The Experts below are selected from a list of 139551 Experts worldwide ranked by ideXlab platform
Robert W Boyd - One of the best experts on this subject based on the ideXlab platform.
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Spatial Sampling of terahertz fields with sub wavelength accuracy via probe beam encoding
Light-Science & Applications, 2019Co-Authors: Jiapeng Zhao, X.-c. Zhang, E Yiwen, Kaia Williams, Robert W BoydAbstract:Recently, computational Sampling methods have been implemented to Spatially characterize terahertz (THz) fields. Previous methods usually rely on either specialized THz devices such as THz Spatial light modulators or complicated systems requiring assistance from photon-excited free carriers with high-speed synchronization among multiple optical beams. Here, by Spatially encoding an 800-nm near-infrared (NIR) probe beam through the use of an optical SLM, we demonstrate a simple Sampling approach that can probe THz fields with a single-pixel camera. This design does not require any dedicated THz devices, semiconductors or nanofilms to modulate THz fields. Using computational algorithms, we successfully measure 128 × 128 field distributions with a 62-μm transverse Spatial resolution, which is 15 times smaller than the central wavelength of the THz signal (940 μm). Benefitting from the non-invasive nature of THz radiation and sub-wavelength resolution of our system, this simple approach can be used in applications such as biomedical sensing, inspection of flaws in industrial products, and so on. A convenient and practical scheme for Spatially characterizing terahertz (THz) signals has been invented by scientists in the US. The approach could prove useful for applications such as beam profiling, biomedical sensing, flaw detection and security screening. Jiapeng Zhao and coworkers from the University of Rochester make use of the electrooptic effect in a ZnTe crystal to indirectly measure the Spatial profile of a THz signal with a resolution of λ/15 (62 microns), 128 × 128 sample points and kilohertz Sampling speed. The approach relies on the fact that when a THz signal interacts with the ZnTe crystal it induces birefringence, which modifies the polarization of a near-infrared (NIR) probe beam. Spatially-modulating the NIR probe and measuring its polarization rotation via a single-pixel detector thus makes it possible to determine the profile of the THz beam.
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Spatial Sampling of terahertz fields with sub wavelength accuracy via probe beam encoding
arXiv: Optics, 2019Co-Authors: Jiapeng Zhao, X.-c. Zhang, E Yiwen, Kaia Williams, Robert W BoydAbstract:Recently, computational Sampling methods have been implemented to Spatially characterize terahertz (THz) fields. Previous methods usually rely on either specialized THz devices such as THz Spatial light modulators, or complicated systems requiring assistance from photon-excited free-carriers with high-speed synchronization among multiple optical beams. Here, by Spatially encoding an 800 nm near-infrared (NIR) probe beam through the use of an optical SLM, we demonstrate a simple Sampling approach that can probe THz fields with a single-pixel camera. This design does not require any dedicated THz devices, semiconductors or nanofilms to modulate THz fields. Through the use of computational algorithms, we successfully measure 128$\times$128 field distributions with a 62 $\mu m$ transverse Spatial resolution, more than 15 times smaller than the central wavelength of the THz signal (940 $\mu m$). Benefitting from the non-invasive nature of THz radiation and sub-wavelength resolution of our system, this simple approach can be used in applications such as biomedical sensing, inspection of flaws. in industrial products, and so on.
Bradley H Shaffer - One of the best experts on this subject based on the ideXlab platform.
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incorporating model complexity and Spatial Sampling bias into ecological niche models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to Spatial Sampling bias. Here, we explore the effects of model complexity and Spatial Sampling bias on niche models for 90 vertebrate taxa of conservation concern.
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incorporating model complexity and Spatial Sampling bias into ecological niche models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to Spatial Sampling bias. Here, we explore the effects of model complexity and Spatial Sampling bias on niche models for 90 vertebrate taxa of conservation concern.
Michael H Cosh - One of the best experts on this subject based on the ideXlab platform.
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estimating Spatial Sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:Abstract The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to Spatial Sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) Spatial Sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture Sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture produ...
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estimating Spatial Sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (.100 km 2 ) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to Spatial Sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) Spatial Sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-tofootprint soil moisture Sampling errors to within 0.0059 m 3 m 23 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
Dan L Warren - One of the best experts on this subject based on the ideXlab platform.
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incorporating model complexity and Spatial Sampling bias into ecological niche models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to Spatial Sampling bias. Here, we explore the effects of model complexity and Spatial Sampling bias on niche models for 90 vertebrate taxa of conservation concern.
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incorporating model complexity and Spatial Sampling bias into ecological niche models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to Spatial Sampling bias. Here, we explore the effects of model complexity and Spatial Sampling bias on niche models for 90 vertebrate taxa of conservation concern.
Diego G Miralles - One of the best experts on this subject based on the ideXlab platform.
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estimating Spatial Sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:Abstract The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to Spatial Sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) Spatial Sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture Sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture produ...
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estimating Spatial Sampling errors in coarse scale soil moisture estimates derived from point scale observations
Journal of Hydrometeorology, 2010Co-Authors: Diego G Miralles, Wade T Crow, Michael H CoshAbstract:The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (.100 km 2 ) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to Spatial Sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) Spatial Sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-tofootprint soil moisture Sampling errors to within 0.0059 m 3 m 23 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
