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
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
Spatio-spectral selectivity,the capability to select a single mode with a specific wavevector(angle)and wavelength,is imperative for light emission and imaging.Continuous band dispersion of a conventional periodic str...Spatio-spectral selectivity,the capability to select a single mode with a specific wavevector(angle)and wavelength,is imperative for light emission and imaging.Continuous band dispersion of a conventional periodic structure,however,sets up an intrinsic locking between wavevectors and wavelengths of photonic modes,making it difficult to single out just one mode.Here,we show that the radiation asymmetry of a photonic mode can be explored to tailor the transmission/reflection properties of a photonic structure,based on Fano interferences between the mode and the background.In particular,we find that a photonic system supporting a band dispersion with certain angledependent radiation-directionality can exhibit Fano-like perfect reflection at a single frequency and a single incident angle,thus overcoming the dispersion locking and enabling the desired spatio-spectral selectivity.We present a phase diagram to guide designing angle-controlled radiation-directionality and experimentally demonstrate double narrow Fano-like reflection in angular(±5°)and wavelength(14 nm)bandwidths,along with high-contrast spatiospectral selective imaging,using a misaligned bilayer metagrating with tens-of-nanometer-scale thin spacer.Our scheme promises new opportunities in applications in directional thermal emission,nonlocal beam shaping,augmented reality,precision bilayer nanofabrication,and biological spectroscopy.展开更多
为了精准捕获和有效应对跨受试者运动想像脑电信号(MI-EEG)的多维度特征差异,从高维脑源空间对域适应(DA)方法进行研究,提出一种基于时频变换和黎曼流行的域适应网络(TFRMDANet)。借助脑源成像技术重构神经电活动,计算分区等效皮层偶极...为了精准捕获和有效应对跨受试者运动想像脑电信号(MI-EEG)的多维度特征差异,从高维脑源空间对域适应(DA)方法进行研究,提出一种基于时频变换和黎曼流行的域适应网络(TFRMDANet)。借助脑源成像技术重构神经电活动,计算分区等效皮层偶极子,并结合小波变换构建多子带时频特征数据;设计两级基于压缩-激励模块的多分支时-频-空特征提取器,依次提取各子带特征数据的局部特征和跨尺度全局特征,并引入通道注意力与多尺度综合特征增强机制;利用基于黎曼流形嵌入的结构特征提取器提取高阶结构特征;通过对抗训练学习域不变特征。在公共数据集BCI Competition IV dataset 2a和High-Gamma上进行实验,TFRMDANet分别取得77.82%和90.47%的分类准确率、0.704和0.826的Kappa值以及0.780和0.905的F1值。结果表明,皮层偶极子具有精确的MI时频特征表征能力,且与所提网络特有的多分支结构和强大的时-频-空-结构特征提取能力相匹配,使从脑源空间提升域适应效果成为可能。展开更多
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.
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National Natural Science Foundation of China grant 62035016National Key Research Development Program of China grant 2023YFB2806800+5 种基金National Key Research Development Program of China grant 2022YFA1404304National Key Research Development Program of China grant 2022YFA1404700National Natural Science Foundation of China grant 12221004National Natural Science Foundation of China grant 62192771Guangdong Basic and Applied Basic Research Foundation grant 2023B1515040023Natural Science Foundation of Shanghai grant 23dz2260100.
文摘Spatio-spectral selectivity,the capability to select a single mode with a specific wavevector(angle)and wavelength,is imperative for light emission and imaging.Continuous band dispersion of a conventional periodic structure,however,sets up an intrinsic locking between wavevectors and wavelengths of photonic modes,making it difficult to single out just one mode.Here,we show that the radiation asymmetry of a photonic mode can be explored to tailor the transmission/reflection properties of a photonic structure,based on Fano interferences between the mode and the background.In particular,we find that a photonic system supporting a band dispersion with certain angledependent radiation-directionality can exhibit Fano-like perfect reflection at a single frequency and a single incident angle,thus overcoming the dispersion locking and enabling the desired spatio-spectral selectivity.We present a phase diagram to guide designing angle-controlled radiation-directionality and experimentally demonstrate double narrow Fano-like reflection in angular(±5°)and wavelength(14 nm)bandwidths,along with high-contrast spatiospectral selective imaging,using a misaligned bilayer metagrating with tens-of-nanometer-scale thin spacer.Our scheme promises new opportunities in applications in directional thermal emission,nonlocal beam shaping,augmented reality,precision bilayer nanofabrication,and biological spectroscopy.
文摘为了精准捕获和有效应对跨受试者运动想像脑电信号(MI-EEG)的多维度特征差异,从高维脑源空间对域适应(DA)方法进行研究,提出一种基于时频变换和黎曼流行的域适应网络(TFRMDANet)。借助脑源成像技术重构神经电活动,计算分区等效皮层偶极子,并结合小波变换构建多子带时频特征数据;设计两级基于压缩-激励模块的多分支时-频-空特征提取器,依次提取各子带特征数据的局部特征和跨尺度全局特征,并引入通道注意力与多尺度综合特征增强机制;利用基于黎曼流形嵌入的结构特征提取器提取高阶结构特征;通过对抗训练学习域不变特征。在公共数据集BCI Competition IV dataset 2a和High-Gamma上进行实验,TFRMDANet分别取得77.82%和90.47%的分类准确率、0.704和0.826的Kappa值以及0.780和0.905的F1值。结果表明,皮层偶极子具有精确的MI时频特征表征能力,且与所提网络特有的多分支结构和强大的时-频-空-结构特征提取能力相匹配,使从脑源空间提升域适应效果成为可能。
