scholarly journals Correction: Ramzan F. et al. “Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations” Genes, 2020, 11, 892

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1199
Author(s):  
Faisal Ramzan ◽  
Mehmet Gültas ◽  
Hendrik Bertram ◽  
David Cavero ◽  
Armin Otto Schmitt
Keyword(s):  
Signal Detection ◽  
Random Forests ◽  
Detection Method ◽  
Robust Detection ◽  
Published Paper

The authors would like to make a correction to the published paper [...]

scholarly journals Rhesus monkey (Macaca mulatta) cool sensitivity measured by a signal detection method

1976 ◽  
Vol 19 (3) ◽  
pp. 246-251 ◽  
Author(s):  
Helen H. Molinari ◽  
Andrew J. Rózsa ◽  
Dan R. Kenshalo
Keyword(s):  
Rhesus Monkey ◽  
Signal Detection ◽  
Macaca Mulatta ◽  
Detection Method

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

Fault Detection in Linear Discrete Dynamic Systems by a Pattern Recognition of a Generalized-Likelihood-Ratio

1990 ◽  
Vol 112 (2) ◽  
pp. 276-282 ◽  
Author(s):  
S. Tanaka ◽  
P. C. Mu¨ller
Keyword(s):  
Pattern Recognition ◽  
Likelihood Ratio ◽  
Dynamic Systems ◽  
Abrupt Change ◽  
Detection Method ◽  
Discrete Dynamical System ◽  
Generalized Likelihood Ratio ◽  
Robust Detection ◽  
Chi Squared ◽  
Discrete Dynamic Systems

The detection of an abrupt change in the parameters of a linear discrete dynamical system is considered in the framework of the easily implemented generalized-likelihood-ratio (GLR) method. This paper proposes a robust detection method based on a pattern recognition of the maximum GLR provided by the conventional step-hypothesized GLR method. A numerical example demonstrates that the proposed method is highly superior to the conventional step-hypothesized GLR method and to the Chi-squared test in both detection rate and detection speed.

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