ABSTRACT. This study proposes use of the DNA genetic artificial fish swarm constant modulus blind equalization algorithm (DNA-G-AFS-CMBEA) to overcome the local convergence of the CMBEA. In this proposed algorithm, after the fusion of the fast convergence of the AFS algorithm and the global search capability of the DNA-G algorithm to drastically optimize the position vector of the artificial fish, the global optimal position vector is obtained and used as the initial optimal weight vector of the CMBEA. The result of application of this improved method in medical image processing demonstrates that the proposed algorithm outperforms the CMBEA and the AFS-CMBEA in removing the noise in a medical image and improving the peak signal to noise ratio.

Highlight:1. The size distribution and seasonal character of carbonaceous aerosols was provided.2. OC, EC, SOC and POC in four seasons were mainly centralized in PM1.1 3. Emission sources were discussed as reasons for OC/EC size distribution and seasonal variation Abstract: In order to investigate the size distributions and seasonal variations of carbonaceous aerosols (OC and EC), the carbonaceous species were collected and then analyzed by using a ), and were mainly centralized in PM1.1 in four seasons. The concentrations of OC in PM1.1 varied in the order of winter > autumn > spring > summer, while EC ranked in the order of autumn > winter > summer > spring. In the PM1.1-2.1 and PM2.1-10, the concentrations of OC and EC decreased in the sequence of winter > spring > autumn > summer. The size spectra of OC, EC and SOC had bimodal distributions in four seasons, except for EC with four peaks in summer. The size spectra of POC varied greatly with seasons, exhibiting bimodal distribution in winter, trimodal distribution in spring and summer, and four peaks in autumn. The OC/EC ratios were 7.0, 6.3, 7.6 and 6.9 in spring, summer, autumn and winter, respectively, which demonstrated the abundance of secondary organic aerosols in Nanjing. The sources of carbonaceous aerosol varied significantly with seasons, and were dominated by vehicle exhaust, coal and biomass burning in PM2.1, and dominant by dust, coal and biomass burning in PM2.1-10.

ABSTRACT. Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multimodulus signals of the constant modulus algorithm (CMA), a multimodulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence 17512 Y.C. Guo et al. ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (4): 17511-17518 (2015) speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.