Direction-of-arrival(DOA)estimation is an important task in many unmanned aerial vehicle(UAV)applications.However,the complicated electromagnetic wave propagation in urban environments substantially deteriorates the p...Direction-of-arrival(DOA)estimation is an important task in many unmanned aerial vehicle(UAV)applications.However,the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation approaches.To alleviate this,a deep learning based DOA estimation approach is proposed in this paper.Specifically,a complex-valued convolutional neural network(CCNN)is designed to fit the electromagnetic UAV signal with complex envelope better.In the CCNN design,we construct some mapping functions using quantum probabilities,and further analyze some factors which may impact the convergence of complex-valued neural networks.Numerical simulations show that the proposed CCNN converges faster than the real convolutional neural network,and the DOA estimation result is more accurate and robust.展开更多
In this paper,we propose,to our knowledge,a new complex-valued dense atrous neural network(CDANN)for phase-only hologram(POH)generation.The network architecture integrates a complex-valued partial convolution(C-PConv)...In this paper,we propose,to our knowledge,a new complex-valued dense atrous neural network(CDANN)for phase-only hologram(POH)generation.The network architecture integrates a complex-valued partial convolution(C-PConv)module into the down-sampling stages of dual U-Net structures,enhancing computational efficiency through selective channelwise processing.To improve feature extraction,we introduce a novel complex-value dense atrous convolution(DAC)module,which employs four cascaded branches with multi-scale atrous convolutions to capture intricate features while maintaining spatial resolution.Additionally,we integrate a spatial pyramid pooling(SPP)module into the U-Net architecture to encode multi-scale contextual features derived from the DAC module.This hierarchical integration expands the U-Net's receptive field while facilitating cross-layer feature fusion.The proposed method achieves an average peak signal-to-noise ratio(PSNR)of 32.19 d B and an average structural similarity index measure(SSIM)of 0.892 within a running time of 24 ms,outperforming conventional approaches.Experiments confirm significant improvements in both reconstruction quality and computational efficiency,making the CDANN suitable for real-time holographic displays.展开更多
In this paper,we propose a novel complex-valued hierarchical multi-fusion neural network(CHMFNet)for generating highquality holograms.The proposed architecture builds upon a U-Net framework,incorporating a complex-val...In this paper,we propose a novel complex-valued hierarchical multi-fusion neural network(CHMFNet)for generating highquality holograms.The proposed architecture builds upon a U-Net framework,incorporating a complex-valued multi-level perceptron(CMP)module that enhances complex feature representation through optimized convolutional operations and advanced activation functions,enabling effective extraction of intricate holographic patterns.The framework further integrates an innovative complex-valued hierarchical multi-fusion(CHMF)block,which implements multi-scale hierarchical processing and advanced feature fusion through its specialized design.This integration of complex-valued convolution and specialized CHMF design enables superior optical information representation,generating artifact-reduced high-fidelity holograms.The computational results demonstrate the superior performance of the proposed method,achieving an average peak signal-to-noise ratio(PSNR)of 34.11 dB and structural similarity index measure(SSIM)of 0.95,representing significant improvements over conventional approaches.Both numerical simulations and experimental validations confirm CHMFNet's enhanced capability in hologram generation,particularly in terms of detail reproduction accuracy and overall image fidelity.展开更多
文摘Direction-of-arrival(DOA)estimation is an important task in many unmanned aerial vehicle(UAV)applications.However,the complicated electromagnetic wave propagation in urban environments substantially deteriorates the performance of many conventional model-driven DOA estimation approaches.To alleviate this,a deep learning based DOA estimation approach is proposed in this paper.Specifically,a complex-valued convolutional neural network(CCNN)is designed to fit the electromagnetic UAV signal with complex envelope better.In the CCNN design,we construct some mapping functions using quantum probabilities,and further analyze some factors which may impact the convergence of complex-valued neural networks.Numerical simulations show that the proposed CCNN converges faster than the real convolutional neural network,and the DOA estimation result is more accurate and robust.
基金supported by the National Natural Science Foundation of China(No.62265006)the Jiangxi Provincial Department of Science and Technology(Nos.20212BCJL23050 and 20232BAB212017)+4 种基金the Foundation of Jiangxi Educational Commission(No.GJJ200623)the China Postdoctoral Science Foundation(No.2021M691344)the Jiangxi Provincial Postdoctoral Science Foundation of China(No.2020KY16)the Natural Science Foundation of Zhejiang Province(No.LQ22F050005)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.2022YW53)。
文摘In this paper,we propose,to our knowledge,a new complex-valued dense atrous neural network(CDANN)for phase-only hologram(POH)generation.The network architecture integrates a complex-valued partial convolution(C-PConv)module into the down-sampling stages of dual U-Net structures,enhancing computational efficiency through selective channelwise processing.To improve feature extraction,we introduce a novel complex-value dense atrous convolution(DAC)module,which employs four cascaded branches with multi-scale atrous convolutions to capture intricate features while maintaining spatial resolution.Additionally,we integrate a spatial pyramid pooling(SPP)module into the U-Net architecture to encode multi-scale contextual features derived from the DAC module.This hierarchical integration expands the U-Net's receptive field while facilitating cross-layer feature fusion.The proposed method achieves an average peak signal-to-noise ratio(PSNR)of 32.19 d B and an average structural similarity index measure(SSIM)of 0.892 within a running time of 24 ms,outperforming conventional approaches.Experiments confirm significant improvements in both reconstruction quality and computational efficiency,making the CDANN suitable for real-time holographic displays.
基金supported by the National Natural Science Foundation of China(No.62265006)the Jiangxi Provincial Department of Science and Technology(Nos.20212BCJL23050 and 20232BAB212017)+4 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22F050005)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.2022YW53)the Foundation of Jiangxi Educational Commission(No.GJJ200623)the China Postdoctoral Science Foundation(No.2021M691344)the Jiangxi Provincial Postdoctoral Science Foundation of China(No.2020KY16)。
文摘In this paper,we propose a novel complex-valued hierarchical multi-fusion neural network(CHMFNet)for generating highquality holograms.The proposed architecture builds upon a U-Net framework,incorporating a complex-valued multi-level perceptron(CMP)module that enhances complex feature representation through optimized convolutional operations and advanced activation functions,enabling effective extraction of intricate holographic patterns.The framework further integrates an innovative complex-valued hierarchical multi-fusion(CHMF)block,which implements multi-scale hierarchical processing and advanced feature fusion through its specialized design.This integration of complex-valued convolution and specialized CHMF design enables superior optical information representation,generating artifact-reduced high-fidelity holograms.The computational results demonstrate the superior performance of the proposed method,achieving an average peak signal-to-noise ratio(PSNR)of 34.11 dB and structural similarity index measure(SSIM)of 0.95,representing significant improvements over conventional approaches.Both numerical simulations and experimental validations confirm CHMFNet's enhanced capability in hologram generation,particularly in terms of detail reproduction accuracy and overall image fidelity.
