This article presents a review of our research effort on the eigenanalysis of open radiating waveguides and closed resonating structures. A two dimensional (2-D) hybrid Finite Element method in conjunction with a cyli...This article presents a review of our research effort on the eigenanalysis of open radiating waveguides and closed resonating structures. A two dimensional (2-D) hybrid Finite Element method in conjunction with a cylindrical harmonics expansion is established to formulate the open waveguide generalized eigenvalue problem. The key element of this approach refers to the adoption of a vector Dirichlet-to-Neumann map to rigorously enforce the continuity of the two field expansions along a truncation surface. The resulting algorithm was able to evaluate both surface and leaky eigenmodes. The eigenanalysis of three dimensional (3-D) structures involves vast research challenges, especially when they are electrically large and open-radiating. The effort herein is focused on the electrically large case including the losses due to the finite conductivity of metallic walls and objects as well as the loading material losses. The former is introduced through impedance or Leontovich boundary condition, resulting to a non-linear-polynomial generalized eigenvalue problem. A straightforward linearization solution is adopted along with a more efficient alternative technique which mimics analytical approaches. For this one the linear eigenproblem formulated assuming metals as perfect electric conductors is initially solved and their finite conductivity is accounted through impedance boundary conditions enforced locally on the resulting eigenvectors. Finally, some numerical results are presented to verify the performance of these methodologies along with a discussion on their possibilities for extension to open 3D structures as well as to characteristic modes eigenanalysis.展开更多
A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction m...A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction methods usually collect a large number of segments where only the IPN signal is active.To avoid that collection procedure,we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples.Additionally,a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy.The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio(MaxSINR)beamformer.It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression.Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots.Compared to the first proposed method,the modified one can reduce the signal distortion even further.展开更多
An algorithm based on eigenanalysis technique and Walsh-Hadamard transform （WriT） is proposed. The algorithm contains two steps. Firstly, the received sequence is divided into temporal windows, and a covariance matr...An algorithm based on eigenanalysis technique and Walsh-Hadamard transform （WriT） is proposed. The algorithm contains two steps. Firstly, the received sequence is divided into temporal windows, and a covariance matrix is computed. The linear feedback shift register （LFSR） sequence is reconstructed from the first eigenvector of this matrix. Secondly, equations according to the recovered LFSR sequence are constructed, and the Walsh spectrum corresponding to the equations is computed. The feedback polynomial of LFSR is estimated from the Walsh spectrum. The validity of the algorithm is verified by the simulation result. Finally, case studies are presented to illustrate the performance of the blind reconstruction method.展开更多
The Mediterranean durum wheat landraces are genetically diverse and important sources for improving resistance to abiotic and biotic stresses and developing adapted and productive durum wheat varieties in the Mediterr...The Mediterranean durum wheat landraces are genetically diverse and important sources for improving resistance to abiotic and biotic stresses and developing adapted and productive durum wheat varieties in the Mediterranean region. To study the diversity two distant countries (MoroccoandSyria) durum landraces were studied. Fifty-one microsatellites were used as molecular markers tool to determine the genetic structure and spatial adaptation of these landraces. We used two spatially-explicit methods (Bayesian and Eigen) to determine the genetic diversity and structure of a population composed of Moroccan (98) and Syrian (90) durum wheat landraces. Non-spatial methods were also applied for comparison. A significant genetic difference was detected between the landraces originated from Morocco and Syria. Six subpopulations were revealed for each country using the Bayesian method and the Eigenanalysis, which generated PC1 and sPC1, showed similar structure. Eigenanalysis exhibited a significant global genetic structure for both countries landraces;and showed that neighboring landraces tend to have close genetic profile. The two first axes of PC1 and sPC1 had discriminated four out of the six subpopulations revealed by the Bayesian methodology. Also, our study detected the close relationship between the durum landraces from the coastal areas of Syria and the Moroccan landraces from the Atlantic coastal regions where the Phoenicians/Carthaginians had settled in Morocco. These results demonstrate the importance of using the spatial models in genetic analysis of durum wheat landraces;and also recommend the use of the easily usable Eigenanalysis to analyze the genetic diversity and structure.展开更多
Until recently, computational power was insufficient to diagonalize atmospheric datasets of order 108 - 109 elements. Eigenanalysis of tens of thousands of variables now can achieve massive data compression for spatia...Until recently, computational power was insufficient to diagonalize atmospheric datasets of order 108 - 109 elements. Eigenanalysis of tens of thousands of variables now can achieve massive data compression for spatial fields with strong correlation properties. Application of eigenanalysis to 26,394 variable dimensions, for three severe weather datasets (tornado, hail and wind) retains 9 - 11 principal components explaining 42% - 52% of the variability. Rotated principal components (RPCs) detect localized coherent data variance structures for each outbreak type and are related to standardized anomalies of the meteorological fields. Our analyses of the RPC loadings and scores show that these graphical displays can efficiently reduce and interpret large datasets. Data is analyzed 24 hours prior to severe weather as a forecasting aid. RPC loadings of sea-level pressure fields show different morphology loadings for each outbreak type. Analysis of low level moisture and temperature RPCs suggests moisture fields for hail and wind which are more related than for tornado outbreaks. Consequently, these patterns can identify precursors of severe weather and discriminate between tornadic and non-tornadic outbreaks.展开更多
We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximati...We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.展开更多
文摘This article presents a review of our research effort on the eigenanalysis of open radiating waveguides and closed resonating structures. A two dimensional (2-D) hybrid Finite Element method in conjunction with a cylindrical harmonics expansion is established to formulate the open waveguide generalized eigenvalue problem. The key element of this approach refers to the adoption of a vector Dirichlet-to-Neumann map to rigorously enforce the continuity of the two field expansions along a truncation surface. The resulting algorithm was able to evaluate both surface and leaky eigenmodes. The eigenanalysis of three dimensional (3-D) structures involves vast research challenges, especially when they are electrically large and open-radiating. The effort herein is focused on the electrically large case including the losses due to the finite conductivity of metallic walls and objects as well as the loading material losses. The former is introduced through impedance or Leontovich boundary condition, resulting to a non-linear-polynomial generalized eigenvalue problem. A straightforward linearization solution is adopted along with a more efficient alternative technique which mimics analytical approaches. For this one the linear eigenproblem formulated assuming metals as perfect electric conductors is initially solved and their finite conductivity is accounted through impedance boundary conditions enforced locally on the resulting eigenvectors. Finally, some numerical results are presented to verify the performance of these methodologies along with a discussion on their possibilities for extension to open 3D structures as well as to characteristic modes eigenanalysis.
基金Project supported by the National Natural Science Foundation of China(No.61571436)
文摘A robust generalized sidelobe canceller is proposed to combat direction of arrival(DOA)mismatches.To estimate the interference-plus-noise(IPN)statistics characteristics,conventional signal of interest(SOI)extraction methods usually collect a large number of segments where only the IPN signal is active.To avoid that collection procedure,we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples.Additionally,a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy.The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio(MaxSINR)beamformer.It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression.Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots.Compared to the first proposed method,the modified one can reduce the signal distortion even further.
基金supported by the National Natural Science Foundation of China(61072120)
文摘An algorithm based on eigenanalysis technique and Walsh-Hadamard transform （WriT） is proposed. The algorithm contains two steps. Firstly, the received sequence is divided into temporal windows, and a covariance matrix is computed. The linear feedback shift register （LFSR） sequence is reconstructed from the first eigenvector of this matrix. Secondly, equations according to the recovered LFSR sequence are constructed, and the Walsh spectrum corresponding to the equations is computed. The feedback polynomial of LFSR is estimated from the Walsh spectrum. The validity of the algorithm is verified by the simulation result. Finally, case studies are presented to illustrate the performance of the blind reconstruction method.
文摘The Mediterranean durum wheat landraces are genetically diverse and important sources for improving resistance to abiotic and biotic stresses and developing adapted and productive durum wheat varieties in the Mediterranean region. To study the diversity two distant countries (MoroccoandSyria) durum landraces were studied. Fifty-one microsatellites were used as molecular markers tool to determine the genetic structure and spatial adaptation of these landraces. We used two spatially-explicit methods (Bayesian and Eigen) to determine the genetic diversity and structure of a population composed of Moroccan (98) and Syrian (90) durum wheat landraces. Non-spatial methods were also applied for comparison. A significant genetic difference was detected between the landraces originated from Morocco and Syria. Six subpopulations were revealed for each country using the Bayesian method and the Eigenanalysis, which generated PC1 and sPC1, showed similar structure. Eigenanalysis exhibited a significant global genetic structure for both countries landraces;and showed that neighboring landraces tend to have close genetic profile. The two first axes of PC1 and sPC1 had discriminated four out of the six subpopulations revealed by the Bayesian methodology. Also, our study detected the close relationship between the durum landraces from the coastal areas of Syria and the Moroccan landraces from the Atlantic coastal regions where the Phoenicians/Carthaginians had settled in Morocco. These results demonstrate the importance of using the spatial models in genetic analysis of durum wheat landraces;and also recommend the use of the easily usable Eigenanalysis to analyze the genetic diversity and structure.
文摘Until recently, computational power was insufficient to diagonalize atmospheric datasets of order 108 - 109 elements. Eigenanalysis of tens of thousands of variables now can achieve massive data compression for spatial fields with strong correlation properties. Application of eigenanalysis to 26,394 variable dimensions, for three severe weather datasets (tornado, hail and wind) retains 9 - 11 principal components explaining 42% - 52% of the variability. Rotated principal components (RPCs) detect localized coherent data variance structures for each outbreak type and are related to standardized anomalies of the meteorological fields. Our analyses of the RPC loadings and scores show that these graphical displays can efficiently reduce and interpret large datasets. Data is analyzed 24 hours prior to severe weather as a forecasting aid. RPC loadings of sea-level pressure fields show different morphology loadings for each outbreak type. Analysis of low level moisture and temperature RPCs suggests moisture fields for hail and wind which are more related than for tornado outbreaks. Consequently, these patterns can identify precursors of severe weather and discriminate between tornadic and non-tornadic outbreaks.
基金supported by National Statistical Research Project of China(Grant No.2015LY77)National Natural Science Foundation of China(Grant Nos.11571080,11571081,71531006 and 71672042)+3 种基金supported by Engineering and Physical Sciences Research Council(Grant No.EP/L01226X/1)supported by National Natural Science Foundation of China(Grant Nos.11371318 and 11771390)Zhejiang Province Natural Science Foundation(Grant No.R16A010001)the Fundamental Research Funds for the Central Universities。
文摘We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.
