Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Obs...Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.展开更多
【目的】制备抗Luman募集因子N端(Luman-recruiting factor N terminal,LRF-N)蛋白的单克隆抗体。【方法】PCR扩增LRF-N端的194bp序列,构建LRF-N重组质粒pGEX-LRF-N,转染BL21(DE3)菌,诱导表达并纯化GST-LRF-N融合蛋白,以其作为抗原免疫6...【目的】制备抗Luman募集因子N端(Luman-recruiting factor N terminal,LRF-N)蛋白的单克隆抗体。【方法】PCR扩增LRF-N端的194bp序列,构建LRF-N重组质粒pGEX-LRF-N,转染BL21(DE3)菌,诱导表达并纯化GST-LRF-N融合蛋白,以其作为抗原免疫6～8周龄雌性BALB/c小鼠,免疫4次,每次间隔10d,第4次加强免疫3d后采取小鼠脾脏,分离脾细胞,与SP2/0细胞融合,用ELISA和Western blot进行阳性细胞株的筛选和鉴定,对获得的杂交瘤细胞的细胞核型、分泌稳定性以及所分泌的单克隆抗体进行鉴定。【结果】pGEX-LRF-N在大肠杆菌BL21(DE3)中得到高效表达,GST-LRF-N蛋白分子质量约为33ku,与预期结果一致。试验共获得3株稳定分泌抗LRF-N抗体的杂交瘤细胞株,分别命名为2AC9、2AG10和3G7。单抗2AC9、2AG10、3G7能够特异性地识别GST-LRF-N蛋白,亚型鉴定结果显示,其重链分别为IgG2a、IgM和IgG1。【结论】获得的抗LRF-N抗体的杂交瘤细胞株2AC9、2AG10、3G7单抗特异性良好,能够稳定分泌抗体,可用于小鼠LRF蛋白生物学功能及活性的进一步研究。展开更多
为了更快且更准确地对图像进行识别,提出了基于局部感受野的宽度学习算法(Local Receptive Field based Broad Learning System,BLS-LRF),该方法以宽度学习网(Broad Learning System,BLS)为基础模型,与局部感受野(LRF)的思想相结合,从...为了更快且更准确地对图像进行识别,提出了基于局部感受野的宽度学习算法(Local Receptive Field based Broad Learning System,BLS-LRF),该方法以宽度学习网(Broad Learning System,BLS)为基础模型,与局部感受野(LRF)的思想相结合,从局部特征和全局特征两方面对图像进行特征提取。采用两种图像数据集对网络进行研究,将研究结果和许多传统神经网络进行对比,结果表明BLS-LRF网络的测试精度不仅超过了传统网络的测试精度,而且训练过程所需要的时间有了很大程度的缩短。展开更多
Global Navigation Satellite System(GNSS)-based passive radar(GBPR)has been widely used in remote sensing applications.However,for moving target detection(MTD),the quadratic phase error(QPE)introduced by the non-cooper...Global Navigation Satellite System(GNSS)-based passive radar(GBPR)has been widely used in remote sensing applications.However,for moving target detection(MTD),the quadratic phase error(QPE)introduced by the non-cooperative target motion is usually difficult to be compensated,as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective.Consequently,the moving target in GBPR image is usually defocused,which aggravates the difficulty of target detection even further.In this paper,a spawning particle filter(SPF)is proposed for defocused MTD.Firstly,the measurement model and the likelihood ratio function(LRF)of the defocused point-like target image are deduced.Then,a spawning particle set is generated for subsequent target detection,with reference to traditional particles in particle filter(PF)as their parent.After that,based on the PF estimator,the SPF algorithm and its sequential Monte Carlo(SMC)implementation are proposed with a novel amplitude estimation method to decrease the target state dimension.Finally,the effectiveness of the proposed SPF is demonstrated by numerical simulations and pre-liminary experimental results,showing that the target range and Doppler can be estimated accurately.展开更多
文摘Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.
文摘【目的】制备抗Luman募集因子N端(Luman-recruiting factor N terminal,LRF-N)蛋白的单克隆抗体。【方法】PCR扩增LRF-N端的194bp序列,构建LRF-N重组质粒pGEX-LRF-N,转染BL21(DE3)菌,诱导表达并纯化GST-LRF-N融合蛋白,以其作为抗原免疫6～8周龄雌性BALB/c小鼠,免疫4次,每次间隔10d,第4次加强免疫3d后采取小鼠脾脏,分离脾细胞,与SP2/0细胞融合,用ELISA和Western blot进行阳性细胞株的筛选和鉴定,对获得的杂交瘤细胞的细胞核型、分泌稳定性以及所分泌的单克隆抗体进行鉴定。【结果】pGEX-LRF-N在大肠杆菌BL21(DE3)中得到高效表达,GST-LRF-N蛋白分子质量约为33ku,与预期结果一致。试验共获得3株稳定分泌抗LRF-N抗体的杂交瘤细胞株,分别命名为2AC9、2AG10和3G7。单抗2AC9、2AG10、3G7能够特异性地识别GST-LRF-N蛋白,亚型鉴定结果显示,其重链分别为IgG2a、IgM和IgG1。【结论】获得的抗LRF-N抗体的杂交瘤细胞株2AC9、2AG10、3G7单抗特异性良好,能够稳定分泌抗体,可用于小鼠LRF蛋白生物学功能及活性的进一步研究。
文摘为了更快且更准确地对图像进行识别,提出了基于局部感受野的宽度学习算法(Local Receptive Field based Broad Learning System,BLS-LRF),该方法以宽度学习网(Broad Learning System,BLS)为基础模型,与局部感受野(LRF)的思想相结合,从局部特征和全局特征两方面对图像进行特征提取。采用两种图像数据集对网络进行研究,将研究结果和许多传统神经网络进行对比,结果表明BLS-LRF网络的测试精度不仅超过了传统网络的测试精度,而且训练过程所需要的时间有了很大程度的缩短。
基金supported by the National Natural Science Foundation of China(62101014)the National Key Laboratory of Science and Technology on Space Microwave(6142411203307).
文摘Global Navigation Satellite System(GNSS)-based passive radar(GBPR)has been widely used in remote sensing applications.However,for moving target detection(MTD),the quadratic phase error(QPE)introduced by the non-cooperative target motion is usually difficult to be compensated,as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective.Consequently,the moving target in GBPR image is usually defocused,which aggravates the difficulty of target detection even further.In this paper,a spawning particle filter(SPF)is proposed for defocused MTD.Firstly,the measurement model and the likelihood ratio function(LRF)of the defocused point-like target image are deduced.Then,a spawning particle set is generated for subsequent target detection,with reference to traditional particles in particle filter(PF)as their parent.After that,based on the PF estimator,the SPF algorithm and its sequential Monte Carlo(SMC)implementation are proposed with a novel amplitude estimation method to decrease the target state dimension.Finally,the effectiveness of the proposed SPF is demonstrated by numerical simulations and pre-liminary experimental results,showing that the target range and Doppler can be estimated accurately.
