The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed....The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed.However,this progress has also led to security concerns related to the transmission of confidential data.Nevertheless,safeguarding these data during communication through insecure channels is crucial for obvious reasons.The emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation learning.According to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification direction.Through simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual quality.During the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.展开更多
Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such a...Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental monitoring.However,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell analysis.In this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image processing.Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and analyzed.The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples.This work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.展开更多
文摘The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed.However,this progress has also led to security concerns related to the transmission of confidential data.Nevertheless,safeguarding these data during communication through insecure channels is crucial for obvious reasons.The emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation learning.According to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification direction.Through simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual quality.During the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.
基金supported by the National Key R&D Program of China(2023YFF0723300)National Natural Science Foundation of China(62475198,62075200,12374295)+8 种基金Fundamental Research Funds for the Central Universities(2042024kf0003,2042024kf1010,2042023kf0105)Hubei Provincial Natural Science Foundation of China(2023AFB133)Jiangsu Science and Technology Program(BK20221257)Shenzhen Science and Technology Program(JCYJ20220530140601003,JCYJ20230807090207014)Translational Medicine and Multidisciplinary Research Project of Zhongnan Hospital of Wuhan University(ZNJC202217,ZNJC202232)The Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University(JCRCYR-2022-006)Hubei Province Young Science and Technology Talent Morning Hight Lift Project(202319)The Fund of National Key Laboratory of Plasma Physics(6142A04230201)We gratefully acknowledge Serendipity Lab for facilitating collaboration opportunities.
文摘Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental monitoring.However,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell analysis.In this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image processing.Cells flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and analyzed.The capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal samples.This work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.
基金supported by National Spark Program(No.2011GA780037)Science and Technology Planning Project of Guangdong Province,China(No.2010A0507001-144)+2 种基金Natural Science Foundation of China(No.30871450)the earmarked fund for China Agriculture Research System(CARS-27)Special Fund for Agro-scientific Research in the Public Interest of China(No.201203016)
