A new matting algorithm based on color distance and differential distance is proposed to deal with the problem that many matting methods perform poorly with complex natural images.The proposed method combines local sa...A new matting algorithm based on color distance and differential distance is proposed to deal with the problem that many matting methods perform poorly with complex natural images.The proposed method combines local sampling with global sampling to select foreground and background pairs for unknown pixels and then a new cost function is constructed based on color distance and differential distance to further optimize the selected sample pairs.Finally,a quadratic objective function is used based on matte Laplacian coming from KNN matting which is added with texture feature.Through experiments on various test images,it is confirmed that the results obtained by the proposed method are more accurate than those obtained by traditional methods.The four-error-metrics comparison on benchmark dataset among several algorithms also proves the effectiveness of the proposed method.展开更多
Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for co...Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for complex texture distribution.In order to extract the nature images with complex texture distribution,we design an information entropy approach to estimate the scalable variance.Secondly,when the opacity is near the boundary of the value range,Bayesian matting method may be failure because of the error computation of opacity.Therefore,a rectification approach is proposed to adjust the computation model and keep the opacity within the valid value range.Thirdly,Bayesian matting is a local sample method which may miss some valid samples of matting.We propose a selection function to integrate valid global sample matting result into above matting framework as a supplement to the local sample matting result.The proposed function is compose of three criteria,that is,the similarity of results,the overlapping degree of samples,and the similarity of neighborhood.Fourthly,in order to obtain a smooth and vivid matte,the result is further refined by considering correlation between neighbouring pixels.Finally,We use online benchmark for image matting to evaluate the proposed method with both qualitative observation and quantitative analysis.The experiments show that our method achieves a competitive advantages over other methods.展开更多
This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several...This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.展开更多
Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask （Trimap）. This step has no r...Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask （Trimap）. This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories： static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.展开更多
We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human images.We utilize the intrinsic structures of the human body as seen in images ...We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human images.We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory costs.Our model can process real images downloaded from the Internet for use in composition applications.展开更多
Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecti...Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs.It is essentially a large-scale multi-peak optimization problem of pixel pairs.Previous study shows that particle swarm optimization(PSO)can effectively optimize the pixel pairs.However,it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima.To address this problem,this work presents a parameter-free strategy for PSO called adaptive convergence speed controller(ACSC).ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator(CPPRO)and pixel pair reset operator(PPRO)during the iteration.ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution.PPRO is performed to avoid premature convergence when the alpha mattes regarding two selecleu particles ae liglly siimilau.Expeiilfental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.展开更多
Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods ...Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.展开更多
Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, ...Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.展开更多
This paper presents a multi-task gradual inference model,MTGINet,for automatic portrait matting.It handles the subtasks of automatic portrait matting,namely portrait–transition–background trimap segmentation and tra...This paper presents a multi-task gradual inference model,MTGINet,for automatic portrait matting.It handles the subtasks of automatic portrait matting,namely portrait–transition–background trimap segmentation and transition region matting,with a single encoder–decoder structure.First,we enrich the highest stage of features from the encoder with portrait shape context via a shape context aggregation(SCA)module for trimap segmentation.Then,we fuse the SCA-enhanced features with detailed clues from the encoder for transition-region-aware alpha matting.The gradual inference model naturally allows sufficient interaction between the subtasks via forward computation and backwards propagation during training,and therefore achieves high accuracy while maintaining low complexity.In addition,considering the discrepancies in feature requirements across subtasks,we adapt the features from the encoders before reusing them via a feature rectification module.In addition to the MTGINet model,we have constructed a new large-scale dataset,HPM-17K,for half-body portrait matting.It consists of 16,967 images with diverse backgrounds.Comparative experiments with existing deep models on the public P3M-10K dataset and our HPM-17K dataset demonstrate that the proposed model exhibits state-of-the-art performance.展开更多
无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散...无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散射簇生成算法,即通过把Matérn硬核点过程和泊松簇过程相结合来模拟真实V2X信道中的障碍物。在算法中,依据真实环境障碍物的方位设置散射簇的坐标位置,根据周围障碍物密度合理设置簇内散射点数量。利用传播图论进行仿真,考虑直射路径和单跳散射路径,基于信道冲激响应(channel impulse response,CIR)分别研究了功率延迟分布(power delay profile,PDP)和多普勒功率谱密度(Doppler power spectrum density,DPSD),并分析了不同移动轨迹下的均方根(root mean square,RMS)时延扩展的累计分布函数(cumulative distribution function,CDF),以及莱斯K因子的分布特性和角度功率谱(power angular spectrum,PAS)的分布。本文研究验证得到,所提出的模型有助于分析车辆-基础设施(vehicle to infrastructure,V2I)通信场景下的时域非平稳特性,为V2X通信系统的设计和优化提供了重要参考。展开更多
Ultra-wide absorption band and flexibility are needed in multi-scenario applications,however,current electromagnetic wave absorption materials(EMWAMs)are not capable enough to deliver due to rigid structure.Here,we ha...Ultra-wide absorption band and flexibility are needed in multi-scenario applications,however,current electromagnetic wave absorption materials(EMWAMs)are not capable enough to deliver due to rigid structure.Here,we have designed a porous flexible mat composed of Zn-doped carbon(Zn@C)nanofibers(NFs)having encapsulated uniformly dispersed FeCo nanoparticles(NPs)(FeCo/Zn@C)as ultra-wideband absorber.During the electrospinning,the Fe^(3+),Co^(2+)and Zn^(2+)are uniformly immobilized within the NFs nanocrystallization process.Subsequently,the Kirkendall effect is deployed to trigger the generation of FeCo NPs and porous framework under thermal annealing.The FeCo/Zn@C NFs effectively favor magnetic-dielectric synergies due to the coexistence of magnetic FeCo NPs and dielectric carbon components.One-dimensional porous fiber prolongs the attenuation path and enhances multi-scattering and reflection.While the FeCo NPs encapsulated in Zn-doped carbon NFs provide abundant dipole and interfacial polarization.These favorable factors synergistically enhance absorption performance,resulting in a reflection loss value of-71.58 dB.Moreover,by varying the thickness of absorbers,effective absorption bandwidth spans from 4.26 to 18.00 GHz.Hence,this work offers innovative insights for fabricating advanced EMWAMs.展开更多
Wenlan FENG,Pierre MARIOTTE,Jun GU,XiaodongSONG,JinlingYANG,Fei YANG,Yuguo ZHAOand Ganlin ZHANG In the fourth and fifth lines of the study area section on Page 903,the mean annual temperature(MAT)and precipitation(MAP...Wenlan FENG,Pierre MARIOTTE,Jun GU,XiaodongSONG,JinlingYANG,Fei YANG,Yuguo ZHAOand Ganlin ZHANG In the fourth and fifth lines of the study area section on Page 903,the mean annual temperature(MAT)and precipitation(MAP)values are incorrect.They should be—17 to 24.2°C and 18.3 to 3155 mm,respectively.展开更多
基金Supported by the National Natural Science Foundation of China(No.61133009,U1304616)
文摘A new matting algorithm based on color distance and differential distance is proposed to deal with the problem that many matting methods perform poorly with complex natural images.The proposed method combines local sampling with global sampling to select foreground and background pairs for unknown pixels and then a new cost function is constructed based on color distance and differential distance to further optimize the selected sample pairs.Finally,a quadratic objective function is used based on matte Laplacian coming from KNN matting which is added with texture feature.Through experiments on various test images,it is confirmed that the results obtained by the proposed method are more accurate than those obtained by traditional methods.The four-error-metrics comparison on benchmark dataset among several algorithms also proves the effectiveness of the proposed method.
文摘Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for complex texture distribution.In order to extract the nature images with complex texture distribution,we design an information entropy approach to estimate the scalable variance.Secondly,when the opacity is near the boundary of the value range,Bayesian matting method may be failure because of the error computation of opacity.Therefore,a rectification approach is proposed to adjust the computation model and keep the opacity within the valid value range.Thirdly,Bayesian matting is a local sample method which may miss some valid samples of matting.We propose a selection function to integrate valid global sample matting result into above matting framework as a supplement to the local sample matting result.The proposed function is compose of three criteria,that is,the similarity of results,the overlapping degree of samples,and the similarity of neighborhood.Fourthly,in order to obtain a smooth and vivid matte,the result is further refined by considering correlation between neighbouring pixels.Finally,We use online benchmark for image matting to evaluate the proposed method with both qualitative observation and quantitative analysis.The experiments show that our method achieves a competitive advantages over other methods.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 600330107 Zhejiang Provincial Natural Science Foundation of China under Grant No, Y105324 and Planned Program of Science and Technology Department of Zhejiang Province, China （Grant No. 2006C31065）,
文摘This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.
文摘Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask （Trimap）. This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories： static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.
基金supported by National Natural Science Foundation of China(Grant Nos.61561146393 and61521002)supported by a Victoria Early-Career Research Excellence Award。
文摘We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human images.We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory costs.Our model can process real images downloaded from the Internet for use in composition applications.
基金supported by the National Nat-ural Science Foundation of China(Grant Nos.61772225,61876207,61502088)National Key R&D Program of China(2018YFCO823803,2018YFCO823802)+7 种基金Zhongshan Science and Technology Research Project of Social welfare(2019B2010)Guangdong Natural Science Fundsfor Distinguished Young Scholar(2014A030306050)Guangdong High-level personnel of special support program(2014TQ01X664)International Cooperator Project of Guangzhou(201807010047)National Natural Scicnce Foundation of Guangdong(2018B030311046)Guangdong University Key Platforms and Research Projects(2018KZDXMO66,2017KZDXM081,2015KQNCX153)Guangzhou Science and Technology Projects(201802010007,201804010276)Youth science and technologytalents cultivating object of Guizhou province(Qian education cooperation KY word[2016]165).
文摘Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs.It is essentially a large-scale multi-peak optimization problem of pixel pairs.Previous study shows that particle swarm optimization(PSO)can effectively optimize the pixel pairs.However,it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima.To address this problem,this work presents a parameter-free strategy for PSO called adaptive convergence speed controller(ACSC).ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator(CPPRO)and pixel pair reset operator(PPRO)during the iteration.ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution.PPRO is performed to avoid premature convergence when the alpha mattes regarding two selecleu particles ae liglly siimilau.Expeiilfental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.
基金This work was supported and funded by the Directorate ASR&TD of UET-Taxila.
文摘Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.
基金supported by the National Natural Science Foundation of China under Grant No.62076162the Shanghai Municipal Science and Technology Major Project under Grant Nos.2021SHZDZX0102 and 20511100300.
文摘Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.
基金supported by the National Natural Science Foundation of China(Nos.62176010 and 61771026).
文摘This paper presents a multi-task gradual inference model,MTGINet,for automatic portrait matting.It handles the subtasks of automatic portrait matting,namely portrait–transition–background trimap segmentation and transition region matting,with a single encoder–decoder structure.First,we enrich the highest stage of features from the encoder with portrait shape context via a shape context aggregation(SCA)module for trimap segmentation.Then,we fuse the SCA-enhanced features with detailed clues from the encoder for transition-region-aware alpha matting.The gradual inference model naturally allows sufficient interaction between the subtasks via forward computation and backwards propagation during training,and therefore achieves high accuracy while maintaining low complexity.In addition,considering the discrepancies in feature requirements across subtasks,we adapt the features from the encoders before reusing them via a feature rectification module.In addition to the MTGINet model,we have constructed a new large-scale dataset,HPM-17K,for half-body portrait matting.It consists of 16,967 images with diverse backgrounds.Comparative experiments with existing deep models on the public P3M-10K dataset and our HPM-17K dataset demonstrate that the proposed model exhibits state-of-the-art performance.
文摘无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散射簇生成算法,即通过把Matérn硬核点过程和泊松簇过程相结合来模拟真实V2X信道中的障碍物。在算法中,依据真实环境障碍物的方位设置散射簇的坐标位置,根据周围障碍物密度合理设置簇内散射点数量。利用传播图论进行仿真,考虑直射路径和单跳散射路径,基于信道冲激响应(channel impulse response,CIR)分别研究了功率延迟分布(power delay profile,PDP)和多普勒功率谱密度(Doppler power spectrum density,DPSD),并分析了不同移动轨迹下的均方根(root mean square,RMS)时延扩展的累计分布函数(cumulative distribution function,CDF),以及莱斯K因子的分布特性和角度功率谱(power angular spectrum,PAS)的分布。本文研究验证得到,所提出的模型有助于分析车辆-基础设施(vehicle to infrastructure,V2I)通信场景下的时域非平稳特性,为V2X通信系统的设计和优化提供了重要参考。
基金supported by the National Natural Science Foundation of China(No.51972045).
文摘Ultra-wide absorption band and flexibility are needed in multi-scenario applications,however,current electromagnetic wave absorption materials(EMWAMs)are not capable enough to deliver due to rigid structure.Here,we have designed a porous flexible mat composed of Zn-doped carbon(Zn@C)nanofibers(NFs)having encapsulated uniformly dispersed FeCo nanoparticles(NPs)(FeCo/Zn@C)as ultra-wideband absorber.During the electrospinning,the Fe^(3+),Co^(2+)and Zn^(2+)are uniformly immobilized within the NFs nanocrystallization process.Subsequently,the Kirkendall effect is deployed to trigger the generation of FeCo NPs and porous framework under thermal annealing.The FeCo/Zn@C NFs effectively favor magnetic-dielectric synergies due to the coexistence of magnetic FeCo NPs and dielectric carbon components.One-dimensional porous fiber prolongs the attenuation path and enhances multi-scattering and reflection.While the FeCo NPs encapsulated in Zn-doped carbon NFs provide abundant dipole and interfacial polarization.These favorable factors synergistically enhance absorption performance,resulting in a reflection loss value of-71.58 dB.Moreover,by varying the thickness of absorbers,effective absorption bandwidth spans from 4.26 to 18.00 GHz.Hence,this work offers innovative insights for fabricating advanced EMWAMs.
文摘Wenlan FENG,Pierre MARIOTTE,Jun GU,XiaodongSONG,JinlingYANG,Fei YANG,Yuguo ZHAOand Ganlin ZHANG In the fourth and fifth lines of the study area section on Page 903,the mean annual temperature(MAT)and precipitation(MAP)values are incorrect.They should be—17 to 24.2°C and 18.3 to 3155 mm,respectively.
