For problems such as low accuracy and poor real-time detection of existing radar non-contact vital signs detection， a human vital signs detection algorithm based on Frequency Modulated Continuous Wave （FMCW） radar was proposed. Firstly，the vital signs signal was obtained through the millimeter wave radar. Then， the adaptive decomposition and reconstruction of the vital signs signal were achieved using the improved Empirical Wavelet Transformation （EWT） algorithm. The best value of the spectrum division line was found by introducing Sparrow Search Algorithm （SSA） and Fuzzy Entropy （FE）. Finally，the heart rate and respiratory rate were calculated using the estimation algorithm with improved frequency interpolation. The superiority and robustness of the proposed algorithm were verified through comparative experiments with a medical critical care monitor. The experimental results showed that compared with Wavelet Transform （WT） algorithm， Complementary Ensemble Empirical Mode Decomposition （CEEMD） algorithm and Variational Mode Decomposition （VMD） algorithm， the Mean Square Error （MSE） was reduced by 77.65， 27.25 and 21.05， the Mean Absolute Percentage （MAPE） was reduced by 7.33， 4.33 and 3.42 percentage points， and the real-time performance was improved by 0.72 s， 16.74 s and 1.87 s. At the same time， the proposed algorithm also achieves the detection of Heart Rate Variability （HRV）.

Time slicing methods in dynamic networks greatly influence the accuracy of community evolution analysis results. As communities vary nonlinearly with time and network topology， both the existing uniform time slicing method and network topology variance-based nonuniform time slicing method are unsatisfactory in capturing community evolution events. Therefore， a nonuniform time slicing method based on prediction of community variance was proposed， where the community variance is quantitatively described by the difference between the community modularity expected to be achieved by the updated network and the community modularity obtained by directly applying the community detection results of the network before changing. Firstly， the prediction model of community modularity was established on the basis of time series analysis. Secondly， with the established model， the expected community modularity of the updated network was predicted， and the prediction value of community variance was obtained. Finally， once the prediction value surpassed a previously set threshold， a new time slice was generated. Experimental results on two real network datasets show that compared with the traditional uniform time slicing method and the nonuniform time slicing method based on network topology variance， on the dynamic network dataset Arxiv HEP-PH， the proposed method identifies community disappearance events 1.10 days and 1.30 days earlier， respectively， and identifies the community forming events 8.34 days and 3.34 days earlier， respectively， and the total number of identified community shrinking and growing events increased by 10 and 1 respectively. On Sx?MathOverflow dataset， the proposed method identifies community disappearance events 3.30 days and 1.80 days earlier， and identifies the community forming events 6.41 days and 2.97 days earlier respectively， and the total number of identified community shrinking and growing events increased by 15 and 7， respectively.

Forged and tampered data frames should be identified and filtered out to ensure network security and efficiency. However， the existing schemes usually fail to work when verification devices are attacked or maliciously controlled in the Software Defined Network （SDN）. To solve the above problem， a blockchain-based data frame security verification mechanism was proposed. Firstly， a Proof of Frame Forwarding （PoFF） consensus algorithm was designed and used to build a lightweight blockchain system. Then， an efficient data frame security verifying scheme for SDN data frame was proposed on the basis of this blockchain system. Finally， a flexible semi-random verifying scheme was presented to balance the verification efficiency and the resource cost. Simulation results show that compared with the hash chain based verifying scheme， the proposed scheme decreases the missed detection rate significantly when an equal proportion of switches are maliciously controlled. Specifically， when the proportion is 40%， the decrease effect is very obvious， the missed detection rate can still be kept no more than 32% in the basic verification mode， and can be further reduced to 7% with the assistance of the semi-random verifying scheme. Both are much lower than the missed detection rate of 72% in the hash chain based verifying scheme， and the resource overhead and communication cost introduced by the proposed mechanism are within a reasonable range. Additionally， the proposed scheme can still maintain good verification performance and efficiency even when the SDN controller is completely unable to work.