Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesae...Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesaegypti, such as high-temperature days interlaced with afternoon or nocturnal rainstorms in summer. This work has the objective of investigating the relationships between variabilities of the El Ni?o-South Oscillation (ENSO) and greater epidemics of dengue in Rio de Janeiro city. To accomplish this goal, the analysis and signal decomposition by cross-wavelet transform (WT) was applied to obtain the cross variability associated with variations of power and phase of both signals by characteristic periods and along with the time series. Data considered in the analysis are (the decimal logarithm of normalized value) of the monthly available notifications of dengue worsening, provided by the public health system of Brazil, and the Southern Oscillation Index (SOI) Ni?o 3.4 data, provided by the National Oceanic and Atmospheric Administration (NOAA), in the period 2000-2017. A maximum cross-wavelet power close to 0.45 was obtained for the representative period of 1 year and also to periods between 3 and 4 years, associated with the positive phase of the SOI index (i.e. , La Ni?a) or with a transition to the positive phase. The evolution of the combined variability of SOI and dengue can be expressed by progressive differences in phase along the time, eventually resulting in yielding phases (i.e., La Niña-Dengue epidemic).展开更多
针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈...针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。展开更多
文摘Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesaegypti, such as high-temperature days interlaced with afternoon or nocturnal rainstorms in summer. This work has the objective of investigating the relationships between variabilities of the El Ni?o-South Oscillation (ENSO) and greater epidemics of dengue in Rio de Janeiro city. To accomplish this goal, the analysis and signal decomposition by cross-wavelet transform (WT) was applied to obtain the cross variability associated with variations of power and phase of both signals by characteristic periods and along with the time series. Data considered in the analysis are (the decimal logarithm of normalized value) of the monthly available notifications of dengue worsening, provided by the public health system of Brazil, and the Southern Oscillation Index (SOI) Ni?o 3.4 data, provided by the National Oceanic and Atmospheric Administration (NOAA), in the period 2000-2017. A maximum cross-wavelet power close to 0.45 was obtained for the representative period of 1 year and also to periods between 3 and 4 years, associated with the positive phase of the SOI index (i.e. , La Ni?a) or with a transition to the positive phase. The evolution of the combined variability of SOI and dengue can be expressed by progressive differences in phase along the time, eventually resulting in yielding phases (i.e., La Niña-Dengue epidemic).
文摘针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。
