scholarly journals Study of Very Low Frequency Signal by Independent Component Analysis Observed Form DEMETER Satellite

2012 ◽  
Vol 2 (5) ◽  
pp. 21-28 ◽  
Author(s):  
Shivali Verma
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Frequency Signal ◽  
Very Low Frequency ◽  
Demeter Satellite

Digital Audio Watermarking Scheme Based on Independent Component Analysis in DWT Domain

2011 ◽  
Vol 219-220 ◽  
pp. 1121-1125 ◽  
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Reza Asharif Mohammad
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Extraction Process ◽  
Independent Component ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Digital Audio Watermarking ◽  
Watermarking Scheme

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in DWT domain. The embedding process make full use of the multi-resolution characteristic of discrete wavelet transform (DWT), performing 3-level DWT. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. Then constructing the ICA model to embed the watermarking. The extraction process is similar with ICA’s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency.

scholarly journals Investigations into resting-state connectivity using independent component analysis

2005 ◽  
Vol 360 (1457) ◽  
pp. 1001-1013 ◽  
Author(s):  
Christian F Beckmann ◽  
Marilena DeLuca ◽  
Joseph T Devlin ◽  
Stephen M Smith
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Temporal Structure ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Analytical Technique ◽  
Spatio Temporal ◽  
Resting State Connectivity

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

Digital Audio Watermarking Scheme Based on Independent Component Analysis in Stereo Sound

2012 ◽  
Vol 459 ◽  
pp. 469-473
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Mohammad Reza Asharif
Keyword(s):  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Extraction Process ◽  
Independent Component ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Digital Audio Watermarking ◽  
Watermarking Scheme

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in stereo sound. In order to make full use of the multi-channel characteristic of stereo sound, The watermarking embedded into the two channel, half respectively. Also using the multi-resolution characteristic of discrete wavelet transform, performing 3-level DWT in each channel. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. The extraction process is similar with ICA’ s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency

scholarly journals Characterization of Strombolian events by using independent component analysis

2004 ◽  
Vol 11 (4) ◽  
pp. 453-461 ◽  
Author(s):  
A. Ciaramella ◽  
E. De Lauro ◽  
S. De Martino ◽  
B. Di Lieto ◽  
M. Falanga ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Frequency ◽  
Low Frequency ◽  
Component Analysis ◽  
Frequency Component ◽  
Independent Component ◽  
Stromboli Volcano ◽  
Pipe Model ◽  
Organ Pipe

Abstract. We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s.

Functional Networks in the Anesthetized Rat Brain Revealed by Independent Component Analysis of Resting-State fMRI

2010 ◽  
Vol 103 (6) ◽  
pp. 3398-3406 ◽  
Author(s):  
R. Matthew Hutchison ◽  
Seyed M. Mirsattari ◽  
Craig K. Jones ◽  
Joseph S. Gati ◽  
L. Stan Leung
Keyword(s):  
Independent Component Analysis ◽  
Rat Brain ◽  
A Priori ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Anterior Cingulate ◽  
Functional Networks ◽  
Seed Region ◽  
Subcortical Structures

The rodent brain is organized into functional networks that can be studied through examination of synchronized low-frequency spontaneous fluctuations (LFFs) of the functional magnetic resonance imaging -blood-oxygen-level-dependent (BOLD) signal. In this study, resting networks of LFFs were estimated from the whole-brain BOLD signals using independent component analysis (ICA). ICA provides a hypothesis-free technique for determining the functional connectivity map that does not require a priori selection of a seed region. Twenty Long-Evans rats were anesthetized with isoflurane (1%, n = 10) or ketamine/xylazine (50/6 mg · kg−1 · h−1 ip, n = 10) and imaged for 5–10 min in a 9.4 T MR scanner without experimental stimulation or task requirement. Independent, synchronous LFFs of BOLD signals were found to exist in clustered, bilaterally symmetric regions of both cortical and subcortical structures, including primary and secondary somatosensory cortices, motor cortices, visual cortices, posterior and anterior cingulate cortices, hippocampi, caudate-putamen, and thalamic and hypothalamic nuclei. The somatosensory and motor cortices typically demonstrated both symmetric and asymmetric components with unique frequency profiles. Similar independent network components were found under isoflurane and ketamine/xylazine anesthesia. The report demonstrates, for the first time, 12 independent resting networks that are bilaterally synchronous in different cortical and subcortical areas of the rat brain.

SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN

2010 ◽  
Vol 20 (04) ◽  
pp. 279-292 ◽  
Author(s):  
FENGYU CONG ◽  
IGOR KALYAKIN ◽  
TIINA HUTTUNEN-SCOTT ◽  
HONG LI ◽  
HEIKKI LYYTINEN ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Digital Filter ◽  
Mismatch Negativity ◽  
Low Frequency ◽  
Component Analysis ◽  
Event Related Potential ◽  
Independent Component ◽  
Frequency Noise ◽  
Theoretical Expectation ◽  
Single Trial

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential — mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

An Enhanced CICA Method and Its Application to Multistage Gearbox Low-frequency Fault Feature Extraction

2020 ◽  
Vol 13 (2) ◽  
pp. 285-294
Author(s):  
Junfa Leng ◽  
Penghui Shi ◽  
Shuangxi Jing ◽  
Chenxu Luo
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Signals ◽  
Gear Fault ◽  
Weak Fault ◽  
Constrained Independent Component Analysis ◽  
Ica Algorithm

Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise. Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise and measured noise are introduced into the independent component analysis (ICA) algorithm model, and then an enhanced Constrained Independent Component Analysis (CICA) method is proposed. The proposed method is implemented by combining the traditional Wavelet Transform (WT) with Constrained Independent Component Analysis (CICA). Results: In this method, the role of a supplementary step of WT before CICA analysis is explored to effectively reduce the influence of strong noise. Conclusion: Through the simulations and experiments, the results show that the proposed method can effectively decrease noise and enhance feature extraction effect of CICA method, and extract the desired gear fault feature, especially the low-frequency and weak fault feature.

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