Real-time target detection for hyperspectral images (HSI) has received considerable interest in recent years. However, owing to enormous data volume provided by HSI, detection algorithms are generally computationally complex, thus developing rapid processing techniques for target detection has encountered several challenging issues. It seems that using a deep pipelined structure can improve the detection speed, and implementing on field programmable gate arrays (FPGAs) can also achieve concurrent operations rather than run streams of sequential instruction. This paper presents a deep pipelined background statistics (DPBS) approach to optimizing and implementing a well-known subpixel target detection algorithm, called constrained energy minimization (CEM) on FPGA by using high-level synthesis (HLS). This approach offers significant benefits in terms of increasing data throughput and improving design efficiency. To overcome a drawback of HLS on implementing a task-level pipelined circuit that includes a feedback data path, a script based circuit design method is further developed to make connections between some of the modules created by HLS. Experimental results show that the proposed method can detect targets on a real-hyperspectral data set (HyMap Data) only in 0.15 s without compromising detection accuracy.
This research is designed to investigate interlanguage fossilization in Chinese college students' written output.Twelve common linguistic errors from 20 Chinese EFL (English as a foreign language) learners' writing assignments are observed. Results show that among 12 typical errors, five types of errors are declining while the rest are increasing, indicating a tendency towards fossilization. Analysis shows that negative/corrective feedback has played a key role in reducing fossilization of some errors, but it does not work in every case. Some errors tend to be fossilized for several reasons. First, language items that do not have a direct form-function relationship are likely to be fossilized. Second, advanced learners create their own language system and neglect the basic rules of grammar. Third, task difficulty takes learners' attention away from form to meaning. Fourth, ingrained thinking patterns have a great impact on how learners organize their thoughts in writing. It is true that many Chinese EFL advanced learners reach a plateau in the process of acquiring English. However, attention, consciousness, and training of self-monitoring ability will help destabilize their interlanguage system.
Background The incubation period is a key index of epidemiology in understanding of the spread of infectious diseases and the decision-making of the disease control. However, the incubation period of the emerging COVID-19 is still unclear. Methods Between January 19, 2020 and September 21, 2020, we collected information on 11545 patients in Mainland China outside Hubei. The 218 patients with precise data was validation population. The incubation period was fitted with lognormal model by the coarseDataTools package in R. Results In 11545 patients, the mean incubation period of COVID-19 was 7.1 days (95% Confidence interval [CI], 7.0–7.2). About 5.4% of patients had precise incubation period less than 3 days, 10.2% longer than 14 days, and 2.1% longer than 21 days. There was no statistically significant difference in incubation period between male and female (P = 0.603). It was similar in the 218 patients. The mean accurate incubation period was 6.8 days (6.2–7.4). Of which, 14.7% (32/218) of patients had incubation period less than 3 days, 12.4% (27/218) longer than 14 days, and 0.9% (2/218) longer than 21 days. Conclusions For COVID-19, the mean incubation period is 7.1 days and 10.2% of patients developed disease 14 days after infection, which challenges the current 14-day quarantine strategy.
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