This paper proposes a new Zernike modal gray map reconstruction algorithm used in the nematic liquid crystal adaptive optics system. Firstly, the new modal algorithm is described. Secondly, a single loop correction ex...This paper proposes a new Zernike modal gray map reconstruction algorithm used in the nematic liquid crystal adaptive optics system. Firstly, the new modal algorithm is described. Secondly, a single loop correction experiment was conducted, and it showed that the modal method has a higher precision in gray map reconstruction than the widely used slope method. Finally, the contrast close-loop correction experiment was conducted to correct static aberration in the laboratory. The experimental results showed that the average peak to valley （PV） and root mean square （RMS） of the wavefront corrected by mode method were reduced from 2.501A （λ= 633 nm） and 0.610A to 0.0334λ and 0.00845A, respectively. The corrected PV and RMS were much smaller than those of 0.173A and 0.048A by slope method. The Strehl ratio and modulation transfer function of the system corrected by mode method were much closer to diffraction limit than with slope method. These results indicate that the mode method can take good advantage of the large number of pixels of the liquid crystal corrector to realize high correction precision.展开更多
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods...Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.展开更多
The inverse problem of determining two convection coefficients of an elliptic partial differential equation by Dirichlet to Neumann map is discussed.It is well known that this is a severely ill-posed problem with high...The inverse problem of determining two convection coefficients of an elliptic partial differential equation by Dirichlet to Neumann map is discussed.It is well known that this is a severely ill-posed problem with high nonlinearity.By the inverse scattering technique for first order elliptic system in the plane and the theory of generalized analytic functions,we give a constructive method for this inverse problem.展开更多
基金Project supported by the National Natural Science Foundation of China (Grants Nos.60736042,60578035 and 50703039)Science and Technology Cooperation Project between Chinese Academy of Sciences and Jilin Province (Grant No.2008SYHZ0005)
文摘This paper proposes a new Zernike modal gray map reconstruction algorithm used in the nematic liquid crystal adaptive optics system. Firstly, the new modal algorithm is described. Secondly, a single loop correction experiment was conducted, and it showed that the modal method has a higher precision in gray map reconstruction than the widely used slope method. Finally, the contrast close-loop correction experiment was conducted to correct static aberration in the laboratory. The experimental results showed that the average peak to valley （PV） and root mean square （RMS） of the wavefront corrected by mode method were reduced from 2.501A （λ= 633 nm） and 0.610A to 0.0334λ and 0.00845A, respectively. The corrected PV and RMS were much smaller than those of 0.173A and 0.048A by slope method. The Strehl ratio and modulation transfer function of the system corrected by mode method were much closer to diffraction limit than with slope method. These results indicate that the mode method can take good advantage of the large number of pixels of the liquid crystal corrector to realize high correction precision.
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金National Key Scientific Instrument and Equipment Development Project under Grant No.61827801the open research fund of State Key Laboratory of Integrated Services Networks,No.ISN22-11+1 种基金Natural Science Foundation of Jiangsu Province,No.BK20211182open research fund of National Mobile Communications Research Laboratory,Southeast University,No.2022D04。
文摘Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.
基金partly supported by the National Natural Science Foundation of China(Grant No.10271032)Shuguang Project and E-Institute of Shanghai Municipal Education Commission(N.E03004).
文摘The inverse problem of determining two convection coefficients of an elliptic partial differential equation by Dirichlet to Neumann map is discussed.It is well known that this is a severely ill-posed problem with high nonlinearity.By the inverse scattering technique for first order elliptic system in the plane and the theory of generalized analytic functions,we give a constructive method for this inverse problem.
