为解决传统数字滤波器在有限精度实现时因有限字长(Finite Word Length,FWL)效应导致滤波器性能下降的问题,提出一种L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法.推导前向差分算子数字滤波器结构传输函数及其等效状态空间实现...为解决传统数字滤波器在有限精度实现时因有限字长(Finite Word Length,FWL)效应导致滤波器性能下降的问题,提出一种L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法.推导前向差分算子数字滤波器结构传输函数及其等效状态空间实现,根据可控及可观格莱姆矩阵得到基于相似变换矩阵的L_(2)灵敏度表达式,并进行稀疏化校准,将L_(2)灵敏度最小化问题转换为凸函数求最值问题,求导得到L_(2)灵敏度最小化表达式,代回即得前向差分算子数字滤波器的稀疏化状态空间实现.仿真结果表明,所提方法设计的数字滤波器具有更好的抗FWL效应.展开更多
With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direc...With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.展开更多
Background:Hepatocellular carcinoma(HCC)is the fourth leading cause of cancer-related deaths globally.Splicing factor proline and glutamine-rich(SFPQ)is a multifunctional protein that controls various biological funct...Background:Hepatocellular carcinoma(HCC)is the fourth leading cause of cancer-related deaths globally.Splicing factor proline and glutamine-rich(SFPQ)is a multifunctional protein that controls various biological functions.As a potential therapeutic target and a promising prognostic indicator,the potential effects and processes of SFPQ in HCC require further investigation.Methods:The RNA sequencing data were obtained from the Gene Expression Omnibus,International Cancer Genome Consortium,and The Cancer Genome Atlas databases to analyze SFPQ expression and differentially expressed genes(DEGs).We utilized the LinkedOmics database to identify co-expressed genes.A Venn diagram was constructed to determine the overlapping genes between the DEGs and the co-expressed genes.Functional enrichment analysis was performed on the overlapping genes and DEGs.Furthermore,our study involved functional enrichment analysis,a protein-protein interaction network analysis,and an analysis of immune cell infiltration.The cBioPortal and Tumor Immune Single-cell Hub were utilized to investigate the genetic alterations of SFPQ and the single-cell transcriptome visualization of the tumor microenvironment.A ceRNA network was established with the assistance of the ENCORI website.Finally,we elucidated the clinical significance of SFPQ in HCC by employing Kaplan-Meier survival analysis,univariate and multivariate Cox regression,and prognostic nomogram models.Results:The expression of SFPQ in HCC tissues was significantly elevated compared to normal tissues.GSEA results indicated that increased expression of SFPQ was associated with pathways related to HCC.The ceRNA network,including SFPQ,hsa-miR-101-3p,AC023043.4,AC124798.1,AC145207.5,and GSEC,was constructed with the assistance of ENCORI.High SFPQ expression was related to a poor prognosis in HCC and its subtypes.Univariate and multivariate Cox regression analysis showed that elevated SFPQ expression is an independent predictive factor.Conclusions:The overexpression of SFPQ may serve as a potential prognostic biomarker,indicating a poor prognosis in HCC.展开更多
文摘为解决传统数字滤波器在有限精度实现时因有限字长(Finite Word Length,FWL)效应导致滤波器性能下降的问题,提出一种L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法.推导前向差分算子数字滤波器结构传输函数及其等效状态空间实现,根据可控及可观格莱姆矩阵得到基于相似变换矩阵的L_(2)灵敏度表达式,并进行稀疏化校准,将L_(2)灵敏度最小化问题转换为凸函数求最值问题,求导得到L_(2)灵敏度最小化表达式,代回即得前向差分算子数字滤波器的稀疏化状态空间实现.仿真结果表明,所提方法设计的数字滤波器具有更好的抗FWL效应.
基金supported by the National Basic Research Program of China。
文摘With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
文摘Background:Hepatocellular carcinoma(HCC)is the fourth leading cause of cancer-related deaths globally.Splicing factor proline and glutamine-rich(SFPQ)is a multifunctional protein that controls various biological functions.As a potential therapeutic target and a promising prognostic indicator,the potential effects and processes of SFPQ in HCC require further investigation.Methods:The RNA sequencing data were obtained from the Gene Expression Omnibus,International Cancer Genome Consortium,and The Cancer Genome Atlas databases to analyze SFPQ expression and differentially expressed genes(DEGs).We utilized the LinkedOmics database to identify co-expressed genes.A Venn diagram was constructed to determine the overlapping genes between the DEGs and the co-expressed genes.Functional enrichment analysis was performed on the overlapping genes and DEGs.Furthermore,our study involved functional enrichment analysis,a protein-protein interaction network analysis,and an analysis of immune cell infiltration.The cBioPortal and Tumor Immune Single-cell Hub were utilized to investigate the genetic alterations of SFPQ and the single-cell transcriptome visualization of the tumor microenvironment.A ceRNA network was established with the assistance of the ENCORI website.Finally,we elucidated the clinical significance of SFPQ in HCC by employing Kaplan-Meier survival analysis,univariate and multivariate Cox regression,and prognostic nomogram models.Results:The expression of SFPQ in HCC tissues was significantly elevated compared to normal tissues.GSEA results indicated that increased expression of SFPQ was associated with pathways related to HCC.The ceRNA network,including SFPQ,hsa-miR-101-3p,AC023043.4,AC124798.1,AC145207.5,and GSEC,was constructed with the assistance of ENCORI.High SFPQ expression was related to a poor prognosis in HCC and its subtypes.Univariate and multivariate Cox regression analysis showed that elevated SFPQ expression is an independent predictive factor.Conclusions:The overexpression of SFPQ may serve as a potential prognostic biomarker,indicating a poor prognosis in HCC.
