Shengdong Hu

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Assessing Google Correlate Queries for Influenza H1N1 Surveillance in Asian Developing Countries Xichuan Zhou1∗, Qin Li2, Han Zhao2, Shengli Li 1, Lei Yu 1, Fang Tang 1, Shengdong Hu 1, Guojun Li4 , Yujie Feng3 1 College of Communication Engineering, Chongqing University, Chongqing, China 2 Chongqing Centers for Disease Control and Prevention, Chongqing,(More)
The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the(More)
The 2009 influenza pandemic teaches us how fast the influenza virus could spread globally within a short period of time. To address the challenge of timely global influenza surveillance, this paper presents a spatial-temporal method that incorporates heterogeneous data collected from the Internet to detect influenza epidemics in real time. Specifically, the(More)
Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data. However, implementing a deep neural network in embedded systems is a challenging task, because a typical deep neural network, such as a Deep Belief Network using 128×128 images as input, could exhaust Giga bytes of memory and result in(More)
Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an(More)
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