topic matrix was defined, and Robust Principal Component Analysis (RPCA) was introduced to realize the decomposition. The experimental result on news corpus shows that the model complexity is 25 percent lower than that of Latent Dirichlet Allocation (LDA). In practical applications, the low-rank component reduces the features needed in text classification by 28.7 percent, which helps to reduce the dimension of features; And the sparse component improves the precision of information retrieval result by 10.8 percent compared with LDA, which improves the hit rate of information retrieval result.

To solve the problem of the regulation of badminton dynamic stable equilibrium, the particle influence coefficient method of feather piece was put forward. The method combined badminton quality models and quality feather piece, bending camber degree, angle of attack, and other related factors. The feather piece of particle influence coefficient was obtained by adjusting the height centroid which satisfied badminton dynamic stability requirements got by striking tilt minimum square. Compared with the traditional badminton dynamic stabilization which must depend on the experience accumulated for a long time, the badminton particle influence coefficient method of feather piece that was put forward by this paper formed a theoretical system. And it had less time consumption, high efficiency, etc. The numerical results show that the proposed method is correct and effective.

In this paper, to protect data integrity in data aggregation of Wireless Sensor Network (WSN), a secure and efficient data aggregation scheme was proposed, which was based on Dual-head Cluster Based Secure Aggregation (DCSA). By setting symmetric keys between nodes and using distributed authentication method, this scheme performed node authentication and aggregation simultaneously, as integrity-checking of child node was completed immediately in the process of aggregation. Also, by using the oversight features of red and black cluster head, this scheme could locate malicious nodes and enhance the capability of anti-collusion attack. The experimental results show that the proposed scheme ensures the same security level with DCSA, and this scheme is able to detect and discard erroneous data immediately. It improves the efficiency of integrity detection mechanism and it has lower network energy consumption.

The near-surface defects are hard to identify in ultrasonic phased array Non-Destructive Testing (NDT), thus a new intelligent identification method based on fractal theory was proposed to solve this problem. A box-counting dimension algorithm based on linear interpolation was described to calculate the box-counting dimension of 140 groups of ultrasonic A-Scan time domain signals. Then the distribution of box-counting dimension was analyzed using the statistical method. The experimental results show that ultrasonic A-Scan signal is obviously fractal and it is effective to analyze the A-Scan signal with the fractal approach. This method has the potential to identify near-surface defects since the values of the box counting dimension of defective signals are different from those of defective signals. As a result, the detection rate of near-surface defects can be improved and the omission rate caused by man-made factors can be reduced in ultrasonic phased array automatic testing.

The existing algorithms of Top-k query can not make full use of the powerful parallel throughput of Graphic Processing Unit (GPU) to timely return the query results. So, a segmented query algorithm based on Compute Unified Device Architecture (CUDA) model was proposed. By dividing the query process and using the strategy of segmented parallel process, the maximal calculation and comparison efficiency in query process could be obtained in this algorithm. The experimental results show that this algorithm has obvious performance advantages compared with four-thread parallel optimization algorithm on multi-core CPU. When the number of ordered lists is 6 and the traversal stride is 120, the optimal performance can be obtained which is 40 times faster than multi-core CPU algorithm.

To meet the requirements of the military and merchant marine radar simulation, and enhance the simulation reality of radar image, a real-time scan simulation approach based on sector-banded texture blending model was presented to simulate highly realistic radar echo image. In this method, Electronic Navigation Chart (ENC) was regarded as the resource data of the radar echo signal, and according to the principle of the radar echo, the sector-banded texture blending algorithm was proposed to replace the traditional radar image simulation method based on the pixel-scan model and generate the radar echo texture data. Based on that, the simulation models of the radar echo signal processing were presented to implement the basic functions of the marine radar, such as gain adjustment, sea clutter suppression and rain/snow clutter suppression. The experimental results show the proposed approach improves distinctly the efficiency and effectiveness of the radar echo simulation, and it is a promising means to address the problem of radar and Electronic Chart Display Information System (ECDIS) simulation.

Currently, to develop context-aware applications that are flexible and adaptable is complex and laborious. There are many unexpected cases to handle. As one of the important approaches to achieve context-awareness, predicate detection can represent context effectively. However, how predicate detection supports the development of context-aware applications on a real device is still largely unknown. In order to cope with these issues, a simple scenario was created. Predicate detection was practically applied to control the car running in a designated environment. The original context was formally modeled and contextual properties were specified into snapshot predicates and sequence predicates. By detecting these specified predicates in the case study, predicate detection was applied to the robot car. The performance analysis shows that predicate detection can effectively detect the car's contextual properties and successfully help the car finish the running task.

To solve the node deployment problem caused by unreliable information provided by the sensors, four different forms of static Wireless Sensor Network (WSN) deployment were addressed. The four problems were formalized as combinatorial optimization problems, which were Non-deterministic Polynomial (NP)-complete. Furthermore, an uncertainty-aware deployment algorithm based on dynamic programming was proposed. Firstly, the K-best placements of sensor nodes within the region of interest were found, and then the best deployment scheme was selected over the K-best placements. The proposed algorithm was able to determine the minimum number of sensors and their locations to achieve both coverage and connectivity. The simulation results show that, compared with the state-of-the-art deployment strategies, the performance of the proposed algorithm is better than the existing methods in terms of the uniform coverage requirement, the preferential coverage requirement and the network connectivity.

The scene of unstructured road is complex and easy to be influenced by many factors. In order to solve the detection difficulty, a road detection algorithm based on contour features and two-dimensional maximum entropy was proposed. Quadratic two-dimensional maximum entropy segmentation method combined with invariant color feature was used for road image segmentation. Afterwards, contour features were extracted from segmentation result by boundary tracking algorithm, and then the maximum contour was chosen. Finally, the improved mid-to-side algorithm was used to search road edge points, then road boundary was reconstructed through road model and road direction was judged. The experimental results show that the detection accuracy rate is improved about 25% in three kinds of unstructured scene compared with traditional algorithm. In addition, this method is robust against shadows and can recognize road direction efficiently.

In order to protect Android platforms from the application-level privilege escalation attacks, this paper analyzed the XManDroid access control model, which has better ability on fighting these attacks, especially the collusion attack on the covert channel. To address the problem that XManDroid could not detect the multi-application and multi-permissions collusion attacks, this paper proposed an improved mandatory access control model which recorded the communication history of applications by building an IPC links colored diagram. At last, the test result on the prototype system show that the new model can solve the problem in the XManDroid well.

Labeling ordered XML documents can process XML data without accessing the data files. The present labeling schemes have achieved better results in queries, however, the labeling schemes for insertions incurs sacrifices of query performance, lower updates efficiency, and other problems. This paper proposed a new labeling scheme for insertions, EDL（Extended Dewey Labeling）, which efficiently realizes the calculations in the insertions of XML documents without degrading query performance . The conducted experiments have shown that EDL is superior to the similar labeling schemes for updates.

For various applications in today’s service-oriented enterprise computing systems, such as process-oriented service discovering or clustering, it is necessary to measure the distance between two process models. In this paper, we propose a quantitative measure to calculate the distance or similarity between different structured processes. We first introduce a structured workflow model and transform each process into a process structure tree, and then calculate the process distance and its similarity based on the tree edit distance of two structure trees. The proposed distance metric satisfies three distance measure properties, i.e., identity of indiscernible, symmetry and triangle inequality. These properties make the distance metric can be used as a quantitative tool in effective process model management activities. Experiment studies show that the method is feasible. Compared to the adjacency matrix method, the proposed method is more reasonable due to the semantic distance between different structures is considered.

With the purpose of stabilization control of servo motor, according to the dynamic matrix control principle, a servo motor control scheme was put forward using dynamic matrix control and Proportion-Integration-Differentiation （PID） control. Then, the unification model for direct/alternating servo model was analyzed; dynamic matrix control was used to design the current loop; rise time and stabilization value were used to form equivalent inertial element, and PID method was used to realize the control of speed loop and position loop. The calculation and simulation results show that the constant time of equivalent inertial element is in the inverse ratio of -ln(0.386); that the controller designed by PID mixed with Model Predictive Control (MPC) can well make the servo motor run smoothly and fast.

To manage the risk of service, it is necessary to obtain stochastic character of Quality of Service (QoS) that is represented as accurate probability distribution. This paper presented an approach to estimate probability distribution of Web service QoS in the case of small number of samples. Using max entropy principle, the analytical formula of the probability density function can be obtained by transforming the probability distribution estimation problem into an optimal problem with constraints obtained from sampling QoS data. Then an algorithm to estimate parameters of the probability density function was designed. The experimental and simulation results based on real Web service QoS data show the effectiveness of the proposed approach for probability distribution estimation of different QoS attribute. The efficiency and feasibility of the distribution estimation algorithm have got validated by experiments too.

The fast increasing large and complex networks make the research of graph structure more and more important, in which approximate subgraph query is of big concern. Constructing index for each vertex by the adjacency characteristics was able to reduce the number of matched vertices, and partitioning the large graph based on label and structure information was able to reduce the matching search space. Using the dual index built in offline time, large amount of candidate vertices were filtered out according to the adjacency discriminant formula, and then the edge matching was carried out in some partition spaces. The experiments on real dataset show that, compared with many other existing methods, the dual-index query mechanism improves the efficiency and accuracy of subgraph matching significantly.

Cross-layer design is an effective technique for future mobile communication systems. A cross-layer resource allocation algorithm with partial channel state information was explored to maximize the total system throughput for multi-user MIMO-OFDM （Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing） system. The objective function of the optimization problem was designed based on the power limitation constraint, transmission rate, average queue length and sub-carrier occupancy, Quality of Service (QoS) requirements of different services and queue state information of data link layer. Under the condition of finite-length user buffer in data link layer, the mean feedback model was utilized to describe the feedback process of channel state information, and then the corresponding cross-layer resource allocation criteria could be derived. The simulation results show that compared with the existing schemes, the proposed algorithms obtain reasonable throughput performance and reduce lost package rate while providing better QoS requirement for each user of different services.