In order to ensure the security of audio signals in communication transmission, a fractional domain audio encryption algorithm based on cascaded chaotic system was proposed. Firstly, the audio signal was grouped. Secondly, the chaotic system was used to obtain the orders of fractional Fourier transform, and the order corresponding to each group data changed dynamically. Thirdly, the sampling fractional Fourier discrete transform with less computational complexity was used to obtain the fractional domain spectrum data of each group. Finally, the cascaded chaotic system was used to perform data encryption to the fractional domain of each group in turn, so as to realize the overall encryption of the audio signals. Experimental results show that the proposed algorithm is extremely sensitive to the key, and has the waveform and fractional domain spectrum of obtained encrypted signal more uniformly distributed and less correlated compared with those of the original signal. At the same time, compared with the frequency domain encryption and fixed-order fractional domain encryption methods, the proposed algorithm can effectively increase the key space while reducing the computational complexity. It can be seen that the proposed algorithm can satisfy the real-time and secure transmission requirements of audio signals effectively.

A new Particle Swarm Optimization (PSO) algorithm based on the blast wave model (referred to as BW-PSO algorithm) was proposed aiming at the problem that the basic PSO algorithm when solving complex multimodal problems is easy to fall into local optimal solution. The supervision conditions of population diversity were added to the basic PSO algorithm so that the process of particle shock was triggered when the population decreased to a given threshold value. Crossover and mutation occurred between optimal and suboptimal particles so that the particles within the blast radius by the traction were subjected to accelerate convergence to the current extreme and the particles outside the blast radius were subjected to spread out, which increased the possibility of finding the global optimum value. BW-PSO algorithm not only improved the accuracy of the current solution by the mutation between optimal and suboptimal particles, but also increased the population diversity with the shock wave process of the particles and enhances the ability of the global space development of the particles. Compared with the mutative PSO and charged PSO, the results indicate that the BW-PSO algorithm has a better performance to solve multi-modal optimization problem.

In view of real-time issue of the Hidden Conditional Random Field (HCRF) and marked deviation problem of the Latent-Dynamic Conditional Random Field (LDCRF) during behavior transforming, this article proposed a kind of behavior recognition algorithm based on Stratified Fractal Conditional Random Field (SFCRF). The proposed algorithm improved LDCRF and put forward the concept of score mark, which made the integrity and direction of human behavior specific. The experimental results show that the proposed algorithm can obtain better recognition effect than Conditional Random Field (CRF), HCRF and LDCRF.