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Blind equalization of switching channels by ICA and learning of learning rate
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
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For switching channels, we use an updating rule to tune the learning rate of on-line algorithms automatically to follow the channel change. ...
The idea is applicable to improve all blind equalization algorithms to equalize switching channels. ...
To demonstrate the eectiveness of the learning of learning rate, we consider the blind equalization of a switching channel in Figure 4 . ...
doi:10.1109/icassp.1997.598898
dblp:conf/icassp/YangA97
fatcat:ct3csras3jdddkclx2xsen2ms4
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
1999
Neural Computation
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is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub-and supergaussian regimes. ...
This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. ...
L. was supported by the German Academic Exchange Program. M. ...
doi:10.1162/089976699300016719
pmid:9950738
fatcat:qe2yi2ikazcj7pyoalw65qeiu4
An Implementation of Nonlinear Multiuser Detection in Rayleigh Fading Channel
2006
EURASIP Journal on Wireless Communications and Networking
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Several comparisons and experiments are performed based on examining BER performance in AWGN and Rayleigh fading in order to verify the validity of the proposed blind ICA multiuser detector. ...
Unlike the conventional MMSE receiver, the proposed blind ICA multiuser detector is shown to be robust without training sequences and with only knowledge of the signature waveforms. ...
We also wish to acknowledge the constructive comments and suggestions provided by the reviewers. Their kind effort certainly contributed to the quality of this publication. ...
doi:10.1155/wcn/2006/45647
fatcat:rnw3l4h6ujfwblpcjknomxbmja
ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation
2000
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The algorithm can learn efficient codes for images containing both natural scenes and text. ...
AbstractÐAn unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their detailed comments and questions which improved the quality of the presentation of this paper. T.-W. ...
doi:10.1109/34.879789
fatcat:lqkvyynrsbaphnz7s6ybqvkmki
Mixed-signal VLSI microsystem for acoustic source separation
2013
2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS)
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We present an architecture of a microsystem designed for the task of blind source separation of acoustic sources that impinge on the miniature microphone array. ...
The spatial gradients of the acoustic wavefront are computed in the continuous-time domain and decomposed in the 16 subbands. In each band, we perform static independent component analysis. ...
The each channel implements a first-order band-pass filter with the factor Q being equal to 4. ...
doi:10.1109/mwscas.2013.6674863
dblp:conf/mwscas/LiLS13
fatcat:pkn3nymjrrgvlabtfkrmttgj5m
My Traces Learn What You Did in the Dark: Recovering Secret Signals Without Key Guesses
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we ask whether it is possible to take the other way around-directly learning the intermediate states from the side channel leakages. ...
Specifically, we propose several methods to convert the side channel leakages into effective ICA observations. ...
This work is supported by the National Basic Research Program of China (No.2013CB338002) and National Natural Science Foundation of China (No. 61272476, 61672509 and 61232009). ...
doi:10.1007/978-3-319-52153-4_21
fatcat:3d3imujaerap7bzquw45lvdk34
A Completed Adaptive De-Mixing Algorithm On Stiefel Manifold For Ica
2009
Zenodo
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Based on the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, Ma et al proposed a one-bit-matching learning algorithm on the Stiefel manifold ...
In this paper, an algorithm which can extract kurtosis and its sign of each independent source component directly from observation data is firstly introduced.With the algorithm , the one-bit-matching learning ...
INTRODUCTION O NE of the main problems in independent component analysis (ICA) is to recover independent sources which have been mixed by an unknown channel. ...
doi:10.5281/zenodo.1082024
fatcat:6l7bbeewmndtzaaflccln6mk3a
Blind Identification and Separation of Noisy Source Signals : Neural Network Approaches
1998
Systems, Control and Information
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Fbr the SISO blind equalization we have recently developed the fo11owing adaptive learning alg} rithms27),25),29):1. ...
-a new learning algorithm Let us consider a recurrent neural network for noisy ICA with the same number of inputs and outputs (m=n) described by (see Fig. 4) y(t)=x(t)-"VV'(t)y(t). (31) For m=n this model ...
, Control and Information Engineers Institute ofSystems, Control and Information Engineers ...
doi:10.11509/isciesci.42.2_63
fatcat:6xkansmiungsbngdudq2pxmwpy
Mixtures of Independent Component Analyzers for EEG Prediction
[chapter]
2012
Communications in Computer and Information Science
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The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. ...
Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated. ...
This work has been supported by Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006. ...
doi:10.1007/978-3-642-35251-5_46
fatcat:ifyo3ndex5amrb3tavzxlzwm5e
Harmonic Separation Based on Independent Component Analysis Method
2013
Journal of Computers
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During the harmonic separation, ICA is used to determine harmonic frequencies firstly. Then the least square method is used to determine amplitudes and phases of harmonic components. ...
This paper models the problem of harmonic separation as a blind source separation (BSS) task and the paper presents an algorithm called independent component analysis, which is widely used in the field ...
The method of neural network can apply neurons' self-learning algorithm to the harmonic detection, but its convergence will be influenced by the size of learning rate. ...
doi:10.4304/jcp.8.2.433-440
fatcat:hsdy2gomyfdjjomxyturrcea4a
A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera
2018
IEEE Access
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Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method ...
to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. ...
Color
Space
Blind Source
Separation
Method
Noise Reduction
Light
Equalization
Component selection
[4]
RGB
ICA
A Band pass Filter
N/A
Second Component
[5]
RGB
ICA
Moving Average ...
doi:10.1109/access.2018.2872756
fatcat:42ofwq7bp5dspeuk3oaeyvyn5e
Blind Nonnegative Source Separation Using Biological Neural Networks
2017
Neural Computation
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2017 pre-print arXiv:1706.00382v1 fatcat:kooknjbw3fflbewmdelifmg2k4
Blind source separation, i.e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing. ...
The novelty of our approach is that we formulate blind nonnegative source separation as a similarity matching problem and derive neural networks from the similarity matching objective. ...
Acknowledgments We thank Andrea Giovannucci, Eftychios Pnevmatikakis, Anirvan Sengupta and Sebastian Seung for useful discussions. DC is grateful to the IARPA MICRONS program for support. ...
doi:10.1162/neco_a_01007
pmid:28777718
fatcat:jwdinatjtjgcrpdoeqpaz64ixu
Modeling brain dynamic state changes with adaptive mixture independent component analysis
2018
NeuroImage
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We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. ...
learned portion of the data into statistically independent sources. ...
Makeig's participation was funded by a grant from the U.S. National Institutes of Health (R01 NS047293-13A1) and by a gift from The Swartz Foundation (Old Field, NY). ...
doi:10.1016/j.neuroimage.2018.08.001
pmid:30086409
pmcid:PMC6205696
fatcat:llnowq4nhbehxojg5sjtv6am4a
Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis
2002
Biological cybernetics
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In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. ...
In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. ...
A at step t; lðtÞ is a learning-rate parameter; Fig. 14. ...
doi:10.1007/s00422-001-0298-6
pmid:11956810
fatcat:7hfmdmppqndf5no5f73pcqmyyq
Sparse Correlation Kernel Analysis and Evolutionary Algorithm-Based Modeling of the Sensory Activity within the Rat's Barrel Cortex
[chapter]
2002
Lecture Notes in Computer Science
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for learning an overcomplete basis of the EP components by viewing it as probabilistic model of the observed data. ...
First, a slightly modified approach to the Independent Component Analysis (ICA) algorithm and its application to the investigation of Evoked Potentials (EP), and second, an Evolutionary Algorithm (EA) ...
Three of the components of the averaged 3 rd channel signal, presented in Fig. 5 are very similar to those obtained by ICA. ...
doi:10.1007/3-540-45665-1_16
fatcat:ge4dw76bire5hhfsli7uyycvza
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