scholarly journals Convolutive Complex-Valued Independent Component Analysis for Nonlinear Radar Signal Processing and Maritime Weak Target Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hamzeh Ghahramani ◽  
Naser Parhizgar ◽  
Bijan Abbasi Arand ◽  
Morteza Barari
Keyword(s):  
Independent Component Analysis ◽  
Real Life ◽  
Component Analysis ◽  
Independent Component ◽  
Sea Surface ◽  
Radar Signal ◽  
Computational Time ◽  
Detector Performance ◽  
Maritime Surveillance ◽  
Complex Valued

This paper first establishes a new complex independent component analysis (cICA) algorithm based on the spatiotemporal extension of complex-valued entropy bound minimization (CEBM) to separate received complex-valued radar signals. Next, we propose a new cICA-based detector with an open structure to find Swerling model targets, lognormal targets, and sea-surface small floating targets in coherent high-resolution maritime surveillance radars. The detector encountered three major problems when adopting cICA for detection and solved them using three effective suggestions. After performing cICA on the time series received by the radar, we obtained two different sources. Using the first and second theoretical and empirical moment estimates of the K-distribution, the target was selected between these two output source signals. Detector performance was verified quantitatively and qualitatively using the real-life IPIX radar database. Comprehensive experiments on this database with synthetic injected targets showed promising results. The computational time and sample size dependency of the proposed cICA algorithm were also discussed. Finally, a comparison of the detector with several novel detectors for detecting sea-surface floating small targets of the IPIX radar database demonstrated the proposed detector’s superiority.

Separation and Reproduction of Mixed Images Using 2-D Complex Wavelet Transform

2010 ◽  
Vol 36 ◽  
pp. 466-475
Author(s):  
Tsutomu Matsuura ◽  
Amirul Faiz ◽  
Kouji Kiryu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Wavelet Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Complex Wavelet ◽  
Complex Valued

The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.

scholarly journals Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

2011 ◽  
Vol 58 (10) ◽  
pp. 2794-2803 ◽  
Author(s):  
Hualiang Li ◽  
N. M. Correa ◽  
P. A. Rodriguez ◽  
V. D. Calhoun ◽  
T. Adali
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Density Model ◽  
Fmri Data ◽  
Complex Valued

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

scholarly journals Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Machine Learning ◽  
Independent Component Analysis ◽  
Singular Spectrum Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Computational Time ◽  
Learning Models ◽  
Eeg Signals ◽  
Ica Algorithm ◽  
Machine Learning Models

<pre>Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and non-alcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.</pre>

scholarly journals Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

2019 ◽  
Vol 15 (1) ◽  
pp. 1-12
Author(s):  
Ali Al-Saegh
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Speaker Identification ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Signals ◽  
Artifact Detection ◽  
Eeg Data ◽  
Ica Algorithm ◽  
Complex Valued

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

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