Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on ...Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.展开更多
Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.Howe...Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.展开更多
Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intell...Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intelligent approach utilizes predefined class information for supervised learning in order to solve the converting problem and keep the fuzziness and imprecision of the whole sensory information. The method is validated by the experiment on stimulation evaluation of cigarette sensory.展开更多
Objective:To systematically explore the effect and mechanism of traditional Chinese herbs in the treatment of allergic rhinitis.Methods:Magnolia flower,xanthium fruit,astragalus root,paniculate cynanchum,scutellaria r...Objective:To systematically explore the effect and mechanism of traditional Chinese herbs in the treatment of allergic rhinitis.Methods:Magnolia flower,xanthium fruit,astragalus root,paniculate cynanchum,scutellaria root,cicada molting,stephania root,small centipeda herb,and licorice root as traditional Chinese herbs are widely used to treat allergic rhinitis.With multiple database integration and entity grammar systems,a molecular interaction network of traditional Chinese herbs for treating allergic rhinitis was established.Bioinformatics approaches were adopted to integrate relevant data and biological information by multiple databases.Molecular interaction network of Chinese herbs for treating allergic rhinitis was constructed based on entity grammar systems model.The network was then used for elucidating the antiallergic rhinitis mechanism of the Chinese herbs on the molecular level.The subnetworks were also extracted to explicitly display the pathway and targets where effective components acted on.Results:Traditional Chinese herbs could influence various pathological aspects in allergic rhinitis including the production of pro-inflammatory substances,such as histamine and cytokine interleukin-4(IL-4),interleukin-13(IL-13),lowering immunoglobulin E(IgE)level or blocking antigen binding,altering the biological processes of IgE,modulating the balance of T helper(Th)or other cells by cell proliferation and differentiation,mediating cellecell signaling and second-messenger-mediated signaling,stabilizing the cell membrane,and affecting regulation of cellular defense response.Conclusion:The research theoretically confirms the mechanism of anti-allergic rhinitis by traditional Chinese herbs,provides important fundamental research information for treatment of allergic rhinitis,and may serve as a reference for new drug development and effective ingredients of compatibility.展开更多
With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so ...With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.展开更多
Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets wi...Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets with the larger computational load,a method combining MHSS ARAIM with gross error detection is proposed in this paper.The gross error detection method is used to identify and eliminate the gross data in the original data first,then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection.Therefore,this makes up for the weakness of the MHSS ARAIM algorithm.With the data processing and analysis from several international GNSS service(IGS)and international GNSS monitoring and assessment system(iGMAS)stations,the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service(LPV-200)when using GPS and BDS measure data.Under the assumption of a single-faulty satellite,the effective monitoring threshold(EMT)is improved about 22.47%and 9.63%,and the vertical protection level(VPL)is improved about 32.28%and 12.98%for GPS and BDS observations,respectively.Moreover,under the assumption of double-faulty satellites,the EMT is improved about 80.85%and 29.88%,and the VPL is improved about 49.66%and 18.24%for GPS and BDS observations,respectively.展开更多
With melon Jiningqing 1 as the test material, the control effects of 20% difenoconazole EC and 15% prochloraz EC against melon anthracnose in the initial stage of incidence were studied in the paper. The results showe...With melon Jiningqing 1 as the test material, the control effects of 20% difenoconazole EC and 15% prochloraz EC against melon anthracnose in the initial stage of incidence were studied in the paper. The results showed that when 20% difenoconazole EC and 15% prochloraz EC were sprayed before incidence and in the initial stage of incidence, their control effects against melon anthracnose were greater than 90% and 80%, respectively, and the control effect of 25% pro- chloraz EC was greater than 60%. Therefore, 20% difenoconazole EC and 15% prochloraz EC could be used as the effective reagents to control melan anthracnose.展开更多
This paper describes a new approach to regulate the photoelectric properties of two-dimensional SiC materials.The first-principles pseudo-potential plane wave method is used to calculate the geometric structure,electr...This paper describes a new approach to regulate the photoelectric properties of two-dimensional SiC materials.The first-principles pseudo-potential plane wave method is used to calculate the geometric structure,electronic structure and optical properties of two-dimensional(2D)SiC co-doped by the adjacent elements of C-Si(such as B and N).The results show that:after B-N co-doping,the supercell lattices of 2D SiC are observed obviously deformation near the doped atoms.Meanwhile,the band structures of 2D SiC co-doped by B-N become rich.As the impurity level enters the forbidden band,the band gap decreases,and the distribution of density of states near the Fermi level changes accordingly.The calculation of optical properties shows that the ability to absorb electromagnetic waves of 2D SiC has been enhanced obviously in the low energy range after B-N co-doping.The reason is originated from the transition of the 2p state of B and N.At the same time,the static dielectric constant increases and the peak of reflectivity decreases.The above results indicate that the optoelectronic properties of 2D SiC can be modulated by co-doping B-N.展开更多
Recent change detection(CD)methods focus on the extraction of deep change semantic features.However,existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time infor...Recent change detection(CD)methods focus on the extraction of deep change semantic features.However,existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information,which leads to the micro changes missing and the edges of change types smoothing.In this paper,a potential transformer-based semantic change detection(SCD)model,Pyramid-SCDFormer is proposed,which precisely recognizes the small changes and fine edges details of the changes.The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features,which is crucial for extraction information of remote sensing images(RSIs)with multiple changes from different scales.Moreover,we create a well-annotated SCD dataset,Landsat-SCD with unprecedented time series and change types in complex scenarios.Comparing with three Convolutional Neural Network-based,one attention-based,and two transformer-based networks,experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1.11/0.76%,0.57/0.50%,and 8.75/8.59%on the LEVIR-CD,WHU_CD,and Landsat-SCD dataset respectively.For change classes proportion less than 1%,the proposed model improves the MIoU by 7.17–19.53%on Landsat-SCD dataset.The recognition performance for small-scale and fine edges of change types has greatly improved.展开更多
Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effecti...Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.展开更多
We demonstrate the subsurface imaging of an articular cartilage using Fourier-domain common-path optical coherence tomography. The bare fiber probe integrated with a hypodermic needle provides the rigidness required t...We demonstrate the subsurface imaging of an articular cartilage using Fourier-domain common-path optical coherence tomography. The bare fiber probe integrated with a hypodermic needle provides the rigidness required to perform lateral scanning with less microscale bending. By submerging both the probe and the specimen into saline solution, we not only reduce the beam divergence, but also increase the signal-to-noise ratio compared with the measurement in free space. Our system can differentiate the characteristic cartilage zones and identity various micro-structured defects in an ex vivo chicken knee cartilage, thus demonstrating that it could be used to conduct early arthritis diagnosis and intraoperative endo-microscopy.展开更多
基金the National Natural Science Foundation of China(No.62271466)the Natural Science Foundation of Beijing(No.4202025)+1 种基金the Tianjin IoT Technology Enterprise Key Laboratory Research Project(No.VTJ-OT20230209-2)the Guizhou Provincial Sci-Tech Project(No.ZK[2022]-012)。
文摘Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.
基金supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps,under grants No.2020DB005 and No.2017DB005supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions fund.
文摘Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.
文摘Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intelligent approach utilizes predefined class information for supervised learning in order to solve the converting problem and keep the fuzziness and imprecision of the whole sensory information. The method is validated by the experiment on stimulation evaluation of cigarette sensory.
基金This study was supported by the National Natural Science Foundation of China(81373985,and 81430094).
文摘Objective:To systematically explore the effect and mechanism of traditional Chinese herbs in the treatment of allergic rhinitis.Methods:Magnolia flower,xanthium fruit,astragalus root,paniculate cynanchum,scutellaria root,cicada molting,stephania root,small centipeda herb,and licorice root as traditional Chinese herbs are widely used to treat allergic rhinitis.With multiple database integration and entity grammar systems,a molecular interaction network of traditional Chinese herbs for treating allergic rhinitis was established.Bioinformatics approaches were adopted to integrate relevant data and biological information by multiple databases.Molecular interaction network of Chinese herbs for treating allergic rhinitis was constructed based on entity grammar systems model.The network was then used for elucidating the antiallergic rhinitis mechanism of the Chinese herbs on the molecular level.The subnetworks were also extracted to explicitly display the pathway and targets where effective components acted on.Results:Traditional Chinese herbs could influence various pathological aspects in allergic rhinitis including the production of pro-inflammatory substances,such as histamine and cytokine interleukin-4(IL-4),interleukin-13(IL-13),lowering immunoglobulin E(IgE)level or blocking antigen binding,altering the biological processes of IgE,modulating the balance of T helper(Th)or other cells by cell proliferation and differentiation,mediating cellecell signaling and second-messenger-mediated signaling,stabilizing the cell membrane,and affecting regulation of cellular defense response.Conclusion:The research theoretically confirms the mechanism of anti-allergic rhinitis by traditional Chinese herbs,provides important fundamental research information for treatment of allergic rhinitis,and may serve as a reference for new drug development and effective ingredients of compatibility.
基金This research was supported by the National Natural Science Foundation of China under Grant(No.42050102)the Postgraduate Education Reform Project of Jiangsu Province under Grant(No.SJCX22_0343)Also,this research was supported by Dou Wanchun Expert Workstation of Yunnan Province(No.202205AF150013).
文摘With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.
基金National Natural Science Foundation of China(No.4130403341504006+2 种基金41604001)The Grand Projects of the Beidou-2 System(No.GFZX0301040308)The Foundation of State Key Laboratory of Geo-information Engineering(No.SKLGIE2017-Z-2-1)。
文摘Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring(MHSS ARAIM)algorithm,such as the weaker robustness,a number of computational subsets with the larger computational load,a method combining MHSS ARAIM with gross error detection is proposed in this paper.The gross error detection method is used to identify and eliminate the gross data in the original data first,then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection.Therefore,this makes up for the weakness of the MHSS ARAIM algorithm.With the data processing and analysis from several international GNSS service(IGS)and international GNSS monitoring and assessment system(iGMAS)stations,the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service(LPV-200)when using GPS and BDS measure data.Under the assumption of a single-faulty satellite,the effective monitoring threshold(EMT)is improved about 22.47%and 9.63%,and the vertical protection level(VPL)is improved about 32.28%and 12.98%for GPS and BDS observations,respectively.Moreover,under the assumption of double-faulty satellites,the EMT is improved about 80.85%and 29.88%,and the VPL is improved about 49.66%and 18.24%for GPS and BDS observations,respectively.
文摘With melon Jiningqing 1 as the test material, the control effects of 20% difenoconazole EC and 15% prochloraz EC against melon anthracnose in the initial stage of incidence were studied in the paper. The results showed that when 20% difenoconazole EC and 15% prochloraz EC were sprayed before incidence and in the initial stage of incidence, their control effects against melon anthracnose were greater than 90% and 80%, respectively, and the control effect of 25% pro- chloraz EC was greater than 60%. Therefore, 20% difenoconazole EC and 15% prochloraz EC could be used as the effective reagents to control melan anthracnose.
基金the Natural Science Foundation of Guizhou Province of China(No.[2015]2001)the Innovation Team of Anshun University(No.2015PT02)+1 种基金the Doctoral Fund of Anshun University(No.Asxybsjj201503)the Discipline Platform of Anshun University(No.Asxyxkpt201803).
文摘This paper describes a new approach to regulate the photoelectric properties of two-dimensional SiC materials.The first-principles pseudo-potential plane wave method is used to calculate the geometric structure,electronic structure and optical properties of two-dimensional(2D)SiC co-doped by the adjacent elements of C-Si(such as B and N).The results show that:after B-N co-doping,the supercell lattices of 2D SiC are observed obviously deformation near the doped atoms.Meanwhile,the band structures of 2D SiC co-doped by B-N become rich.As the impurity level enters the forbidden band,the band gap decreases,and the distribution of density of states near the Fermi level changes accordingly.The calculation of optical properties shows that the ability to absorb electromagnetic waves of 2D SiC has been enhanced obviously in the low energy range after B-N co-doping.The reason is originated from the transition of the 2p state of B and N.At the same time,the static dielectric constant increases and the peak of reflectivity decreases.The above results indicate that the optoelectronic properties of 2D SiC can be modulated by co-doping B-N.
基金supported by National Key Research and Development Program of China[Grant number 2017YFB0504203]Xinjiang Production and Construction Corps Science and Technology Project:[Grant number 2017DB005].
文摘Recent change detection(CD)methods focus on the extraction of deep change semantic features.However,existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information,which leads to the micro changes missing and the edges of change types smoothing.In this paper,a potential transformer-based semantic change detection(SCD)model,Pyramid-SCDFormer is proposed,which precisely recognizes the small changes and fine edges details of the changes.The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features,which is crucial for extraction information of remote sensing images(RSIs)with multiple changes from different scales.Moreover,we create a well-annotated SCD dataset,Landsat-SCD with unprecedented time series and change types in complex scenarios.Comparing with three Convolutional Neural Network-based,one attention-based,and two transformer-based networks,experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1.11/0.76%,0.57/0.50%,and 8.75/8.59%on the LEVIR-CD,WHU_CD,and Landsat-SCD dataset respectively.For change classes proportion less than 1%,the proposed model improves the MIoU by 7.17–19.53%on Landsat-SCD dataset.The recognition performance for small-scale and fine edges of change types has greatly improved.
基金Key Program of the National Natural Science Foundation of China (Grant No. U1301253)Guangdong Provincial Science and Technology Project (Nos. 2017A050501031 and2017A040406022)+1 种基金Guangzhou Science and Technology Projects (Nos. 201807010048 and 201804020034)the International Postdoctoral Exchange Fellowship Program 2017 (No. 20170029). The authors would like to express their thanks to European Space Agency for providing Sentinel-1 SAR data as well as ESA-SNAP software in conducting research, our colleagues Haiyan Deng and Li Zhao for their assistance in collecting field validation, and processing images, and the colleagues from the Guangzhou Urban Renewal Bureau for their good suggestions. We also would like to thank the editors and anonymous reviewers for their instructive comments.
文摘Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.
基金supported by the second stage of the Brain Korea 21 Project in 2009a Korean Science and Engineering Foundation(KOSEF) grant funded by the Korea Government(MEST)(No.R01-2008-000-20089-0).
文摘We demonstrate the subsurface imaging of an articular cartilage using Fourier-domain common-path optical coherence tomography. The bare fiber probe integrated with a hypodermic needle provides the rigidness required to perform lateral scanning with less microscale bending. By submerging both the probe and the specimen into saline solution, we not only reduce the beam divergence, but also increase the signal-to-noise ratio compared with the measurement in free space. Our system can differentiate the characteristic cartilage zones and identity various micro-structured defects in an ex vivo chicken knee cartilage, thus demonstrating that it could be used to conduct early arthritis diagnosis and intraoperative endo-microscopy.
