Machine learning(ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the...Machine learning(ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning(DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system(for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.展开更多
Purpose: This study was to present national estimates of participating in moderate-to-vigorous physical activity(MVPA) and adherence to the recommendations of 60 min/day of MVPA among Chinese school-aged children and ...Purpose: This study was to present national estimates of participating in moderate-to-vigorous physical activity(MVPA) and adherence to the recommendations of 60 min/day of MVPA among Chinese school-aged children and to assess demographic differences in MVPA.Methods: Cross-sectional analyses of data from the 2016 Physical Activity and Fitness in China—The Youth Study. Participants were 90,712 primary, junior middle, and junior high school children(boy: 47%; girl: 53%), recruited from 1204 rural and urban schools across 32 administrative provinces and regions in the Mainland of China. Main outcomes were(a) average MVPA minutes per day in the previous 7 days by self-reports and(b) percentage meeting MVPA recommendations.Results: Average MVPA time was 45.4 min/day, with boys having more MVPA(47.2 min/day) than girls(43.7 min/day) overall and across the 3 school grade categories. About 30% of participants met MVPA recommendations, with a higher percentage of boys(32%) than girls(28%) overall and across the 3 grades categories. Urban school children outperformed rural children in terms of MVPA time. Overall, boys were more likely to meet MVPA recommendations(adjusted odds ratio(a OR) = 1.19, 95% confidence interval(CI): 1.16–1.22) compared with girls; children in higher grades(junior middle(a OR = 0.92, 95%CI: 0.87–0.98) and junior high(a OR = 0.59, 95%CI: 0.53–0.66)) were less likely to meet recommendations compared with primary school children. The odds of meeting recommendations did not differ between urban and rural children(p = 0.07),but urban boys were found to be more likely to meet recommendations compared with rural boys(a OR = 1.14, 95%CI: 1.06–1.19).Conclusion: Overall, the average MVPA minutes per day among Chinese school-aged children is low, and less than one-third of them meet MVPA recommendations. These results were most evident among junior middle and junior high school children and those living rural areas.展开更多
The LAGFD-WAM wave model is a third generation wave model. In the present paper the physical aspect of the model was shown in great detail including energy spectrum balance equation, complicated characteristics equati...The LAGFD-WAM wave model is a third generation wave model. In the present paper the physical aspect of the model was shown in great detail including energy spectrum balance equation, complicated characteristics equations and source functions.展开更多
文摘Machine learning(ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning(DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system(for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.
基金supported in part by the Key Project of the National Social Science Foundation of China(No.16ZDA227 and No.16CTY012)a grant from the Research Program of School Physical Education of Shanghai Municipal Education Commission(No.HJTY-2016-D31)+2 种基金a grant from the Program for Professors of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning(No.TP2014057)Shanghai Philosophy and Social Science Planning Project(No.2015ETY001)Shanghai Pu Jiang Talents Program(No.15PJC065).
文摘Purpose: This study was to present national estimates of participating in moderate-to-vigorous physical activity(MVPA) and adherence to the recommendations of 60 min/day of MVPA among Chinese school-aged children and to assess demographic differences in MVPA.Methods: Cross-sectional analyses of data from the 2016 Physical Activity and Fitness in China—The Youth Study. Participants were 90,712 primary, junior middle, and junior high school children(boy: 47%; girl: 53%), recruited from 1204 rural and urban schools across 32 administrative provinces and regions in the Mainland of China. Main outcomes were(a) average MVPA minutes per day in the previous 7 days by self-reports and(b) percentage meeting MVPA recommendations.Results: Average MVPA time was 45.4 min/day, with boys having more MVPA(47.2 min/day) than girls(43.7 min/day) overall and across the 3 school grade categories. About 30% of participants met MVPA recommendations, with a higher percentage of boys(32%) than girls(28%) overall and across the 3 grades categories. Urban school children outperformed rural children in terms of MVPA time. Overall, boys were more likely to meet MVPA recommendations(adjusted odds ratio(a OR) = 1.19, 95% confidence interval(CI): 1.16–1.22) compared with girls; children in higher grades(junior middle(a OR = 0.92, 95%CI: 0.87–0.98) and junior high(a OR = 0.59, 95%CI: 0.53–0.66)) were less likely to meet recommendations compared with primary school children. The odds of meeting recommendations did not differ between urban and rural children(p = 0.07),but urban boys were found to be more likely to meet recommendations compared with rural boys(a OR = 1.14, 95%CI: 1.06–1.19).Conclusion: Overall, the average MVPA minutes per day among Chinese school-aged children is low, and less than one-third of them meet MVPA recommendations. These results were most evident among junior middle and junior high school children and those living rural areas.
文摘The LAGFD-WAM wave model is a third generation wave model. In the present paper the physical aspect of the model was shown in great detail including energy spectrum balance equation, complicated characteristics equations and source functions.
