IEEE Organizations related to Blind Source Separation

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Conferences related to Blind Source Separation

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Blind Source Separation

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Most published Xplore authors for Blind Source Separation

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Xplore Articles related to Blind Source Separation

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Per tone blind signal separation for a DMT-DS-CDMA system

2000 10th European Signal Processing Conference, 2000

In this paper, we discuss a per tone blind signal separation method for a discrete multi-tone direct-sequence code-division multiple-access (DMT-DS- CDMA) system. We show that the use of a cyclic prefix reduces the inter-chip interference (ICI). As a result, when we increase the length of the cyclic prefix, using the minimal required amount of temporal smoothing, the computational complexity of ...


Blind source separation by simultaneous third-order tensor diagonalization

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

We develop a technique for Blind Source Separation based on simultaneous diagonalization of (linear combinations of) third-order tensor "slices" of the fourth-order cumulant. It will be shown that, in a Jacobi-type iteration scheme, the computation of an elementary rotation can be reformulated in terms of a simultaneous matrix diagonalization.


Fast convolutive blind speech separation via subband adaptation

2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684), 2003

Summary form only given. We consider the problem of blind source separation (BSS) applied to speech signals. Due to reverberation, BSS in the time domain is usually expensive in terms of computations. We propose a subband BSS system based on the use of adaptive feedback de-mixing networks in an oversampled uniform DFT filter bank structure. We show that the computational ...


Analyzing satellite image with blind separation of sources, knowledge based system and fusion of multisource images

Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004., 2004

In this paper, an intelligent satellite image analysis is proposed from blind source separation (BSS) method, knowledge based system and fusion of multisource images. The experimental results demonstrate that the proposed hybrid method provides an effective intelligent technique for remotely sensed images classification. This approach is validated on optical images of the satellite SPOT4 and radar images of the satellite ...


Blind source separation based on multi-user kurtosis criteria

2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060), 2000

A novel technique for the blind source separation (BSS) of mutually independent and identically distributed i.i.d. discrete-time sequences is presented. The observed signals are assumed mixed through a narrow-band (memoryless) multiple-input-multiple-output (MIMO) noisy channel and are then processed by a linear MIMO receiver, whose outputs should ideally match the transmitted signals. In the proposed approach (called the multi-user kurtosis (MUK) ...


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Educational Resources on Blind Source Separation

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IEEE.tv Videos

GHTC 2012 Jim Fruchterman Keynote
Bug Labs: How an Open Source Gadget Works
Open Source Software: Opportunities for Social Innovation from Around the World
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
Dual-Core 60GHz Push-Push VCO - Vadim Issakov - RFIC Showcase 2018
Xun Luo - University of Calgary Talk
2011 IEEE Richard W. Hamming Medal - Toby Berger
IEEE World Forum on Internet of Things - Milan, Italy - Benjamin Cabe and Charalampos Doukas - IoT Device Management: Using Eclipse IoT Open-Source Tools and Frameworks - Part 2
Driving Community Engagement as Part of Open Innovation - Nithya Ruff - IEEE Sarnoff Symposium, 2019
IEEE World Forum on Internet of Things - Milan, Italy - Benjamin Cabe and Charalampos Doukas - IoT Device Management: Using Eclipse IoT Open-Source Tools and Frameworks - Part 3
Niobium Manufacturing for Superconductivity - ASC-2014 Plenary series - 5 of 13 - Tuesday 2014/8/12
Accelerating Autonomy at the Edge - Stan Schneider at Fog World Congress 2018
Fog/Edge Computing and Robotics - Flavio Bonomi at Fog World Congress 2018
Real-Time, Event-Driven Applications on the Edge - Blaine Mathieu at Fog World Congress 2018
Edge to Fog Panel Discussion
Envelope Time-Domain Characterizations to Assess In-Band Linearity Performances of Pre-Matched MASMOS Power Amplifier: RFIC Interactive Forum 2017
IEEE World Forum on Internet of Things - Milan, Italy - Benjamin Cabe and Charalampos Doukas - IoT Device Management: Using Eclipse IoT Open-Source Tools and Frameworks - Part 4
Bringing AI to Edge at Scale - Edy Liongosari at Fog World Congress 2018
Stochastic Sampling Machine for Bayesian Inference - Raphael Frisch at INC 2019
Artificial Retinas give Second Sight - LSGC - Gianluca Lazzi

IEEE-USA E-Books

  • Per tone blind signal separation for a DMT-DS-CDMA system

    In this paper, we discuss a per tone blind signal separation method for a discrete multi-tone direct-sequence code-division multiple-access (DMT-DS- CDMA) system. We show that the use of a cyclic prefix reduces the inter-chip interference (ICI). As a result, when we increase the length of the cyclic prefix, using the minimal required amount of temporal smoothing, the computational complexity of the proposed method decreases. Moreover, we demonstrate that this reduction in computational complexity even comes with a performance improvement.

  • Blind source separation by simultaneous third-order tensor diagonalization

    We develop a technique for Blind Source Separation based on simultaneous diagonalization of (linear combinations of) third-order tensor "slices" of the fourth-order cumulant. It will be shown that, in a Jacobi-type iteration scheme, the computation of an elementary rotation can be reformulated in terms of a simultaneous matrix diagonalization.

  • Fast convolutive blind speech separation via subband adaptation

    Summary form only given. We consider the problem of blind source separation (BSS) applied to speech signals. Due to reverberation, BSS in the time domain is usually expensive in terms of computations. We propose a subband BSS system based on the use of adaptive feedback de-mixing networks in an oversampled uniform DFT filter bank structure. We show that the computational cost can be significantly decreased if BSS is carried out in subbands due to the possibility of reducing the sampling rate. Experiments with real speech signals, conducted with two-input two-output BSS systems using oversampled 32-subband and fullband adaptation, indicate that separation quality and distortion are similar for both systems. However, the proposed subband system is more than 10 times faster computationally than the fullband one.

  • Analyzing satellite image with blind separation of sources, knowledge based system and fusion of multisource images

    In this paper, an intelligent satellite image analysis is proposed from blind source separation (BSS) method, knowledge based system and fusion of multisource images. The experimental results demonstrate that the proposed hybrid method provides an effective intelligent technique for remotely sensed images classification. This approach is validated on optical images of the satellite SPOT4 and radar images of the satellite ERS2 acquiring on the central Tunisian region for five classes of land.

  • Blind source separation based on multi-user kurtosis criteria

    A novel technique for the blind source separation (BSS) of mutually independent and identically distributed i.i.d. discrete-time sequences is presented. The observed signals are assumed mixed through a narrow-band (memoryless) multiple-input-multiple-output (MIMO) noisy channel and are then processed by a linear MIMO receiver, whose outputs should ideally match the transmitted signals. In the proposed approach (called the multi-user kurtosis (MUK) algorithm), the linear receiver's matrix setting is computed adaptively based on the optimization of a constrained statistical criterion that involves only second and fourth order statistics of the receiver's output. At each iteration, the algorithm combines a stochastic gradient adaptation with a Gram-Shmidt orthogonalization that enforces its criterion's constraints. The analysis of its stationary points, reveals that it is globally convergent to a zero forcing -ZF (or decorrelating) solution, both in the absence of noise and in the presence of spatio-temporally white additive Gaussian noise.

  • Independent component analysis with mixture density model and its application to localize the brain alpha activity in fMRI and EEG

    Recently, independent component analysis (ICA) has been introduced to solve the blind source separation problem. In the original and extended versions of ICA, nonlinearity functions are fixed to have specific forms such as supergaussian or subgaussian, limiting their performance. In this paper, we utilized ICA with mixture density model such that any assumption about the source density is not required, thus better separation is possible by matching flexible parametric nonlinearity to any kind of density of sources. Through simulation studies, the algorithm was validated and its better performance was demonstrated in comparison to other versions of ICA. Then mixture density ICA was applied to functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) data to localize the independent sources for alpha activity. We found that there is a strong spatial correlation between the sources in fMRI and EEG, proving the usefulness of our approach in its application to source separation problem in biomedical signal processing.

  • Adaptive blind source separation for virtually any source probability density function

    Blind source separation (BSS) aims to recover a set of statistically independent source signals from a set of linear mixtures of the same sources. In the noiseless real-mixture two-source two-sensor scenario, once the observations are whitened (decorrelated and normalized), only a Givens rotation matrix remains to be identified in order to achieve the source separation. In this paper an adaptive estimator of the angle that characterizes such a rotation is derived. It is shown to converge to a stable valid separation solution with the only condition that the sum of source kurtosis be distinct from zero. An asymptotic performance analysis is carried out, resulting in a closed-form expression for the asymptotic probability density function of the proposed estimator. It is shown how the estimator can be incorporated into a complete adaptive source separation system by combining it with an adaptive prewhitening strategy and how it can be useful in a general BSS scenario of more than two signals by means of a pairwise approach. A variety of simulations assess the accuracy of the asymptotic results, display the properties of the estimator (such as its robust fast convergence), and compare this on-line BSS implementation with other adaptive BSS procedures.

  • Spatial evolutionary spectrum for DOA estimation and blind signal separation

    We combine the concepts of evolutionary spectrum and array processing. We present a cross-power stationary periodogram for both direction-of-arrival (DOA) estimation and blind separation of nonstationary signals. We model the nonstationary signals received by each array sensor as a sum of complex sinusoids with time-varying amplitudes. These magnitudes carry information about the DOA that may also be time-varying. We first estimate the time- varying amplitudes using estimators obtained by minimizing the mean-squared error. Then using the estimated time-varying amplitudes, we estimate the evolutionary cross-power distributions of the sensor. Next, using cross-power estimates at time-frequency points interest, we estimate the DOAs using one of the existing methods. If the directions are time varying, we choose time- frequency points around the time of interest to estimate spontaneous source locations. If the sources are stationary, time-frequency points of interest can be combined for the estimation of fixed directions. Whitening and subspace methods used to find the mixing matrix and separate nonstationary signals received by the array. We present examples illustrating the performance of the proposed algorithms.

  • A common neural-network model for unsupervised exploratory data analysis and independent component analysis

    This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised exploratory data analysis and data visualization. Experimental results confirm the attractiveness of this technique for exploratory data analysis and an empirical comparison is made with the recently proposed generative topographic mapping (GTM) and standard principal component analysis (PCA). Based on standard probability density models a generic nonlinearity is developed which allows both (1) identification and visualization of dichotomised clusters inherent in the observed data and (2) separation of sources with arbitrary distributions from mixtures, whose dimensionality may be greater than that of number of sources. The resulting algorithm is therefore also a generalized neural approach to independent component analysis (ICA) and it is considered to be a promising method for analysis of real-world data that will consist of sub- and super- Gaussian components such as biomedical signals.

  • A blind signal separation method for multiuser communications

    A new approach based on the constant modulus (CM) criterion is proposed to separate instantaneous linear mixtures of signals using a linear memoryless multiple input multiple output (MIMO) system. Even though a nonconvex cost function is minimized, analyses show that minima correspond to parameter settings where perfect separation is achieved.



Standards related to Blind Source Separation

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Jobs related to Blind Source Separation

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