The Aspect-Based Sentiment Analysis （ABSA） task aims to identify the sentiment polarity of a specific aspect. However， the existing related models lack the short-distance constraints on the context of the aspect word for the natural sentences with uncertain structure， and easily ignore the syntactic relations， so it is difficult to accurately determine the sentiment polarity of the aspect. Aiming at the above problems， an ABSA model with Embedding Different Neighborhood Representations （EDNR） was proposed. In this model， on the basis of obtaining the word order information of sentences， the nearest neighbor strategy combining with Convolution Neural Network （CNN） was used to obtain aspect neighborhood information， so as to reduce the influence of far irrelevant information on the model. At the same time， the grammatical information of sentences was introduced to increase the dependency between words. After fusing the two features， Mask and attention mechanism were used to pay special attention to the aspect information and reduce the interference of useless information to the sentiment analysis model. Besides， in order to evaluate the influence degree of contextual and grammatical information on sentiment polarity， an information evaluation coefficient was proposed. Experiments were carried out on five public datasets， and the results show that compared with the sentiment analysis model AGCN-MAX （Aggregated Graph Convolutional Network-MAX）， the EDNR model has the accuracy and F1 score on dataset 14Lap improved by 2.47 percentage points and 2.83 percentage points respectively. It can be seen that the EDNR model can effectively capture emotional features and improve the classification performance.

The abundant structure and association information contained in Knowledge Graph (KG) can not only alleviate the data sparseness and cold-start in the recommender systems, but also make personalized recommendation more accurately. Therefore, a knowledge graph driven end-to-end recommendation model of graph neural network, named KGLN, was proposed. First, a signal-layer neural network framework was used to fuse the features of individual nodes in the graph, then the aggregation weights of different neighbor entities were changed by adding influence factors. Second, the single-layer was extended to multi-layer by iteration, so that the entities were able to obtain abundant multi-order associated entity information. Finally, the obtained features of entities and users were integrated to generate the prediction score for recommendation. The effects of different aggregation methods and influence factors on the recommendation results were analyzed. Experimental results show that on the datasets MovieLen-1M and Book-Crossing, compared with the benchmark methods such as Factorization Machine Library (LibFM), Deep Factorization Machine (DeepFM), Wide&Deep and RippleNet, KGLN obtains an AUC (Area Under ROC (Receiver Operating Characteristic) curve) improvement of 0.3%-5.9% and 1.1%-8.2%, respectively.

Graph matching is widely used in reality, of which subgraph isomorphic matching is a research hotspot and has important scientific significance and practical value. Most existing subgraph isomorphism algorithms build constraints based on neighbor relationships, ignoring the local neighborhood information of nodes. In order to solve the problem, a subgraph isomorphism matching algorithm based on neighbor information aggregation was proposed. Firstly, the aggregated local neighborhood information of the nodes was obtained by importing the graph attributes and structure into the improved graph convolutional neural network to perform the representation learning of feature vector. Then, the efficiency of the algorithm was improved by optimizing the matching order according to the characteristics such as the label and degree of the graph. Finally, the Constraint Satisfaction Problem (CSP) model of subgraph isomorphism was established by combining the obtained feature vector and the optimized matching order with the search algorithm, and the model was solved by using the CSP backtracking algorithm. Experimental results show that the proposed algorithm significantly improves the solving efficiency of subgraph isomorphism compared with the traditional tree search algorithm and constraint solving algorithm.

Large amount of uncertainty in PPI network and the incompleteness of the known protein complex data add inaccuracy to the methods only considering the topological structural information to search or performing supervised learning to the known complex data. In order to solve the problem, a search method called XGBoost model for Predicting protein complex (XGBP) was proposed. Firstly, feature extraction was performed based on the topological structural information of complexes. Then, the extracted features were trained by XGBoost model. Finally, a mapping relationship between features and protein complexes was constructed by combining topological structural information and supervised learning method, in order to improve the accuracy of protein complex prediction. Comparisons were performed with eight popular unsupervised algorithms: Markov CLustering (MCL), Clustering based on Maximal Clique (CMC), Core-Attachment based method (COACH), Fast Hierarchical clustering algorithm for functional modules discovery in Protein Interaction (HC-PIN), Cluster with Overlapping Neighborhood Expansion (ClusterONE), Molecular COmplex DEtection (MCODE), Detecting Complex based on Uncertain graph model (DCU), Weighted COACH （WCOACH）； and three supervisedmethods Bayesian Network (BN), Support Vector Machine (SVM), Regression Model (RM). The results show that the proposed algorithm has good performance in terms of precision, sensitivity and F-measure.

In the detection of components with uneven thickness by X-ray, the problems of low contrast or uneven contrast and low illumination often occur, which make it difficult to observe and analyze some details of components in the images obtained. To solve this problem, an X-ray image enhancement algorithm based on gradient field was proposed. The algorithm takes gradient field enhancement as the core and is divided into two steps. Firstly, an algorithm based on logarithmic transformation was proposed to compress the gray range of an image, remove redundant gray information of the image and improve image contrast. Then, an algorithm based on gradient field was proposed to enhance image details, improve local image contrast and image quality, so that the details of components were able to be clearly displayed on the detection screen. A group of X-ray images of components with uneven thickness were selected for experiments, and the comparisons with algorithms such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and homomorphic filtering were carried out. Experimental results show that the proposed algorithm has more obvious enhancement effect and can better display the detailed information of the components. The quantitative evaluation criteria of calculating average gradient and No-Reference Structural Sharpness (NRSS) texture analysis further demonstrate the effectiveness of this algorithm.

In order to improve the recognition ability for online behavior continuous sequences and enhance the stability of behavior recognition model, a novel online behavior recognition method based on Probabilistic Latent-Dynamic Conditional Random Field (PLDCRF) from surveillance video was proposed. Firstly, the Space-Time Interest Point (STIP) was used to extract behavior features. Then, the PLDCRF model was applied to identify the activity state of indoor human body. The proposed PLDCRF model incorporates the hidden state variables and can construct the substructure of gesture sequences. It can select the dynamic features of gesture and mark the unsegmented sequences directly. At the same time, it can also mark the conversion process between behaviors correctly to improve the effect of behavior recognition greatly. Compared with Hidden Conditional Random Field (HCRF), Latent-Dynamic Conditional Random Field (LDCRF) and Latent-Dynamic Conditional Neural Field (LDCNF), the recognition rate comparison results of 10 different behaviors show that, the proposed PLDCRF model has a stronger recognition ability for continuous behavior sequences and better stability.

In order to alleviate the problem of energy hole in the Wireless Sensor Network (WSN), a Multi-level Energy Heterogeneous algorithm (MEH) was proposed. The energy consumption's characteristics of WSN were analyzed. Then the nodes with different initial energies were deployed according to the energy consumption's characteristics. To balance the energy consumption rate of each region, alleviate the energy hole problem and prolong the network lifecycle, nodes in the heavy communication load region would be configured with higher initial energy. The simulation results show that, compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy-Balanced Unequal Clustering routing protocol (DEBUC), and Nonuniform Distributed Strategy (NDS), the utilization rate of network energy, network lifecycle and period ratio of network energy of MEH were increased nearly 10 percentage points respectively. The proposed MEH has a good balance of energy consumption as well. The experimental results show that, the proposed MEH can effectively prolong the network lifecycle and ease the energy hole problem.

With the extensive application of wideband receivers, the blind detection of non-cooperation burst signal in broadband is increasingly important. It is difficult to detect burst signals with low duty cycle time and to distinguish the burst signals with high duty cycle time from continuous-time signals. The problem was solved by constructing two broadband spectral statistics including maximum spectrum and maximum difference spectrum. By keeping the maximum value of instantaneous spectrum, the maximum spectrum has the information of both burst and non-burst signals; by keeping the maximum value of difference between adjacent instantaneous spectrums, the maximum difference spectrum can extract burst information and suppress continuous-time signals. By using these two spectrums, the detection of burst signals in broadband is completed. The test results show that the proposed algorithm can handle burst signals of all the duty cycle time.

In the traditional method of vibration measurement system, the ability of the network construction is weak and the transmission rate is slow. In order to solve these problems, a new kind of vibration measurement was designed using ZigBee and Ethernet. There are three layers in the system. ZigBee based on XBee-PRO was used to establish the communication between collector nodes and router nodes to suit the multipoint and long-span measurement. Ethernet based on LwIP was used to make the data transmitted accurately in real-time. On the end device layer, the data were stored in SD-card in a server node and offered to computers. The experimental results show that the three layers structure of the measurement system combines the strength of ZigBee's network construction ability and Ethernet's high speed and good stability. It can not only realize an effective control to the measure points, but also meet the requirements of a long-span measurement and a real-time data transmission.

For the traditional method of digestive tract disease diagnosis, the accuracy rate is low and the process is painful. In order to solve these problems, a wireless capsule endoscope system was designed using the wireless communication technology to transmit the image of the tract out of the body. Firstly, the image gathering module was used to capture the image of the digestive tract. Secondly, the image data was transmitted out of the body by the digital wireless communication system. Finally, the data was quickly uploaded to PC by the receiving module to decompress and display the image. The experimental results show that the wireless communication system with MSP430 and ZL70102 has several excellent features such as small-size, low-power and high-rate. Compared with the existing capsule endoscope that transmits analog signal, this digital wireless communication system has strong anti-interference capacity. Also, the accuracy of transmitting image data can reach 80% and the power consumption is only 31.6 mW.

To resist recaptured image's attack towards face recognition system, an algorithm based on predicting face image's gradient direction was proposed. The contrast of real image and recaptured image was enhanced by adaptive Gauss homomorphic's illumination compensation. A Support Vector Machine (SVM) classifier was chosen for training and testing two kinds of pictures with convoluting 8-direction Sobel operator. Using 522 live and recaptured faces come from domestic and foreign face databases including NUAA Imposter Database and Yale Face Database for experiment, the detection rate reached 99.51%; Taking 261 live face photos using Samsung Galaxy Nexus phone, then remaked them to get 522 samples library, the detection rate was 98.08% and the time of feature extraction was 167.04s. The results show that the proposed algorithm can classify live and recaptured faces with high extraction efficiency.

To tackle multi-label data with high dimensionality and label correlations, a multi-label classification approach based on Singular Value Decomposition (SVD)-Partial Least Squares Regression (PLSR) was proposed, which aimed at performing dimensionality reduction and regression analysis. Firstly, the label space was taken into a whole so as to exploit the label correlations. After that, the score vectors of both the instance space and label space were obtained by SVD, which was used for dimensionality reduction. Finally, the model of multi-label classification was established based on PLSR. The experiments performed on four real data sets with higher dimensionality verify the effectiveness of the proposed method.

In view of the problem that data for Named Data Networking (NDN) cache is replaced efficiently, a new replacement policy that considered popularity and request cost of data was proposed in this paper. It dynamically allocated proportion of popularity factor and request cost factor according to the interval time between the two requests of the same data. Therefore, nodes would cache data with high popularity and request cost. Users could get data from local node when requesting data next time, so it could reduce the response time of data request and reduce link congestion. The simulation results show that the proposed replacement policy can efficiently improve the in-network hit rate, reduce the delay and distance for users to fetch data.

For a better trade-off between fault-tolerance mechanism and fault-tolerance overhead in processor reliability research, a fault detection approach for Multi-Processor System-on-Chip (MPSoC) that placed the calculation task of detecting code on redundancy core was proposed in this paper. The approach achieved MPSoC failure detection by placing the calculation and comparison parts of detecting code on redundancy core. The technique required no additional hardware modification, and shortened the design cycle while reducing performance and memory overheads. The verification experiment was implemented on a MPSoC by fault injection and running multiple benchmark programs. Comparing several previous methods of fault detection in terms of capability, area, memory and performance overhead, the experiment results show that the approach is effective and able to achieve a better trade-off between performance and overhead.

To overcome the influence from both the light change and blurring in actual shooting for the three-dimensional reconstruction based on the stereo vision technique, the new illumination-robust combined invariant moments were put forward. Meanwhile, for the purpose of improving the performance of the image feature matching which solely depended on similarity, the dual constraints of the slope and the distance were involved into the similarity measurement, and then the matching process was carried out with their combined actions. Finally the three-dimensional reconstruction of the whole belly contour was built automatically. The parameters of the belly shape obtained by the proposed method can achieve the same accuracy as the 3D scanner and the measurement error with the actual value was less than 0.5cm. The experimental results show that the hardware of this system is simple, low cost as well as fast and reliable for information collection. The system is suitable for apparel design.

In view of the kinematic model of mobile robot, a tracking controller of global asymptotic stability was proposed. The design of tracking controller was divided into two parts: The first part designed the control law of angular velocity by using global fast terminal sliding mode in order to asymptotically stabilize the tracking error of the heading angle; the second part designed the control law of linear velocity by using the Lyapunov method in order to asymptotically stabilize the tracking error of the planar coordinate. By combining Lyapunov stability theorem and two control laws, the mobile robot can track the desired trajectory in a global asymptotic sense when the angular velocity and the linear velocity satisfy these control laws. The experimental results show that the mobile robot can track desired trajectory effectively. It is helpful for promoting the practical application.

In this paper, a Self-Organizing Fuzzy Neural Network (SOFNN) based on Unscented Particle Filter (UPF) was designed and developed. The UPF was used to estimate the parameters of the SOFNN and better result was gotten. The generating criterion of fuzzy rules based on the pruning strategy of the error reduction ratio was introduced. The width of membership function was established as the state and the ideal output as the measurement. The UPF was used to learn parameters. The two typical simulations, nonlinear function approximation and system identification, were done to validate the UPF-SOFNN. It can be seen from the results of simulation that the UPF-SOFNN has a more compact structure and better generalization than the other algorithms.

The multicast routing method has been applied into the Network on Chip (NoC) since traditional unicast communication cannot meet the increasingly rich application requirements of NoC. Three kinds of path-based multicast routing algorithms including XY routing, UpDown routing and SubPartition routing algorithms were applied to 2D Mesh or Torus NoC. The congestion control strategy was proposed. The simulation results show multicast routing algorithms have shorter average latency and higher throughput and balanced applied load compared with unicast routing algorithms. SubPartition routing algorithm was confirmed to have a more stable and better performance as the network size increases. Finally, multicast congestion control techniques for NoC were employed to make multicast communications more efficient and enhance the NoC performance.