The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the r...The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.展开更多
We propose a compensation technique based on pulse reference for intensity-modulated optical fiber sensors that can compensate the power fluctuation of the light source, the change of optical components transmission l...We propose a compensation technique based on pulse reference for intensity-modulated optical fiber sensors that can compensate the power fluctuation of the light source, the change of optical components transmission loss, and the coupler splitting ratio. The theoretical principle of this compensation technique is analyzed and a temperature sensor based on fiber coating-covered optical microfiber is carried out to demonstrate the compensation effect. The system noise is measured to be 0.0005 dB with the temperature sensitivity reaching -0.063 dB/℃, and the output drift is 0.006 dB in 2 h at room temperature. The output shows a slight variation （0.0061 dB） when the light source and the common liKht path suffer a 3 dB attenuation fluctuation.展开更多
Background:Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics,environmental microbiology,a...Background:Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics,environmental microbiology,and biodiversity.As sequencing technologies evolve,this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated.Especially in intensive care units(ICUs),infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients,significantly impacting the effectiveness of treatments and patient prognoses.Therefore,obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections,which enables precisely targeted anti-infection therapies,and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.Methods:We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach.It facilitates the rapid and accurate identification and analysis of pathogens.The tool is designed to be user-friendly and efficient on standard personal computers,allowing its use across a wide variety of settings.We validated MetaGeneMiner using eight metagenomic samples from ICU patients,which demonstrated its efficiency and accuracy.Results:The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes.All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner.Conclusions:It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains,including clinical diagnostics,environmental microbiology,gut microbiome research,as well as biodiversity and conservation biology.Particularly in ICU settings,MetaGeneMiner introduces a novel,rapid,and precise method for diagnosing and treating infections in critically ill patients.This tool is capable of efficiently identifying infectious pathogens,guiding personalized and precise treatment strategies,and monitoring the development of antibiotic resistance,significantly impacting the diagnosis and treatment of severe infections.展开更多
Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as colo...Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as color guidance,particularly when colorizing related characters or an animation sequence.Reference-guided colorization is more intuitive than colorization with other hints,such as color points or scribbles,or text-based hints.Unfortunately,reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading.In this paper,we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image.Our framework contains a color style extractor to extract the color feature from a color image,a colorization network to generate multi-scale output images by combining a sketch and a color feature,and a multi-scale discriminator to improve the reality of the output image.Extensive qualitative and quantitative evaluations show that our method outperforms existing methods,providing both superior visual quality and style reference consistency in the task of reference-based colorization.展开更多
In recent years,the convolutional neural networks(CNNs)for single image super-resolution(SISR)are becoming more and more complex,and it is more challenging to improve the SISR performance.In contrast,the reference ima...In recent years,the convolutional neural networks(CNNs)for single image super-resolution(SISR)are becoming more and more complex,and it is more challenging to improve the SISR performance.In contrast,the reference image guided super-resolution(RefSR)is an effective strategy to boost the SR(super-resolution)performance.In RefSR,the introduced high-resolution(HR)references can facilitate the high-frequency residual prediction process.According to the best of our knowledge,the existing CNN-based RefSR methods treat the features from the references and the low-resolution(LR)input equally by simply concatenating them together.However,the HR references and the LR inputs contribute differently to the final SR results.Therefore,we propose a progressive channel attention network(PCANet)for RefSR.There are two technical contributions in this paper.First,we propose a novel channel attention module(CAM),which estimates the channel weighting parameter by weightedly averaging the spatial features instead of using global averaging.Second,considering that the residual prediction process can be improved when the LR input is enriched with more details,we perform super-resolution progressively,which can take advantage of the reference images in multi-scales.Extensive quantitative and qualitative evaluations on three benchmark datasets,which represent three typical scenarios for RefSR,demonstrate that our method is superior to the state-of-the-art SISR and RefSR methods in terms of PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity).展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51775410)Science Challenge Project of China(Grant No.TZ2018007).
文摘The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.
基金supported by the National Natural Science Foundation of China(Nos.61505258 and 11574397)the Scientific Research Project of the National University of Defense Technology(No.JC15-11-02)
文摘We propose a compensation technique based on pulse reference for intensity-modulated optical fiber sensors that can compensate the power fluctuation of the light source, the change of optical components transmission loss, and the coupler splitting ratio. The theoretical principle of this compensation technique is analyzed and a temperature sensor based on fiber coating-covered optical microfiber is carried out to demonstrate the compensation effect. The system noise is measured to be 0.0005 dB with the temperature sensitivity reaching -0.063 dB/℃, and the output drift is 0.006 dB in 2 h at room temperature. The output shows a slight variation （0.0061 dB） when the light source and the common liKht path suffer a 3 dB attenuation fluctuation.
基金supported by grants from the National Natural Science Foundation of China(Nos.32071666 and 32271552)the Science&Technology Fundamental Resources Investigation Program(No.2022FY101000).
文摘Background:Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics,environmental microbiology,and biodiversity.As sequencing technologies evolve,this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated.Especially in intensive care units(ICUs),infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients,significantly impacting the effectiveness of treatments and patient prognoses.Therefore,obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections,which enables precisely targeted anti-infection therapies,and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.Methods:We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach.It facilitates the rapid and accurate identification and analysis of pathogens.The tool is designed to be user-friendly and efficient on standard personal computers,allowing its use across a wide variety of settings.We validated MetaGeneMiner using eight metagenomic samples from ICU patients,which demonstrated its efficiency and accuracy.Results:The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes.All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner.Conclusions:It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains,including clinical diagnostics,environmental microbiology,gut microbiome research,as well as biodiversity and conservation biology.Particularly in ICU settings,MetaGeneMiner introduces a novel,rapid,and precise method for diagnosing and treating infections in critically ill patients.This tool is capable of efficiently identifying infectious pathogens,guiding personalized and precise treatment strategies,and monitoring the development of antibiotic resistance,significantly impacting the diagnosis and treatment of severe infections.
基金supported in part by a CIHE Institutional Development Grant No.IDG200107the National Natural Science Foundation of China under Grant No.61973221the Natural Science Foundation of Guangdong Province of China under Grant Nos.2018A030313381 and 2019A1515011165.
文摘Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading.During colorization,the artist usually takes an existing cartoon image as color guidance,particularly when colorizing related characters or an animation sequence.Reference-guided colorization is more intuitive than colorization with other hints,such as color points or scribbles,or text-based hints.Unfortunately,reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading.In this paper,we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image.Our framework contains a color style extractor to extract the color feature from a color image,a colorization network to generate multi-scale output images by combining a sketch and a color feature,and a multi-scale discriminator to improve the reality of the output image.Extensive qualitative and quantitative evaluations show that our method outperforms existing methods,providing both superior visual quality and style reference consistency in the task of reference-based colorization.
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos.61672378,61771339,and 61520106002.
文摘In recent years,the convolutional neural networks(CNNs)for single image super-resolution(SISR)are becoming more and more complex,and it is more challenging to improve the SISR performance.In contrast,the reference image guided super-resolution(RefSR)is an effective strategy to boost the SR(super-resolution)performance.In RefSR,the introduced high-resolution(HR)references can facilitate the high-frequency residual prediction process.According to the best of our knowledge,the existing CNN-based RefSR methods treat the features from the references and the low-resolution(LR)input equally by simply concatenating them together.However,the HR references and the LR inputs contribute differently to the final SR results.Therefore,we propose a progressive channel attention network(PCANet)for RefSR.There are two technical contributions in this paper.First,we propose a novel channel attention module(CAM),which estimates the channel weighting parameter by weightedly averaging the spatial features instead of using global averaging.Second,considering that the residual prediction process can be improved when the LR input is enriched with more details,we perform super-resolution progressively,which can take advantage of the reference images in multi-scales.Extensive quantitative and qualitative evaluations on three benchmark datasets,which represent three typical scenarios for RefSR,demonstrate that our method is superior to the state-of-the-art SISR and RefSR methods in terms of PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity).
