A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
Objective: To evaluate the clinical and radiological factors that affect therecurrence of the meningioma patient so as to effectively prevent and cure recurrence of meningiomapatients more earlier. Methods: The clinic...Objective: To evaluate the clinical and radiological factors that affect therecurrence of the meningioma patient so as to effectively prevent and cure recurrence of meningiomapatients more earlier. Methods: The clinical features and radiological aspects in 145 cases ofmeningiomas undergoing operation during 1993-1997 were retrospectively studied. The data of only 83cases of all 145 cases were available. The factors were evaluated with univariate and multivariateanalysis. Results: With univariate analysis, 7 factors showed highly significance to recurrence ofmeningiomas: tumor size, tumor location, tumor shape, edema, extent of resection, pathologicalgrade, CT enhancement. With multivariate analysis, 4 factors showed significant danger to recurrenceof meningiomas: pathological grade, extent of resection, tumor shape and CT enhancement.Conclusion: The main factors that affect the recurrence of meningioma patients are pathologicalgrade, extent of resection, tumor shape and CT enhancement.展开更多
A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directl...A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.展开更多
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
文摘Objective: To evaluate the clinical and radiological factors that affect therecurrence of the meningioma patient so as to effectively prevent and cure recurrence of meningiomapatients more earlier. Methods: The clinical features and radiological aspects in 145 cases ofmeningiomas undergoing operation during 1993-1997 were retrospectively studied. The data of only 83cases of all 145 cases were available. The factors were evaluated with univariate and multivariateanalysis. Results: With univariate analysis, 7 factors showed highly significance to recurrence ofmeningiomas: tumor size, tumor location, tumor shape, edema, extent of resection, pathologicalgrade, CT enhancement. With multivariate analysis, 4 factors showed significant danger to recurrenceof meningiomas: pathological grade, extent of resection, tumor shape and CT enhancement.Conclusion: The main factors that affect the recurrence of meningioma patients are pathologicalgrade, extent of resection, tumor shape and CT enhancement.
基金The National Natural Science Foundation of China (No.61374194)
文摘A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.
