On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

Hafiane, Mohamed Lamine; Dibi, Z.; Manck, O.

doi:10.3390/s90402884

Cited by 9 publications

(7 citation statements)

References 20 publications

(15 reference statements)

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“…As it is well-known, one of the main advantages of neural networks lays in their ability to represent both linear and non-linear models by learning directly from data measurements [15]. …”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 99%

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Juez

Lasheras

Roqueñí

et al. 2012

Sensors

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In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).

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“…As it is well-known, one of the main advantages of neural networks lays in their ability to represent both linear and non-linear models by learning directly from data measurements [15]. …”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 99%

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Juez

Lasheras

Roqueñí

et al. 2012

Sensors

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“…On the other hand, artificial neural network (ANN) has been widely employed for different applications of estimation in science and engineering to achieve complex inputoutput relationship as well as nonlinear mapping ability in recent years [15][16][17][18][19][20][21][22][23]. It outperforms other conventional nonlinear methods since it does not require much prior inputoutput relationships on the nature of nonlinearity existing between the input and output patterns [21,22].…”

Section: Introductionmentioning

confidence: 99%

Signal processing using artificial neural network for BOTDA sensor system

et al. 2016

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We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

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“…In this paper, we propose the implementation of an artificial neural network to identify the relationship and solve the problem. An artificial neural network can map the implicit relationship of inputs and outputs through the training and testing of measured data, which has been applied to compensate for the various nonlinear errors in system designs [ 20 , 21 , 22 , 23 , 24 ]. Through proper training, the artificial neural network can compensate for the nonlinear errors, enabling a direct read-out of the applied ultrasound intensity.…”

Section: Introductionmentioning

confidence: 99%

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

Xing

Chen

2015

Sensors

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In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous design, the new design reduced sensing time from 20 s to 12 s, and the sensor’s average error from 3.97 mW/cm2 to 1.31 mW/cm2 respectively.

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On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

Cited by 9 publications

References 20 publications

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Signal processing using artificial neural network for BOTDA sensor system

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

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