6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is...6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.展开更多
Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management.However,traditional rainfall measurement methods face limitations reg...Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management.However,traditional rainfall measurement methods face limitations regarding spatial coverage,temporal resolution,and data accessibility,particularly in urban settings.Here,we show a novel rainfall estimation framework that leverages surveillance cameras to enhance estimation accuracy and spatiotemporal data coverage.Our hybrid approach consists of two complementary modules:the first employs an image-quality signature technique to detect rain streaks from video frames and selects optimal regions of interest(ROIs).The second module integrates depthwise separable convolution(DSC)layers with gated recurrent units(GRU)in a regression model to accurately estimate rainfall intensity using these ROIs.We evaluate the framework using video data from two locations with distinct rainfall patterns and environmental conditions.The DSCeGRU model achieves high predictive performance,with coefficient of determination(R^(2))values ranging from 0.89 to 0.93 when validated against rain gauge measurements.Remarkably,the model maintains strong performance during daytime and nighttime conditions,outperforming existing video-based rainfall estimation methods and demonstrating robust adaptability across variable environmental scenarios.The model's lightweight architecture facilitates efficient training and deployment,enabling practical realtime urban rainfall monitoring.This work represents a substantial advancement in rainfall estimation technology,significantly reducing estimation errors and expanding measurement coverage,and provides a practical,low-cost solution for urban hydrological monitoring.展开更多
基金supported by the Inner Mongolia Natural Science Fund Project(2019MS06013)Ordos Science and Technology Plan Project(2022YY041)Hunan Enterprise Science and Technology Commissioner Program(2021GK5042).
文摘6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.
基金supported by the National Key R&D Planof China(Grant No.2021YFC3001400)。
文摘Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management.However,traditional rainfall measurement methods face limitations regarding spatial coverage,temporal resolution,and data accessibility,particularly in urban settings.Here,we show a novel rainfall estimation framework that leverages surveillance cameras to enhance estimation accuracy and spatiotemporal data coverage.Our hybrid approach consists of two complementary modules:the first employs an image-quality signature technique to detect rain streaks from video frames and selects optimal regions of interest(ROIs).The second module integrates depthwise separable convolution(DSC)layers with gated recurrent units(GRU)in a regression model to accurately estimate rainfall intensity using these ROIs.We evaluate the framework using video data from two locations with distinct rainfall patterns and environmental conditions.The DSCeGRU model achieves high predictive performance,with coefficient of determination(R^(2))values ranging from 0.89 to 0.93 when validated against rain gauge measurements.Remarkably,the model maintains strong performance during daytime and nighttime conditions,outperforming existing video-based rainfall estimation methods and demonstrating robust adaptability across variable environmental scenarios.The model's lightweight architecture facilitates efficient training and deployment,enabling practical realtime urban rainfall monitoring.This work represents a substantial advancement in rainfall estimation technology,significantly reducing estimation errors and expanding measurement coverage,and provides a practical,low-cost solution for urban hydrological monitoring.
