MAP Decoder for Nonlinear Compensation in WDM DP-QPSK Systems

2018 ◽  
Author(s):  
Shuai Yuan ◽  
Koji Igarashi
Keyword(s):  
Nonlinear Compensation ◽  
Map Decoder

Recent Progress on Nonlinear Compensation Technique in Digital Coherent Receiver

2010 ◽  
Author(s):  
Takeshi Hoshida ◽  
Takahito Tanimura ◽  
Shoichiro Oda ◽  
Toshiki Tanaka ◽  
Hisao Nakashima ◽  
...  
Keyword(s):  
Recent Progress ◽  
Compensation Technique ◽  
Nonlinear Compensation ◽  
Coherent Receiver

Development of Control System by Nonlinear Compensation Using Digital Acceleration Control

2010 ◽  
Author(s):  
Takanori Emaru ◽  
Kazuo Imagawa ◽  
Yohei Hoshino ◽  
Yukinori Kobayashi
Keyword(s):  
Control System ◽  
System Identification ◽  
Mechanical System ◽  
Pid Control ◽  
Estimation Method ◽  
Proportional Integral Derivative ◽  
Inertia Term ◽  
Modeling Errors ◽  
Nonlinear Compensation ◽  
Simulation Results

Proportional-Integral-Derivative (PID) control has been most commonly used to operate mechanical systems. In PID control, however, there are limits to the accuracy of the resulting movement because of the influence of gravity, friction, and interaction of joints. We have proposed a digital acceleration control (DAC) that is robust over these modeling errors. One of the most practicable advantages of DAC is robustness against modeling errors. However, it does not always work effectively. If there are modeling errors in the inertia term of the model, the DAC controller cannot control a mechanical system properly. Generally an inertia term is easily modeled in advance, but it has a possibility to change. Therefore, we propose an online estimation method of an inertia term by using a system identification method. By using the proposed method, the robustness of DAC is considerably improved. This paper shows the simulation results of the proposed method using 2-link manipulator.

scholarly journals Mass Imbalance Compensation Control for Stabilized Platform Based on UKF Identification

2018 ◽  
Vol 198 ◽  
pp. 01005
Author(s):  
WeiMing Zhang ◽  
ZeLin Shi
Keyword(s):  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Parameters Estimation ◽  
Feed Forward ◽  
Mass Imbalance ◽  
Feed Forward Control ◽  
Nonlinear Compensation ◽  
The Stability ◽  
Stabilized Platform ◽  
Platform System

Due to the mass imbalance about the center of rotation, the stability of stabilized platform system degrades with carrier’s disturbances. Various feed-forward control methods are provided by reaserchers to solve this problem, however these methods are not well applied because the eccentricity of stabilized platform could not be measured directly. The dynamics model of a typical 2-axis stabilized platform is given. The eccentricity vector is identified through Unscented Kalman Filter(UKF) algorithm. Imbalance torque is precisely observed so that the real-time nonlinear compensation for mass imbalance is achieved through a feed-forward loop. The simulation result indicates that the Root Mean Squared Error (RMSE) of parameters estimation is 0.024 after convergence. the LOS stabilization with carrier’s 2.5Hz vibration is 0.04 rad/s, which improves 78% compared to conventional feed-back control.

Ground-Constrained Motion Target Tracking for Monocular Sequence Images

2018 ◽  
Vol 32 (12) ◽  
pp. 1850044 ◽  
Author(s):  
Ling Guo
Keyword(s):  
Background Subtraction ◽  
Target Position ◽  
Video Monitoring ◽  
Moving Target ◽  
Constrained Motion ◽  
Otsu Method ◽  
Test Platform ◽  
Monitoring Test ◽  
Nonlinear Compensation ◽  
Nonlinear Imaging

For the detection of a moving target position in video monitoring images, the existing locating tracking systems mainly adopt binocular or structured light stereoscopic technology, which has drawbacks such as system design complexity and slow detection speed. In light of these limitations, a tracking method for monocular sequence moving targets is presented, with the introduction of ground constraints into monocular visual monitoring; the principle and process of the method are introduced in detail in this paper. This method uses camera installation information and geometric imaging principles combined with nonlinear compensation to derive the calculation formula for the actual position of the ground moving target in monocular asymmetric nonlinear imaging. The footprint location of a walker is searched in the sequence imaging of a monitoring test platform that is built indoors. Because of the shadow of the walker in the image, the multi-threshold OTSU method based on test target background subtraction is used here to segment the images. The experimental results verify the effectiveness of the proposed method.

scholarly journals Multiscale Aggregate Networks with Dense Connections for Crowd Counting

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pengfei Li ◽  
Min Zhang ◽  
Jian Wan ◽  
Ming Jiang
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Image Features ◽  
Convolutional Network ◽  
Crowd Counting ◽  
Squared Error ◽  
Crowd Density ◽  
Density Maps ◽  
Density Map ◽  
Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

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