In order to improve the accuracy of foot plantar pressure acquisition and reduce the influence on the experimenter’s natural gait during the measurement, the improved design of plantar pressure acquisition system and the optimization of sampling timing are proposed, which can effectively reduce the noise interference introduced by the sensor and the circuit system, and finally realize the sampling measurement of large size (71.9 cm × 33.5 cm) and high spatial resolution (2.8/cm², a total of 6720 sensor units) plantar pressure sensing system with the sampling rate of 100 SPS. The system is used to test in the Department of Rehabilitation in Guangzhou First People’s Hospital. The collection and analysis of foot plantar pressure can be applied to the postoperative gait rehabilitation training, providing effective quantitative data reference for doctors to evaluate the rehabilitation of patients and formulate targeted treatment plans.

The authors use Passive DNS to log domain name query history of real campus network environment, and construct eighteen feature sets grouping by diversity, time, growth, and relevance, and then propose a model detect Fast-Flux Domains using random forest algorithm. The result shows that the proposed model can classify domains with accuracy over 90% by cross validation experiments. The model can detect Fast-Flux domains in the datasets used in this study more effectively compared with Fluxbuster.

Because of the inherent node (link) failures in wireless networks, virtual backbones should be fault-tolerant. Fault-tolerant virtual backbones were modeled as 2-connected k-dominating sets. An approximation algorithm was designed to find a 2-connected k-dominating virtual backbone in wireless ad-hoc networks by analyzing the properties of maximal independent sets in unit disk graphs and the block-cutvertex tree structure of connected graphs. The time complexity and the performance ratio of the algorithm were analyzed and proved to be a constant, respectively.