The outer-product decomposition algorithm(OPDA)performs well at blindly identifying system function.However,the direct use of the OPDA in systems using bandpass source will lead to errors.This study proposes an approa...The outer-product decomposition algorithm(OPDA)performs well at blindly identifying system function.However,the direct use of the OPDA in systems using bandpass source will lead to errors.This study proposes an approach to enhance the channel estimation quality of a bandpass source that uses OPDA.This approach performs frequency domain transformation on the received signal and obtains the optimal transformation parameter by minimizing the p-norm of an error matrix.Moreover,the proposed approach extends the application of OPDA from a white source to a bandpass white source or chirp signal.Theoretical formulas and simulation results show that the proposed approach not only reduces the estimation error but also accelerates the algorithm in a bandpass system,thus being highly feasible in practical blind system identification applications.展开更多
This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method ...This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://github.com/qwerty1319/PC-SR).展开更多
Conventionally,soil cadmium(Cd)measurements in the laboratory are expensive and timeconsuming,involving complex processes of sample preparation and chemical analysis.This study aimed to identify the feasibility of usi...Conventionally,soil cadmium(Cd)measurements in the laboratory are expensive and timeconsuming,involving complex processes of sample preparation and chemical analysis.This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance(Vis-NIR)spectroscopy and portable X-ray fluorescence spectrometry(PXRF)to estimate regional soil Cd concentration in a time-and cost-savingmanner.The sensor data of Vis-NIR and PXRF,and Cd concentrations of 128 surface soils from Yunnan Province,China,were measured.Outer-product analysis(OPA)was used for synthesizing the sensor data and Granger-Ramanathan averaging(GRA)was applied to fuse the model results.Artificial neural network(ANN)models were built using Vis-NIR data,PXRF data,and OPA data,respectively.Results showed that:(1)ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation;(2)Fusion methods of both OPA and GRA had higher predictive power(R^(2))=0.89,ratios of performance to interquartile range(RPIQ)=4.14,and lower root mean squared error(RMSE)=0.06,in ANN model based on OPA fusion;R^(2)=0.88,RMSE=0.06,and RPIQ=3.53 in GRA model)than those based on either Vis-NIR data or PXRF data.In conclusion,there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.展开更多
基金This study is supported by the Natural Science Foundation of China(NSFC)under Grant Nos.11774073 and 51279033.
文摘The outer-product decomposition algorithm(OPDA)performs well at blindly identifying system function.However,the direct use of the OPDA in systems using bandpass source will lead to errors.This study proposes an approach to enhance the channel estimation quality of a bandpass source that uses OPDA.This approach performs frequency domain transformation on the received signal and obtains the optimal transformation parameter by minimizing the p-norm of an error matrix.Moreover,the proposed approach extends the application of OPDA from a white source to a bandpass white source or chirp signal.Theoretical formulas and simulation results show that the proposed approach not only reduces the estimation error but also accelerates the algorithm in a bandpass system,thus being highly feasible in practical blind system identification applications.
基金supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No.U1909210the National Nature Science Foundation of China under Grant Nos.61761136010,61772163。
文摘This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://github.com/qwerty1319/PC-SR).
基金supported by the National Key Research and Development Project(No.2020YFC1807405)the China Postdoctoral Science Foundation(No.2021M703301)+1 种基金the Key-Area Research and Development Program of Guangdong Province(No.2020B0202010006)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2019312).
文摘Conventionally,soil cadmium(Cd)measurements in the laboratory are expensive and timeconsuming,involving complex processes of sample preparation and chemical analysis.This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance(Vis-NIR)spectroscopy and portable X-ray fluorescence spectrometry(PXRF)to estimate regional soil Cd concentration in a time-and cost-savingmanner.The sensor data of Vis-NIR and PXRF,and Cd concentrations of 128 surface soils from Yunnan Province,China,were measured.Outer-product analysis(OPA)was used for synthesizing the sensor data and Granger-Ramanathan averaging(GRA)was applied to fuse the model results.Artificial neural network(ANN)models were built using Vis-NIR data,PXRF data,and OPA data,respectively.Results showed that:(1)ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation;(2)Fusion methods of both OPA and GRA had higher predictive power(R^(2))=0.89,ratios of performance to interquartile range(RPIQ)=4.14,and lower root mean squared error(RMSE)=0.06,in ANN model based on OPA fusion;R^(2)=0.88,RMSE=0.06,and RPIQ=3.53 in GRA model)than those based on either Vis-NIR data or PXRF data.In conclusion,there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.
