Bayesian Blind Identification of Nonlinear Distortion with Memory for Audio Applications

2016 ◽  
Vol 23 (4) ◽  
pp. 414-418 ◽  
Author(s):  
Flavio R. Avila ◽  
Hugo T. Carvalho ◽  
Luiz W. P. Biscainho
Keyword(s):  
Nonlinear Distortion ◽  
Blind Identification ◽  
With Memory

Distortion Analysis of Memory-Less Nonlinear Sensors

2003 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Nonlinear Distortion ◽  
Proper Choice ◽  
Signal Recovery ◽  
Distortion Compensation ◽  
Convergence Parameter ◽  
Distortion Analysis ◽  
Nonlinear Sensors ◽  
Simulation Results ◽  
With Memory ◽  
Recovery Procedure

Distortion associated with memory-less nonlinear sensors is analyzed and several distortion compensation techniques are presented. Sensor nonlinearity is considered a defect in sensory systems because it introduces distortion into the system. Due to the fact that no efficient technique is available to deal with the issues related sensor nonlinearity, nonlinear primary sensors tend to be ignored. In this paper, we point out that there are certain advantages of using nonlinear sensor and nonlinear distortion caused by sensor nonlinearity may be completely compensated. A robust and efficient signal recovery procedure is derived to facilitate the design of nonlinear sensors. Not having an accurate sensor will result in errors and it is shown that the error can be minimized with a proper choice of a convergence parameter whereby stability of the developed algorithm is established. Simulation results are presented to validate the algorithms developed.

Signal Conditioning With Memory-Less Nonlinear Sensors

2004 ◽  
Vol 126 (2) ◽  
pp. 284-293 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Nonlinear Distortion ◽  
Recursive Algorithm ◽  
Noise Removal ◽  
Signal Recovery ◽  
Signal Conditioning ◽  
Recovery Algorithm ◽  
Sensor Model ◽  
Nonlinear Sensors ◽  
Almost All ◽  
With Memory

Proposed in this paper is an off-line signal conditioning scheme for memoryless nonlinear sensors. In most sensor designs, a linear input-output response is desired. However, nonlinearity is present in one form or another in almost all real sensors and therefore it is very difficult if not impossible to achieve a truly linear relationship. Often sensor nonlinearity is considered a disadvantage in sensory systems because it introduces distortion into the system. Due to the lack of efficient techniques to deal with the issues of sensor nonlinearity, primarily nonlinear sensors tend to be ignored. In this paper, it is shown that there are certain advantages of using nonlinear sensors and nonlinear distortion caused by sensor nonlinearity may be effectively compensated. A recursive algorithm utilizing certain characteristics of nonlinear sensor functions is proposed for the compensation of nonlinear distortion and sensor noise removal. A signal recovery algorithm that implements this idea is developed. Not having an accurate sensor model will result in errors and it is shown that the error can be minimized with a proper choice of a convergence accelerator whereby stability of the developed algorithm is established.

Capito-Lunate Fusion with Memory Staples

2015 ◽  
Vol 04 (S 01) ◽  
Author(s):  
M. Solomons
Keyword(s):  
With Memory

Nonlinear distortion measurement of FM DDS signals

2018 ◽  
pp. 43-48
Author(s):  
A. V. Zenkovich ◽  
V. L. Ballo ◽  
V. B. Dobrovolsky
Keyword(s):  
Nonlinear Distortion
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