Publications by authors named "Jehn-Yih Juang"

To quickly isolate suspected cases to control the epidemics, this study proposes a body temperature monitoring system with a thermography based on the Internet of Things (IoT) architecture. The collected data are transmitted to a back-end platform via wireless communication. Using the analyzed data, the platform provides services, such as instant alerts for any anomalies, infectious disease outbreak prediction, and risk level assessment for a given area, and it will be a great help to epidemic prevention.

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A new reconstruction of changes in Taiwan's land cover and estimated uncertainty between 1904 and 2015 is presented. The reconstruction is made by integrating geographical information from historical maps and SPOT satellite images, to obtain spatially explicit land cover maps with a resolution of 500 × 500 m and distinguishes six land cover classes: forests, grasslands, agricultural land, inland water, built-up land, and bare soil. The temporal resolution is unbalanced being derived from four historical maps describing the land cover between 1904 and 1994 and five mosaic satellite images describing the land cover between 1995 and 2015.

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The southeastern United States is experiencing a rapid regional increase in the ratio of pine to deciduous forest ecosystems at the same time it is experiencing changes in climate. This study is focused on exploring how these shifts will affect the carbon sink capacity of southeastern US forests, which we show here are among the strongest carbon sinks in the continental United States. Using eight-year-long eddy covariance records collected above a hardwood deciduous forest (HW) and a pine plantation (PP) co-located in North Carolina, USA, we show that the net ecosystem exchange of CO2 (NEE) was more variable in PP, contributing to variability in the difference in NEE between the two sites (ΔNEE) at a range of timescales, including the interannual timescale.

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Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings.

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For mission-critical applications of wireless sensor networks (WSNs) involving extensive battlefield surveillance, medical healthcare, etc., it is crucial to have low-power, new protocols, methodologies and structures for transferring data and information in a network with full sensing coverage capability for an extended working period. The upmost mission is to ensure that the network is fully functional providing reliable transmission of the sensed data without the risk of data loss.

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Flow disturbances near tall forest edges are receiving significant attention in diverse disciplines including ecology, forest management, meteorology, and fluid mechanics. Current theories suggest that near a forest edge, when the flow originates from a forest into a large clearing, the flow retains its forest canopy turbulence structure at the exit point. Here, we propose that this framework is not sufficiently general for dense forested edges and suggest that the flow shares several attributes with backward-facing step (BFS) flow.

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A number of recent studies have attributed a large proportion of soil respiration (R(soil)) to recently photoassimilated carbon (C). Time lags (tau(PR)) associated with these pulses of photosynthesis and responses of R(soil) have been found on time scales of hours to weeks for different ecosystems, but most studies find evidence for tau(PR) on the order of 1-5 d. We showed that such time scales are commensurate with CO(2) diffusion time scales from the roots to the soil surface, and may thus be independent from photosynthetic pulses.

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Orthonormal wavelet transformation (OWT) is a computationally efficient technique for quantifying underlying frequencies in nonstationary and gap-infested time series, such as eddy-covariance-measured net ecosystem exchange of CO2 (NEE). We employed OWT to analyze the frequency characteristics of synchronously measured and modeled NEE at adjacent pine (PP) and hardwood (HW) ecosystems. Wavelet cospectral analysis showed that NEE at PP was more correlated to light and vapor pressure deficit at the daily time scale, and NEE at HW was more correlated to leaf area index (LAI) and temperature, especially soil temperature, at seasonal time scales.

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