Brick tea-drinking fluorosis is an unusual environmental problem. As a result of an investigation of tea-drinking habits, total fluoride intakes, dental fluorosis, and skeletal fluorosis, this disease has been found in the Sichuan Province of China in Tibetans with a long history of drinking brick tea. The dental fluorosis investigation of 375 Tibetan children (213 males, 162 females) and 161 Han children (86 males, 75 females), 8-15 years of age, was carried out in Daofu County, Sichuan Province. According to the standard of the Chinese Health Ministry, a skeletal fluorosis survey of 658 Tibetans (264 males, 394 females) and 41 Hans (20 males, 11 females), all over 16 years old, was performed. The total fluoride intake and fluorosis were determined from a question--calculation method in all participants. The morbidities of dental fluorosis in Tibetan and Han children are 51.2% and 11.05%, respectively, and the indexes of dental fluorosis are 1.33 and 0.17 (chi 2 = 75.7, p < 0.01) respectively. The morbidity of skeletal fluorosis is 32.83% for Tibetan children and zero for the Han children. The fluoride intakes of Tibetan children and adults were 5.49 mg/person/day and 10.43 mg/person/day, respectively, in this area. Of total everyday fluoride intake, 94.2% by children and 94.4% by adults was from brick tea and zanba (r = 0.99).
Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.
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