Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

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Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1733835100 ◽

2003 ◽

Vol 100 (18) ◽

pp. 10158-10163 ◽

Cited By ~ 1857

Author(s):

J. H. Ardenkjaer-Larsen ◽

B. Fridlund ◽

A. Gram ◽

G. Hansson ◽

L. Hansson ◽

...

Keyword(s):

Liquid State ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Noise Ratio

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Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

Neurocomputers ◽

10.18127/j19998554-202102-02 ◽

2021 ◽

Author(s):

S.V. Zimina

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Output Signal ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Useful Signal ◽

Artificial Neural ◽

Noise Ratio ◽

Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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Research on Speaker Recognition of DRNN in Different Noise Environment

10.21203/rs.3.rs-124941/v1 ◽

2020 ◽

Author(s):

chaofeng lan ◽

yuanyuan Zhang ◽

hongyun Zhao

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Recurrent Neural Network ◽

Speaker Recognition ◽

Signal To Noise Ratio ◽

Recognition Rate ◽

Noisy Environment ◽

Signal To Noise ◽

Noise Ratio ◽

Improved Model

Abstract This paper draws on the training method of Recurrent Neural Network (RNN), By increasing the number of hidden layers of RNN and changing the layer activation function from traditional Sigmoid to Leaky ReLU on the input layer, the first group and the last set of data are zero-padded to enhance the effective utilization of data such that the improved reduction model of Denoise Recurrent Neural Network (DRNN) with high calculation speed and good convergence is constructed to solve the problem of low speaker recognition rate in noisy environment. According to this model, the random semantic speech signal with a sampling rate of 16 kHz and a duration of 5 seconds in the speech library is studied. The experimental settings of the signal-to-noise ratios are − 10dB, -5dB, 0dB, 5dB, 10dB, 15dB, 20dB, 25dB. In the noisy environment, the improved model is used to denoise the Mel Frequency Cepstral Coefficients (MFCC) and the Gammatone Frequency Cepstral Coefficents (GFCC), impact of the traditional model and the improved model on the speech recognition rate is analyzed. The research shows that the improved model can effectively eliminate the noise of the feature parameters and improve the speech recognition rate. When the signal-to-noise ratio is low, the speaker recognition rate can be more obvious. Furthermore, when the signal-to-noise ratio is 0dB, the speaker recognition rate of people is increased by 40%, which can be 85% improved compared with the traditional speech model. On the other hand, with the increase in the signal-to-noise ratio, the recognition rate is gradually increased. When the signal-to-noise ratio is 15dB, the recognition rate of speakers is 93%.

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Effect of signal to noise ratio on a convolutional neural network for source ranging and environmental classification

The Journal of the Acoustical Society of America ◽

10.1121/1.5137292 ◽

2019 ◽

Vol 146 (4) ◽

pp. 2961-2962

Author(s):

Kira Howarth ◽

David F. Van Komen ◽

Tracianne B. Neilsen ◽

David P. Knobles ◽

Peter H. Dahl ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Environmental Classification ◽

Noise Ratio

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Low-field 1 H NMR spectroscopy: Factors impacting signal-to-noise ratio and experimental time in the context of mixed microstructure polyisoprenes

Magnetic Resonance in Chemistry ◽

10.1002/mrc.5022 ◽

2020 ◽

Author(s):

Michael J. Minkler ◽

Jung Min Kim ◽

Vinita V. Shinde ◽

Bryan S. Beckingham

Keyword(s):

Nmr Spectroscopy ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Experimental Time ◽

H Nmr ◽

Low Field ◽

Mixed Microstructure ◽

Noise Ratio

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Super resolution DOA estimation based on deep neural network

Scientific Reports ◽

10.1038/s41598-020-76608-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Wanli Liu

Keyword(s):

Neural Network ◽

Potential Application ◽

Deep Neural Network ◽

State Of The Art ◽

Signal To Noise Ratio ◽

Super Resolution ◽

Doa Estimation ◽

Angular Distance ◽

Signal To Noise ◽

Simulation Results

AbstractRecently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.

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