The Suppression of Powerline Noise for TEM with Coded Source Based on Independent Component Analysis

2019 ◽  
Vol 24 (4) ◽  
pp. 513-523
Author(s):  
Xin Wu ◽  
Guoqiang Xue ◽  
Shun Wang ◽  
Yongqiang Feng ◽  
Qimao Zhang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Noise Suppression ◽  
Binary Sequence ◽  
Component Analysis ◽  
Independent Component ◽  
Electromagnetic Signal ◽  
Electrical Measurements ◽  
Synchronous Sampling ◽  
Transient Electromagnetic ◽  
The Time Domain

Powerline noise is one of the most common contaminating types of the observed transient electromagnetic signal. For the conventional TEM method using the bipolar square wave as transmitter waveform, synchronous sampling is the main technology for powerline noise suppression, and notching filtering is sometimes used also. When the transmitter wave has been encoded based on pseudo-random binary sequence, it has proven difficult to achieve the effect by using the above-mentioned conventional methods for the suppression of powerline noise. This is due to the fact that the duration of each logic states of the pseudo-coded transmitter waveform is normally inconsistent. In this study, a method for the suppression of powerline noise is proposed, which is based on the independent component analysis method (ICA). In order to introduce the observation and processing details of this method more clearly, we attempt to apply this method to the time-domain electromagnetic method with coded source researched and developed by the institute of geology and geophysics of Chinese academy of sciences on the basis of the MTEM method. In terms of specific processes, the electrical measurements need to be simultaneously observed in the inline and crossline directions firstly, and then the data will be input into the processing procedures based on ICA method to realize the effective separation of the powerline noise and the useful signals. The processing results of the simulation data and field data show that the method proposed in this paper can suppress the powerline noise effectively, and the processing results build a good data foundation for the subsequent processing.

scholarly journals An Enhanced Impulsive Noise Suppression Method Based on Wavelet Denoising and ICA for Power Line Communication

2021 ◽  
Vol 13 (2) ◽  
pp. 25-31
Author(s):  
Wei Zhang ◽  
Zhongqiang Luo ◽  
Xingzhong Xiong ◽  
Kai Deng
Keyword(s):  
Independent Component Analysis ◽  
Prior Knowledge ◽  
Noise Suppression ◽  
Single Channel ◽  
Component Analysis ◽  
Wavelet Denoising ◽  
Independent Component ◽  
Analysis Algorithm ◽  
Source Signal ◽  
Independent Component Analysis Algorithm

Aiming at the problem of noise suppression in power lines, traditional noise suppression methods need to know prior knowledge and other defects. In this paper, blind source separation methods that do not need prior knowledge are selected. In the case of low signal-to-noise ratio, the basic independent component analysis algorithm has poor denoising effect. Therefore, this paper proposes a joint independent component analysis algorithm based on Wavelet denoising and Power independent component analysis (WD-PowerICA). In this work, firstly, the pseudo observation signal is constructed by weighted processing, and the blind separation model of single channel is transformed into a multi-channel determined model. Then, the proposed WD-PowerICA algorithm is used to separate noise and source signals. Finally, the simulation results demonstrate that the proposed algorithm in this paper can effectively separate noise and source signal under low SNR. At the same time, the stronger the α pulse noise is, the closer the WD-PowerICA separated signal is to the source signal. The proposed algorithm is better than the state of the art PowerICA algorithm.

DENOISING NONINVASIVE LASER-ULTRASOUND SIGNALS BASED ON COMBINATION OF WAVELET TRANSFORM AND INDEPENDENT COMPONENT ANALYSIS

2012 ◽  
Vol 24 (05) ◽  
pp. 411-423 ◽  
Author(s):  
Ali Sadr ◽  
Amirkeyvan Momtaz
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Component Analysis ◽  
Independent Component ◽  
Superior Performance ◽  
Laser Ultrasound ◽  
Wide Range ◽  
Ultrasound Signals

One of the medical applications of noninvasive laser-ultrasound is in the diagnosis of eye diseases. In such applications, specific features are detected in the received signals produced by generation and reflection of ultrasonic pulses in the intraocular interfaces. Due to the noisy nature of the process, the desired features are typically faded in the received signal. Therefore, denoising the signal is inevitable. The noise suppression technique, with the combination of wavelet transform and independent component analysis (WICA), is widely used for biomedical signals. However, when signals are not obtained by the multichannel recording systems, independent components (ICs) of the wavelet coefficients are not extracted completely by independent component analysis, and rejecting any extracted signals will cause data loss. This paper develops a new technique using improved version of WICA to eliminate this drawback for laser-ultrasound signals obtained from a single channel recording system. The approach is based on extracting ICs of wavelet detail coefficients of the noisy signal and applying the threshold on the ICs to reduce the noise. The proposed method is evaluated on two real laser-ultrasound signals by artificially adding white Gaussian noise to study the distortion measures of the filter outputs. The results of the study demonstrate superior performance compared with conventional denoising approaches such as WICA, wavelet denoising and median, Wiener and lowpass filters over a wide range of laser-ultrasound signal-to-noise ratios.

scholarly journals Independent Component Analysis of Climate Data: A New Look at EOF Rotation

2009 ◽  
Vol 22 (11) ◽  
pp. 2797-2812 ◽  
Author(s):  
A. Hannachi ◽  
S. Unkel ◽  
N. T. Trendafilov ◽  
I. T. Jolliffe
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Climate Data ◽  
Statistical Tool ◽  
Modes Of Variability ◽  
The North ◽  
Simple Structure Rotation ◽  
The Time Domain ◽  
The North Atlantic Oscillation

Abstract The complexity inherent in climate data makes it necessary to introduce more than one statistical tool to the researcher to gain insight into the climate system. Empirical orthogonal function (EOF) analysis is one of the most widely used methods to analyze weather/climate modes of variability and to reduce the dimensionality of the system. Simple structure rotation of EOFs can enhance interpretability of the obtained patterns but cannot provide anything more than temporal uncorrelatedness. In this paper, an alternative rotation method based on independent component analysis (ICA) is considered. The ICA is viewed here as a method of EOF rotation. Starting from an initial EOF solution rather than rotating the loadings toward simplicity, ICA seeks a rotation matrix that maximizes the independence between the components in the time domain. If the underlying climate signals have an independent forcing, one can expect to find loadings with interpretable patterns whose time coefficients have properties that go beyond simple noncorrelation observed in EOFs. The methodology is presented and an application to monthly means sea level pressure (SLP) field is discussed. Among the rotated (to independence) EOFs, the North Atlantic Oscillation (NAO) pattern, an Arctic Oscillation–like pattern, and a Scandinavian-like pattern have been identified. There is the suggestion that the NAO is an intrinsic mode of variability independent of the Pacific.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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