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
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).
