A Neural Network-Based Noise Compensation Method for Pronunciation Assessment

2021 ◽  
Author(s):  
Binghuai Lin ◽  
Liyuan Wang
Keyword(s):  
Neural Network ◽  
Compensation Method ◽  
Noise Compensation

A Residual Phase Noise Compensation Method for IEEE 802.15.4 Compliant Dual-Mode Receiver for Diverse Low Power IoT Applications

2019 ◽  
Vol 6 (2) ◽  
pp. 3437-3447 ◽  
Author(s):  
Abdullah Zubair Mohammed ◽  
Ajay Kumar Nain ◽  
Jagadish Bandaru ◽  
Ajay Kumar ◽  
D. Santhosh Reddy ◽  
...  
Keyword(s):  
Low Power ◽  
Phase Noise ◽  
Ieee 802.15.4 ◽  
Compensation Method ◽  
Dual Mode ◽  
Noise Compensation ◽  
Iot Applications ◽  
Residual Phase

scholarly journals Clipping Noise Compensation with Neural Networks in OFDM Systems

Signals ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 100-109
Author(s):  
Tzu-Hsien Sang ◽  
You-Cheng Xu
Keyword(s):  
Neural Network ◽  
Orthogonal Frequency Division Multiplexing ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Inverse Discrete Fourier Transform ◽  
Noise Compensation ◽  
The Neural Network ◽  
Frequency Domains ◽  
Iterative Procedures ◽  
Ofdm Signals

The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike conventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based method learns to directly reconstruct the clipped part of the signal while the unclipped part is protected. Furthermore, an interpretation of the learned weight matrices of the neural network is presented. It is observed that parts of the network, in effect, implement transformations very similar to the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and frequency domains. The simulation results show that the proposed method outperforms existing algorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit Error Rate (BER).

scholarly journals A Nonlinear Multiparameters Temperature Error Modeling and Compensation of POS Applied in Airborne Remote Sensing System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jianli Li ◽  
Wenjian Wang ◽  
Feng Jiao ◽  
Jiancheng Fang ◽  
Tao Yu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Error Modeling ◽  
Compensation Method ◽  
Temperature Error ◽  
Bayesian Regularization ◽  
Airborne Remote Sensing ◽  
Accuracy Requirement ◽  
Sensing System ◽  
Single Temperature

The position and orientation system (POS) is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.

Noise compensation method study on APD photoelectronic detector

1998 ◽  
Author(s):  
Dianren Chen ◽  
Huilin Jiang ◽  
Guiying Li
Keyword(s):  
Compensation Method ◽  
Noise Compensation

scholarly journals A neural network combined with sliding mode controller for the two-wheel self-balancing robot

2021 ◽  
Vol 10 (3) ◽  
pp. 592
Author(s):  
Duc-Minh Nguyen ◽  
Van-Tiem Nguyen ◽  
Trong-Thang Nguyen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Sliding Mode ◽  
Lyapunov Theory ◽  
Sliding Mode Controller ◽  
Control Laws ◽  
Noise Compensation ◽  
Simulation Results ◽  
Balancing Robot ◽  
The Stability

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

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