Interstitial Lung Disease （ILD） segmentation labels are highly costly， leading to small sample sizes in existing datasets and resulting in poor performance of trained models. To address this issue， a segmentation algorithm for ILD based on multi-task learning was proposed. Firstly， a multi-task segmentation model was constructed based on U-Net. Then， the generated lung segmentation labels were used as auxiliary task labels for multi-task learning. Finally， a method of dynamically weighting the multi-task loss functions was used to balance the losses of the primary task and the secondary task. Experimental results on a self-built ILD dataset show that the Dice Similarity Coefficient （DSC） of the multi-task segmentation model reaches 82.61%， which is 2.26 percentage points higher than that of U-Net. The experimental results demonstrate that the proposed algorithm can improve the segmentation performance of ILD and can assist clinical doctors in ILD diagnosis.

Federated Learning （FL） allows users to share and interact with multiple parties without directly uploading the original data， effectively reducing the risk of privacy leaks. However， existing research suggests that the adversary can still reconstruct raw data through shared gradient information. To further protect the privacy of federated learning， a deep shadow defense scheme of federated learning based on Generative Adversarial Network （GAN） was proposed. The original real data distribution features were learned by GAN and replaceable shadow data was generated. Then， the original model trained on real data was replaced by a shadow model trained on shadow data and was not directly accessible to the adversary. Finally， the real gradient was replaced by the shadow gradient generated by the shadow data in the shadow model and was not accessible to the adversary. Experiments were conducted on CIFAR10 and CIFAR100 datasets for comparison of the proposed scheme with the five defense schemes of adding noise， gradient clipping， gradient compression， representation perturbation and local regularization and sparsification. On CIFAR10 dataset， the Mean Square Error （MSE） and the Feature Mean Square Error （FMSE） of the proposed scheme were 1.18-5.34 and 4.46-1.03×10⁷ times， and the Peak Signal-to-Noise Ratio （PSNR） of the proposed scheme was 49.9%-90.8%. On CIFAR100 dataset， the MSE and the FMSE of the proposed scheme were 1.04-1.06 and 5.93-4.24×10³ times， and the PSNR of the proposed scheme was 96.0%-97.6%. Compared with the deep shadow defense method， the proposed scheme takes into account the actual attack capability of the adversary and the problems in shadow model training， and designs threat models and shadow model generation algorithms. It performs better in theory analysis and experiment result that of the comparsion schemes， and it can effectively reduce the risk of federated learning privacy leaks while ensuring accuracy.

To meet the application demand of high speed scanning and massive data transmission in industrial Computed Tomography (CT) of low-energy X-ray, a system of high-speed data acquisition and transmission for low-energy X-ray industrial CT was designed. X-CARD 0.2-256G of DT company was selected as the detector. In order to accommodate the needs of high-speed analog to digital conversion, high-speed time division multiplexing circuit and ping-pong operation for the data cache were combined; a gigabit Ethernet design was conducted with Field Programmable Gate Array (FPGA) selected as the master chip,so as to meet the requirements of high-speed transmission of multi-channel data. The experimental result shows that the speed of data acquisition system reaches 1MHz, the transmission speed reaches 926Mb/s and the dynamic range is greater than 5000. The system can effectively shorten the scanning time of low energy X-ray detection, which can meet the requirements of data transmission of more channels.

In view of the correction error problem which is caused by some factors such as dithering, the authors presented a new optical tilt correction method based on grating projection. The method was based on the analysis of each pixel of the data array in a sequence of fringe patterns having multiple frequencies, and setup model for pixel coordinates and pixel-slope. Then skew angles of fringes were calculated by trigonometry with the relationship between tilt angle and pixel-slope. At last, tilt correction was realized. The experimental results show that, the algorithm is capable of accurately detecting angle within the range [-90°,90°],accuracy is 99%. Compared with other algorithms such as Hough transform, the proposed algorithm improves precision and accuracy significantly.

Under the cloud computing environment, it has become a significant problem to decrease the power consumption while the makespan is shortened in the process of scheduling resource. Thus, this paper made span and power consumption as the optimization objectives and established power-aware resource scheduling model, then improved the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) by adopting special initialization and the learning algorithm, to solve the problem of power-aware scheduling. Consequently, the simulation results prove that the proposed scheduling algorithm not only shortens the makespan, but also decreases the power consumption effectively.

A new method, called Grayscale Iteration Threshold Pulse Coupled Neural Network (GIT-PCNN), was proposed for image segmentation. The GIT-PCNN reduced the required parameters of conventional PCNN and the exponentially decaying threshold was improved to be related to the grayscale statistics of the original image. When GIT-PCNN was applied to image segmentation, no parameter or iteration time needs to be determined since the segmentation could be completed by one time of PCNN firing process. Therefore, GIT-PCNN did not require specific rule as the iteration stop condition. GIT-PCNN made good use of the grayscale information of the original image and the pulse characteristics of PCNN that the neurons associated with each group of spatially connected pixels with similar intensities tended to pulse together when partitioning images. The experimental results show that GIT-PCNN is better than classical PCNN-based segmentation algorithms on visual evaluation, subjective indices and speed performance.

This paper made a systematic analysis on a few of current trust models, provided a new mixed authenticated trust model, and gave a particular frame and function design. Then the study carried out a simulation and property analysis of the new model. The simulation results indicate that the new mixed authentication model can resolve the defaults of the static key mechanism and improve the security of the grid authentication.

Concerning the deficiency that the study on elliptic curve cryptosystem over Optimal Extension Fields (OEF) mainly focuses on the operation about addition, subtraction, multiplication and inverse of field's element, the preparative base point method was presented and a simple algorithm of computing the order on elliptic curve was designed. Making use of the methods and the algorithm, a fast generating algorithm of secure elliptic curve over optimal extension fields was implemented on general PC. Experiments show that the efficient is preferable.