scholarly journals Revisiting the nonlinear Gaussian noise model for hybrid fiber spans

2021 ◽  
Vol 2 (1) ◽  
pp. 30-49
Author(s):  
Ioannis Roudas ◽  
Jaroslaw Kwapisz ◽  
Xin Jiang
Keyword(s):  
Gaussian Noise ◽  
Noise Model ◽  
Hybrid Fiber

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

Additive Gaussian noise model and Kalman filter to enhance controllability of gesture-controlled teleoperated soft actuators

2020 ◽  
pp. 095440542094100
Author(s):  
PDSH Gunawardane ◽  
APTD Pathirana ◽  
REA Pallewela ◽  
Nimali T Medagedara
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Noise Model ◽  
The Internet ◽  
Linear Calibration ◽  
Spherical Object ◽  
Gesture Control ◽  
Soft Actuators ◽  
Human Finger ◽  
And Control

Soft actuators are used in bilateral systems as slave-side actuators. To broaden the applications and usage of these soft actuators in teleoperated applications required high accuracy and control. This article focuses on the improvement and control of the soft actuators operated through the Internet using a glove-based gesture control system. A hand-worn sensor glove (a glove with flex sensors and accelerometers) is used to detect hand gestures (specifically bending angle and force generated in a finger while gripping a spherical object) of an operator. The operator’s finger movements are mimicked in a remote location, through the Internet, in a soft actuator. The bending angles of the human finger have converted into the pressure variations inside the actuator using a linear calibration technique. A vertically mounted actuator and a spherical object are used to demonstrate the gripping action. The main problems that occurred during the controlling of this setup are noise and delays. Electronic noise (line noise and circuit noise), mechanical noise (vibration, nonuniformities in actuator fabrication, wear, and tear), elastic effect (energy absorbed during the state transformation), communication delays (delays occurred due to geography, telecommunication infrastructure, and round trip transmission), and other noises (other environmental effects) degraded the performance. This article considered a Brownian motion model, an additive Gaussian noise model, and a Kalman filter to solve these problems. The experimentations are performed in three different locations (to demonstrate the teleoperation) and the recorded improvement in the performance is approximately 17%.

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