This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of...This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a <i>detached</i> brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce <i>beta buzz</i>. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.展开更多
Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral het...Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images,especially on both multispectral and hyperspectral airborne images.In this study,four airborne images,Airborne Thematic Mapper,Compact Airborne Spectrographic Imager,Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood,UK,were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling.Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength.Within the visible,near-infrared spectra and short wave infrared spectra,greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra.There are dramatic changes across the red and red edge spectra,and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively.In all,for real multisensor airborne images,the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength.Besides,if with close spatial resolution,the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved.A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.展开更多
Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolu...Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.展开更多
文摘This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a <i>detached</i> brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce <i>beta buzz</i>. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.
基金This research is funded by Chinese National Natural Science Foundation(Grant No.41071267)Scientific Research Foundation for Returned Scholars,Ministry of Education of China([2012]940)+1 种基金the Science&technology department of Fujian province of China(Grant Nos.2012I0005,2012J01167)The authors would like to thank the Natural Environment Research Council of UK for the provision of the airborne remote sensing data.Part of the work for this study was carried out while Qiu Bingwen was a Visiting Scholar at the Department of Geography,University of Cambridge,England.The authors would like to acknowledge the advice of Robert Haining during her visit and to thank Ben Taylor and Gabriel Amable who kindly offered help in processing these datasets.
文摘Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images,especially on both multispectral and hyperspectral airborne images.In this study,four airborne images,Airborne Thematic Mapper,Compact Airborne Spectrographic Imager,Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood,UK,were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling.Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength.Within the visible,near-infrared spectra and short wave infrared spectra,greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra.There are dramatic changes across the red and red edge spectra,and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively.In all,for real multisensor airborne images,the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength.Besides,if with close spatial resolution,the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved.A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.
基金This work is supported by the National Key Research and Development Program of China[grant number 2018YFB2100501]the Key Research and Development Program of Yunnan province in China[grant number 2018IB023]+2 种基金the Research Project from the Ministry of Natural Resources of China[grant number 4201⁃⁃240100123]the National Natural Science Foundation of China[grant numbers 41771452,41771454,41890820,and 41901340]the Natural Science Fund of Hubei Province in China[grant number 2018CFA007].
文摘Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.
