Accurate segmentation of the polyp region in the colonoscopic images can assist doctors in diagnosing intestinal diseases. However， the structure information of polyp region is missing in the down sampling process， and the existing methods have the problems of over segmentation and under segmentation.Aiming at the problems above， a Fusing Filter enhancement and Reverse attention segmentation Network （FFRNet） was proposed. Firstly， Filter Enhancement Module （FEM） was added to the skip-connection to enhance the structure information of local lesion region in the down-sampling features. Secondly， the global features were obtained by aggregating the shallow features. Finally， Multiscale reverse Attention Fusion Mechanism （MAFM） was adopted in the up-sampling process， by combining the global features and up-sampling features to generate the reverse attention weight， the polyp region information was mined in the features layer by layer， and the relationship between the target region and the boundary was established by the guidance network to improve the integrity of the model on polyp region segmentation. On Kvasir and CVC-ClinicDB datasets， compared with Uncertainty Augmented Context Attention Network （UACANet）， FFRNet has Dice Similarity Coefficient （DSC） increased by 0.22% and 0.54% respectively. Experimental results show that FFRNet can effectively improve the accuracy of polyp image segmentation and has good generalization ability.

As a key technology of land use/land cover detection, change detection aims to detect the changed part and its type in the remote sensing data of the same region in different periods. In view of the problems in traditional change detection methods, such as heavy manual labor and poor detection results, a large number of change detection methods based on remote sensing images have been proposed. In order to further understand the change detection technology based on remote sensing images and further study on the change detection methods, a comprehensive review of change detection was carried out by sorting, analyzing and comparing a large number of researches on change detection. Firstly, the development process of change detection was described. Then, the research progress of change detection was summarized in detail from three aspects:data selection and preprocessing, change detection technology, post-processing and precision evaluation, where the change detection technology was mainly summarized from analysis unit and comparison method respectively. Finally, the summary of the problems in each stage of change detection was performed and the future development directions were proposed.

With the continuous improvement of information technology, the amount of image-text data with orientation on various social platforms is growing rapidly, and the sentiment analysis with image and text fusion is widely concerned. The single sentiment analysis method can no longer meet the demand of multi-modal data. Aiming at the technical problems of image and text sentiment feature extraction and fusion, firstly, the widely used image and text emotional analysis datasets were listed, and the extraction methods of text features and image features were introduced. Then, the current fusion modes of image features and text features were focused on and the problems existing in the process of image-text sentiment analysis were briefly described. Finally, the research directions of sentiment analysis in the future were summarized and prospected for. In order to have a deeper understanding of image-text fusion technology, literature research method was adopted to review the study of image-text sentiment analysis, which is helpful to compare the differences between different fusion methods and find more valuable research schemes.

As one of intelligent video surveillance technologies, person Re-identification (Re-id) has great research significance for maintaining social order and stability, and it aims to retrieve the specific person in different camera views. For traditional hand-crafted feature methods are difficult to address the complex camera environment problem in person Re-id task, a large number of deep learning-based person Re-id methods were proposed, so as to promote the development of person Re-id technology greatly. In order to deeply understand the person Re-id technology based on deep learning, a large number of related literature were collated and analyzed. First, a comprehensive introduction was given from three aspects: image, video and cross-modality. The image-based person Re-id technology was divided into two categories: supervised and unsupervised, and the two categories were generalized respectively. Then, some related datasets were listed, and the performance of some algorithms in recent years on image and video datasets were compared and analyzed. At last, the development difficulties of person Re-id technology were summarized, and the possible future research directions of this technology were discussed.

Concerning the problem of wide applications and low efficiency of two-person interaction behavior recognition, a method of two-person interaction behavior recognition based on key gestures was proposed. First, the key frames were extracted by comparing the differences between frames. Second, the key gestures in the key frames were determined by using the variance and spatial relationship of the angle changes of the bone points. Then, the key gestures were represented by features such as joint distance, angle, and joint motion. Every key gesture was expressed as a feature matrix. Finally, the combination with the best recognition rate was selected by comparing different dimension reductions and classification combinations. The proposed recognition method was evaluated on the SBU interaction dataset and the self-built interaction dataset, and the recognition rate of it reached 92.47% and 94.14% respectively. Experimental results show that the proposed method of representing actions by extracting the features of key gestures to form feature matrices can effectively improve the recognition result of two-person interaction behavior.

The traditional Total Variation (TV) minimization algorithm is a classical iterative reconstruction algorithm based on Compressed Sensing (CS), and can accurately reconstruct images from sparse and noisy data. However, the block artifacts may be brought by the algorithm during the reconstruction of image having not obvious piecewise constant feature. Researches show that the use of High Order Total Variation (HOTV) in the image denoising can effectively suppress the block artifacts brought by the TV model. Therefore, a HOTV image reconstruction model and its Chambolle-Pock (CP) solving algorithm were proposed. Specifically, the second order TV norm was constructed by using the second order gradient, then a data fidelity constrained second order TV minimization model was designed, and the corresponding CP algorithm was derived. The Shepp-Logan phantom in wave background, grayscale gradual changing phantom and real CT phantom were used to perform the image reconstruction experiments and qualitative and quantitative analysis under ideal data projection and noisy data projection conditions. The reconstruction results of ideal data projection show that compared to the traditional TV algorithm, the HOTV algorithm can effectively suppress the block artifacts and improve the reconstruction accuracy. The reconstruction results of noisy data projection show that both the traditional TV algorithm and the HOTV algorithm have good denoising effect but the HOTV algorithm is able to protect the image edge information better and has higher anti-noise performance. The HOTV algorithm is a better reconstruction algorithm than the TV algorithm in the reconstruction of image having not obvious piecewise constant feature and obvious grayscale fluctuation feature. The proposed HOTV algorithm can be extended to CT reconstruction under different scanning modes and other imaging modalities.

The data in internet social media has the characteristics of fast transmission, high user participation and complete coverage compared with traditional media under the background of the rise of various platforms on the internet.There are various topics that people pay attention to and publish comments in, and there may exist deeper and more fine-grained sub-topics in the related information of one topic. A survey of sub-topic detection based on internet social media, as a newly emerging and developing research field, was proposed. The method of obtaining topic and sub-topic information through social media and participating in the discussion is changing people’s lives in an all-round way. However, the technologies in this field are not mature at present, and the researches are still in the initial stage in China. Firstly, the development background and basic concept of the sub-topic detection in internet social media were described. Secondly, the sub-topic detection technologies were divided into seven categories, each of which was introduced, compared and summarized. Thirdly, the methods of sub-topic detection were divided into online and offline methods, and the two methods were compared, then the general technologies and the frequently used technologies of the two methods were listed. Finally, the current shortages and future development trends of this field were summarized.

A tampered image recognition system with better universality based on convolutional neural network of three-stream feature extraction was proposed to improve the recognition accuracy of three main tampering methods stitching, scaling and rotating, copying and pasting. Firstly, by comparing the similarity of feature sub-blocks according to image local color invariant feature, comparing the noise correlation coefficients of tampered region edges with noise correlation, and calculating the standard deviation contrast of sub-blocks based on image resampling trace, the features of the RGB stream, noise stream and signal stream of the image were extracted separately. Then, through multilinear pooling, combined with an improved piecewise AdaGrad gradient algorithm, the feature dimension reduction and parameter self-adaptive updating were realized. Finally, through network training and classification, three main image tampering methods of stitching, scaling and rotating, copying and pasting were identified and the corresponding tampered areas were located. In order to measure the performance of this model, experiments were carried out on VOC2007 and CIFAR-10 datasets. The experimental results of about 9 000 images show that the proposed model can accurately identify and locate the three tampering methods stitching, scaling and rotating, copying and pasting, and its recognition rates are 0.962,0.956 and 0.935 respectively. Compared with the two-stream feature extraction method in the latest literature, the model has the recognition rates increased by 1.050%, 2.137% and 2.860% respectively. The proposed three-stream model enriches the image feature extraction by convolutional neural network, improves the training performance and recognition accuracy of the network. Meanwhile, controlling the descent speed of parameter learning rate piecewisely by the improved gradient algorithm reduces the over-fitting and convergence oscillation, as well as increases the convergence speed, so as to realize the optimization design of the algorithm.

When the areas to be inpainted in the face image are large, there are some visual defects caused by the inpainting of the existing methods, such as unreasonable image semantic understanding and incoherent boundary. To solve this problem, an end-to-end image inpainting model of U-Net structure based on dense blocks was proposed to achieve the inpainting of semantic face of any mask. Firstly, the idea of generative adversarial network was adopted. In the generator, the convolutional layers in U-Net were replaced with dense blocks to capture the semantic information of the missing regions of the image and to make sure the features of the previous layers were reused. Then, the skip connections were adopted to reduce the information loss caused by the down-sampling, so as to extract the semantics of the missing regions. Finally, by introducing the joint loss function combining adversarial loss, content loss and local Total Variation (TV) loss to train the generator, the visual consistency between the inpainted boundary and the surrounding real image was ensured, and Hinge loss was used to train the discriminator. The proposed model was compared with Globally and Locally Consistent image completion(GLC),Deep Fusion(DF) and Gated Convolution(GC) on CelebA-HQ face dataset. Experimental results show that the proposed model can effectively extract the semantic information of face images, and its inpainting results have the boundaries with natural transition and clear local details. Compared with the second-best GC, the proposed model has the Structure SIMilarity index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) increased by 5.68% and 7.87% respectively, while the Frechet Inception Distance (FID) decreased by 7.86% for the central masks; and has the SSIM and PSNR increased by 7.06% and 4.80% respectively while the FID decreased by 6.85% for the random masks.

In consideration of the problems of low utilization rate of action information and insufficient attention of temporal information in video human action recognition, a human action recognition model based on tightly coupled spatiotemporal two-stream convolutional neural network was proposed. Firstly, two 2D convolutional neural networks were used to separately extract the spatial and temporal features in the video. Then, the forget gate module in the Long Short-Term Memory (LSTM) network was used to establish the feature-level tightly coupled connections between different sampled segments to achieve the transfer of information flow. After that, the Bi-directional Long Short-Term Memory (Bi-LSTM) network was used to evaluate the importance of each sampled segment and assign adaptive weight to it. Finally, the spatiotemporal two-stream features were combined to complete the human action recognition. The accuracy rates of this model on the datasets UCF101 and HMDB51 selected for the experiment and verification were 94.2% and 70.1% respectively. Experimental results show that the proposed model can effectively improve the utilization rate of temporal information and the ability of overall action representation, thus significantly improving the accuracy of human action recognition.

Because of the complexity of human language, text sentiment classification algorithms mostly have the problem of excessively huge vocabulary due to redundancy. Deep Belief Network (DBN) can solve this problem by learning useful information in the input corpus and its hidden layers. However, DBN is a time-consuming and computationally expensive algorithm for large applications. Aiming at this problem, a semi-supervised sentiment classification algorithm called text sentiment classification algorithm based on Feature Selection and Deep Belief Network (FSDBN) was proposed. Firstly, the feature selection methods including Document Frequency (DF), Information Gain (IG), CHI-square statistics (CHI) and Mutual Information (MI) were used to filter out some irrelevant features to reduce the complexity of vocabulary. Then, the results of feature selection were input into DBN to make the learning phase of DBN more efficient. The proposed algorithm was applied to Chinese and Uygur language. The experimental results on hotel review dataset show that the accuracy of FSDBN is 1.6% higher than that of DBN and the training time of FSDBN halves that of DBN.

In classical heuristic attribute reduction algorithm for positive region, the attribute with the maximum dependency degree of the current positive domain should be added into the selected feature attribute subset in each iteration, leading to the large number of iterations and the low efficiency of the algorithm, and making the algorithm hard to be applied in the feature selection of high-dimensional and large-scale datasets. In order to solve the problems, the monotonic relationship between the positive regions in a decision system was studied and the formal description for the Multi-Scale Attribute Granule (MSAG) was given, and a Multi-scale Attribute Granule based Quick Positive Region reduction algorithm (MAG-QPR) was proposed. Each MSAG contains several attributes and can provide a large positive region for the selected feature attribute subset. As a result, adding MSAG in each iteration can reduce the number of the iteration and make the selected feature attribute subset more quickly approach to the positive region resolving ability of the condition attribute universal set. Therefore, the computational efficiency of the heuristic attribute reduction algorithm for positive region is improved. With 8 UCI datasets used for experiments, on the datasets Lung Cancer, Flag and German, the running time acceleration ratios of MAG-QPR to the general improved Feature Selection algorithm based on the Positive Approximation-Positive Region (FSPA-PR), the general improved Feature Selection algorithm based on the Positive Approximation-Shannon's Conditional Entropy (FSPA-SCE), the Backward Greedy Reduction Algorithm for positive region Preservation (BGRAP) and the Backward Greedy Reduction Algorithm for Generalized decision preservation (BGRAG) are 9.64, 15.70, 5.03, 2.50; 3.93, 7.55, 1.69, 4.57; and 3.61, 6.49, 1.30, 9.51 respectively. The experimental results show that, the proposed algorithm MAG-QPR can improve the algorithm efficiency and has better classification accuracy.

In order to reduce the harm caused by bursty events in social media, a multi-feature based microblog bursty events detection algorithm was proposed. The algorithm combines text emotion filtering and user influence calculation methods. Firstly, the microblog text with negative emotion was obtained through noise filtering and emotion filtering. Then the proposed user influence calculation method was combined with the burst word extraction algorithm to extract the characteristics of burst words. Finally, a cohesive hierarchical clustering algorithm was introduced to cluster bursty word sets, and extract bursty events from them. In the experimental test, the accuracy is 66.84%, which proves that the proposed method can effectively detect bursty events.

For the routing problem that nodes in mobile wireless sensor network, based on random moving model, a low energy consumption routing strategy named DTVC (Data Transmission based on Virtual Currency) was proposed. When two nodes met each other, the buyer and the seller determined the price of data message and selected relay node according to node attributes and data message attributes. To improve the network performance, the number of the data message's replicas was controlled according to node type and data messages in the queue were sorted according to each message's delay tolerance. The nodes in the network were divided into source nodes and relay nodes for each data message and only the source node could copy it. The smaller the delay tolerance was, the greater the priority was. In order to reduce the energy consumption in the network, the data message in the storage queue that had been transmitted successfully was deleted according to the message broadcast by the sink node. The simulation results on Matlab showed that the data delivery rate of DTVC was increased by at least 2.5%, and the average number of replicas was reduced by at least 25% than those of FAD (the message Fault tolerance-based Adaptive data Delivery scheme), FLDEAR (Fuzzy-Logic based Distance and Energy Aware Routing protocol) and a routing algorithm based on energy consumption optional evolution mechanism.

Exemplar-based image inpainting methods may cause local mosaic effects and visual incoherence, since the interference of image tiny texture and noise often result in invalid priority terms that makes the inpainting order abnormal. Besides, when searching for the best matching patch, the inter structure information of patches are ignored, which leads to non-unique best matching patches. To tackle these aforementioned issues, a new image inpainting model based on structure-texture decomposition and local total variation minimization was proposed. Three improvements were presented and detailed. Firstly, for an given image to be inpainted, the structure image was extracted by using the logarithm total variation minimization model, then the inpainting priority was calculated on this auxiliary image. In this way, a more robust filling mechanism can be achieved, since the isophote direction of the structure image is less noisy than the original image. Secondly, the priority term was redefined as the weighted summation of data term and confidence term to eliminate the product effect and ensure that the data term was always effective. As a result, the image mismatching rate caused by unreasonable inpainting order was reduced. Finally, the problem of choosing the best matching patch was converted into a 0-1 optimization problem aiming to reach a minimal local total variation. Comprehensive comparisons with the state-of-the-art three inpainting methods show that the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm is improved by 1.12-3.56 dB, and the Structural Similarity Index Measure (SSIM) is improved by 0.02-0.04. The proposed model can ensure a better selection of pixel candidates to fill in, and achieve a better global coherence of the reconstruction; therefore, the results are more visually appealing and with less block artifacts for inpainting large damaged images.

Total variation minimization based image impainting method is easy to cause staircase effect in smooth regions. In order to solve the problem, a novel non-local total variation image inpainting model based on logarithmic function was proposed. The integrand function of the new total variation energy function is a logarithmic function concerning the magnitude of gradient. Under the framework of partial differential equations of total variation model and anisotropic diffusion model, firstly, the proposed model was proven theoretically to satisfy all the properties required for good diffusion. Besides, the local diffusion behavior was theoretically analyzed, and its good properties of diffusion in equal illumination direction and gradient direction were proved. Then, in order to consider the similarity of image blocks and avoid local blur, non-local logarithmic total variation was used for numerical implementation. The experimental results demonstrate that, compared with a classical total variation image inpainting model, the proposed non-local total variation image inpainting model based on logarithmic function has good effect on image inpainting, avoids local blur, and can better suppress the staircase effect in image smooth region; in the meantime, compared with the exemplar-based inpainting model, the proposed model can obtain more natural inpainting effect for texture images. The experimental results show that, compared with three types of total variation models and the exemplar-based inpainting model, the proposed model has the best performance. Compared with the average results of the comparison models (figure 2, figure 3, figure 4), the Structural Similarity Index Measure (SSIM) of the proposed model is improved by 0.065, 0.022 and 0.051, while its Peak Signal-to-Noise Ratio (PSNR) is improved by 5.94 dB、4.00 dB and 6.22 dB. The inpainting results of noisy images show that the proposed model has good robustness and can also get good inpainting results for noisy images.

Current researches on the assessment of image aesthetic quality are based on visual content of images to give assessment results, ignoring the fact that aesthetics is a person's cognitive activity and not considering the user's understanding towards image semantic information during the evaluating process. In order to solve this problem, an approach to image aesthetic quality assessment based on semantic perception was proposed to apply both the object category information and scene category information of images to the aesthetic quality assessment. Using the transfer learning concept, a hybrid network integrating multiple features of the images was constructed. For each input image, the object category features, scene category features, and aesthetic features were extracted respectively by network, and the three features were combined to achieve better image aesthetic quality evaluation. The classification accuracy of the method on the AVA data set reached 89.5%, which was 19.9% higher than that of the traditional method, and the generalization performance on the CUKHPQ data set was greatly improved. The experimental results show that the proposed approach can achieve better classification performance on the aesthetic quality evaluation of images.

In most of the graph-based segmentation methods, prior saliency regions are often obtained by analyzing motion and appearance information and then the energy model was minimized for further segmentation. These methods often ignore refined analysis of appearance information, and are not robust to complex scenarios. Since information entropy can measure sample purity and information entropy minimization has a consistent goal with energy model minimization, a video object segmentation via information entropy constraint was proposed. Firstly, the segmentation results of the first stage were obtained by combining with optical flow vector and the point-in-polygon principle from the computational geometry. Secondly, the uniform movement and performance were gained through presenting superpixel as the basic division unit. Finally, video segmentation was formulated as a pixel labeling optimization problem with two labels by introducing information entropy constraint into energy function, and more accurate segmentation results were obtained by minimizing the energy function. The experimental results on public datasets show that the proposed method can effectively improve the robustness of video object segmentation.

Energy efficient routing is a challenging problem in resource constrained Mobile Wireless Sensor Network (MWSN). Focused on the issue that the energy consumption of the inter-community routing in the mobile sensor network is too fast, an Energy-balanced Routing Algorithm for Inter-community (ERAI) was proposed. In ERAI, a new routing metric FC (Forwarding Capacity) based on the residual energy of nodes and the probability of encounter was designed. Then, this metric FC and the directional information of encountered nodes were used for selection of a relay node to forward the messages. The experimental data show that the death time of the first node of ERAI was later than that of Epidemic and PROPHET by 12.6%-15.6% and 4.5%-8.3% respectively, and the residual energy mean square deviation of ERAI was less than that of Epidemic and PROPHET. The experimental results show that the ERAI can balance the energy consumption of each node to a certain extent, and thus prolongs the network lifetime.

It is difficult for existing rumor detection methods to overcome the disadvantage of data collection and detection delay. To resolve this problem, a rumor detection method based on burst topic detection inspired by the momentum model and domain expert discovery was proposed. The dynamics theory in physics was introduced to model the topic features spreading among the Weibo platform, and dynamic physical quantities of the topic features were used to describe the burst characteristics and tendency of topic development. Then, emergent topics were extracted after feature clustering. Next, according to the domain relativity between the topic and the expert, domain experts for each emergent topic were selected within experts pool, which is responsible for identifying the credibility of the emergent topic. The experimental results show that the proposed method gets 13 percentage points improvement on accuracy comparing with the Weibo rumor identification method based merely on supervised machine learning, and the detection time is reduced to 20 hours compared with dominating manual methods, which means that the proposed method is applicable for real rumor detection situation.

In order to solve the problem that the Single Gaussian Model (SGM) for background could not adapt to non-stationary scenes and the "ghost" phenomenon due to sudden moving of a motionless object. An SGM for background using block-based gradient and linear prediction was put forward. Firstly, SGM was implemented on the pixel level and updated adaptively according to the changes of the pixels' values, at the same time the frame was processed by the block-based gradient algorithm, obtaining the background by judging whether the gradient of sub-block was within the threshold value and eliminating "ghost"; and then foreground from the block-based gradient algorithm and that from the SGM were made "AND" operation, improving the judgment of the background in non-stationary scenes; lastly the linear prediction was employed to process the foreground acquired from the previous operation, resetting the connected regions whose area was less than the threshold value as the background. Simulation experiments were conducted on the CDNET 2012 dataset and Wallflower dataset. In the scenes which varied by a large margin, the accuracy of the proposed method was 40% higher than that of the Gaussian Mixture Model (GMM) in spite of the fact that the detection rate of the proposed method was lower than that of GMM; but in other scenes, the rate of detection was 10% higher and the accuracy was 25% higher. The simulation results show that the proposed method is able to accommodate to the non-stationary scenes and achieve the goal of wiping the "ghost" off, as well as obtain a better result of the background and more detailed foreground than GMM.

Responding to the problem of the unreasonable distribution of cluster head nodes and "hot spots" caused by uneven load energy in Wireless Sensor Network (WSN), an Unequal partition Area Uneven Clustering routing protocol (UAUC) was proposed. The network was divided according to unequal partition area, and the appropriate cluster head nodes in each area were selected on the basis of the energy factor, the distance factor and the intensity factor. Meanwhile, a load balancing path tree was built between cluster head nodes to solve the problem of "hot spots" in data transmission. In the comparison experiments with LEACH (Low Energy Adaptive Clustering Hierarchy) protocol, DEBUC (Distributed Energy-Balanced Unequal Clustering routing) protocol and HRPNC (Hierarchical Routing Protocol based on Non-uniform Clustering) protocol, UAUC achieved more reasonable distribution of cluster head nodes. The network cycle of UAUC was increased than that of LEACH, DEBUC and HRPNC by 88%, 12% and 17.5% respectively. The average residual energy of UAUC was higher than LEACH, DEBUC and HRPNC. And the variance of node residual energy of UAUC was less than LEACH, DEBUC and HRPNC. What is more, the aggregate of data packet of UAUC was higher than that of LEACH, DEBUC and HRPNC by 400%, 87.5% and 17.5% respectively. The experimental results show that UAUC can effectively improve the energy efficiency and the aggregate of data packet, balance energy consumption and prolong the network lifetime.

To solve the gesture segmentation deviation problem under the interference of other skins and overlapping objects, a method of using depth data and skeleton tracking to segment gesture accurately was proposed. The minimum circumscribed circle, the average and the maximal inscribed circle of convexity defect, were combined to improve the detection of palm and the palm region's radius of various gesture. A fingertip candidate set was got through integrating the finger arc with convex hull, then real fingertips were obtained with three-step filtering. Six gestures have been tested in four transform cases, the recognition rate of flip, parallel, overlapping are all higher than 90% but the rate decreases obviously when tilting more than 70 degree and yawing more than 60 degree. The experimental results show that the accuracy of the proposed method is high in a variety of real scenes.

In order to manage the metadata of massive spatial data storage effectively, a distributed metadata server management structure based on consistent hashing was introduced, and on this basis, a metadata wheeled backup strategy was proposed in this paper, which stored Hash metadata node after excuting a consistent Hash algorithm according to the method of data backup, and it effectively alleviated the single point of metadata management and access bottleneck problems. Finally testing wheel backup strategy, it obtained the optimum number of metadata node backup solution. Compared with the single point of metadata servers, the proposed strategy improves the metadata safety, reduces the access delay, and improves the load balance of distributed metadata server combined with virtual nodes.

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.

Aiming at the problem that the performance of parallel computing cannot be improved by extending its scale under the constraint of fixed structure, a method of proportionally adjusting graph weights was proposed to handle such extension problem. The method firstly investigated the factors from architecture and parallel tasks which affected its scalability, and then modeled the architecture as well as parallel tasks by using weighted graph. Finally, it realized an extension in parallel computing by adjusting proportionally the weights of the vertices and edges in the graph model for parallel computing. The experimental results show that the proposed extension method can realize isospeed-efficiency extension for parallel computing under the constraint of fixed structure.

To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.

Due to the uncertainty of complex networks, traditional community structures division algorithm of the complex network could easily lead to premature convergence and decreased accuracy. And because of the large amount of computation, time complexity is high. To overcome the above shortcomings, the paper adopted GEP's global search ability and adaptability, and other characteristics with parallel calculations, optimized the network structure of the division of community, and proposed a community structure division algorithm of complex network based on GEP, and verified the validity of the new algorithm by experiment. The new algorithm has more accurate community division of the complex network in the case of no prior information.

Taking comprehensive use of 2.45GHz active Radio Frequency Identification (RFID) technique, information processing technology, wireless communication technology of General Packet Radio Service (GPRS), Global Positioning System (GPS) technique, mobile computing and network technology, this paper designed the software and hardware structure of the taxi anti-counterfeiting management system, developed the 2.45GHz active tags and information terminals with the functions of recognition, orientation navigation and mobile communication. Meanwhile, on the basis of the analysis of the main application models, it developed the upper application software of the system. By setting up the application system and testing, the results show that the system can work with ultra-low power, the peak current is only 2mA; And the data transmit in real-time while delaying less than 4 seconds; Also the identifiable distance of the RFID terminal is about 110m and it can read no less than 150 tags at the same time.

This paper proposed a new classification method based on Multi-class Support Vector Machine (MSVM) which aimed at solving the problems in online classification of cotton foreign fibers. Firstly the features of color, shape and texture of the foreign fiber objects were extracted to create the feature vectors. Secondly three kinds of multi-class support vector machines were constructed for foreign fiber classification. These three MSVMs were tested with the obtained feature vectors using leave-one-out cross validation. The experimental results show that the one-against-one directed acyclic graph MSVM is the fastest one and is fitter for online classification of foreign fibers.