scholarly journals Joint Estimation of Linear and Non-linear Signal-to-Noise Ratio based on Neural Networks

2018 ◽  
Author(s):  
F. J. Vaquero Caballero ◽  
David Ives ◽  
Qunbi Zhuge ◽  
Maurice O’Sullivan ◽  
Seb J. Savory
Keyword(s):  
Neural Networks ◽  
Signal To Noise Ratio ◽  
Joint Estimation ◽  
Signal To Noise ◽  
Non Linear ◽  
Linear Signal ◽  
Noise Ratio

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

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.

scholarly journals Joint estimation of channel parameters for very low signal-to-noise ratio environment in mobile radio propagations

Radio Science ◽  
2010 ◽  
Vol 45 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jingyu Hua ◽  
Limin Meng ◽  
Gang Li ◽  
Dongming Wang ◽  
Bin Sheng ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Mobile Radio ◽  
Joint Estimation ◽  
Signal To Noise ◽  
Channel Parameters ◽  
Noise Ratio

scholarly journals A Blind Spectrum Sensing Method Based on Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2270 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Xueqiong Li
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Spectrum Sensing ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Noise Ratio

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

Non-linear filtering of rare events with large signal-to-noise ratio

1987 ◽  
Vol 24 (04) ◽  
pp. 929-948
Author(s):  
A. J. Heunis
Keyword(s):  
Conditional Distribution ◽  
Signal To Noise Ratio ◽  
Sample Path ◽  
Rare Event ◽  
Dirac Measure ◽  
Linear Filtering ◽  
Large Signal ◽  
Signal To Noise ◽  
Non Linear ◽  
Noise Ratio

The theory of robust non-linear filtering in Clark (1978) and Davis (1980), (1982) is used to evaluate the limiting conditional distribution of a diffusion, given an observation of a ‘rare-event' sample-path of the diffusion, as the signal-to-noise ratio and the diffusion noise-intensity converge to infinity and zero respectively. Under mild conditions it is shown that the limiting conditional distribution is a Dirac measure concentrated at a trajectory which solves a variational problem parametrised by the sample-path of the observed signal.

Extracting symptoms of bearing faults from noise using a non-linear neural filter

2002 ◽  
Vol 216 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Y Shao ◽  
K Nezu
Keyword(s):  
Adaptive Filter ◽  
Signal To Noise Ratio ◽  
Effective Means ◽  
Failure Detection ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Non Linear ◽  
Detection Capabilities ◽  
Noise Ratio

Improving the signal-to-noise ratio is an important feature for the early detection of faults in bearings subject to large amounts of environmental noises. A method is proposed for improving the signal-to-noise ratio by adaptive neural filtering (ANF). A comparison of failure detection capabilities of a linear adaptive filter using the least mean square (LMS) algorithm and a non-linear adaptive filter using the ANF algorithm in conditions of large amounts of environmental noise is made. Experimental results show that an adaptive filter using a neural filtering algorithm is an effective means for extracting the symptoms of a bearing fault under such conditions.

Financial Trading Systems Using Artificial Neural Networks

2011 ◽  
pp. 1532-1536
Author(s):  
Bruce Vanstone ◽  
Gavin Finnie
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Financial Trading ◽  
Investment Returns ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Mathematical Techniques

Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty, making the approach attractive to those researching within “noisy” realms, where the signal-to-noise ratio is quite low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which include genetic algorithms, chaos theory, etc.). The arena of investment trading is one such field where there is an abundance of noisy data. It is in this area that traditional computing typically gives way to soft computing as the rigid conditions applied by traditional computing cannot be met. This is particularly evident where the same sets of input conditions may appear to invoke different outcomes, or there is an abundance of missing or poor quality data. Artificial neural networks (henceforth ANNs) are a particularly promising branch on the tree of soft computing, as they possess the ability to determine non-linear relationships, and are particularly adept at dealing with noisy datasets. From an investment point of view, ANNs are particularly attractive as they offer the possibility of achieving higher investment returns for two distinct reasons. Firstly, with the advent of cheaper computing power, many mathematical techniques have come to be in common use, effectively minimizing any advantage they had introduced (see Samuel & Malakkal, 1990). Secondly, in order to attempt to address the first issue, many techniques have become more complex. There is a real risk that the signal-to-noise ratio associated with such techniques may be becoming lower, particularly in the area of pattern recognition, as discussed by Blakey (2002). Investment and financial trading is normally divided into two major disciplines: fundamental analysis and technical analysis. Articles concerned with applying ANNs to these two disciplines are reviewed.

scholarly journals Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

IUCrJ ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 1142-1150
Author(s):  
Eugene Palovcak ◽  
Daniel Asarnow ◽  
Melody G. Campbell ◽  
Zanlin Yu ◽  
Yifan Cheng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Pass Filter ◽  
Processing Pipeline ◽  
Low Pass ◽  
Noise Ratio

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.

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