In this study, seven microbial materials (entomogenous fungi Bb3088 mycelia, entomogenous fungi Bb3088 spores, entomogenous fungi Ma2677 mycelia, entomogenous fungi Ma2677 spores, Bacillus subtilis 8204, Staphylococcus aureus 6725, and Saccharomyces cerevisiae 1025) were used to measure electromagnetic (EM) signal extinction. They were subjected to light absorption and reflection measurements in the range of 4000-400 cm(-1) (2.5-25 µm) using Fourier transform infrared spectroscopy. The specular reflection spectrum method was used to calculate the real (n) and imaginary (k) parts of the complex refractive index. The complex refractive index with real part n and imaginary part k in the infrared band satisfies the following conditions n ≥ 1 and k ≥ 0. The mass extinction coefficient was calculated based on Mie theory. Entomogenous fungi Ma2677 spores and entomogenous fungi Bb3088 spores were selected as EM signal extinction materials in the smoke box test. The transmittances of entomogenous fungi Bb3088 spores and entomogenous fungi Ma2677 spores were 11.63% and 5.42%, and the mass extinction coefficients were 1.8337 m(2)/g and 1.227 m(2)/g. These results showed that entomogenous fungi Bb3088 spores and entomogenous fungi Ma2677 spores have higher extinction characteristics than other microbial materials.

Particle filter (PF) has many variations and one of the most popular is the unscented particle filter (UPF). UPF uses the unscented Kalman filter (UKF) to generate particles in the PF framework and has a better performance than the standard PF. However, UPF suffers from its high computation complexity because it has to execute UKF to each particle to obtain proposal distribution. This paper gives an improved UPF aiming at reducing the computation complexity of the algorithm. In comparison to the standard UPF, the new strategy generates proposal distribution from the mean and covariance value of the whole particles instead of from each particle. Thus the improved algorithm utilizes the characteristics of the whole particles and only needs to perform UKF algorithm once to get the proposal distribution at each time step. Experimental results show that, compared to standard UPF, the improved algorithm reduces the time consumption greatly almost without performance degradation.

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