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Pollen influx (number of pollen grains cm−2 year−1) can objectively reflect the dispersal and deposition features of pollen within a certain time and space, and is often used as a basis for the quantitative reconstruction of palaeovegetation; however, little is known about the features and mechanisms of vertical dispersal of pollen. Here we present the results from a 5 year (2006–2010) monitoring program using pollen traps placed at different heights from ground level up to 60 m and surface soil samples in a mixed coniferous and deciduous broad-leaved woodland in the Changbai mountains, northeastern China. The pollen percentages and pollen influx from the traps have very similar characteristics to the highest values for Betula, Fraxinus, Quercus and Pinus, among the tree taxa and Artemisia, Chenopodiaceae and Asteraceae among the herb taxa. Pollen influx values vary significantly with height and show major differences between three distinct layers, above-canopy (≥32 m), within the trunk layer (8 ≤ 32 m) and on the ground (0 m). These differences in pollen influx are explained by differences in (i) the air flows in each of these layers and (ii) the fall speed of pollen of the various taxa. We found that the pollen recorded on the ground surface is a good representation of the major part of the pollen transported in the trunk space of the woodland. Comparison of the pollen influx values with the theoretical, calculated “characteristic pollen source area” (CPSA) of 12 selected taxa indicates that the pollen deposited on the ground surface of the woodland is a fair representation with 85–90 % of the total pollen deposited at a wind speed of 2.4 m s−1 coming from within ca. 1–5 km for Pinus and Quercus, ca. 5–10 km for Ulmus, Tilia, Oleaceae and Betula, ca. 20–40 km for Fraxinus, Poaceae, Chenopodiaceae, Populus and Salix, and ca. 30–60 km for Artemisia; it is also a good representation with 90–98 % of the total pollen deposited coming from within 60 km at a wind speed of 2.4 m s−1, or 100 km at a wind speed: 6 m s−1, for the 12 selected taxa used in the CPSA calculation. Furthermore, comparison with the vegetation map of the area around the sampling site shows that the pollen deposited on the ground represents all plant communities which grow in the study area within 70 km radius of the sampling site. In this study, the pollen percentages obtained from the soil surface samples are significantly biased towards pollen taxa with good preservation due to thick and robust pollen walls. Therefore, if mosses are available instead, soil samples should be avoided for pollen studies, in particular for the study of pollen-vegetation relationships, the estimation of pollen productivities and quantitative reconstruction of past vegetation. The results also indicate that the existing model of pollen dispersal and deposition, Prentice’s model, provides a fair description of the actual pollen dispersal and deposition in this kind of woodland, which suggests that the application of the landscape reconstruction algorithm would be relevant for reconstruction of this type of woodland in the past.
TRIPOD
(2021)
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
VERITAS and Fermi-LAT Observations of TeV Gamma-Ray Sources Discovered by HAWC in the 2HWC Catalog
(2018)
The High Altitude Water Cherenkov (HAWC) collaboration recently published their 2HWC catalog, listing 39 very high energy (VHE; >100 GeV) gamma-ray sources based on 507 days of observation. Among these, 19 sources are not associated with previously known teraelectronvolt (TeV) gamma-ray sources. We have studied 14 of these sources without known counterparts with VERITAS and Fermi-LAT. VERITAS detected weak gamma-ray emission in the 1 TeV-30 TeV band in the region of DA 495, a pulsar wind nebula coinciding with 2HWC J1953+294, confirming the discovery of the source by HAWC. We did not find any counterpart for the selected 14 new HAWC sources from our analysis of Fermi-LAT data for energies higher than 10 GeV. During the search, we detected gigaelectronvolt (GeV) gamma-ray emission coincident with a known TeV pulsar wind nebula, SNR G54.1+0.3 (VER J1930+188), and a 2HWC source, 2HWC J1930+188. The fluxes for isolated, steady sources in the 2HWC catalog are generally in good agreement with those measured by imaging atmospheric Cherenkov telescopes. However, the VERITAS fluxes for SNR G54.1+0.3, DA 495, and TeV J2032+4130 are lower than those measured by HAWC, and several new HAWC sources are not detected by VERITAS. This is likely due to a change in spectral shape, source extension, or the influence of diffuse emission in the source region.