The root multiple signal classification（root-MUSIC） algorithm is one of the most important techniques for direction of arrival（DOA） estimation. Using a uniform linear array（ULA） composed of M sensors, this metho...The root multiple signal classification（root-MUSIC） algorithm is one of the most important techniques for direction of arrival（DOA） estimation. Using a uniform linear array（ULA） composed of M sensors, this method usually estimates L signal DOAs by finding roots that lie closest to the unit circle of a（2M-1）-order polynomial, where L 〈 M. A novel efficient root-MUSIC-based method for direction estimation is presented, in which the order of polynomial is efficiently reduced to 2L. Compared with the unitary root-MUSIC（U-root-MUSIC） approach which involves real-valued computations only in the subspace decomposition stage, both tasks of subspace decomposition and polynomial rooting are implemented with real-valued computations in the new technique,which hence shows a significant efficiency advantage over most state-of-the-art techniques. Numerical simulations are conducted to verify the correctness and efficiency of the new estimator.展开更多
The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that construc...The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.展开更多
Background:DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes.The complex patterns of DNA methylation...Background:DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes.The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.Results:In this review,the main high-throughput detection technologies are summarized,and then the three trends of computational estimation of DNA methylation levels were analyzed,especially the expanding of the methylation data with lower coverage.Furthermore,the detection methods of differential methylation patterns for sequencing and array data were presented.Conclusions:More and more research indicated the great importance of DNA methylation changes across different diseases,such as cancers.Although a lot of enormous progress has been made in understanding the role of DNA methylation,only few methylated genes or functional elements serve as clinically relevant cancer biomarkers.The bottleneck in DNA methylation advances has shifted from data generation to data analysis.Therefore,it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.展开更多
基金supported by the National Natural Science Foundation of China(61501142)the Shandong Provincial Natural Science Foundation(ZR2014FQ003)+1 种基金the Special Foundation of China Postdoctoral Science(2016T90289)the China Postdoctoral Science Foundation(2015M571414)
文摘The root multiple signal classification（root-MUSIC） algorithm is one of the most important techniques for direction of arrival（DOA） estimation. Using a uniform linear array（ULA） composed of M sensors, this method usually estimates L signal DOAs by finding roots that lie closest to the unit circle of a（2M-1）-order polynomial, where L 〈 M. A novel efficient root-MUSIC-based method for direction estimation is presented, in which the order of polynomial is efficiently reduced to 2L. Compared with the unitary root-MUSIC（U-root-MUSIC） approach which involves real-valued computations only in the subspace decomposition stage, both tasks of subspace decomposition and polynomial rooting are implemented with real-valued computations in the new technique,which hence shows a significant efficiency advantage over most state-of-the-art techniques. Numerical simulations are conducted to verify the correctness and efficiency of the new estimator.
基金Sandia Nat.Lab.Sandia is a multiprogram laboratory operated by Sandia Corporation,a Lockheed Martin Company,for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000
文摘The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.
基金supported by the National Natural Science Foundation of China(No.61872063)and Shenzhen Science and Technology Program(No.JCYJ20210324140407021).
文摘Background:DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes.The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.Results:In this review,the main high-throughput detection technologies are summarized,and then the three trends of computational estimation of DNA methylation levels were analyzed,especially the expanding of the methylation data with lower coverage.Furthermore,the detection methods of differential methylation patterns for sequencing and array data were presented.Conclusions:More and more research indicated the great importance of DNA methylation changes across different diseases,such as cancers.Although a lot of enormous progress has been made in understanding the role of DNA methylation,only few methylated genes or functional elements serve as clinically relevant cancer biomarkers.The bottleneck in DNA methylation advances has shifted from data generation to data analysis.Therefore,it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.
