The knowledge tracing task diagnoses a student’s cognitive state in real time based on historical learning data， and predicts the future performance of the student in answering questions. In order to accurately model the forgetting behaviors and the time-series characteristics of the answering sequence in knowledge tracing， a Graph neural network-based Knowledge Tracing blending with Forgetting factors and Memory gate （GKT-FM） model was proposed. Firstly， through the answering record， the correlations of knowledge points were calculated and a knowledge graph was constructed. Then， Graph Neural Network （GNN） was used to model the cognitive state of the student， and seven characteristics that affect forgetting behaviors were considered comprehensively. After that， the memory gate structure was used to model the time-series characteristics in the student’s answering sequence， and the update process of GNN-based knowledge tracing was reconstructed. Finally， the prediction results were obtained by integrating the forgetting factors and the time-series characteristics. Experimental results on public datasets ASSISTments2009 and KDDCup2010 show that compared with GKT （Graph-based Knowledge Tracing） model， GKT-FM model improves the average AUC （Area Under Curve） by 6.9% and 9.5% respectively， and the average ACC （ACCuarcy） by 5.3% and 6.7% respectively， indicating that GKT-FM model can better model students’ forgetting behaviors and trace their cognitive states.

Improving the image quality of IntraVascular Optical Coherence Tomography （IVOCT） through guidewire artifact removal can assist physicians in diagnosing cardiovascular diseases more accurately， which reduces the probabilities of misdiagnosis and missed diagnosis. Aiming at the difficulties of complex structure information and a large proportion of artifact areas in IVOCT images， a Structure-Enhanced Transformer Network （SETN） using Generative Adversarial Network （GAN） architecture was proposed for guidewire artifact removal of IVOCT images. Firstly， based on the ORiginal Image （ORI） backbone generation network for extracting texture features， the generator of GAN was combined with RTV （Relative Total Variation） image enhanced generation network in parallel to obtain image structure information. Next， during the artifact area reconstruction of ORI/RTV image， Transformer encoders focusing on the temporal/spatial domain information respectively were introduced to capture the contextual information and the correlation between texture/structure features of IVOCT image sequence. Finally， the structural feature fusion module was used to integrate the structural features of different levels into the decoding stage of the ORI backbone generation network， so that the generator was cooperated with the discriminator for completing the image reconstruction of the guidewire artifact area. Experimental results show that the guidewire artifact removal results of SETN are excellent in both texture and structure reconstruction. Besides， the improvement of IVOCT image quality after guidewire artifact removal is positive for both vulnerable plaque segmentation and lumen contour extraction tasks of IVOCT image.

The emergence of Mobile Edge Computing （MEC） enables mobile users to easily access services deployed on edge servers with low latency. However， there are various challenges in MEC， especially service deployment issues. The number and resources of edge servers are usually limited and only a limited number of services can be deployed on the edge servers； in addition， the mobility of users changes the popularities of different services in different regions. In this context， deploying suitable services for dynamic service requests becomes a critical problem. To address this problem， by deploying appropriate services by awareness of the dynamic user requirements to minimize interaction delay， the service deployment problem was formulated as a global optimization problem， and a cluster-based resource aggregation algorithm was proposed， which initially deployed suitable services under the resource constraints such as computing and bandwidth. Moreover， considering the influence of dynamic user requests on service popularity and edge server load， a dynamic adjustment algorithm was developed to update the existing services to ensure that the Quality of Service （QoS） always met user expectations. The performance of this deployment strategy was verified through a series of simulation experiments. Simulation results show that compared with the existing benchmark algorithms， the proposed strategy can reduce service interaction delay and achieve a more stable load balance.

Aiming at the problems of current aviation card readers, include poor portability, slow speed and tags' little capacity, a design method of large capacity Radio Frequency Identification (RFID) system based on STM32 was proposed. Using STM32 microprocessor as a core and adopting CR95HF radio chip, a new handled RFID card reader which worked in High Frequency (HF) and supported ISO 15693, ISO 18092 protocols was designed. The design of power, antenna and optimization of software speed, error rate was discussed in detail. A new large compiled capacity passive tag was also designed whose capacity is up to 32KB to form a large capacity RFID system with card reader. The experimental results show that, compared with the traditional card reader, the reading and writing speed of the card reader increases by 2.2 times, error rate reduces by 91.7% and tag capacity increases 255 times. It provides a better choice for fast, accurate and high data requirements of aviation logistics.

Concerning the impact on network performance of the mobile nodes and the constantly changing data transmission rate, the authors proposed a new backoff scheme for IEEE802.15.4, which used Probability Judgment based on Network Load and Exponentially Weighted Moving Average (PJNL_EWMA) method. According to a realtime monitoring of current network status by probability judgment of network load, this method dynamically adjusted backoff exponent by EWMA when Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) began. Compared with the IEEE802.15.4 standard protocol and MBS (Memorized Backoff Scheme)+EWMA algorithm, the simulation experiments on NS2 platform show that the PJNL_EWMA algorithm not only improves the throughput of the network, but also reduces the packet loss rate and the collision ratio, significantly improving the network performance.

To address the problem of the coincidence of the position and size for a same face cannot be guaranteed in different results due to the scale transformation in face detection, research has been done by the method of merging windows in face-detection employing statistical training. And a method based on Euclidean distance was proposed too, without considering the case of error-detected and undetected faces. Judging circle and Euclidean distance were employed to merge face-detection windows according to the distribution of center coordinates. Verifying experiments of the method were conducted according to different pictures, and the experimental results proved the method simple and effective.

Frequency-hopping communication was combined with the Low-Density Parity-Check (LDPC) code to improve anti-jamming performance of frequency hopping communications. By simplifying the complexity of coding algorithm in the “greedy algorithm”, an offset layered quantization decoding called Layered Belief Propagation-Offset Min-Sum (LBP-OMS) algorithm was applied to improve the performance of error correction code words. The simulation results show that when certain frequency bands are covered by strong noise, the anti-interference ability of broadband frequency-hopping communications is improved by using the improved channel coding method.