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.

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.

scholarly journals A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 126 ◽  
Author(s):  
Bin Wang ◽  
Li Wang ◽  
Hao Yu ◽  
Fengming Xin
Keyword(s):  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Steepest Descent Method ◽  
Signal Recovery ◽  
Two Dimensional ◽  
Reconstruction Algorithms ◽  
Experimental Conditions ◽  
One Dimensional ◽  
Recovery Algorithm ◽  
Reconstruction Performance

The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0) algorithm, achieving the reconstruction of the signal well in the presence of noise. However, the ReSL0 reconstruction algorithm still has some flaws. It still chooses the original optimization method of SL0 and the Gauss approximation function, but this method has the problem of a sawtooth effect in the later optimization stage, and the convergence effect is not ideal. Therefore, we make two adjustments to the basis of the ReSL0 reconstruction algorithm: firstly, we introduce another CIPF function which has a better approximation effect than Gauss function; secondly, we combine the steepest descent method and Newton method in terms of the algorithm optimization. Then, a novel regularized recovery algorithm named combined regularized smooth L0 (CReSL0) is proposed. Under the same experimental conditions, the CReSL0 algorithm is compared with other popular reconstruction algorithms. Overall, the CReSL0 algorithm achieves excellent reconstruction performance in terms of the peak signal-to-noise ratio (PSNR) and run-time for both a one-dimensional Gauss signal and two-dimensional image reconstruction tasks.

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

scholarly journals A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dingfei Jin ◽  
Yue Yang ◽  
Tao Ge ◽  
Daole Wu
Keyword(s):  
Compressed Sensing ◽  
Newton Method ◽  
Simulation Experiment ◽  
Sparse Recovery ◽  
Signal Recovery ◽  
Recovery Efficiency ◽  
Recovery Algorithm ◽  
Modified Newton Method ◽  
Computer Simulation Experiment ◽  
Recovery Algorithms

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions.

Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery Algorithm

2020 ◽  
Vol 27 ◽  
pp. 780-784
Author(s):  
Chang-Jen Wang ◽  
Chao-Kai Wen ◽  
Shang-Ho Tsai ◽  
Shi Jin
Keyword(s):  
Phase Retrieval ◽  
Signal Recovery ◽  
Recovery Algorithm

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction

2015 ◽  
Vol 63 ◽  
pp. 66-78 ◽  
Author(s):  
Vidya L. ◽  
Vivekanand V. ◽  
Shyamkumar U. ◽  
Deepak Mishra
Keyword(s):  
Compressed Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Rbf Network ◽  
Recovery Algorithm

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

Psychosocial Status of 192 Out-Patients with HIV Infection and AIDS

1989 ◽  
Vol 154 (2) ◽  
pp. 237-242 ◽  
Author(s):  
Michael B. King
Keyword(s):  
Cognitive Impairment ◽  
Psychological Distress ◽  
Hiv Infection ◽  
Poor Prognosis ◽  
Important Indicator ◽  
Psychosocial Status ◽  
Brief Assessment ◽  
Almost All ◽  
With Memory ◽  
Psychiatric Problems

One hundred and ninety-two out-patients with HIV infection were interviewed in a standardised manner at two London hospitals. Almost all had revealed their diagnosis to others, one-quarter receiving negative reactions from confidants. Thirty-one per cent had significant psychiatric problems, almost half of whom reported emotional problems before HIV infection. Twenty-two per cent complained of difficulties with memory or concentration, of whom 12.5% had objective cognitive impairment on brief assessment. Excessive health ruminations were an important indicator of more extensive psychological problems. This degree of psychological distress is in keeping with reports for patients with other medical conditions, and overall, patients appeared to have adapted well, despite the obvious stigma and poor prognosis of their condition.

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