Applying the compact constraint calculation method of S-box based on Mixed Integer Linear Programming （MILP） model can solve the low efficiency of differential path search of SPONGENT in differential cryptanalysis. To find the best description of S box， a compactness verification algorithm was proposed to verify the inequality constraints in S-box from the perspective of the necessity of the existence of constraints. Firstly， the MILP model was introduced to analyze the inequality constraints of SPONGENT S-box， and the constraint composed of 23 inequalities was obtained. Then， an index for evaluating the existence necessity of constraint inequality was proposed， and a compactness verification algorithm for verifying the compactness of group of constraint inequalities was proposed based on this index. Finally， the compactness of the obtained SPONGENT S-box constraint was verified by using the proposed algorithm. Calculation analysis show that the 23 inequalities have a unique impossible difference mode that can be excluded， that is， each inequality has the necessity of existence. Furthermore， for the same case， the number of inequalities was reduced by 20% compared to that screened by using the greedy algorithm principle. Therefore， the obtained inequality constraint of S-box in SPONGENT is compact， and the proposed compactness verification algorithm outperforms the greedy algorithm.

In Hematoxylin-Eosin （HE）-stained pathological images， the uneven distribution of cell staining and the diversity of various tissue morphologies bring great challenges to automated segmentation. Traditional convolutions cannot capture the correlation features between pixels in a large neighborhood， making it difficult to further improve the segmentation performance. Therefore， a Multi-Channel Segmentation Network with gated axial self-attention （MCSegNet） model was proposed to achieve accurate segmentation of nuclei in pathological images. In the proposed model， a dual-encoder and decoder structure was adopted， in which the axial self-attention encoding channel was used to capture global features， while the convolutional encoding channel based on residual structure was used to obtain local fine features. The feature representation was enhanced by feature fusion at the end of the encoding channel， providing a good information base for the decoder. And in the decoder， segmentation results were gradually generated by cascading multiple upsampling modules. In addition， the improved hybrid loss function was used to alleviate the common problem of sample imbalance in pathological images effectively. Experimental results on MoNuSeg2020 public dataset show that the improved segmentation method is 2.66 percentage points and 2.77 percentage points higher than U-Net in terms of F1-score and Intersection over Union （IoU） indicators， respectively， and effectively improves the pathological image segmentation effect and the reliability of clinical diagnosis.

A Non-Orthogonal Multiple Access （NOMA） based computation offloading and bandwidth allocation strategy was presented to address the issues of insufficient computing capacity of mobile devices and limited spectrum resource in 5G ultra-dense network. Firstly， the system model was analyzed， on this basis， the research problem was defined formally with the objective of minimizing the computation cost of devices. Then， this problem was decomposed into three sub-problems： device computation offloading， system bandwidth allocation， and device grouping and matching， which were solved by adopting simulated annealing， interior point method， and greedy algorithm. Finally， a joint optimization algorithm was used to alternately solve the above sub-problems， and the optimal computation offloading and bandwidth allocation strategy was obtained. Simulation results show that， the proposed joint optimization strategy is superior to the traditional Orthogonal Multiple Access （OMA）， and can achieve lower device computation cost compared to NOMA technology with average bandwidth allocation.

In an actual operation, errhysis in the wound happens frequently during surgery cutting. Most previous studies, focus on skin or organ surface more than dynamic blood oozing in the inner wound. Therefore, upon the requirements of authenticity and real-timing, an oozing simulation model combined with force feedback was proposed. The model used Lagrangian approaches in fluid simulation system, and especially simplified the traditional Lagrangian approaches in this specific liver cutting process. The experimental results show that it can meet the demand of authenticity and real-time in dynamic ooze blood simulation.