scholarly journals Use of independent component analysis to improve signal-to-noise ratio in multi-probe fluorescence microscopy

2014 ◽  
Vol 256 (2) ◽  
pp. 133-144 ◽  
Author(s):  
L. DAO ◽  
B. LUCOTTE ◽  
B. GLANCY ◽  
L.-C. CHANG ◽  
L.-Y. HSU ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Fluorescence Microscopy ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Enhancing the Signal-to-Noise Ratio of X-ray Diffraction Profiles by Smoothed Principal Component Analysis

2005 ◽  
Vol 77 (20) ◽  
pp. 6563-6570 ◽  
Author(s):  
Zeng Ping Chen ◽  
Julian Morris ◽  
Elaine Martin ◽  
Robert B. Hammond ◽  
Xiaojun Lai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Signal To Noise ◽  
X Ray ◽  
Noise Ratio

Application of Fault Diagnosis Method Based on cICA to Gearbox

2014 ◽  
Vol 664 ◽  
pp. 148-152
Author(s):  
Shuang Xi Jing ◽  
Song Tao Guo ◽  
Jun Fa Leng ◽  
Xing Yu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Early Application ◽  
Independent Components ◽  
Gear Fault ◽  
Constrained Independent Component Analysis ◽  
Non Gaussian

Constrained independent component analysis (cICA) is a new theory and new method derived from the independent component analysis (ICA).It can extract the desired independent components (ICs) from the data based on some prior information, thus overcoming the uncertainty of the traditional ICA. Early gearbox fault signals is often very weak ,characterized by non-Gaussian,low signal-to-noise ratio (SNR), which make the existing diagnosis methods in the diagnosis of early application restricted. In this paper,cICA algorithm is applied to gear fault diagnosis. Through the case studies verify the feasibility of this method to extract the desired independent components (ICs), indicating the applicability and effectiveness of the method.

SN Ratio Estimation and Speech Segment Detection of Extracted Signals Through Independent Component Analysis

2010 ◽  
Vol 14 (4) ◽  
pp. 364-374
Author(s):  
Takeshi Koya ◽  
◽  
Nobuo Iwasaki ◽  
Takaaki Ishibashi ◽  
Go Hirano ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Real World ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Snr Estimation ◽  
Speech Segment ◽  
Speech Segments ◽  
Source Signals

In real world environments where acoustic signals are contaminated with various noises, it is difficult to estimate the Signal-to-Noise Ratio (SNR) only from signals observed at microphones; the knowledge of acoustic transfer functions and original source signals is inevitable for SNR estimation. The present paper proposes a method to estimate SNR approximately in the real world environments without the knowledge of transfer functions and source signals: SNR is estimated after application of Independent Component Analysis (ICA) to the signals observed at microphones. Our proposed method also works as a speech segment detector since detection of speech segments are necessarily carried out in the course of SNR estimation. From several experimental results, the proposed method has been confirmed to be valid.

scholarly journals Application of independent component analysis to the iKAGRA data

2020 ◽  
Vol 2020 (5) ◽  
Author(s):  
T Akutsu ◽  
M Ando ◽  
K Arai ◽  
Y Arai ◽  
S Araki ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Gravitational Wave ◽  
Signal To Noise Ratio ◽  
Correlation Method ◽  
Real Data ◽  
Component Analysis ◽  
Independent Component ◽  
Strongly Correlated ◽  
Wave Detector ◽  
First Time

Abstract We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.

scholarly journals Pemampatan Citra Pas Foto dengan Menggunakan Algoritma Kompresi Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA)

2018 ◽  
Vol 17 (1) ◽  
pp. 102
Author(s):  
M. Azman Maricar ◽  
Oka Widyantara
Keyword(s):  
Principal Component Analysis ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Penelitian ini bertujuan untuk membandingkan hasil kompresi dari algoritma Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA) terhadap citra pas foto, guna menemukan hasil terbaik dari hasil citra kompresi yang kualitas hasilnya tidak berbeda jauh dengan citra aslinya. Alat ukur yang digunakan adalah Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil yang diperoleh dalam penelitian ini adalah rata-rata MSE dan PNSR algoritma PCA dapat dikatakan tinggi jika dibandingkan dengan algoritma JPEG. Namun dari segi kualitas citra yang dihasilkan tidak jauh berbeda dengan algoritma JPEG.Dapat dikatakan bahwa algoritma JPEG mampu menghasilkan citra yang lebih baik dibandingkan algoritma PCA. Namun, algoritma PCA tidaklah buruk untuk dijadikan alternatif dalam kompresi citra pas foto.

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