Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate qua...Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.展开更多
With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electr...With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.展开更多
One of the earliest indications of diabetes consequence is Diabetic Retinopathy(DR),the main contributor to blindness worldwide.Recent studies have proposed that Exudates(EXs)are the hallmark of DR severity.The presen...One of the earliest indications of diabetes consequence is Diabetic Retinopathy(DR),the main contributor to blindness worldwide.Recent studies have proposed that Exudates(EXs)are the hallmark of DR severity.The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages.An improved Fusion of Histogram-Based Fuzzy C-Means Clustering(FHBFCM)by a New Weight Assignment Scheme(NWAS)and a set of four selected features from stages of pre-processing to evolve the detection method is proposed.The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage.The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red,Green,and Blue(RGB)color information.This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means(FCM).Afterward,the best expression of NWAS is used for the delicate detection stage.According to the experiment results,the proposed method successfully detects EXs on the retinal image datasets of DiaretDB0(Standard Diabetic Retinopathy Database Calibration level 0),DiaretDB1(Standard Diabetic Retinopathy Database Calibration level 1),and STARE(Structured Analysis of the Retina)with accuracy values of 96.12%,97.20%,and 93.22%,respectively.As a result,this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.展开更多
With the advancement of communication technology,a large number of data are constantly transmitted through the internet for various purposes,which are prone to be illegally accessed by third parties.Therefore,securing...With the advancement of communication technology,a large number of data are constantly transmitted through the internet for various purposes,which are prone to be illegally accessed by third parties.Therefore,securing such data is crucial to protect the transmitted information from falling into the wrong hands.Among data protection schemes,Secret Image Sharing is one of the most popular methods.It protects critical messages or data by embedding them in an image and sharing it with some users.Furthermore,it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method.Despite its advantages,this method may produce noise,making the resulting stego file much different from its cover.Moreover,the size of private data that can be embedded is limited.This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data.To recover the cover image,the SS method based on the Chinese remainder theorem is used.The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.展开更多
基金funded by the Key Research and Development Project for Ecological Civilization Construction in Gansu Province(24YFFA010)the Gansu Province Major Science and Technology Project(22ZD6FA005)+2 种基金the Natural Science Foundation of Gansu Province(24JRRA091)the Shanxi Province Basic Research Program(Free Exploration Category)Youth Project(202403021212316)the Science and Technology Innovation Program for Universities in Shanxi Province(2024L327)。
文摘Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.
基金This work was supported by the National natural science foundation of China(No:62071057)Beijing nature fund(No:3182028).The support is gratefully acknowledged.
文摘With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.
基金This research project was financially supported by Mahasarakham University,Thailand.
文摘One of the earliest indications of diabetes consequence is Diabetic Retinopathy(DR),the main contributor to blindness worldwide.Recent studies have proposed that Exudates(EXs)are the hallmark of DR severity.The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages.An improved Fusion of Histogram-Based Fuzzy C-Means Clustering(FHBFCM)by a New Weight Assignment Scheme(NWAS)and a set of four selected features from stages of pre-processing to evolve the detection method is proposed.The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage.The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red,Green,and Blue(RGB)color information.This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means(FCM).Afterward,the best expression of NWAS is used for the delicate detection stage.According to the experiment results,the proposed method successfully detects EXs on the retinal image datasets of DiaretDB0(Standard Diabetic Retinopathy Database Calibration level 0),DiaretDB1(Standard Diabetic Retinopathy Database Calibration level 1),and STARE(Structured Analysis of the Retina)with accuracy values of 96.12%,97.20%,and 93.22%,respectively.As a result,this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.
基金This research was supported by the Ministry of Education,Culture,Research and Technology,The Republic of Indonesia,Institut Teknologi Sepuluh Nopember,and Universitas 17 Agustus 1945 Surabaya.
文摘With the advancement of communication technology,a large number of data are constantly transmitted through the internet for various purposes,which are prone to be illegally accessed by third parties.Therefore,securing such data is crucial to protect the transmitted information from falling into the wrong hands.Among data protection schemes,Secret Image Sharing is one of the most popular methods.It protects critical messages or data by embedding them in an image and sharing it with some users.Furthermore,it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method.Despite its advantages,this method may produce noise,making the resulting stego file much different from its cover.Moreover,the size of private data that can be embedded is limited.This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data.To recover the cover image,the SS method based on the Chinese remainder theorem is used.The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.
