Convolutive Blind Source Separation Methods
Pedersen, Michael Syskind; Larsen, Jan; Kjems, Ulrik;
2008-01-01
During the past decades, much attention has been given to the separation of mixed sources, in particular for the blind case where both the sources and the mixing process are unknown and only recordings of the mixtures are available. In several situations it is desirable to recover all sources from...
Blind source separation dependent component analysis
Xiang, Yong; Yang, Zuyuan
2015-01-01
This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.
Transform domain steganography with blind source separation
Jouny, Ismail
2015-05-01
This paper applies blind source separation or independent component analysis for images that may contain mixtures of text, audio, or other images for steganography purposes. The paper focuses on separating mixtures in the transform domain such as Fourier domain or the Wavelet domain. The study addresses the effectiveness of steganography when using linear mixtures of multimedia components and the ability of standard blind sources separation techniques to discern hidden multimedia messages. Mixing in the space, frequency, and wavelet (scale) domains is compared. Effectiveness is measured using mean square error rate between original and recovered images.
Blind source separation theory and applications
Yu, Xianchuan; Xu, Jindong
2013-01-01
A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies The book presents an overview of Blind Source Separation, a relatively new signal processing method. Due to the multidisciplinary nature of the subject, the book has been written so as to appeal to an audience from very different backgrounds. Basic mathematical skills (e.g. on matrix algebra and foundations of probability theory) are essential in order to understand the algorithms, although the book is written in an introductory, accessible style. This book offers
Blind Source Separation Using Hessian Evaluation
Jyothirmayi M; Elavaar Kuzhali S; Sethu Selvi S
2012-01-01
This paper focuses on the blind image separation using sparse representation for natural images. The statistics of the natural image is based on one particular statistical property called sparseness, which is closely related to the super-gaussian distribution. Since natural images can have both gaussian and non gaussian distribution, the original infomax algorithm cannot be directly used for source separation as it is better suited to estimate the super-gaussian sources. Hence, we explore the...
Blind Source Separation: the Sparsity Revolution
Bobin, J.; Starck, Jean-Luc; Moudden, Y.; Fadili, Jalal M.
2008-01-01
Over the last few years, the development of multi-channel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-called blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved present some quantitatively measurable diversity. Recently, sparsity and morphological diversity have ...
Blind Source Separation for Speaker Recognition Systems
Unverdorben, Michael; Rothbucher, Martin; Diepold, Klaus
2014-01-01
In this thesis, a combined blind source separation (BSS) and speaker recognition approach for teleconferences is studied. By using a microphone array, consisting of eight microphones, different methods to perform overdetermined independent vector analysis (IVA) are compared. One method is to select a subset of microphones or all available microphones to perform IVA. The second method, the so called subspace method, that utilizes a principal component analysis (PCA) for dimensionality reductio...
Grading learning for blind source separation
张贤达; 朱孝龙; 保铮
2003-01-01
By generalizing the learning rate parameter to a learning rate matrix, this paper proposes agrading learning algorithm for blind source separation. The whole learning process is divided into threestages: initial stage, capturing stage and tracking stage. In different stages, different learning rates areused for each output component, which is determined by its dependency on other output components. Itis shown that the grading learning algorithm is equivariant and can keep the separating matrix from be-coming singular. Simulations show that the proposed algorithm can achieve faster convergence, bettersteady-state performance and higher numerical robustness, as compared with the existing algorithmsusing fixed, time-descending and adaptive learning rates.
Blind Source Separation For Ion Mobility Spectra
Miniaturization is a powerful trend for smart chemical instrumentation in a diversity of applications. It is know that miniaturization in IMS leads to a degradation of the system characteristics. For the present work, we are interested in signal processing solutions to mitigate limitations introduced by limited drift tube length that basically involve a loss of chemical selectivity. While blind source separation techniques (BSS) are popular in other domains, their application for smart chemical instrumentation is limited. However, in some conditions, basically linearity, BSS may fully recover the concentration time evolution and the pure spectra with few underlying hypothesis. This is extremely helpful in conditions where non-expected chemical interferents may appear, or unwanted perturbations may pollute the spectra. SIMPLISMA has been advocated by Harrington et al. in several papers. However, more modern methods of BSS for bilinear decomposition with the restriction of positiveness have appeared in the last decade. In order to explore and compare the performances of those methods a series of experiments were performed.
Bayesian Blind Source Separation of Positive Non Stationary Sources
Ichir, Mahieddine M.; Mohammad-Djafari, Ali
2004-11-01
In this contribution, we address the problem of blind non negative source separation. This problem finds its application in many fields of data analysis. We propose herein a novel approach based on Gamma mixture probability priors: Gamma densities to constraint the unobserved sources to lie on the positive half plane; a mixture density with a first order Markov model on the associated hidden variables to account for eventual non stationarity on the sources. Posterior mean estimates are obtained via appropriate Monte Carlo Markov Chain sampling.
Blind source separation using second-order cyclostationary statistics
Abed-Meraim, Karim; Xiang, Yong; Manton, Jonathan H.; Hua, Yingbo
2001-01-01
This paper studies the blind source separation (BSS) problem with the assumption that the source signals are cyclostationary. Identifiability and separability criteria based on second-order cyclostationary statistics (SOCS) alone are derived. The identifiability condition is used to define an appropriate contrast function. An iterative algorithm (ATH2) is derived to minimize this contrast function. This algorithm separates the sou...
Novel blind source separation algorithm using Gaussian mixture density function
孔薇; 杨杰; 周越
2004-01-01
The blind source separation (BSS) is an important task for numerous applications in signal processing, communications and array processing. But for many complex sources blind separation algorithms are not efficient because the probability distribution of the sources cannot be estimated accurately. So in this paper, to justify the ME(maximum enteropy) approach, the relation between the ME and the MMI(minimum mutual information) is elucidated first. Then a novel algorithm that uses Gaussian mixture density to approximate the probability distribution of the sources is presented based on the ME approach. The experiment of the BSS of ship-radiated noise demonstrates that the proposed algorithm is valid and efficient.
Adaptive blind source separation with HRTFs beamforming preprocessing
Maazaoui, Mounira; Abed-Meraim, Karim; Grenier, Yves
2012-01-01
We propose an adaptive blind source separation algorithm in the context of robot audition using a microphone array. Our algorithm presents two steps: a fixed beamforming step to reduce the reverberation and the background noise and a source separation step. In the fixed beamforming preprocessing, we build the beamforming filters using the Head Related Transfer Functions (HRTFs) which allows us to take into consideration the effect of the robot's head on the near acoustic field. In the source ...
Sparsity in Bayesian Blind Source Separation and Deconvolution
Šmídl, Václav; Tichý, Ondřej
Berlin Heidelberg: Springer, 2013, s. 548-563. (Lecture Notes in Computer Science. vol. 8189. part II). ISBN 978-3-642-40990-5. ISSN 0302-9743. [The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Praha (CZ), 24.09.2013-26.09.2013] R&D Projects: GA ČR GA13-29225S Keywords : Blind Source Separation * Deconvolution * Sparsity * Scintigraphy Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/AS/tichy-sparsity in bayesian blind source separation and deconvolution.pdf
Single channel blind source separation based on ICA feature extraction
无
2007-01-01
A new technique is proposed to solve the blind source separation (BSS) given only a single channel observation. The basis functions and the density of the coefficients of source signals learned by ICA are used as the prior knowledge. Based on the learned prior information the learning rules of single channel BSS are presented by maximizing the joint log likelihood of the mixed sources to obtain source signals from single observation,in which the posterior density of the given measurements is maximized. The experimental results exhibit a successful separation performance for mixtures of speech and music signals.
Sparsity and Morphological Diversity in Blind Source Separation
Bobin, Jérome; Starck, Jean-Luc; Fadili, Jalal M.; Moudden, Yassir
2007-01-01
Over the last few years, the development of multichannel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-caIled blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved present some quantitatively measurable diversity. Recently, sparsity and morphological diversity have e...
Underdetermined Blind Audio Source Separation Using Modal Decomposition
Abdeldjalil Aïssa-El-Bey; Karim Abed-Meraim; Yves Grenier
2007-01-01
This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, we propose a two-step approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (grouping of the components belonging to the same source) using vector clustering. For the signa...
Blind Separation of Piecewise Stationary NonGaussian Sources
Koldovský, Zbyněk; Málek, J.; Tichavský, Petr; Deville, Y.; Hosseini, S.
2009-01-01
Roč. 89, č. 12 (2009), s. 2570-2584. ISSN 0165-1684 R&D Projects: GA MŠk 1M0572; GA ČR GA102/09/1278 Grant ostatní: GA ČR(CZ) GA102/07/P384 Institutional research plan: CEZ:AV0Z10750506 Keywords : Independent component analysis * blind source separation * Cramer-Rao lower bound Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.135, year: 2009 http://library.utia.cas.cz/separaty/2009/SI/tichavsky-blind separationofpiecewisestationarynon-gaussiansources.pdf
Blind source separation advances in theory, algorithms and applications
Wang, Wenwu
2014-01-01
Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms, and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.
FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION
WANG Junfeng; SHI Tielin; HE Lingsong; YANG Shuzi
2006-01-01
The concepts, principles and usages of principal component analysis (PCA) and independent component analysis (ICA) are interpreted. Then the algorithm and methodology of ICA-based blind source separation (BSS), in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not.
Bayesian Blind Source Separation with Unknown Prior Covariance
Tichý, Ondřej; Šmídl, Václav
Cham : Springer, 2015 - (Vincent, E.; Yeredor, A.; Koldovský, Z.; Tichavský, P.), s. 352-359 ISBN 978-3-319-22481-7. ISSN 0302-9743. - (Lecture Notes in Computer Science. 9237). [12th International Conference on Latent Variable Analysis and Signal Separation. Liberec (CZ), 25.08.2015-28.08.2015] R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Blind source separation * Covariance model * Variational Bayes approximation * Non-negative matrix factorization Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2015/AS/tichy-0447092.pdf
Rigid Structure from Motion from a Blind Source Separation Perspective
Fortuna, Jeff
2013-01-01
We present an information theoretic approach to define the problem of structure from motion (SfM) as a blind source separation one. Given that for almost all practical joint densities of shape points, the marginal densities are non-Gaussian, we show how higher-order statistics can be used to provide improvements in shape estimates over the methods of factorization via Singular Value Decomposition (SVD), bundle adjustment and Bayesian approaches. Previous techniques have either explicitly or implicitly used only second-order statistics in models of shape or noise. A further advantage of viewing SfM as a blind source problem is that it easily allows for the inclusion of noise and shape models, resulting in Maximum Likelihood (ML) or Maximum a Posteriori (MAP) shape and motion estimates. A key result is that the blind source separation approach has the ability to recover the motion and shape matrices without the need to explicitly know the motion or shape pdf. We demonstrate that it suffices to know whether the pdf is sub-or super-Gaussian (i.e., semi-parametric estimation) and derive a simple formulation to determine this from the data. We provide extensive experimental results on synthetic and real tracked points in order to quantify the improvement obtained from this technique. PMID:23682206
Underdetermined Blind Audio Source Separation Using Modal Decomposition
Aïssa-El-Bey Abdeldjalil
2007-01-01
Full Text Available This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal components. Based on this representation, we propose a two-step approach consisting of a signal analysis (extraction of the modal components followed by a signal synthesis (grouping of the components belonging to the same source using vector clustering. For the signal analysis, two existing algorithms are considered and compared: namely the EMD (empirical mode decomposition algorithm and a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its validity for both instantaneous and convolutive mixtures and its ability to separate more sources than sensors. Simulation results are given to compare and assess the performance of the proposed algorithms.
Underdetermined Blind Audio Source Separation Using Modal Decomposition
Abdeldjalil Aïssa-El-Bey
2007-03-01
Full Text Available This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal components. Based on this representation, we propose a two-step approach consisting of a signal analysis (extraction of the modal components followed by a signal synthesis (grouping of the components belonging to the same source using vector clustering. For the signal analysis, two existing algorithms are considered and compared: namely the EMD (empirical mode decomposition algorithm and a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its validity for both instantaneous and convolutive mixtures and its ability to separate more sources than sensors. Simulation results are given to compare and assess the performance of the proposed algorithms.
Blind Source Separation for Robot Audition using fixed HRTF beamforming
Maazaoui, Mounira; Abed-Meraim, Karim; Grenier, Yves
2012-01-01
In this article, we present a two-stage blind source separation (BSS) algorithm for robot audition. The first stage consists in a fixed beamforming preprocessing to reduce the reverberation and the environmental noise. Since we are in a robot audition context, the manifold of the sensor array in this case is hard to model due to the presence of the head of the robot, so we use pre-measured head related transfer functions (HRTFs) to estimate the beamforming filters. The use of the HRTF to esti...
Blind source separation for robot audition using fixed HRTF beamforming
Maazaoui, Mounira; Abed-Meraim, Karim; Grenier, Yves
2012-12-01
In this article, we present a two-stage blind source separation (BSS) algorithm for robot audition. The first stage consists in a fixed beamforming preprocessing to reduce the reverberation and the environmental noise. Since we are in a robot audition context, the manifold of the sensor array in this case is hard to model due to the presence of the head of the robot, so we use pre-measured head related transfer functions (HRTFs) to estimate the beamforming filters. The use of the HRTF to estimate the beamformers allows to capture the effect of the head on the manifold of the microphone array. The second stage is a BSS algorithm based on a sparsity criterion which is the minimization of the l 1 norm of the sources. We present different configuration of our algorithm and we show that it has promising results and that the fixed beamforming preprocessing improves the separation results.
An autonomous surveillance system for blind sources localization and separation
Wu, Sean; Kulkarni, Raghavendra; Duraiswamy, Srikanth
2013-05-01
This paper aims at developing a new technology that will enable one to conduct an autonomous and silent surveillance to monitor sound sources stationary or moving in 3D space and a blind separation of target acoustic signals. The underlying principle of this technology is a hybrid approach that uses: 1) passive sonic detection and ranging method that consists of iterative triangulation and redundant checking to locate the Cartesian coordinates of arbitrary sound sources in 3D space, 2) advanced signal processing to sanitizing the measured data and enhance signal to noise ratio, and 3) short-time source localization and separation to extract the target acoustic signals from the directly measured mixed ones. A prototype based on this technology has been developed and its hardware includes six B and K 1/4-in condenser microphones, Type 4935, two 4-channel data acquisition units, Type NI-9234, with a maximum sampling rate of 51.2kS/s per channel, one NI-cDAQ 9174 chassis, a thermometer to measure the air temperature, a camera to view the relative positions of located sources, and a laptop to control data acquisition and post processing. Test results for locating arbitrary sound sources emitting continuous, random, impulsive, and transient signals, and blind separation of signals in various non-ideal environments is presented. This system is invisible to any anti-surveillance device since it uses the acoustic signal emitted by a target source. It can be mounted on a robot or an unmanned vehicle to perform various covert operations, including intelligence gathering in an open or a confined field, or to carry out the rescue mission to search people trapped inside ruins or buried under wreckages.
Exploiting Narrowband Efficiency for Broadband Convolutive Blind Source Separation
Aichner Robert
2007-01-01
Full Text Available Based on a recently presented generic broadband blind source separation (BSS algorithm for convolutive mixtures, we propose in this paper a novel algorithm combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived as a special case of the generic broadband algorithm. This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximations.
Blind source separation problem in GPS time series
Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.
2016-04-01
A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition
Koldovský, Zbyněk; Tichavský, Petr; Málek, J.
Vol. 6365. Heidelberg : Springer-Verlag, 2010 - (Gavrilova, M.; Kumar, V.; Mun, Y.; Tan, C.; Gervasi, O.), s. 17-24 ISBN 978-3-642-15994-7. [Latent Variable Analysis and Signal Separation. St. Malo (FR), 27.09.2010-30.09.2010] R&D Projects: GA MŠk 1M0572; GA ČR GA102/09/1278 Institutional research plan: CEZ:AV0Z10750506 Keywords : blind source separation * audio * convolutive mixture Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/SI/tichavsky-time-domain blind audio source separation method producing separating filters of generalized feedforward structure.pdf
Convergent Bayesian formulations of blind source separation and electromagnetic source estimation
Knuth, Kevin H.; Vaughan Jr, Herbert G.
2015-01-01
We consider two areas of research that have been developing in parallel over the last decade: blind source separation (BSS) and electromagnetic source estimation (ESE). BSS deals with the recovery of source signals when only mixtures of signals can be obtained from an array of detectors and the only prior knowledge consists of some information about the nature of the source signals. On the other hand, ESE utilizes knowledge of the electromagnetic forward problem to assign source signals to th...
Decentralized modal identification using sparse blind source separation
Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time–frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure
From Binaural to Multichannel Blind Source Separation using Fixed Beamforming with HRTFs
Maazaoui, Mounira; Grenier, Yves; Abed-Meraim, Karim
2012-01-01
In this article, we are interested in the problem of blind source separation (BSS) for the robot audition, we study the performance of blind source separation with a varying number of sensors in a microphone array placed in the head of an infant size dummy. We propose a two stage blind source separation algorithm based on a fixed beamforming preprocessing using the head related transfer functions (HRTF) of the dummy and a separation algorithm using a sparsity criterion. We show that in the ca...
Lin Wang
2010-01-01
Full Text Available Frequency-domain blind source separation (BSS performs poorly in high reverberation because the independence assumption collapses at each frequency bins when the number of bins increases. To improve the separation result, this paper proposes a method which combines two techniques by using beamforming as a preprocessor of blind source separation. With the sound source locations supposed to be known, the mixed signals are dereverberated and enhanced by beamforming; then the beamformed signals are further separated by blind source separation. To implement the proposed method, a superdirective fixed beamformer is designed for beamforming, and an interfrequency dependence-based permutation alignment scheme is presented for frequency-domain blind source separation. With beamforming shortening mixing filters and reducing noise before blind source separation, the combined method works better in reverberation. The performance of the proposed method is investigated by separating up to 4 sources in different environments with reverberation time from 100 ms to 700 ms. Simulation results verify the outperformance of the proposed method over using beamforming or blind source separation alone. Analysis demonstrates that the proposed method is computationally efficient and appropriate for real-time processing.
Synthesis of blind source separation algorithms on reconfigurable FPGA platforms
Du, Hongtao; Qi, Hairong; Szu, Harold H.
2005-03-01
Recent advances in intelligence technology have boosted the development of micro- Unmanned Air Vehicles (UAVs) including Sliver Fox, Shadow, and Scan Eagle for various surveillance and reconnaissance applications. These affordable and reusable devices have to fit a series of size, weight, and power constraints. Cameras used on such micro-UAVs are therefore mounted directly at a fixed angle without any motion-compensated gimbals. This mounting scheme has resulted in the so-called jitter effect in which jitter is defined as sub-pixel or small amplitude vibrations. The jitter blur caused by the jitter effect needs to be corrected before any other processing algorithms can be practically applied. Jitter restoration has been solved by various optimization techniques, including Wiener approximation, maximum a-posteriori probability (MAP), etc. However, these algorithms normally assume a spatial-invariant blur model that is not the case with jitter blur. Szu et al. developed a smart real-time algorithm based on auto-regression (AR) with its natural generalization of unsupervised artificial neural network (ANN) learning to achieve restoration accuracy at the sub-pixel level. This algorithm resembles the capability of the human visual system, in which an agreement between the pair of eyes indicates "signal", otherwise, the jitter noise. Using this non-statistical method, for each single pixel, a deterministic blind sources separation (BSS) process can then be carried out independently based on a deterministic minimum of the Helmholtz free energy with a generalization of Shannon's information theory applied to open dynamic systems. From a hardware implementation point of view, the process of jitter restoration of an image using Szu's algorithm can be optimized by pixel-based parallelization. In our previous work, a parallelly structured independent component analysis (ICA) algorithm has been implemented on both Field Programmable Gate Array (FPGA) and Application
Improving the perceptual quality of single-channel blind audio source separation.
Stokes, Tobias W.
2015-01-01
Given a mixture of audio sources, a blind audio source separation (BASS) tool is required to extract audio relating to one specific source whilst attenuating that related to all others. This thesis answers the question “How can the perceptual quality of BASS be improved for broadcasting applications?” The most common source separation scenario, particularly in the field of broadcasting, is single channel, and this is particularly challenging as a limited set of cues are available. Broadcas...
Blind Source Separation with Conjugate Gradient Algorithm and Kurtosis Maximization Criterion
Sanjeev N Jain
2016-02-01
Full Text Available Blind source separation (BSS is a technique for estimating individual source components from their mixtures at multiple sensors. It is called blind because any additional other information will not be used besides the mixtures. Recently, blind source separation has received attention because of its potential applications in signal processing such as in speech recognition systems, telecommunications and medical signal processing. Blind source separation of super and sub-Gaussian Signal is proposed utilizing conjugate gradient algorithm and kurtosis maximization criteria. In our previous paper, ABC algorithm was utilized to blind source separation and here, we improve the technique with changes in fitness function and scout bee phase. Fitness function is improved with the use of kurtosis maximization criterion and scout bee phase is improved with use of conjugate gradient algorithm. The evaluation metrics used for performance evaluation are fitness function values and distance values. Comparative analysis is also carried out by comparing our proposed technique to other prominent techniques. The technique achieved average distance of 38.39, average fitness value of 6.94, average Gaussian distance of 58.60 and average Gaussian fitness as 5.02. The technique attained lowest average distance value among all techniques and good values for all other evaluation metrics which shows the effectiveness of the proposed technique.
Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm
Lei Chen
2014-01-01
Full Text Available The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.
A treatment of EEG data by underdetermined blind source separation for motor imagery classification
Koldovský, Zbyněk; Phan, A. H.; Tichavský, Petr; Cichocki, A.
Bucharest: EURASIP, 2012, s. 1484-1488. ISBN 978-1-4673-1068-0. ISSN 2076-1465. [20th European Signal Processing Conference (EUSIPCO 2012). Bukurešť (RO), 27.08.2012-31.08.2012] Grant ostatní: GA ČR(CZ) GAP103/11/1947 Institutional support: RVO:67985556 Keywords : electroencephalogram * brain-computer Interface * underdetermined blind source separation Subject RIV: FH - Neurology http://library.utia.cas.cz/separaty/2012/SI/tichavsky-a treatment of eeg data by underdetermined blind source separation for motor imagery classification.pdf
Blind source separation with unknown and dynamically changing number of source signals
YE Jimin; ZHANG Xianda; ZHU Xiaolong
2006-01-01
The contrast function remains to be an open problem in blind source separation (BSS) when the number of source signals is unknown and/or dynamically changed.The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.
Semi-Blind Source Separation in a Multi-User Transmission System with Interference Alignment
FADLALLAH, Yasser; AISSA EL BEY, Abdeldjalil; Abed-Meraim, Karim; AMIS CAVALEC, Karine; Pyndiah, Ramesh
2013-01-01
In this paper we address the decoding problem in the K-user MIMO interference channel assuming an interference alignment (IA) design. We aim to decode robustly the desired signal without having a full Channel State Information (CSI) (i.e. precoders knowledge) at the receivers. We show the equivalency between the IA model and the Semi-Blind Source Separation model (SBSS). Then, we prove that this equivalence allows the use of techniques employed in source separation for extracting the desired ...
RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION
Yang Junan; Li Bin; Zhuang Zhenquan
2003-01-01
This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).
ICAR, a tool for Blind Source Separation using Fourth Order Statistics only
Albera, Laurent; Férreol, Anne; Chevalier, Pascal; Comon, Pierre
2005-01-01
The problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in ...
Blind source separation of ship-radiated noise based on generalized Gaussian model
Kong Wei; Yang Bin
2006-01-01
When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model (GGM) is presented in ICA algorithm since it can model nonGaussian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood (ML) approach and natural gradient descent, the learning rules of blind source separation (BSS) based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumulants and Gaussian mixture density function.
Overcomplete Blind Source Separation by Combining ICA and Binary Time-Frequency Masking
Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan;
2005-01-01
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too strict. We propose a...... novel method for over-complete blind source separation. Two powerful source separation techniques have been combined, independent component analysis and binary time-frequency masking. Hereby, it is possible to iteratively extract each speech signal from the mixture. By using merely two microphones we...... can separate up to six mixed speech signals under anechoic conditions. The number of source signals is not assumed to be known in advance. It is also possible to maintain the extracted signals as stereo signals...
Blind separation of sources in nonlinear convolved mixture based on a novel network
胡英; 杨杰; 沈利
2004-01-01
Blind separation of independent sources from their nonlinear convoluted mixtures is a more realistic problem than from linear ones. A solution to this problem based on the Entropy Maximization principle is presented. First we propose a novel two-layer network as the de-mixing system to separate sources in nonlinear convolved mixture. In output layer of our network we use feedback network architecture to cope with convoluted mixtures. Then we derive learning algorithms for the two-layer network by maximizing the information entropy. Based on the comparison of the computer simulation results, it can be concluded that the proposed algorithm has a better nonlinear convolved blind signal separation effect than the H.H. Y' s algorithm.
Takuya Isomura
2015-12-01
Full Text Available Blind source separation is the computation underlying the cocktail party effect--a partygoer can distinguish a particular talker's voice from the ambient noise. Early studies indicated that the brain might use blind source separation as a signal processing strategy for sensory perception and numerous mathematical models have been proposed; however, it remains unclear how the neural networks extract particular sources from a complex mixture of inputs. We discovered that neurons in cultures of dissociated rat cortical cells could learn to represent particular sources while filtering out other signals. Specifically, the distinct classes of neurons in the culture learned to respond to the distinct sources after repeating training stimulation. Moreover, the neural network structures changed to reduce free energy, as predicted by the free-energy principle, a candidate unified theory of learning and memory, and by Jaynes' principle of maximum entropy. This implicit learning can only be explained by some form of Hebbian plasticity. These results are the first in vitro (as opposed to in silico demonstration of neural networks performing blind source separation, and the first formal demonstration of neuronal self-organization under the free energy principle.
Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions
Zhang Yimin
2006-01-01
Full Text Available Blind source separation (BSS based on spatial time-frequency distributions (STFDs provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD. To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.
Semi-blind Source Separation Using Head-Related Transfer Functions
Pedersen, Michael Syskind; Hansen, Lars Kai; Kjems, Ulrik; Rasmussen, Karsten Bo
An online blind source separation algorithm which is a special case of the geometric algorithm by Parra and Fancourt has been implemented for the purpose of separating sounds recorded at microphones placed at each side of the head. By using the assumption that the position of the two sounds are k...... the separation with approximately 1 dB compared to when free-field is assumed. This indicates that the permutation ambiguity is solved more accurate compared to when free-field is assumed....
FPGA-based real-time blind source separation with principal component analysis
Wilson, Matthew; Meyer-Baese, Uwe
2015-05-01
Principal component analysis (PCA) is a popular technique in reducing the dimension of a large data set so that more informed conclusions can be made about the relationship between the values in the data set. Blind source separation (BSS) is one of the many applications of PCA, where it is used to separate linearly mixed signals into their source signals. This project attempts to implement a BSS system in hardware. Due to unique characteristics of hardware implementation, the Generalized Hebbian Algorithm (GHA), a learning network model, is used. The FPGA used to compile and test the system is the Altera Cyclone III EP3C120F780I7.
黄晋英; 潘宏侠; 毕世华; 杨喜旺
2008-01-01
Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.
Šembera, Ondřej; Tichavský, Petr; Koldovský, Z.
Piscataway: IEEE, 2016, s. 4323-4327. ISBN 978-1-4799-9987-3. [IEEE International Conference on Acoustics, Speech, and Signal Processing 2016 (ICASSP2016). Shanghai (CN), 20.03.2016-25.03.2016] R&D Projects: GA ČR(CZ) GA14-13713S Institutional support: RVO:67985556 Keywords : Autoregressive Processes * Cramer-Rao Bound * Blind Source Separation Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2016/SI/tichavsky-0458485.pdf
On Sparsity in Bayesian Blind Source Separation for Dynamic Medical Imaging
Tichý, Ondřej
Praha : Katedra metematiky, FSv ČVUT, 2014, s. 20-21. [Rektorysova Soutěž. Praha (CZ), 3.12.2014] R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : blind source separation * dynamic medical imaging * sparsity constraint Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2014/AS/tichy-0436843.pdf
Blind Source Separation Based of Brain Computer Interface System: A review
Ahmed Kareem Abdullah; Zhang Chao Zhu
2014-01-01
This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and the recent BCI design were explored. Since 1970 when the research of BCI system began in the Californi...
Performance of Blind Source Separation Algorithms for FMRI Analysis using a Group ICA Method
Correa, Nicolle; Adali, Tülay; Calhoun, Vince D.
2006-01-01
Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist, however the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely information max...
Congedo, Marco; Gouy-Pailler, Cedric; Jutten, Christian
2008-01-01
Over the last ten years blind source separation (BSS) has become a prominent processing tool in the study of human electroencephalography (EEG). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG. In this review we begin by placing the BSS linear instantaneous model of EEG within the framework of brain volume conduction theory. We then review the concept and current practic...
Industrial applications of extended output-only Blind Source Separation techniques
Rutten, Christophe; Nguyen, Viet Ha; Golinval, Jean-Claude
2011-01-01
In the field of structural health monitoring or machine condition moni-toring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. This paper presents industrial applications of two possible extensions of output-only Blind Source Separation (BSS) techniqu...
RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION
YangJunan; LiBin; 等
2003-01-01
This letter proposes two algorithns:a novel Quantum Genetic Algorithm(QGA)based on the improvement of Han's Genetic Quantum Algorithm(GQA)and a new Blind Source Separation(BSS)method based on QGA and Independent Component Analysis(ICA).The simulation result shows that the efficiency of the new BSS nethod is obviously higher than that of the Conventional Genetic Algorithm(CGA).
Estimation of Input Function from Dynamic PET Brain Data Using Bayesian Blind Source Separation
Tichý, Ondřej; Šmídl, Václav
2015-01-01
Roč. 12, č. 4 (2015), s. 1273-1287. ISSN 1820-0214 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : blind source separation * Variational Bayes method * dynamic PET * input function * deconvolution Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.477, year: 2014 http://library.utia.cas.cz/separaty/2015/AS/tichy-0450509.pdf
Blind Source Separation for Robot Audition using Fixed Beamforming with HRTFs
Maazaoui, Mounira; Grenier, Yves; Abed-Meraim, Karim
2011-01-01
We present a two stage blind source separation (BSS) algorithm for robot audition. The algorithm is based on a beamforming preprocessing and a BSS algorithm using a sparsity separation criterion. Before the BSS step, we filter the sensors outputs by beamforming filters to reduce the reverberation and the environmental noise. As we are in a robot audition context, the manifold of the sensor array in this case is hard to model, so we use pre-measured Head Related Transfer Functions (HRTFs) to e...
Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA
You Rong-Yi; Chen Zhong
2005-01-01
Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.
Frequency Domain Blind Source Separation for Robot Audition Using a Parameterized Sparsity Criterion
Abed-Meraim, Karim; Grenier, Y.; Maazaoui, Mounira
2011-01-01
In this paper, we introduce a modified lp norm blind source separation criterion based on the source sparsity in the timefrequency domain. We study the effect of making the sparsity constraint harder through the optimization process, making the parameter p of the lp norm vary from 1 to nearly 0 according to a sigmoid function. The sigmoid introduces a smooth lp norm variation which avoids the divergence of the algorithm. We compared this algorithm to the regular l1 norm minimization and an IC...
Difficulties applying recent blind source separation techniques to EEG and MEG
Knuth, Kevin H
2015-01-01
High temporal resolution measurements of human brain activity can be performed by recording the electric potentials on the scalp surface (electroencephalography, EEG), or by recording the magnetic fields near the surface of the head (magnetoencephalography, MEG). The analysis of the data is problematic due to the fact that multiple neural generators may be simultaneously active and the potentials and magnetic fields from these sources are superimposed on the detectors. It is highly desirable to un-mix the data into signals representing the behaviors of the original individual generators. This general problem is called blind source separation and several recent techniques utilizing maximum entropy, minimum mutual information, and maximum likelihood estimation have been applied. These techniques have had much success in separating signals such as natural sounds or speech, but appear to be ineffective when applied to EEG or MEG signals. Many of these techniques implicitly assume that the source distributions hav...
Yan, Jun; Dong, Danan; Chen, Wen
2016-04-01
Due to the development of GNSS technology and the improvement of its positioning accuracy, observational data obtained by GNSS is widely used in Earth space geodesy and geodynamics research. Whereas the GNSS time series data of observation stations contains a plenty of information. This includes geographical space changes, deformation of the Earth, the migration of subsurface material, instantaneous deformation of the Earth, weak deformation and other blind signals. In order to decompose some instantaneous deformation underground, weak deformation and other blind signals hided in GNSS time series, we apply Independent Component Analysis (ICA) to daily station coordinate time series of the Southern California Integrated GPS Network. As ICA is based on the statistical characteristics of the observed signal. It uses non-Gaussian and independence character to process time series to obtain the source signal of the basic geophysical events. In term of the post-processing procedure of precise time-series data by GNSS, this paper examines GNSS time series using the principal component analysis (PCA) module of QOCA and ICA algorithm to separate the source signal. Then we focus on taking into account of these two signal separation technologies, PCA and ICA, for separating original signal that related geophysical disturbance changes from the observed signals. After analyzing these separation process approaches, we demonstrate that in the case of multiple factors, PCA exists ambiguity in the separation of source signals, that is the result related is not clear, and ICA will be better than PCA, which means that dealing with GNSS time series that the combination of signal source is unknown is suitable to use ICA.
Variational Blind Source Separation Toolbox and its Application to Hyperspectral Image Data
Tichý, Ondřej; Šmídl, Václav
Piscataway: IEEE Computer Society, 2015, s. 1336-1340. ISBN 978-0-9928626-4-0. ISSN 2076-1465. [23rd European Signal Processing Conference (EUSIPCO). Nice (FR), 31.08.2015-04.09.2015] R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Blind source separation * Variational Bayes method * Sparse prior * Hyperspectral image Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2015/AS/tichy-0447094.pdf
Blind Source Separation in Farsi Language by Using Hermitian Angle in Convolutive Enviroment
Atefeh Soltani
2013-04-01
Full Text Available This paper presents a T-F masking method for convolutive blind source separation based on hermitian angle concept. The hermitian angle is calculated between T-F domain mixture vector and reference vector. Two different reference vectors are assumed for calculating two different hermitian angles, and then these angles are clustered with k-means or FCM method to estimate unmixing masks. The well-known permutation problem is solved based on k-means clustering of estimated masks which are partitioned to small groups. The experimental results show an improvement in performance when using two different reference vectors compared to only one.
Time-domain Blind Audio Source Separation Using Advanced Component Clustering and Reconstruction
Koldovský, Zbyněk; Tichavský, Petr
Trento: IEEE, 2008, s. 216-219. ISBN 978-1-4244-2337-8; ISBN 978-1-4244-2338-5. [Hands-free Speech Communication and Microphone Arrays 2008. Trento (IT), 06.05.2008-08.05.2008] R&D Projects: GA MŠk 1M0572 Grant ostatní: GA ČR(CZ) GP102/07/P384 Institutional research plan: CEZ:AV0Z10750506 Keywords : blind source separation * audio signals Subject RIV: BI - Acoustics
Extension of EFICA Algorithm for Blind Separation of Piecewise Stationary Non-Gaussian Sources
Koldovský, Zbyněk; Málek, J.; Tichavský, Petr; Deville, Y.; Hosseini, S.
Bryan: Conference Management Services, 2008, s. 1913-1916. ISBN 978-1-4244-1483-3; ISBN 1-4244-1484-9. [ICASSP 2008, IEEE International Conference on Acoustics, Speech adn Signal Processing. Las Vegas (US), 30.03.2008-04.04.2008] R&D Projects: GA MŠk 1M0572 Grant ostatní: GA ČR(CZ) GP102/07/P384 Institutional research plan: CEZ:AV0Z10750506 Keywords : independent component analysis * piecewise stationary signals * blind source separation Subject RIV: BB - Applied Statistics, Operational Research
Kinetic Modeling of the Dynamic PET Brain Data Using Blind Source Separation Methods
Tichý, Ondřej; Šmídl, Václav
Dalian, China: IEEE press, 2014, s. 244-249. ISBN 978-1-4799-5837-5. [The 2014 7th International Conference on BioMedical Engineering and Informatics. Dalian (CN), 14.10.2014-16.10.2014] R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : blind source separation * dynamic PET * input function * deconvolution Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2014/AS/tichy-0433424.pdf
Gang Tang
2016-06-01
Full Text Available In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method.
Tang, Gang; Luo, Ganggang; Zhang, Weihua; Yang, Caijin; Wang, Huaqing
2016-01-01
In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method. PMID:27322268
AN EME BLIND SOURCE SEPARATION ALGORITHM BASED ON GENERALIZED EXPONENTIAL FUNCTION
Miao Hao; Li Xiaodong; Tian Jing
2008-01-01
This letter investigates an improved blind source separation algorithm based on Maximum Entropy (ME) criteria. The original ME algorithm chooses the fixed exponential or sigmoid function as the nonlinear mapping function which can not match the original signal very well. A parameter estimation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.
Generic Uniqueness of a Structured Matrix Factorization and Applications in Blind Source Separation
Domanov, Ignat; Lathauwer, Lieven De
2016-06-01
Algebraic geometry, although little explored in signal processing, provides tools that are very convenient for investigating generic properties in a wide range of applications. Generic properties are properties that hold "almost everywhere". We present a set of conditions that are sufficient for demonstrating the generic uniqueness of a certain structured matrix factorization. This set of conditions may be used as a checklist for generic uniqueness in different settings. We discuss two particular applications in detail. We provide a relaxed generic uniqueness condition for joint matrix diagonalization that is relevant for independent component analysis in the underdetermined case. We present generic uniqueness conditions for a recently proposed class of deterministic blind source separation methods that rely on mild source models. For the interested reader we provide some intuition on how the results are connected to their algebraic geometric roots.
Detection of sudden structural damage using blind source separation and time-frequency approaches
Morovati, V.; Kazemi, M. T.
2016-05-01
Seismic signal processing is one of the most reliable methods of detecting the structural damage during earthquakes. In this paper, the use of the hybrid method of blind source separation (BSS) and time-frequency analysis (TFA) is explored to detect the changes in the structural response data. The combination of the BSS and TFA is applied to the seismic signals due to the non-stationary nature of them. Firstly, the second-order blind identification technique is used to decompose the response signal of structural vibration into modal coordinate signals which will be mono-components for TFA. Then each mono-component signal is analyzed to extract instantaneous frequency of structure. Numerical simulations and a real-world seismic-excited structure with time-varying frequencies show the accuracy and robustness of the developed algorithm. TFA of extracted sources shows that used method can be successfully applied to structural damage detection. The results also demonstrate that the combined method can be used to identify the time instant of structural damage occurrence more sharply and effectively than by the use of TFA alone.
Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms
Pan Qiongfeng
2007-01-01
Full Text Available We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.
Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms
Qiongfeng Pan
2007-04-01
Full Text Available We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.
PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG
Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay
2016-01-01
Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397
Jutten, Christian; Karhunen, Juha
2004-10-01
In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed. PMID:15593377
System identification through nonstationary data using Time-Frequency Blind Source Separation
Guo, Yanlin; Kareem, Ahsan
2016-06-01
Classical output-only system identification (SI) methods are based on the assumption of stationarity of the system response. However, measured response of buildings and bridges is usually non-stationary due to strong winds (e.g. typhoon, and thunder storm etc.), earthquakes and time-varying vehicle motions. Accordingly, the response data may have time-varying frequency contents and/or overlapping of modal frequencies due to non-stationary colored excitation. This renders traditional methods problematic for modal separation and identification. To address these challenges, a new SI technique based on Time-Frequency Blind Source Separation (TFBSS) is proposed. By selectively utilizing "effective" information in local regions of the time-frequency plane, where only one mode contributes to energy, the proposed technique can successfully identify mode shapes and recover modal responses from the non-stationary response where the traditional SI methods often encounter difficulties. This technique can also handle response with closely spaced modes which is a well-known challenge for the identification of large-scale structures. Based on the separated modal responses, frequency and damping can be easily identified using SI methods based on a single degree of freedom (SDOF) system. In addition to the exclusive advantage of handling non-stationary data and closely spaced modes, the proposed technique also benefits from the absence of the end effects and low sensitivity to noise in modal separation. The efficacy of the proposed technique is demonstrated using several simulation based studies, and compared to the popular Second-Order Blind Identification (SOBI) scheme. It is also noted that even some non-stationary response data can be analyzed by the stationary method SOBI. This paper also delineates non-stationary cases where SOBI and the proposed scheme perform comparably and highlights cases where the proposed approach is more advantageous. Finally, the performance of the
Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation
Schmidt, Mikkel N.; Mørup, Morten
2006-01-01
We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to...... individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription....
Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis
Samuel Akwei-Sekyere
2015-07-01
Full Text Available The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.
Musafere, F.; Sadhu, A.; Liu, K.
2016-01-01
In the last few decades, structural health monitoring (SHM) has been an indispensable subject in the field of vibration engineering. With the aid of modern sensing technology, SHM has garnered significant attention towards diagnosis and risk management of large-scale civil structures and mechanical systems. In SHM, system identification is one of major building blocks through which unknown system parameters are extracted from vibration data of the structures. Such system information is then utilized to detect the damage instant, and its severity to rehabilitate and prolong the existing health of the structures. In recent years, blind source separation (BSS) algorithm has become one of the newly emerging advanced signal processing techniques for output-only system identification of civil structures. In this paper, a novel damage detection technique is proposed by integrating BSS with the time-varying auto-regressive modeling to identify the instant and severity of damage. The proposed method is validated using a suite of numerical studies and experimental models followed by a full-scale structure.
Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.
Akwei-Sekyere, Samuel
2015-01-01
The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio. PMID:26157639
Sun, Y; Finlayson-Pitts, B J; Xin, J
2011-01-01
Differential optical absorption spectroscopy (DOAS) is a powerful tool for detecting and quantifying trace gases in atmospheric chemistry \\cite{Platt_Stutz08}. DOAS spectra consist of a linear combination of complex multi-peak multi-scale structures. Most DOAS analysis routines in use today are based on least squares techniques, for example, the approach developed in the 1970s uses polynomial fits to remove a slowly varying background, and known reference spectra to retrieve the identity and concentrations of reference gases. An open problem is to identify unknown gases in the fitting residuals for complex atmospheric mixtures. In this work, we develop a novel three step semi-blind source separation method. The first step uses a multi-resolution analysis to remove the slow-varying and fast-varying components in the DOAS spectral data matrix $X$. The second step decomposes the preprocessed data $\\hat{X}$ in the first step into a linear combination of the reference spectra plus a remainder, or $\\hat{X} = A\\,S +...
Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface
Michael H. Thaut
2005-11-01
Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as Ã¢Â€ÂœyesÃ¢Â€Â or Ã¢Â€ÂœnoÃ¢Â€Â or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct Ã¢Â€ÂœyesÃ¢Â€Â/Ã¢Â€ÂœnoÃ¢Â€Â BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a Ã¢Â€Âœdirect,Ã¢Â€Â single-session BCI.
Farkhan Rosi
2013-09-01
Full Text Available Pada sistem komunikasi bawah air, seringkali sinyal yang diterima oleh sensor berasal dari hasil pencampuran sumber sinyal dengan sinyal-sinyal akustik lain di lingkungan bawah air. Hal ini menjadikan sinyal yang didapatkan menjadi tidak sesuai dengan yang diinginkan. Teknik Blind Source Separation (BSS dipakai di sini untuk memisahkan sinyal-sinyal yang bercampur tersebut. Dalam tugas akhir ini, dilakukan pemisahan sinyal akustik dengan menggunakan Natural Gradient ICA berdasarkan Generalized Gaussian Model yang didapat dari karakteristik distribusi sumber sinyal akustik non-gaussian yakni ship radiated noise dan sea ambient noise. Pemisahan sinyal akustik dilakukan sebanyak tiga kali yakni dengan simulasi, toolbox ICALABS V3, dan menggunakan pemisahan sinyal akustik dari data riil pengukuran. Dari hasil simulasi menunjukkan pemisahan dengan algoritma Natural Gradien ICA berdasarkan Generalized Gaussian Model berjalan dengan baik. Hal ini ditunjukkan dengan nilai SIR shrimp.wav = 48.9946 dB dan ferry.wav = 46.9309. dB. Sedangkan rata-rata MSE shrimp.wav = 1.2605 x 10-5 dan ferry.wav = 2.0272 x 10 -5.
The distressed brain: a group blind source separation analysis on tinnitus.
Dirk De Ridder
Full Text Available BACKGROUND: Tinnitus, the perception of a sound without an external sound source, can lead to variable amounts of distress. METHODOLOGY: In a group of tinnitus patients with variable amounts of tinnitus related distress, as measured by the Tinnitus Questionnaire (TQ, an electroencephalography (EEG is performed, evaluating the patients' resting state electrical brain activity. This resting state electrical activity is compared with a control group and between patients with low (N = 30 and high distress (N = 25. The groups are homogeneous for tinnitus type, tinnitus duration or tinnitus laterality. A group blind source separation (BSS analysis is performed using a large normative sample (N = 84, generating seven normative components to which high and low tinnitus patients are compared. A correlation analysis of the obtained normative components' relative power and distress is performed. Furthermore, the functional connectivity as reflected by lagged phase synchronization is analyzed between the brain areas defined by the components. Finally, a group BSS analysis on the Tinnitus group as a whole is performed. CONCLUSIONS: Tinnitus can be characterized by at least four BSS components, two of which are posterior cingulate based, one based on the subgenual anterior cingulate and one based on the parahippocampus. Only the subgenual component correlates with distress. When performed on a normative sample, group BSS reveals that distress is characterized by two anterior cingulate based components. Spectral analysis of these components demonstrates that distress in tinnitus is related to alpha and beta changes in a network consisting of the subgenual anterior cingulate cortex extending to the pregenual and dorsal anterior cingulate cortex as well as the ventromedial prefrontal cortex/orbitofrontal cortex, insula, and parahippocampus. This network overlaps partially with brain areas implicated in distress in patients suffering from pain, functional somatic
2015-01-01
Distributed temperature sensing (DTS) provides an important technology support for the earth-rock junctions of dike projects (ERJD), which are binding sites between culvert, gates, and pipes and dike body and dike foundation. In this study, a blind source separation model is used for the identification of leakages based on the temperature data of DTS in leakage monitoring of ERJD. First, a denoising method is established based on the temperature monitoring data of distributed optical fiber in...
He Peng Ju
2016-01-01
Full Text Available Nowadays, detecting fetal ECG using abdominal signal is a commonly used method, but fetal ECG signal will be affected by maternal ECG. Current FECG extraction algorithms are mainly aiming at multiple channels signal. They often assume there is only one fetus and did not consider multiple births. This paper proposed a single channel blind source separation (SCBSS algorithm based on source number estimation using multi-algorithm fusion to process single abdominal signal. The method decomposed collected single channel signal into multiple intrinsic mode function (IMF utilizing Empirical Mode Decomposition (EMD, mapping single channel into multiple channels. Four multiple channel source number estimation (MCSNE methods (Bootstrap, Hough, AIC and PCA were weighting fused to estimate accurate source number and the particle swarm optimization algorithm (PSO was employed to determine weighted coefficient. According to source number and IMF, nonnegative matrix was constructed and nonnegative matrix factorization (NMF was employed to separate mixed signals. Experiments used single channel signal mixed by four man-made signals and single channel ECG mixed by two to verify the proposed algorithm. Results showed that the proposed algorithm could determine number of independent signal in single acquired signal. FECG could be extracted from single channel observed signal and the algorithm can be used to solve separation of MECG and FECG.
Yang, Yang; Li, Xiukun
2016-06-01
Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation (BSS) can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution (WVD) observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. An experimental simulation has been used, with changes in the pulse width of the transmitted signal, the relative amplitude and the time delay parameter, in order to analyzing the feasibility of this new method. Simulation results show that the new method is not only able to separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.
Time-domain beamforming and blind source separation speech input in the car environment
Bourgeois, Julien
2009-01-01
The development of computer and telecommunication technologies led to a revolutioninthewaythatpeopleworkandcommunicatewitheachother.One of the results is that large amount of information will increasingly be held in a form that is natural for users, as speech in natural language. In the presented work, we investigate the speech signal capture problem, which includes the separation of multiple interfering speakers using microphone arrays. Adaptive beamforming is a classical approach which has been developed since the seventies. However it requires a double-talk detector (DTD) that interrupts th
The Distressed Brain: A Group Blind Source Separation Analysis on Tinnitus
De Ridder, Dirk; Vanneste, Sven; Congedo, Marco
2011-01-01
Background: Tinnitus, the perception of a sound without an external sound source, can lead to variable amounts of distress. Methodology: In a group of tinnitus patients with variable amounts of tinnitus related distress, as measured by the Tinnitus Questionnaire (TQ), an electroencephalography (EEG) is performed, evaluating the patients' resting state electrical brain activity. This resting state electrical activity is compared with a control group and between patients with low (N = 30) and h...
Kellermann Walter
2007-01-01
Full Text Available We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.
利用盲源分离算法实现DOA估计%DOA estimation method based on blind source separation algorithm
徐先峰; 刘义艳; 段晨东
2012-01-01
A new DOA (direction-of-arrival) estimation method based on an algorithm for fast blind source separation (FBSS-DOA) is proposed in this paper. A group of correlation matrices possessing diagonal structure is generated. A cost function of joint diagonalization for blind source separation is introduced. For solving this cost function, a fast multiplied iterative algorithm in complex-valued domain is utilized. The demixing matrix was then estimated and the estimation of DOA was realized. Compared with familiar algorithms, the algorithm has more generality and better estimation performance. The simulation results illustrate its efficiency.%提出一种基于快速盲源分离算法实现波达方向(DOA)估计的方法.构造了具有对角化结构的相关矩阵组,引入解盲源分离问题的联合对角化代价函数,采用一种快速的复数域乘性迭代算法求解代价函数,得到混迭矩阵逆的估计,进而实现DOA估计.与同类算法相比,该算法具有更广的适用性和更精确的DOA估计性能.仿真实验结果验证了算法的快速收敛性和优越的估计性能.
Mavaddaty, Samira; Ebrahimzadeh, Ata
2011-01-01
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sourc...
ITERATIVE MULTICHANNEL BLIND DECONVOLUTION METHOD FOR TEMPORALLY COLORED SOURCES
Zhang Mingjian; Wei Gang
2004-01-01
An iterative separation approach, i.e. source signals are extracted and removed one by one, is proposed for multichannel blind deconvolution of colored signals. Each source signal is extracted in two stages: a filtered version of the source signal is first obtained by solving the generalized eigenvalue problem, which is then followed by a single channel blind deconvolution based on ensemble learning. Simulation demonstrates the capability of the approach to perform efficient mutichannel blind deconvolution.
Jiménez-Gonzalez, Aída; James, Christopher J.
2013-11-01
Today, it is generally accepted that current methods for biophysical antenatal surveillance do not facilitate a comprehensive and reliable assessment of foetal well-being and thus, that continuing research into alternative methods is necessary to improve antenatal monitoring procedures. Here, attention has been paid to the abdominal phonogram, a signal that is recorded by positioning an acoustic sensor on the maternal womb and contains valuable information about foetal status, but which is hidden by maternal and environmental sources. To recover such information, this work describes single-channel independent component analysis (SCICA) as an alternative signal processing approach for analyzing the abdominal phonogram. The approach, based on the method of delays, the Temporal Decorrelation Source SEParation implementation (TDSEP) of Independent Components Analysis (ICA), and an automatic grouping algorithm, has managed to successfully retrieve estimates of: (1) the foetal cardiac activity (in the form of the foetal phonocardiogram, FPCG), (2) the maternal cardiovascular activity (in the form of the maternal phonocardiogram, MPCG, and/or pulse wave), (3) the maternal respiratory activity (in the form of the maternal respirograma, MResp), and (4) noise (N). These results have been obtained from a dataset of 25 single-channel phonograms and point at the possibilities of using SCICA to address a fundamental problem faced in antenatal surveillance, i.e. the extraction of information from a non-invasive signal like the abdominal phonogram. Future work will test the possibility of using SCICA to recover information regarding the foetal breathing movements (FBM), another physiological parameter of interest in foetal surveillance.
Chengjie Li
2016-01-01
Full Text Available In Passive Radar System, obtaining the mixed weak object signal against the super power signal (jamming is still a challenging task. In this paper, a novel framework based on Passive Radar System is designed for weak object signal separation. Firstly, we propose an Interference Cancellation algorithm (IC-algorithm to extract the mixed weak object signals from the strong jamming. Then, an improved FastICA algorithm with K-means cluster is designed to separate each weak signal from the mixed weak object signals. At last, we discuss the performance of the proposed method and verify the novel method based on several simulations. The experimental results demonstrate the effectiveness of the proposed method.
A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics
Guangkuo Lu; Manlin Xiao; Ping Wei; Huaguo Zhang
2015-01-01
Methods of utilizing independent component analysis (ICA) give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments b...
Hiroshi Sawada
2007-01-01
Full Text Available We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the ℓ1-norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.
单观测通道船舶辐射噪声盲源分离%Blind source separation of ship-radiated noise using single observing channel
刘佳; 杨士莪; 朴胜春; 黄益旺
2011-01-01
提出了一种适用于单观测通道的船舶辐射噪声盲源分离方法.该方法依据船舶辐射噪声远场的空间分布规律,通过将单观测通道延时和滤波的方法构造虚拟通道,使单通道转化为多通道,以实现单通道的盲源分离.仿真及实验数据分析的结果显示,分离后信号的相关系数在不同信噪比下有稳定的提高,说明该方法能在一定程度上利用单观测通道在海洋环境噪声背景下分离船舶辐射噪声,实验数据分析同时表明该方法对双目标船的分离也有一定效果.%A method of blind source separation for ship-radiated noise is proposed based on single observing channel.According to the spatial characteristic of ship-radiated noise in far field, a virtual channel is constructed from the observed channel by time delay and data filtering. It overcomes the limitation of channel numbers. The simulation and experimental results using this method are presented. The results show that under the sea ambient noise the shipradiated noise can be separated by this method with single observing channel, and it is effective for two object ships to be separated.
A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics
Guangkuo Lu
2015-01-01
Full Text Available Methods of utilizing independent component analysis (ICA give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments by applying a modified segmentation algorithm. Then the state space is reconstructed and the single-channel signal is transformed into a pseudo multiple input multiple output (MIMO mode using a method of nonlinear analysis based on the high order statistics (HOS. In the last step, ICA is performed on the pseudo MIMO data to decompose the single channel recording into its underlying independent components (ICs and the interested ICs are then extracted. Finally, the effectiveness and excellence of the higher order single-channel ICA (SCICA method are validated with measured data throughout experiments. Also, the proposed method in this paper is proved to be more robust under different SNR and/or embedding dimension via explicit formulae and simulations.
Adaptive Time-Domain Blind Separation of Speech Signals
Málek, J.; Koldovský, Zbyněk; Tichavský, Petr
Vol. 6365. Heidelberg: Springer-Verlag, 2010 - (Gavrilova, M.; Kumar, V.; Mun, Y.; Tan, C.; Gervasi, O.), s. 9-16 ISBN 978-3-642-15994-7. [Latent Variable Analysis and Signal Separation. St. Malo (FR), 27.09.2010-30.09.2010] R&D Projects: GA MŠk 1M0572; GA ČR GA102/09/1278 Grant ostatní: GA ČR(CZ) GA102/08/0707 Institutional research plan: CEZ:AV0Z10750506 Keywords : blind source separation * speech * convolutive mixture * adaptive algorithms Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/SI/tichavsky-adaptive time-domain blind separation of speech signals.pdf
李志农; 张芬; 肖尧先
2015-01-01
动态盲源分离问题是多故障源盲分离的一个热点。传统的机械故障源分离方法要求满足统计特征保持稳定，且混合系统保持不变等假设，而忽略了时序信息。针对此不足，结合规范变量分析(Canonical variate analysis, CVA)和独立分量分析(Independent component analysis, ICA)，提出一种基于CVA-ICA的机械多故障源动态盲分离方法。该方法的基本思想是将源信号看成状态空间的状态变量，观测信号看成状态空间的输出变量，从而将动态混合盲源分离问题转化为状态空间盲源分离问题，利用规范变量分析作为降维工具来构造状态空间，再利用传统的 ICA 算法对规范的观测信号进行盲源分离。仿真研究表明，在处理动态混合的盲分离中，提出的方法明显优于静态 ICA 方法，取得了满意的分离效果。将该方法应用到滚动轴承内圈和滚动体的故障盲分离中，试验结果进一步验证了该方法的有效性。%Dynamic blind source separation is a focus in the blind source separation of multi-fault. Traditional blind source separation (BSS) is restricted to the stable statistical characteristics and static mixture system, and ignores the sequential information. Based on this deficiency, combining to canonical variate analysis (CVA) and independent component analysis (ICA), a dynamic blind source separation method based on CVA-ICA is proposed. In the proposed method, the source signal is regarded as state variable in the state space, observation signal as output variable, thus the dynamics ICA is transform into the state space ICA. The proposed method employs CVA as a reduction tool to construct a state space, then the statistically independent sources are separated by the conventional ICA algorithm. The simulation results show that the CVA-ICA method is superior to traditional blind source separation in the dynamic blind source separation, and has satisfactory
Informed source separation: source coding meets source separation
Ozerov, Alexey; Liutkus, Antoine; Badeau, Roland; Richard, Gaël
2011-01-01
We consider the informed source separation (ISS) problem where, given the sources and the mixtures, any kind of side-information can be computed during a so-called encoding stage. This side-information is then used to assist source separation, given the mixtures only, at the so-called decoding stage. State of the art ISS approaches do not really consider ISS as a coding problem and rely on some purely source separation-inspired strategies, leading to performances that can at best reach those ...
基于行列式和稀疏性约束的NMF的欠定盲分离方法%Algorithm for underdetermined blind source separation based on DSNMF
卢宏; 赵知劲; 杨小牛
2011-01-01
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank,which limits of its application to Underdetermined Blind Source Separation (UBSS). To address this issue, an algorithm for UBSS based on determinant and sparsity constraint of NMF, named DSNMF, was proposed in this paper. On the basis of standard NMF, determinant criterion was used for constraining the left matrix of NMF, while sparsity was used for constraining the right one. In this way, the reconstruction error, the uniqueness of mixing matrix and the spasity of original sources can be equipoised, which leads to the underdetermined blind separation of mixing matrix and original sources. The simulation results show that DSNMF both works well for good and poor sparsity of sources separation.%非负矩阵分解(NMF)要求分解得到的左矩阵为列满秩,这限制了它在欠定盲分离(UBSS)中的应用.针对此问题,提出基于带行列式和稀疏性约束的NMF的欠定盲分离算法--DSNMF.该算法在基本NMF的基础上,对NMF得到的左矩阵进行行列式准则约束,对右矩阵进行稀疏性约束.平衡了重构误差、混合矩阵的唯一性以及分离信号的稀疏特性,实现了对混合矩阵和源信号的欠定盲分离.仿真结果表明,在源信号稀疏性较好和较差两种情况下,DSNMF都能取得良好的分离效果.
基于小波半软阈值消噪的盲源分离方法%Blind Source Separation Based on Wavelet Semi-soft Threshold Denoising
孟宗; 马钊; 刘东; 李晶
2016-01-01
为了有效提取含噪机械故障信号中的故障特征信息，研究了一种基于小波半软阈值消噪的盲源分离方法。利用小波半软阈值对故障信号进行消噪处理；采用联合近似对角化算法对信号进行盲源分离；考虑在噪声干扰下预消噪常常不足以消除全部噪声，因此在盲源分离后再进行适当的消噪处理，以提高其分离性能。实验验证了所提出方法的有效性和可行性。%In order to extract fault feature informations from the mechanical malfunction signals with noise,a method of blind source separation was proposed based on wavelet semi-soft threshold de-noising.First,wavelet semi-soft threshold was used to filter the failure signals.Then,joint approxi-mate diagonalization was used as blind source separation method to separate signals.Pretreatment was often not enough to eliminate all noises,therefore,it was necessary to denoise again to improve the separation performance.Finally,the feasibility and validity of this method was verified by experi-ments.
Blind image separation based on exponentiated transmuted Weibull distribution
Adam, A. M.; Farouk, R. M.; El-aziz, M. E. Abd
2016-01-01
In recent years the processing of blind image separation has been investigated. As a result, a number of feature extraction algorithms for direct application of such image structures have been developed. For example, separation of mixed fingerprints found in any crime scene, in which a mixture of two or more fingerprints may be obtained, for identification, we have to separate them. In this paper, we have proposed a new technique for separating a multiple mixed images based on exponentiated t...
Research and Simulation of FECG Signal Blind Separation Algorithm Based on Gradient Method
Yu Chen
2012-08-01
Full Text Available Independent Component Analysis (ICA is a new developed signal separation and digital analysis technology in recent years. ICA has widely used because it does not need to know the signal prior information, which has became the hot spot in signal processing field research. In this study, we firstly introduce the principle, meaning and blind source separation algorithm based on the gradient. By using the traditional natural gradient algorithm and Equi-variant Adaptive Source Separation via Independent (EASI blind separation algorithm, mixing ECG signals with noises had been separated effectively into the Maternal Electrocardiograph (MECG signal, Fetal Electrocardiograph (FECG signal and noise signal. The algorithm separation test showed that EASI algorithm can better separate the fetal ECG signal and because the gradient algorithm is a kind of online algorithm, which can be used for clinical fetal ECG signal of the real-time detection with important practical value and research significance.
Camilo Andrés Lemus
2012-12-01
Full Text Available La separación ciega de fuentes, conocida como BSS por sus siglas en inglés (Blind Source Separation, es una técnica de procesamiento de señales que consiste en estimar fuentes en señales mezcladas linealmente, utilizando métodos como el ICA, para señales fuentes estadísticamente independientes. Uno de los algoritmos BSS más conocidos es el algoritmo JADE, el cual exige que el número de señales independientes coincida con el número de señales observadas (sensores. En situaciones reales, el número de sensores es menor al número de señales fuentes (BSS no-determinado y el problema no tiene solución. En este proyecto se propone una solución para BSS no-determinado, adicionando una etapa de preprocesamiento y una etapa de descomposición basada en la transformada wavelet discreta. Nuestro modelo, el cual hemos denominado DWT+BSS, crea una señal virtual observada a partir de una señal real observada y utiliza los coeficientes wavelet de las señales observadas como entradas al algoritmo clásico JADE. El modelo se valida con señales de voz y audio, obteniendo índices de similitud entre las señales fuentes y las estimadas por encima de 0,7.Blind Source Separation, BSS, is a signal processing technique which estimates sources from linearly mixed signals and it uses methods such as ICA for sources that are statistically independent. Among the best known BSS algorithms is the JADE method, which requires that the number of independent signals match the number of observed signals (sensors. In the real world, the number of sensors is lower than the number of sources (undetermined BSS and therefore the problem has no solution. This work proposes a solution for undetermined BSS by pre-processing and decomposition stages based on the Discrete Wavelet Transform (DWT. Our proposal, which it is known as DWT+BSS, creates a virtual observed signal from a real observed signal and it uses the wavelet coefficients of the observed signals as the
Low Complexity Bayesian Single Channel Source Separation
Beierholm, Thomas; Pedersen, Brian Dam; Winther, Ole
We propose a simple Bayesian model for performing single channel speech separation using factorized source priors in a sliding window linearly transformed domain. Using a one dimensional mixture of Gaussians to model each band source leads to fast tractable inference for the source signals....... Simulations with separation of a male and a female speaker using priors trained on the same speakers show comparable performance with the blind separation approach of G.-J. Jang and T.-W. Lee (see NIPS, vol.15, 2003) with a SNR improvement of 4.9 dB for both the male and female speaker. Mixing coefficients...... keeping the complexity low using machine learning and CASA (computational auditory scene analysis) approaches (Jang and Lee, 2003; Roweis, S.T., 2001; Wang, D.L. and Brown, G.J., 1999; Hu, G. and Wang, D., 2003)....
刘佳; 杨士莪; 朴胜春
2012-01-01
A method of single channel blind source separation for underwater acoustic signal was proposed under condition of multipath transmission. Firstly, the multipath time delay was estimated with autocorrelation. Then, the successive multipath signals were added in phase, so as to increase the signal-noise ratio. At last, virtual channels were constructed with the observed data using resample method, the limitation of single channel was overcomed. The simulation results showed that the ship-radiated noise can be separated from ambient noise using this method with single observing channel under condition of multipath transmission, and the method works stably under different SNRs.%提出一种适用于多途环境下的单观测通道水声信号盲源分离方法.该方法首先利用自相关估计多途时延,使经各途径到达的多途信号同相叠加,信号能量得到增强,而噪声由于随机性能量不会增加,进而提高信噪比.然后采用间隔重采样的方法,虚拟多接收通道,解决单通道的欠定问题.仿真分析表明该方法可以有效解决多途环境下的舰船辐射噪声与环境噪声分离,而且在不同信噪比下有较稳定的分离性能.
Real-time adaptive concepts in acoustics blind signal separation and multichannel echo cancellation
Schobben, Daniel W E
2001-01-01
Blind Signal Separation (BSS) deals with recovering (filtered versions of) source signals from an observed mixture thereof. The term `blind' relates to the fact that there are no reference signals for the source signals and also that the mixing system is unknown. This book presents a new method for blind signal separation, which is developed to work on microphone signals. Acoustic Echo Cancellation (AEC) is a well-known technique to suppress the echo that a microphone picks up from a loudspeaker in the same room. Such acoustic feedback occurs for example in hands-free telephony and can lead to a perceived loud tone. For an application such as a voice-controlled television, a stereo AEC is required to suppress the contribution of the stereo loudspeaker setup. A generalized AEC is presented that is suited for multi-channel operation. New algorithms for Blind Signal Separation and multi-channel Acoustic Echo Cancellation are presented. A background is given in array signal processing methods, adaptive filter the...
Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors
Tichý, Ondřej; Šmídl, Václav
2015-01-01
Roč. 34, č. 1 (2015), s. 258-266. ISSN 0278-0062 R&D Projects: GA ČR GA13-29225S Keywords : Functional imaging * Blind source separation * Computer-aided detection and diagnosis * Probabilistic and statistical methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.390, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/tichy-0431090.pdf
The Application of FastICA Combined with Related Function in Blind Signal Separation
Dengao Li; Junmin Zhao; Hongyan Liu; Defeng Hao
2014-01-01
Blind source separation (BSS) has applications in the fields of data compression, feature recognition, speech, audio, and biosignal processing. Identification of ECG signal is one of the challenges in the biosignal processing. Proposed in this paper is a new method, which is the combination of related function relevance to estimated signal and negative entropy in fast independent component analysis (FastICA) as objective function, and the iterative formula is derived without any assumptions; ...
A TIME-FREQUENCY BLIND SEPARATION METHOD FOR UNDERDETERMINED SPEECH MIXTURES
Lv Yao; Li Shuangtian
2008-01-01
The proposed Blind Source Separation method (BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources. The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique (DUET) which assumes the number of sources a priori. In the proposed algorithm,the Short-Time Fourier Transform (STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes (URCP) which can accurately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures. Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio (SIR),and maintains good speech quality in the separation results.
陶襄樊; 陈美霞; 魏建辉
2012-01-01
水下双层加筋圆柱壳振动和辐射声场的预估对其噪声控制具有重要意义.以此为出发点,基于双层加筋圆柱壳干模态叠加原理,运用欠定盲分离方法,实现了有限数目测点预报结构振动和辐射声场的目的,并用数值方法对预报结果的有效性进行了验证.结果表明,欠定盲分离方法可以对水下双层加筋圆柱壳辐射声场进行预报,并且该预报方法对测点的位置、选取的模态阶数没有严格的要求,所需要的测点数目也较少,有很好的适用性.%The prediction of vibration and radiated acoustic field for double ring-stiffened cylindrical shell under water is of great importance to noise control. Based on the theory of modal superposition of double ring-stiffened cylindrical shell in vacuum, this paper proposes an underdetermined blind source separation method which achieves the aim of predicting the vibration and radiated acoustic field of the double ring-stiffened cylindrical shell under water with a few measuring points. The validity of the prediction results is analyzed through numerical method. It demonstrates that the prediction is reliable. And it does not has rigorous requirements on the location of the measuring points and the number of selected modes, otherwise, the number of the measuring points needed is acceptable, consequently, this method has much better applicability.
Ibrahim Missaoui
2011-05-01
Full Text Available In this paper, we address the problem of blind separation of speech mixtures. We propose a new blind speech separation system, which integrates a perceptual filterbank and independent component analysis (ICA and using kurtosis criterion. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD tree in order to accord to critical band characteristics of psycho-acoustic model. Our proposed technique consists on transforming the observations signals into an adequate representation using UWPD and Kurtosis maximization criterion in a new preprocessing step in order to increase the non-Gaussianity which is a pre-requirement for ICA. Experiments were carried out with the instantaneous mixture of two speech sources using two sensors. The obtained results show that the proposed method gives a considerable improvement when compared with FastICA and other techniques.
Blind instantaneous noisy mixture separation with best interference-plus-noise rejection
Koldovský, Zbyněk; Tichavský, Petr
2007-01-01
Roč. 2007, Č. 4666 (2007), s. 730-737. ISSN 0302-9743. [Independent Component Analysis and Signal Separation. Londyn, 09.09.2007-12.09.2007] R&D Projects: GA MŠk 1M0572 Grant ostatní: GA ČR(CZ) GP102/07/P384 Institutional research plan: CEZ:AV0Z10750506 Keywords : blind source separation * independent component analysis Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005
Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Tichavský, Petr; Šembera, Ondřej; Koldovský, Zbyněk
Heidelberg : Springer, 2015 - (Vincent, E.; Yeredor, A.; Koldovský, Z.; Tichavský, P.), s. 304-311 ISBN 978-3-319-22482-4. ISSN 0302-9743. - (Lecture Notes in Computer Science). [Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015. Liberec (CZ), 25.08.2015-28.08.2015] R&D Projects: GA ČR(CZ) GA14-13713S Institutional support: RVO:67985556 Keywords : Autoregressive processes * Cramer-Rao bound * Blind source separation Subject RIV: BI - Acoustics http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0447196.pdf
Blind Component Separation in Wavelet Space: Application to CMB Analysis
2005-01-01
It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse ast...
Blind component separation in wavelet space. Application to CMB analysis
J. Delabrouille; J.-L. Starck; J. -F. Cardoso; Moudden, Y.
2004-01-01
It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse ast...
Blind signal separation of underdetermined mixtures based on clustering algorithms on planes
Xie Shengli; Tan Beihai; Fu Yuli
2007-01-01
Based on clustering method on planes, blind signal separation (BSS) of underdetermined mixtures with three observed signals is discussed. The condition of sufficient sparsity of the source signals is not necessary when clustering method on planes is used. In other words, it needs not that only one source signal plays the main role among others at one time. The proposed method uses normal line clustering of planes first. Then, the mixing matrix can be identified via deciding the intersection lines of the planes. This method is an effective implement of the new theory presented by Georgiev. Simulations illustrate accuracy and restoring capability of the method to estimate the mixing matrix.
Jammer Suppression in DS-CDMA Communications using Parafac-based Blind Separation
Xu Lingyun
2016-01-01
Full Text Available In this paper we propose to apply parafac-based source separation techniques for jammer suppression in direct spread spectrum communication systems. The jammer excision is formulated as an optimization problem and a new algorithm is presented which is based on the parafac tri-iterative least square algorithm. By jointly diagonalizing the time delay correlation matrix of the observed signals and using the new proposed method, a better solution is achieved. The proposed algorithm can successfully separate communication signals and jamming signals. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm doesn’t require whitening processing. Moreover our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of antennas.
Complex-wave retrieval based on blind signal separation
Xiaodong Chai; Chengpeng Zhou; Zhaoyan Feng; Yinhua Wang; Yansheng Zuo
2006-01-01
In the process of the reconstruction of digital holography, the traditional methods of diffraction and filtration are commonly adopted to recover the original complex-wave signal. Influenced by twin-image and zero-order terms, the above-mentioned methods, however, either limit the field of vision or result in the loss of the amplitude and phase. A new method for complex-wave retrieval is presented, which is based on blind signal separation. Three frames of holograms are captured by a charge coupled device (CCD)camera to form an observation signal. The term containing only amplitude and phase of complex-wave is separated, by means of independent component analysis, from the observation signal, which effectively eliminates the zero-order term. Finally, the complex-wave retrieval of pure phase wavefront is achieved.Experimental results show that this method can better recover the amplitude and phase of the original complex-wave even when there is a frequency spectrum mixture in the hologram.
Blind Separation of Two Users Based on User Delays and Optimal Pulse-Shape Design
Poor HVincent
2010-01-01
Full Text Available A wireless network is considered, in which two spatially distributed users transmit narrow-band signals simultaneously over the same channel using the same power. User separation is achieved by oversampling the received signal and formulating a virtual multiple-input multiple-output (MIMO system based on the resulting polyphase components. Because of oversampling, high correlations can occur between the columns of the virtual MIMO system matrix which can be detrimental to user separation. A novel pulse-shape waveform design is proposed that results in low correlation between the columns of the system matrix, while it exploits all available bandwidth as dictated by a spectral mask. It is also shown that the use of successive interference cancelation in combination with blind source separation further improves the separation performance.
Compressing Data by Source Separation
Schmidt, A.; Tréguier, E.; Schmidt, F.; Moussaoui, S.
2012-04-01
We interpret source separation of hyperspectral data as a way of applying lossy compressing. In settings where datacubes can be interpreted as a linear combination of source spectra and their abundances and the number of sources is small, we try to quantify the trade-offs and the benefits of source separation and its implementation with non-negative source factorisation. While various methods to implement non-negative matrix factorisation have been used successfully for factoring hyperspectral images into physically meaningful sources which linearly combine to an approximation of the original image. This is useful for modelling the processes which make up the image. At the same time, the approximation opens up the potential for a significant reduction of the data by keeping only the sources and their corresponding abundances, instead of the original complete data cube. This presentation will try to explore the potential of the idea and also to establish limits of its use. Formally, the setting is as follows: we consider P pixels of a hyperspectral image which are acquired at L frequency bands and which are represented as a PxL data matrix X. Each row of this matrix represents a spectrum at a pixel with spatial index p=1..P; this implies that the original topology may be disregarded. Since we work under the assumption of linear mixing, the p-th spectrum, 1<=p<=P, can be expressed as a linear combination of r, 1<=r<=R, source spectra. Thus, X=AxS+E, E being an error matrix to be minimised, and X, A, and S only have non-negative entries. The rows of matrix S are the estimations of the R source spectra, and each entry of A expresses the contribution of the r-th component to the pixel with spatial index p. There are applications where we may interpret the rows of S as physical sources which can be combined using the columns of A to approximate the original data. If the source signals are few and strong (but not even necessarily meaningful), the data volume that has to
Blind Component Separation in Wavelet Space: Application to CMB Analysis
J. Delabrouille
2005-09-01
Full Text Available It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.
... Got Homework? Here's Help White House Lunch Recipes Blindness KidsHealth > For Kids > Blindness Print A A A ... help, are sometimes called "legally blind." What Causes Blindness? Vision problems can develop before a baby is ...
Hiekata Takashi
2006-01-01
Full Text Available A new two-stage blind source separation (BSS method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO-model-based independent component analysis (ICA and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.
WANG Gang; RAO NiNi; ZHANG Ying
2008-01-01
The analysis and the characterization of atrial fibrillation (AF) requires,in a previous key step,the extraction of the atrial activity (AA) free from 12-lead electrocardiogram (ECG).This contribution proposes a novel non-invasive approach for the AA estimation in AF episodes.The method is based on blind source extraction (BSE) using high order statistics (HOS).The validity and performance of this algorithm are confirmed by extensive computer simulations and experiments on realworld data.In contrast to blind source separation (BSS) methods,BSE only extract one desired signal,and it is easy for the machine to judge whether the extracted signal is AA source by calculating its spectrum concentration,while it is hard for the machine using BSS method to judge which one of the separated twelve signals is AA source.Therefore,the proposed method is expected to have great potential in clinical monitoring.
Blind component separation in wavelet space. Application to CMB analysis
Moudden, Y; Starck, J L; Delabrouille, J
2004-01-01
It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse astrophysical emissions in Cosmic Microwave Background observations. We show here that wavelets, which are standard tools in processing non stationary data, can profitably be used to extend SMICA. Among possible applications, it is shown that gaps in data are dealt with more conveniently and with better results using this extension, wSMICA, in place of the original SMICA. The performances of these two methods are compared on simulated CMB data sets, demonstrating the advantageous use of wavelets.
The Application of FastICA Combined with Related Function in Blind Signal Separation
Dengao Li
2014-01-01
Full Text Available Blind source separation (BSS has applications in the fields of data compression, feature recognition, speech, audio, and biosignal processing. Identification of ECG signal is one of the challenges in the biosignal processing. Proposed in this paper is a new method, which is the combination of related function relevance to estimated signal and negative entropy in fast independent component analysis (FastICA as objective function, and the iterative formula is derived without any assumptions; then the independent components are found by maximizing the objective function. The improved algorithm shorthand for R-FastICA is applied to extract random mixed signals and ventricular late potential (VLP signal from normal ECG signal; simultaneously the performance of R-FastICA algorithm is compared with traditional FastICA through simulation. Experimental results show that R-FastICA algorithm outperforms traditional FastICA with higher similarity coefficient and separation precision.
王权锋; 胥德平; 詹泽东; 吴海洋
2012-01-01
This paper explores the method of seismic exploration data noise reduction in deep metal, and proposes wavelet domain blind separation algorithm processing procedure. The actual noise reduction process and research of the low signal-to-noise ratio (SNR) in a metal mine of Yunnan Province shows that the effect would be better than JADE blind source separation algorithm noise reduction in pure time domain when the seismic data are transformed into wavelet domain blind source separation algorithm of JADE noise reduction processing, and then the wavelet domain is transformed into time domain; and in the de-noising processing, parameter combination must be tested many times according to different parts of the seismic data so as to achieve the best effect of de-noise. It is of practical value and instruction for prospecting blind ores and deep concealed ore deposits in large areas by seismic techniques to combine blind signal theory with wavelet transformation for deep seismic exploration data noise reduction.%探索深部金属矿地震勘查中数据降噪方法,提出小波域盲分离算法处理流程,通过对我国云南地区某金属矿低信噪比资料,进行实际降噪处理研究,结果表明:①将地震数据变换到小波域的盲分离JADE算法降噪处理后,再从小波域变换到时间域,比单纯的时间域内盲分离JADE算法降噪效果要好；②在降噪处理过程中,必须根据不同地区的地震数据多次试验参数组合,以其达到最佳降噪效果.将盲信号理论与小波变换有机结合进行深部金属矿地震勘查数据降噪,对利用地震方法大面积地寻找盲矿和深部隐伏矿有一定的实用价值和指导意义.
Separating More Sources Than Sensors Using Time-Frequency Distributions
Belouchrani Adel
2005-01-01
Full Text Available We examine the problem of blind separation of nonstationary sources in the underdetermined case, where there are more sources than sensors. Since time-frequency (TF signal processing provides effective tools for dealing with nonstationary signals, we propose a new separation method that is based on time-frequency distributions (TFDs. The underlying assumption is that the original sources are disjoint in the time-frequency (TF domain. The successful method recovers the sources by performing the following four main procedures. First, the spatial time-frequency distribution (STFD matrices are computed from the observed mixtures. Next, the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then, the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources; each class, now containing the auto-source points of only one source, gives an estimation of the TFD of this source. Finally, the source waveforms are recovered from their TFD estimates using TF synthesis. Simulated experiments indicate the success of the proposed algorithm in different scenarios. We also contribute with two other modified versions of the algorithm to better deal with auto-source point selection.
Separating More Sources Than Sensors Using Time-Frequency Distributions
Linh-Trung, Nguyen; Belouchrani, Adel; Abed-Meraim, Karim; Boashash, Boualem
2005-12-01
We examine the problem of blind separation of nonstationary sources in the underdetermined case, where there are more sources than sensors. Since time-frequency (TF) signal processing provides effective tools for dealing with nonstationary signals, we propose a new separation method that is based on time-frequency distributions (TFDs). The underlying assumption is that the original sources are disjoint in the time-frequency (TF) domain. The successful method recovers the sources by performing the following four main procedures. First, the spatial time-frequency distribution (STFD) matrices are computed from the observed mixtures. Next, the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then, the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources; each class, now containing the auto-source points of only one source, gives an estimation of the TFD of this source. Finally, the source waveforms are recovered from their TFD estimates using TF synthesis. Simulated experiments indicate the success of the proposed algorithm in different scenarios. We also contribute with two other modified versions of the algorithm to better deal with auto-source point selection.
Ibrahim Missaoui; Zied Lachiri
2011-01-01
In this paper, we address the problem of blind separation of speech mixtures. We propose a new blind speech separation system, which integrates a perceptual filterbank and independent component analysis (ICA) and using kurtosis criterion. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree in order to accord to critical band characteristics of psycho-acoustic model. Our proposed technique consists on transforming the observations signals in...
Informed Source Separation: A Bayesian Tutorial
Knuth, Kevin
2013-01-01
Source separation problems are ubiquitous in the physical sciences; any situation where signals are superimposed calls for source separation to estimate the original signals. In this tutorial I will discuss the Bayesian approach to the source separation problem. This approach has a specific advantage in that it requires the designer to explicitly describe the signal model in addition to any other information or assumptions that go into the problem description. This leads naturally to the idea...
... Prevalent Cases of Blindness (in thousands) by Age, Gender, and Race/Ethnicity Table for 2010 U.S. Prevalent ... Prevalent Cases of Blindness (in thousands) by Age, Gender, and Race/Ethnicity Table for 2000 U.S. Prevalent ...
A new blind fault component separation algorithm for a single-channel mechanical signal mixture
Wang, Dong; Tse, Peter W.
2012-10-01
A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two parts: the periodic and transient subsets. The periodic subset is related to the imbalance, misalignment and eccentricity of a machine. The transient subset refers to abnormal impulsive phenomena, such as those caused by localized bearing faults. The proposed method includes two individual strategies to deal with these different characteristics. The first extracts the sub-Gaussian periodic signal by minimizing the kurtosis of the equalized signals. The second detects the super-Gaussian transient signal by minimizing the smoothness index of the equalized signals. Here, the equalized signals are derived by an eigenvector algorithm that is a successful solution to the blind equalization problem. To reduce the computing time needed to select the equalizer length, a simple optimization method is introduced to minimize the kurtosis and smoothness index, respectively. Finally, simulated multiple-fault signals and a real multiple-fault signal collected from an industrial machine are used to validate the proposed method. The results show that the proposed method is able to effectively decompose the multiple-fault vibration mixture into periodic components and random non-stationary transient components. In addition, the equalizer length can be intelligently determined using the proposed method.
How Many Separable Sources? Model Selection In Independent Components Analysis
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis...... computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher’s iris data set and Howells’ craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....
Tichavský, Petr; Yeredor, A.; Koldovský, Zbyněk
Taipei : IEEE, 2009, s. 3133-3136. ISBN 978-1-4244-2354-5. [IEEE International Conference on Acoustics , Speech and Signal Processing (ICASSP) 2009 /34./. Taipei (TW), 19.04.2009-24.04.2009] R&D Projects: GA MŠk 1M0572 Grant ostatní: GA ČR(CZ) GP102/07/P384 Institutional research plan: CEZ:AV0Z10750506 Keywords : Approximate joint diagonalization * blind source separation, * autoregressive processes * second-order statistics Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/SI/tichavsky-a fast asymptotically efficient algorithm for blind separation of a linear mixture of block-wise stationary autoregressive processes.pdf
Blind Separation of DS-CDMA Signals with ICA Method
Miao Yu; Jianzhong Chen; Lei Shen; Shiju Li
2011-01-01
The estimation of pseudo noise sequence and information sequence is of great importance in the security of DS-CDMA system, which remains a hot research problem in reconnaissance and supervision of wireless communication. In DS-CDMA system, the pseudo noise sequences of different users are uncorrelated and the information sequences of different users are statistical independent, thus independent component analysis (ICA) could be introduced to separate the DS-CDMA signals with little prior know...
Removal of micropollutants in source separated sanitation
Butkovskyi, A.
2015-01-01
Source separated sanitation is an innovative sanitation method designed for minimizing use of energy and clean drinking water, and maximizing reuse of water, organics and nutrients from waste water. This approach is based on separate collection and treatment of toilet wastewater (black water) and th
Audio Source Separation Using a Deep Autoencoder
Jang, Giljin; Kim, Han-Gyu; Oh, Yung-Hwan
2014-01-01
This paper proposes a novel framework for unsupervised audio source separation using a deep autoencoder. The characteristics of unknown source signals mixed in the mixed input is automatically by properly configured autoencoders implemented by a network with many layers, and separated by clustering the coefficient vectors in the code layer. By investigating the weight vectors to the final target, representation layer, the primitive components of the audio signals in the frequency domain are o...
Kazi Takpaya; Wei Gang
2003-01-01
Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.
AaziTakpaya; WeiGang
2003-01-01
Blind identification-blind equalization for finite Impulse Response(FIR)Multiple Input-Multiple Output(MIMO)channels can be reformulated as the problem of blind sources separation.It has been shown that blind identification via decorrelating sub-channels method could recover the input sources.The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators,which decorrelate the output signals of subchannels,and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix.In this paper,a new qpproximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed.The proposed method outperforms BIDS in the presence of additive white Garssian noise.
A source separation approach to enhancing marine mammal vocalizations.
Gur, M Berke; Niezrecki, Christopher
2009-12-01
A common problem in passive acoustic based marine mammal monitoring is the contamination of vocalizations by a noise source, such as a surface vessel. The conventional approach in improving the vocalization signal to noise ratio (SNR) is to suppress the unwanted noise sources by beamforming the measurements made using an array. In this paper, an alternative approach to multi-channel underwater signal enhancement is proposed. Specifically, a blind source separation algorithm that extracts the vocalization signal from two-channel noisy measurements is derived and implemented. The proposed algorithm uses a robust decorrelation criterion to separate the vocalization from background noise, and hence is suitable for low SNR measurements. To overcome the convergence limitations resulting from temporally correlated recordings, the supervised affine projection filter update rule is adapted to the unsupervised source separation framework. The proposed method is evaluated using real West Indian manatee (Trichechus manatus latirostris) vocalizations and watercraft emitted noise measurements made within a typical manatee habitat in Florida. The results suggest that the proposed algorithm can improve the detection range of a passive acoustic detector five times on average (for input SNR between -10 and 5 dB) using only two receivers. PMID:20000920
Phase recovery in NMF for audio source separation: an insightful benchmark
Magron, Paul; Badeau, Roland; David, Bertrand
2016-01-01
Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. In applications such as source separation, the phase recovery for each extracted component is a major issue since it often leads to audible artifacts. In this paper, we present a methodology for evaluating various NMF-based source separation techniques involving phase reconstruction. For each model considered, a comparison between two approaches (blind separat...
Removal of micropollutants in source separated sanitation
Butkovskyi, A.
2015-01-01
Source separated sanitation is an innovative sanitation method designed for minimizing use of energy and clean drinking water, and maximizing reuse of water, organics and nutrients from waste water. This approach is based on separate collection and treatment of toilet wastewater (black water) and the rest of the domestic wastewater (grey water). Different characteristics of wastewater streams facilitate recovery of energy, nutrients and fresh water. To ensure agricultural or ecological reuse ...
Non-Stationary Brain Source Separation for Multi-Class Motor Imagery
Gouy-Pailler, Cedric; Congedo, Marco; Brunner, Clemens; Jutten, Christian; Pfurtscheller, Gert
2010-01-01
International audience This article describes a method to recover taskrelated brain sources in the context of multi-class Brain- Computer Interfaces (BCIs) based on non-invasive electroencephalography (EEG). We extend the method Joint Approximate Diagonalization (JAD) for spatial filtering using a maximum likelihood framework. This generic formulation (1) bridges the gap between the Common Spatial Patterns (CSP) and Blind Source Separation (BSS) of non-stationary sources, and (2) leads to ...
Iterative compressive sampling for hyperspectral images via source separation
Kamdem Kuiteing, S.; Barni, Mauro
2014-03-01
Compressive Sensing (CS) is receiving increasing attention as a way to lower storage and compression requirements for on-board acquisition of remote-sensing images. In the case of multi- and hyperspectral images, however, exploiting the spectral correlation poses severe computational problems. Yet, exploiting such a correlation would provide significantly better performance in terms of reconstruction quality. In this paper, we build on a recently proposed 2D CS scheme based on blind source separation to develop a computationally simple, yet accurate, prediction-based scheme for acquisition and iterative reconstruction of hyperspectral images in a CS setting. Preliminary experiments carried out on different hyperspectral images show that our approach yields a dramatic reduction of computational time while ensuring reconstruction performance similar to those of much more complicated 3D reconstruction schemes.
Reconstruction of Zeff profiles at TEXTOR through Bayesian source separation
We describe a work in progress on the reconstruction of radial profiles for the ion effective charge Zeff on the TEXTOR tokamak, using statistical data analysis techniques. We introduce our diagnostic for the measurement of Bremsstrahlung emissivity signals. Zeff profiles can be determined by Abel inversion of line-integrated measurements of the Bremsstrahlung emissivity (εff) from the plasma and the plasma electron density (ne) and temperature (Te). However, at the plasma edge only estimated values are routinely used for ne and Te, which are moreover determined at different toroidal locations. These various uncertainties hinder the interpretation of a Zeff profile outside the central plasma. In order to circumvent this problem, we propose several scenarios meant to allow the extraction by (Bayesian) Blind Source Separation techniques of either (line-integrated) Zeff wave shapes or absolutely calibrated signals from (line-integrated) emissivity signals, using also density and temperature signals, as required. (authors)
Multichannel audio signal source separation based on an Interchannel Loudness Vector Sum
Park, Taejin; Lee, Taejin
2015-01-01
In this paper, a Blind Source Separation (BSS) algorithm for multichannel audio contents is proposed. Unlike common BSS algorithms targeting stereo audio contents or microphone array signals, our technique is targeted at multichannel audio such as 5.1 and 7.1ch audio. Since most multichannel audio object sources are panned using the Inter-channel Loudness Difference (ILD), we employ the ILVS (Inter-channel Loudness Vector Sum) concept to cluster common signals (such as background music) from ...
Blind source identification from the multichannel surface electromyogram
The spinal circuitries combine the information flow from the supraspinal centers with the afferent input to generate the neural codes that drive the human skeletal muscles. The muscles transform the neural drive they receive from alpha motor neurons into motor unit action potentials (electrical activity) and force. Thus, the output of the spinal cord circuitries can be examined noninvasively by measuring the electrical activity of skeletal muscles at the surface of the skin i.e. the surface electromyogram (EMG). The recorded multi-muscle EMG activity pattern is generated by mixing processes of neural sources that need to be identified from the recorded signals themselves, with minimal or no a priori information available. Recently, multichannel source separation techniques that rely minimally on a priori knowledge of the mixing process have been developed and successfully applied to surface EMG. They act at different scales of information extraction to identify: (a) the activation signals shared by synergistic skeletal muscles, (b) the specific neural activation of individual muscles, separating it from that of nearby muscles i.e. from crosstalk, and (c) the spike trains of the active motor neurons. This review discusses the assumptions made by these methods, the challenges and limitations, as well as examples of their current applications. (topical review)
Blind, high-resolution, space-time separation of multipaths in an ionospheric propagation
Chenu-Tournier, M.; Larzabal, P.; Barbot, J. P.; Grouffaud, J.; Ferreol, A.
2000-01-01
The ionospheric radio electrical transmissions have multiple paths due to the inhomogeneity of the propagation medium, that is, the ionospheric layers. Tactical applications such as radiolocation and radiocommunications systems need blind, high-resolution identification of multipath channels. This work concerns the separation of the ionospheric paths and is based on recent work done on blind deconvolution which can estimate the impulse responses of a propagation channel. In this way, on the basis of a parametric model of the paths, we propose a blind, spatiotemporal identification of the propagation channel. The parameters that characterize the propagation model are the directions of arrivals (DOA) θ, time delays τ, and complex gains α (also called fading). We propose an algorithm that can both estimate the multipath parameters and test them on real life data. This new method needs fewer snapshots than other methods recently proposed, and thus can monitor more quickly varying channels. Moreover, compared to recent work we have relaxed the problem of making successive estimates of the impulse responses. The proposed method can also identify more paths than the number of sensors. An extension of the algorithm will be presented by including polarization diversity and thus increases the resolution. The proposed methods are illustrated on experimental data.
A method for blind separation of components information from mixed pixel
FAN Wenjie; XU Xiru
2006-01-01
In the field of remote sensing, it is important to separate the component information from mixed pixel. If the physical process of remote sensing can be expressed by a set of linear equations, the remote sensing information matrix is equal to the weight matrix multiplied by the component information matrix. Generally speaking, the precondition of retrieval of component information matrix is that the weight matrix is known. However, the blind signal separation (BSS) method can separate the matrix unconditionally, whose basic principle is that the additive information needing separation can be achieved from the statistical characteristics contained in a mass of samples in the remotely sensed information matrix. Therefore, the values of the component information matrix and the weight matrix can be estimated. The wave shape of components can be retrieved by BSS, but the amplitude cannot. In this paper, the plant-soil mixed pixels were chosen as the studying targets in this paper to quantitatively separate the component information and solve the uncertainty of BSS.Simulation and field test verify the reliability of the method. Results show that the BSS can be one of the effective methods of mixed pixel separation, and the foreground of application is very promising.
A blind separation method of overlapped multi-components based on time varying AR model
无
2008-01-01
A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.
A Modified Infomax ICA Algorithm for fMRI Data Source Separation
Amir A. Khaliq
2013-05-01
Full Text Available This study presents a modified infomax model of Independent Component Analysis (ICA for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA. ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extracted sources for which different techniques are used like kurtosis, negentropy, information maximization etc. The infomax method of ICA extracts unknown sources from a number of mixtures by maximizing the negentropy thus ensuring independence. In this proposed modified infomax model a higher order contrast function is used which results in fast convergence and accuracy. The Proposed algorithm is applied to general simulated signals and simulated fMRI data. Comparison of correlation results of the proposed algorithm with the conventional infomax algorithm shows better performance.
Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices
Baccigalupi C
2005-01-01
Full Text Available This paper proposes a new strategy to separate astrophysical sources that are mutually correlated. This strategy is based on second-order statistics and exploits prior information about the possible structure of the mixing matrix. Unlike ICA blind separation approaches, where the sources are assumed mutually independent and no prior knowledge is assumed about the mixing matrix, our strategy allows the independence assumption to be relaxed and performs the separation of even significantly correlated sources. Besides the mixing matrix, our strategy is also capable to evaluate the source covariance functions at several lags. Moreover, once the mixing parameters have been identified, a simple deconvolution can be used to estimate the probability density functions of the source processes. To benchmark our algorithm, we used a database that simulates the one expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.
Blind Source Parameters for Performance Evaluation of Despeckling Filters
Nagashettappa Biradar; Dewal, M. L.; ManojKumar Rohit; Sanjaykumar Gowre; Yogesh Gundge
2016-01-01
The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, spec...
Kazunobu Kondo
2012-01-01
Full Text Available Small agglomerative microphone array systems have been proposed for use with speech communication and recognition systems. Blind source separation methods based on frequency domain independent component analysis have shown significant separation performance, and the microphone arrays are small enough to make them portable. However, the level of computational complexity involved is very high because the conventional signal collection and processing method uses 60 microphones. In this paper, we propose a band selection method based on magnitude squared coherence. Frequency bands are selected based on the spatial and geometric characteristics of the microphone array device which is strongly related to the dodecahedral shape, and the selected bands are nonuniformly spaced. The estimated reduction in the computational complexity is 90% with a 68% reduction in the number of frequency bands. Separation performance achieved during our experimental evaluation was 7.45 (dB (signal-to-noise ratio and 2.30 (dB (cepstral distortion. These results show improvement in performance compared to the use of uniformly spaced frequency band.
Douglas Scott C
2007-01-01
Full Text Available We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.
Zhang, Le; Karakci, Ata; Korotkov, Andrei; Sutter, P M; Timbie, Peter T; Tucker, Gregory S; Wandelt, Benjamin D
2016-01-01
We present in this paper a new Bayesian semi-blind approach for foreground removal in observations of the 21-cm signal with interferometers. The technique, which we call HIEMICA (HI Expectation-Maximization Independent Component Analysis), is an extension of the Independent Component Analysis (ICA) technique developed for two-dimensional (2D) CMB maps to three-dimensional (3D) 21-cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from signal based on the diversity of their power spectra. Only relying on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21-cm signal and, the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21-cm intensity mapping observations. Based on ...
Blind Source Parameters for Performance Evaluation of Despeckling Filters
Nagashettappa Biradar
2016-01-01
Full Text Available The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI, and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink embedded with Stein’s unbiased risk estimation (SURE. The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.
Blind Source Parameters for Performance Evaluation of Despeckling Filters.
Biradar, Nagashettappa; Dewal, M L; Rohit, ManojKumar; Gowre, Sanjaykumar; Gundge, Yogesh
2016-01-01
The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein's unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images. PMID:27298618
BLUES from Music: BLind Underdetermined Extraction of Sources from Music
Pedersen, Michael Syskind; Lehn-Schiøler, Tue; Larsen, Jan
2006-01-01
In this paper we propose to use an instantaneous ICA method (BLUES) to separate the instruments in a real music stereo recording. We combine two strong separation techniques to segregate instruments from a mixture: ICA and binary time-frequency masking. By combining the methods, we are able to make...
Coupled Blind Signal Separation and Spectroscopic Database Fitting of the Mid Infrared PAH Features
Rosenberg, M J F; Boersma, C; Allamandola, L J; Tielens, A G G M
2011-01-01
The aromatic infrared bands (AIBs) observed in the mid infrared spectrum are attributed to Polycyclic Aromatic Hydrocarbons (PAHs). We observe the NGC 7023-North West (NW) PDR in the mid-infrared (10 - 19.5 micron) using the Infrared Spectrometer (IRS), on board Spitzer. Clear variations are observed in the spectra, most notably the ratio of the 11.0 to 11.2 micron bands, the peak position of the 11.2 and 12.0 micron bands, and the degree of asymmetry of the 11.2 micron band. The observed variations appear to change as a function of position within the PDR. We aim to explain these variations by a change in the abundances of the emitting components of the PDR. A Blind Signal Separation (BSS) method, i.e. a Non-Negative Matrix Factorization algorithm is applied to separate the observed spectrum into components. Using the NASA Ames PAH IR Spectroscopic Database, these extracted signals are fit. The observed signals alone were also fit using the database and these components are compared to the BSS components. Th...
Zhang, Hongjuan; Shi, Zhenwei; Guo, Chonghui; Feng, Enmin
2009-01-01
Blind source extraction (BSE) has become one of the promising methods in the field of signal processing and analysis, which only desires to extract "interesting" source signals with specific stochastic property or features so as to save lots of computing time and resources. This paper addresses BSE problem, in which desired source signals have some available reference signals. Based on this prior information, we develop an objective function for extraction of temporally correlated sources. Maximizing this objective function, a semi-blind source extraction fixed-point algorithm is proposed. Simulations on artificial electrocardiograph (ECG) signals and the real-world ECG data demonstrate the better performance of the new algorithm. Moreover, comparisons with existing algorithms further indicate the validity of our new algorithm, and also show its robustness to the estimated error of time delay.
Plokhoi, V. V.; Kandiev, Ya. Z.; Samarin, S. I.; Malyshkin, G. N.; Baidin, G. V.; Litvinenko, I. A.; Nikitin, V. P.
2001-09-01
The paper considers designs of moderators where fast positron stopping medium consists of very fine tungsten strips separated by vacuum gaps and the strips are arranged into Venetian blinds- or honeycomb-type structures. Moderator efficiency is evaluated through Monte-Carlo simulations. According to the maximal estimate, the efficiency of conversion of fast positrons into slow ones in the Venetian blinds and honeycomb-type moderators is ˜5×10 -3 for the reasonable thickness of the tungsten foil. If such moderator is used, the intensity of slow positron source on the hard synchrotron of SPring-8 storage ring can reach the level of ˜5×10 10 e +/s.
Measurment of gas-liquid two-phase slug flow with a Venturi meter based on blind source separation☆
Weiwei Wang; Xiao Liang; Mingzhu Zhang
2015-01-01
We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source sep-aration technique. The flow measurement model is established based on the fluctuation characteristics of differ-ential pressure (DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle re-lationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis (ICA) technique. The mixture matrix could be described using the variances of two DP sig-nals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid two-phase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%. We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.
基于新概率密度函数的ICA盲源分离%ICA Blind Signal Separation Based on a New Probability Density Function
张娟娟; 邸双亮
2014-01-01
This paper is concerned with the blind source separation (BSS) problem of super-Gaussian and sub-Gaussian mixed signal by using the maximum likelihood method, which is based on independent component analysis (ICA) method. In this paper, we construct a new type of probability density function (PDF) which is different from the already existing PDF used to separate mixed signals in the previously published papers. Applying the new constructed PDF to estimate probability density of super-Gaussian and sub-Gaussian signals (assuming the source signals are independent of each other), it is not necessary to change the parameter values artificially, and the separation work may be performed adaptively. Numerical experiments verify the feasibility of the newly constructed PDF, and the convergence time and the separation effect are improved compared with the original algorithm.%基于独立分量分析(Independent Component Analysis, ICA)，利用极大似然估计法，研究了超高斯和亚高斯的混合信号的盲源分离(Blind Sources Separation, BSS)问题。文中构造了一种新的、不同于以往文章中用来分离混合信号的概率密度函数(Probability Density Function, PDF)。新构造的PDF无需改变函数中的参数值，可用来对于超高斯和亚高斯信号的概率密度进行估计(假设未知源信号是相互独立的)。数值实验验证了新构造的PDF的可行性，与原算法相比，收敛时间和分离效果都得到了较大的改善。
Magnetic source separation in Earth's outer core.
Hoffman, Kenneth A; Singer, Brad S
2008-09-26
We present evidence that the source of Earth's axial dipole field is largely independent from the sources responsible for the rest of the geomagnetic field, the so-called nonaxial dipole (NAD) field. Support for this claim comes from correlations between the structure of the historic field and the behavior of the paleomagnetic field recorded in precisely dated lavas at those times when the axial dipole was especially weak or nearly absent. It is argued that a "stratification" of magnetic sources exists in the fluid core such that the axial dipole is the only observed field component that is nearly immune from the influence exerted by the lowermost mantle. It follows that subsequent work on spherical harmonic-based field descriptions may now incorporate an understanding of a dichotomy of spatial-temporal dynamo processes. PMID:18818352
May, Tobias; van de Par, Steven
2012-01-01
In this paper we present a new approach for estimating the number of active speech sources in the presence of interfering noise sources and reverberation. First, a binaural front-end is used to detect the spatial positions of all active sound sources, resulting in a binary mask for each candidate...... on a support vector machine (SVM) classifier. A systematic analysis shows that the proposed algorithm is able to blindly determine the number and the corresponding spatial positions of speech sources in multisource scenarios and generalizes well to unknown acoustic conditions...
Lee, Intae; Jang, Gil-Jin
2012-12-01
A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a spherical dependency model to all pairs of frequency bins. The dependency model of IVA is equivalent to a single clique in an undirected graph; a clique in graph theory is defined as a subset of vertices in which any pair of vertices is connected by an undirected edge. Therefore, IVA imposes the same amount of statistical dependency on every pair of frequency bins, which may not match the characteristics of real-world signals. The proposed method allows variable amounts of statistical dependencies according to the correlation coefficients observed in real acoustic signals and, hence, enables more accurate modeling of statistical dependencies. A number of cliques constitutes the new dependency graph so that neighboring frequency bins are assigned to the same clique, while distant bins are assigned to different cliques. The permutation ambiguity is resolved by overlapped frequency bins between neighboring cliques. For speech signals, we observed especially strong correlations across neighboring frequency bins and a decrease in these correlations with an increase in the distance between bins. The clique sizes are either fixed, or determined by the reciprocal of the mel-frequency scale to impose a wider dependency on low-frequency components. Experimental results showed improved performances over conventional IVA. The signal-to-interference ratio improved from 15.5 to 18.8 dB on average for seven different source locations. When we varied the clique sizes according to the observed correlations, the stability of the proposed method increased with a large number of cliques.
Source separation of household waste: A case study in China
A pilot program concerning source separation of household waste was launched in Hangzhou, capital city of Zhejiang province, China. Detailed investigations on the composition and properties of household waste in the experimental communities revealed that high water content and high percentage of food waste are the main limiting factors in the recovery of recyclables, especially paper from household waste, and the main contributors to the high cost and low efficiency of waste disposal. On the basis of the investigation, a novel source separation method, according to which household waste was classified as food waste, dry waste and harmful waste, was proposed and performed in four selected communities. In addition, a corresponding household waste management system that involves all stakeholders, a recovery system and a mechanical dehydration system for food waste were constituted to promote source separation activity. Performances and the questionnaire survey results showed that the active support and investment of a real estate company and a community residential committee play important roles in enhancing public participation and awareness of the importance of waste source separation. In comparison with the conventional mixed collection and transportation system of household waste, the established source separation and management system is cost-effective. It could be extended to the entire city and used by other cities in China as a source of reference
Separation of synchronous sources through phase locked matrix factorization.
Almeida, Miguel S B; Vigário, Ricardo; Bioucas-Dias, José
2014-10-01
In this paper, we study the separation of synchronous sources (SSS) problem, which deals with the separation of sources whose phases are synchronous. This problem cannot be addressed through independent component analysis methods because synchronous sources are statistically dependent. We present a two-step algorithm, called phase locked matrix factorization (PLMF), to perform SSS. We also show that SSS is identifiable under some assumptions and that any global minimum of PLMFs cost function is a desirable solution for SSS. We extensively study the algorithm on simulated data and conclude that it can perform SSS with various numbers of sources and sensors and with various phase lags between the sources, both in the ideal (i.e., perfectly synchronous and nonnoisy) case, and with various levels of additive noise in the observed signals and of phase jitter in the sources. PMID:25291741
Manjunath V. Joshi
2008-08-01
Full Text Available Given blurred observations of a stationary scene captured using a static camera but with different and unknown light source positions, we estimate the light source positions and scene structure (surface gradients and perform blind image restoration. The images are restored using the estimated light source positions, surface gradients, and albedo. The surface of the object is assumed to be Lambertian. We first propose a simple approach to obtain a rough estimate of the light source position from a single image using the shading information which does not use any calibration or initialization. We model the prior information for the scene structure as a separate Markov random field (MRF with discontinuity preservation, and the blur function is modeled as Gaussian. A proper regularization approach is then used to estimate the light source position, scene structure, and blur parameter. The optimization is carried out using the graph cuts approach. The advantage of the proposed approach is that its time complexity is much less as compared to other approaches that use global optimization techniques such as simulated annealing. Reducing the time complexity is crucial in many of the practical vision problems. Results of experimentation on both synthetic and real images are presented.
Wang, Fa-Yu; Chi, Chong-Yung; Chan, Tsung-Han; Wang, Yue
2010-05-01
Although significant efforts have been made in developing nonnegative blind source separation techniques, accurate separation of positive yet dependent sources remains a challenging task. In this paper, a joint correlation function of multiple signals is proposed to reveal and confirm that the observations after nonnegative mixing would have higher joint correlation than the original unknown sources. Accordingly, a new nonnegative least-correlated component analysis (n/LCA) method is proposed to design the unmixing matrix by minimizing the joint correlation function among the estimated nonnegative sources. In addition to a closed-form solution for unmixing two mixtures of two sources, the general algorithm of n/LCA for the multisource case is developed based on an iterative volume maximization (IVM) principle and linear programming. The source identifiability and required conditions are discussed and proven. The proposed n/LCA algorithm, denoted by n/LCA-IVM, is evaluated with both simulation data and real biomedical data to demonstrate its superior performance over several existing benchmark methods. PMID:20299711
Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation
Derry FitzGerald
2008-01-01
Full Text Available Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously.
Heungkyu Lee
2013-02-01
Full Text Available This paper proposes a method for the simultaneous separation and recognition of speech mixtures in noisy environments using two‐channel based independent vector analysis (IVA on a home‐robot cleaner. The issues to be considered in our target application are speech recognition at a distance and noise removal to cope with a variety of noises, including TV sounds, air conditioners, babble, and so on, that can occur in a house, where people can utter a voice command to control a robot cleaner at any time and at any location, even while a robot cleaner is moving. Thus, the system should always be in a recognition‐ready state to promptly recognize a spoken word at any time, and the false acceptance rate should be lower. To cope with these issues, the keyword spotting technique is applied. In addition, a microphone alignment method and a model‐based real‐time IVA approach are proposed to effectively and simultaneously process the speech and noise sources, as well as to cover 360‐degree directions irrespective of distance. From the experimental evaluations, we show that the proposed method is robust in terms of speech recognition accuracy, even when the speaker location is unfixed and changes all the time. In addition, the proposed method shows good performance in severely noisy environments.
Separation of core and crustal magnetic field sources
Shure, L.; Parker, R. L.; Langel, R. A.
1985-01-01
Fluid motions in the electrically conducting core and magnetized crustal rocks are the two major sources of the magnetic field observed on or slightly above the Earth's surface. The exact separation of these two contributions is not possible without imposing a priori assumptions about the internal source distribution. Nonetheless models like these were developed for hundreds of years Gauss' method, least squares analysis with a truncated spherical harmonic expansion was the method of choice for more than 100 years although he did not address separation of core and crustal sources, but rather internal versus external ones. Using some arbitrary criterion for appropriate truncation level, we now extrapolate downward core field models through the (approximately) insulating mantle. Unfortunately our view can change dramatically depending on the degree of truncation for describing core sources.
Ser Javier Del
2005-01-01
Full Text Available We consider the case of two correlated sources, S 1 and S 2 . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source S 1 over an additive white Gaussian noise (AWGN channel when the output of the other source S 2 is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source S 1 . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
Ser Javier Del
2005-01-01
Full Text Available We consider the case of two correlated sources, and . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source over an additive white Gaussian noise (AWGN channel when the output of the other source is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
Plokhoi, V V; Samarin, S I; Malyshkin, G N; Baidin, G V; Litvinenko, I A; Nikitin, V P
2001-01-01
The paper considers designs of moderators where fast positron stopping medium consists of very fine tungsten strips separated by vacuum gaps and the strips are arranged into Venetian blinds- or honeycomb-type structures. Moderator efficiency is evaluated through Monte-Carlo simulations. According to the maximal estimate, the efficiency of conversion of fast positrons into slow ones in the Venetian blinds and honeycomb-type moderators is approx 5x10 sup - sup 3 for the reasonable thickness of the tungsten foil. If such moderator is used, the intensity of slow positron source on the hard synchrotron of SPring-8 storage ring can reach the level of approx 5x10 sup 1 sup 0 e sup + /s.
Jank, Anna; Müller, Wolfgang; Schneider, Irene; Gerke, Frederic; Bockreis, Anke
2015-05-01
An efficient biological treatment of source separated organic waste from household kitchens and gardens (biowaste) requires an adequate upfront mechanical preparation which possibly includes a hand sorting for the separation of contaminants. In this work untreated biowaste from households and gardens and the screen overflow >60mm of the same waste were mechanically treated by a Waste Separation Press (WSP). The WSP separates the waste into a wet fraction for biological treatment and a fraction of dry contaminants for incineration. The results show that it is possible to replace a hand sorting of contaminants, the milling and a screening of organic waste before the biological treatment by using the WSP. A special focus was put on the contaminants separation. The separation of plastic film from the untreated biowaste was 67% and the separation rate of glass was about 92%. About 90% of the organics were transferred to the fraction for further biological treatment. When treating the screen overflow >60mm with the WSP 86% of the plastic film and 88% of the glass were transferred to the contaminants fraction. 32% of the organic was transferred to the contaminants fraction and thereby lost for a further biological treatment. Additionally it was calculated that national standards for glass contaminants in compost can be met when using the WSP to mechanically treat the total biowaste. The loss of biogas by transferring biodegradable organics to the contaminants fraction was about 11% when preparing the untreated biowaste with the WSP. PMID:25761398
Source separation and clustering of phase-locked subspaces.
Almeida, Miguel; Schleimer, Jan-Hendrik; Bioucas-Dias, José Mario; Vigário, Ricardo
2011-09-01
It has been proven that there are synchrony (or phase-locking) phenomena present in multiple oscillating systems such as electrical circuits, lasers, chemical reactions, and human neurons. If the measurements of these systems cannot detect the individual oscillators but rather a superposition of them, as in brain electrophysiological signals (electro- and magneoencephalogram), spurious phase locking will be detected. Current source-extraction techniques attempt to undo this superposition by assuming properties on the data, which are not valid when underlying sources are phase-locked. Statistical independence of the sources is one such invalid assumption, as phase-locked sources are dependent. In this paper, we introduce methods for source separation and clustering which make adequate assumptions for data where synchrony is present, and show with simulated data that they perform well even in cases where independent component analysis and other well-known source-separation methods fail. The results in this paper provide a proof of concept that synchrony-based techniques are useful for low-noise applications. PMID:21791409
Blind noisy image separation based on a new robust independent component analysis network
Jian Ji; Zheng Tian
2006-01-01
The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis(ICA) network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms.
陈永强; 王宏霞
2012-01-01
A method applicable to underdetermined blind separation of non-sparse signals is proposed, which is based on the Markov chain Monte Carlo (MCMC)Bayesian framework. The generalized Gaussian distribution (GGD) is used to model the source signals distribution, the model parameters and hidden variables are estimated by MCMC sampling to obtain the least mean square error estimation (MMSE) of source signals, by which the problem is solved that the GGD parameter estimation easily falls into a local extremum point and has poor robustness. Making use of the local stationarity of speech, a blind separation method based on non-sparse judgment criterions is proposed to enhance speech separation accuracy, which separates the speech in the non-sparse zone by MCMC sampling. Computer simulation shows that the proposed method can improve separation performance of non-sparse and speech signals, it also has better robustness.%本文提出一种基于马尔科夫链蒙特卡洛方法(MCMC)的贝叶斯非稀疏盲源分离算法.用广义高斯分布(GGD)来拟合源信号的分布,通过MCMC抽样得到GGD参数和隐变量的估计,并由此得到源信号的最小均方误差估计(MMSE),解决了GGD参数估计容易陷入局部极值点、鲁棒性差的问题.根据语音信号的局部平稳性,提出基于非稀疏度评判准则的盲分离算法,用MCMC方法分离非稀疏区的语音信号,进一步提高了语音信号分离精度.仿真实验证明,本文方法改善了非稀疏信号和语音信号的分离效果,而且具有更好的鲁棒性.
Evaluating source separation of plastic waste using conjoint analysis.
Nakatani, Jun; Aramaki, Toshiya; Hanaki, Keisuke
2008-11-01
Using conjoint analysis, we estimated the willingness to pay (WTP) of households for source separation of plastic waste and the improvement of related environmental impacts, the residents' loss of life expectancy (LLE), the landfill capacity, and the CO2 emissions. Unreliable respondents were identified and removed from the sample based on their answers to follow-up questions. It was found that the utility associated with reducing LLE and with the landfill capacity were both well expressed by logarithmic functions, but that residents were indifferent to the level of CO2 emissions even though they approved of CO2 reduction. In addition, residents derived utility from the act of separating plastic waste, irrespective of its environmental impacts; that is, they were willing to practice the separation of plastic waste at home in anticipation of its "invisible effects", such as the improvement of citizens' attitudes toward solid waste issues. PMID:18207727
Seema Sud
2016-04-01
Full Text Available The Fractional Fourier Transform (FrFT has been used for signal separation in numerous applications, such as speech, radar, and image processing. It is a useful signal processing tool that enables significantly greater signal separation by rotating signals in a time-frequency plane known as the Wigner Distribution (WD where they may be totally separable, when they are not separable in the time or frequency domains alone. In this paper, we apply the FrFT to the problem of separating the heartbeats of twin fetuses, as seen on an electrocardiogram (ECG, from each other. Most techniques fail here because the two heartbeat patterns are similar. The proposed algorithm takes advantage of the fact that two or more fetal heartbeats can be approximated as damped, chirp signals and may be slightly offset in time or amplitude. Thus, the WDs of the two signals will be slightly different from each other. By finding and rotating to the proper axis in the WD plane, where the chirp heartbeats become narrowband, separable signals, we can notch much of the weaker heartbeat to extract the stronger one. The estimate of the stronger signal is then subtracted from the ECG signal to obtain the weaker one. We show that the technique operates well using simulations, even when the two heartbeats are nearly equal in power. Specifically, we show that the mean-square error (MSE between the true heartbeats and the estimated ones are only on the order of 10−4 −10−3 . This method will enable identification of problems in an unborn child early in when the pregnant mother is expecting twins
Source separation as an exercise in logical induction
Knuth, Kevin H.
2002-01-01
We examine the relationship between the Bayesian and information-theoretic formulations of source separation algorithms. This work makes use of the relationship between the work of Claude E. Shannon and the "Recent Contributions" by Warren Weaver (Shannon & Weaver 1949) as clarified by Richard T. Cox (1979) and expounded upon by Robert L. Fry (1996) as a duality between a logic of assertions and a logic of questions. Working with the logic of assertions requires the use of probability as a me...
Blidholm, O.; Wiklund, S.E. [AaF-Energikonsult (Sweden); Bauer, A.C. [Energikonsult A. Bauer (Sweden)
1997-02-01
The basic idea of this project is to study the possibilities to use source separated combustible material for energy conversion in conventional solid fuel boilers (i.e. not municipal waste incineration plants). The project has been carried out in three phases. During phase 1 and 2 a number of fuel analyses of different fractions were carried out. During phase 3 two combustion tests were carried out; (1) a boiler with grate equipped with cyclone, electrostatic precipitator and flue gas condenser, and (2) a bubbling fluidized bed boiler with electrostatic precipitator and flue gas condenser. During the tests source separated paper and plastic packagings were co-fired with biomass fuels. The mixing rate of packagings was approximately 15%. This study reports the results of phase 3 and the conclusions of the whole project. The technical terms of using packaging as fuel are good. The technique is available for shredding both paper and plastic packaging. The material can be co-fired with biomass. The economical terms of using source separated packaging for energy conversion can be very advantageous, but can also form obstacles. The result is to a high degree guided by such facts as how the fuel is collected, transported, reduced in size and handled at the combustion plant. The results of the combustion tests show that the environmental terms of using source separated packaging for energy conversion are good. The emissions of heavy metals into the atmosphere are very low. The emissions are well below the emission standards for waste incineration plants. 35 figs, 13 tabs, 8 appendices
Abed-Meraim K; Barkat B
2004-01-01
We propose novel algorithms to select and extract separately all the components, using the time-frequency distribution (TFD), of a given multicomponent frequency-modulated (FM) signal. These algorithms do not use any a priori information about the various components. However, their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms, we applied them for the estimati...
Defining the force between separated sources on a light front
The Newtonian character of gauge theories on a light front requires that the longitudinal momentum P+, which plays the role of Newtonian mass, be conserved. This requirement conflicts with the standard definition of the force between two sources in terms of the minimal energy of quantum gauge fields in the presence of a quark and anti-quark pinned to points separated by a distance R. We propose that, on a light front, the force be defined by minimizing the energy of gauge fields in the presence of a quark and an anti-quark pinned to lines (1-branes) oriented in the longitudinal direction singled out by the light front and separated by a transverse distance R. Such sources will have a limited 1+1 dimensional dynamics. We study this proposal for weak coupling gauge theories by showing how it leads to the Coulomb force law. For QCD we also show how asymptotic freedom emerges by evaluating the S matrix through one loop for the scattering of a particle in the Nc representation of color SU(Nc) on a 1-brane by a particle in the bar Nc representation of color on a parallel 1-brane separated from the first by a distance RQCD. Potential applications to the problem of confinement on a light front are discussed. copyright 1999 The American Physical Society
Heungkyu Lee
2013-01-01
This paper proposes a method for the simultaneous separation and recognition of speech mixtures in noisy environments using two‐channel based independent vector analysis (IVA) on a home‐robot cleaner. The issues to be considered in our target application are speech recognition at a distance and noise removal to cope with a variety of noises, including TV sounds, air conditioners, babble, and so on, that can occur in a house, where people can utter a voice command to control a robot cleaner at...
B. Barkat
2004-10-01
Full Text Available We propose novel algorithms to select and extract separately all the components, using the time-frequency distribution (TFD, of a given multicomponent frequency-modulated (FM signal. These algorithms do not use any a priori information about the various components. However, their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms, we applied them for the estimation of the instantaneous frequency coefficients of a multicomponent signal and the results are compared with those of the higher-order ambiguity function (HAF algorithm. Monte Carlo simulation results show the superiority of the proposed algorithms over the HAF.
Barkat, B.; Abed-Meraim, K.
2004-12-01
We propose novel algorithms to select and extract separately all the components, using the time-frequency distribution (TFD), of a given multicomponent frequency-modulated (FM) signal. These algorithms do not use any a priori information about the various components. However, their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms, we applied them for the estimation of the instantaneous frequency coefficients of a multicomponent signal and the results are compared with those of the higher-order ambiguity function (HAF) algorithm. Monte Carlo simulation results show the superiority of the proposed algorithms over the HAF.
The Leuven isotope separator on-line laser ion source
An element-selective laser ion source has been used to produce beams of exotic radioactive nuclei and to study their decay properties. The operational principle of the ion source is based on selective resonant laser ionization of nuclear reaction products thermalized and neutralized in a noble gas at high pressure. The ion source has been installed at the Leuven Isotope Separator On-Line (LISOL), which is coupled on-line to the cyclotron accelerator at Louvain-la-Neuve. 54,55Ni and 54,55Co isotopes were produced in light-ion-induced fusion reactions. Exotic nickel, cobalt and copper nuclei were produced in proton-induced fission of 238U. The b decay of the 68-74Ni, 67-70Co, 70-75Cu and 110-114Rh isotopes has been studied by means of β-γ and γ-γ spectroscopy. Recently, the laser ion source has been used to produce neutron-deficient rhodium and ruthenium isotopes (91-95Rh, 98Rh, 90,91Ru) near the N=Z line in heavy ion-induced fusion reactions
Compressive Source Separation: Theory and Methods for Hyperspectral Imaging
Golbabaee, Mohammad; Arberet, Simon; Vandergheynst, Pierre
2013-12-01
With the development of numbers of high resolution data acquisition systems and the global requirement to lower the energy consumption, the development of efficient sensing techniques becomes critical. Recently, Compressed Sampling (CS) techniques, which exploit the sparsity of signals, have allowed to reconstruct signal and images with less measurements than the traditional Nyquist sensing approach. However, multichannel signals like Hyperspectral images (HSI) have additional structures, like inter-channel correlations, that are not taken into account in the classical CS scheme. In this paper we exploit the linear mixture of sources model, that is the assumption that the multichannel signal is composed of a linear combination of sources, each of them having its own spectral signature, and propose new sampling schemes exploiting this model to considerably decrease the number of measurements needed for the acquisition and source separation. Moreover, we give theoretical lower bounds on the number of measurements required to perform reconstruction of both the multichannel signal and its sources. We also proposed optimization algorithms and extensive experimentation on our target application which is HSI, and show that our approach recovers HSI with far less measurements and computational effort than traditional CS approaches.
The Leuven isotope separator on-line laser ion source
Kudryavtsev, Y; Franchoo, S; Huyse, M; Gentens, J; Kruglov, K; Müller, W F; Prasad, N V S; Raabe, R; Reusen, I; Van den Bergh, P; Van Duppen, P; Van Roosbroeck, J; Vermeeren, L; Weissman, L
2002-01-01
An element-selective laser ion source has been used to produce beams of exotic radioactive nuclei and to study their decay properties. The operational principle of the ion source is based on selective resonant laser ionization of nuclear reaction products thermalized and neutralized in a noble gas at high pressure. The ion source has been installed at the Leuven Isotope Separator On-Line (LISOL), which is coupled on-line to the cyclotron accelerator at Louvain-la-Neuve. sup 5 sup 4 sup , sup 5 sup 5 Ni and sup 5 sup 4 sup , sup 5 sup 5 Co isotopes were produced in light-ion-induced fusion reactions. Exotic nickel, cobalt and copper nuclei were produced in proton-induced fission of sup 2 sup 3 sup 8 U. The b decay of the sup 6 sup 8 sup - sup 7 sup 4 Ni, sup 6 sup 7 sup - sup 7 sup 0 Co, sup 7 sup 0 sup - sup 7 sup 5 Cu and sup 1 sup 1 sup 0 sup - sup 1 sup 1 sup 4 Rh isotopes has been studied by means of beta-gamma and gamma-gamma spectroscopy. Recently, the laser ion source has been used to produce neutron-d...
The Effects of Environmental Management Systems on Source Separation in the Work and Home Settings
Chris von Borgstede
2012-06-01
Full Text Available Measures that challenge the generation of waste are needed to address the global problem of the increasing volumes of waste that are generated in both private homes and workplaces. Source separation at the workplace is commonly implemented by environmental management systems (EMS. In the present study, the relationship between source separation at work and at home was investigated. A questionnaire that maps psychological and behavioural predictors of source separation was distributed to employees at different workplaces. The results show that respondents with awareness of EMS report higher levels of source separation at work, stronger environmental concern, personal and social norms, and perceive source separation to be less difficult. Furthermore, the results support the notion that after the adoption of EMS at the workplace, source separation at work spills over into source separation in the household. The potential implications for environmental management systems are discussed.
Using the FASST source separation toolbox for noise robust speech recognition
Ozerov, Alexey; Vincent, Emmanuel
2011-01-01
We describe our submission to the 2011 CHiME Speech Separation and Recognition Challenge. Our speech separation algorithm was built using the Flexible Audio Source Separation Toolbox (FASST) we developed recently. This toolbox is an implementation of a general flexible framework based on a library of structured source models that enable the incorporation of prior knowledge about a source separation problem via user-specifiable constraints. We show how to use FASST to develop an efficient spee...
Contributions to theory and algorithms of independent component analysis and signal separation
Eriksson, Jan
2004-01-01
This thesis addresses the problem of blind signal separation (BSS) using independent component analysis (ICA). In blind signal separation, signals from multiple sources arrive simultaneously at a sensor array, so that each sensor array output contains a mixture of source signals. Sets of sensor outputs are processed to recover the source signals or to identify the mixing system. The term blind refers to the fact that no explicit knowledge of source signals or mixing system is available. Indep...
Nguyen, V. H.; C. Rutten; J.-C. Golinval
2012-01-01
In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. The aim of this paper is to propose an extension of fault detection techniques that may be used when a reduced set of sensors...
Sebastian Halder
2007-01-01
that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA and one BSS algorithm (AMUSE are evaluated to determine their ability to isolate electromyographic (EMG and electrooculographic (EOG artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.
Separating Underdetermined Convolutive Speech Mixtures
Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan;
2006-01-01
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a...... method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...... techniques with binary time-frequency masking. In the proposed method, the number of source signals is not assumed to be known in advance and the number of sources is not limited to the number of microphones. Our approach needs only two microphones and the separated sounds are maintained as stereo signals....
Residents’ Household Solid Waste (HSW Source Separation Activity: A Case Study of Suzhou, China
Hua Zhang
2014-09-01
Full Text Available Though the Suzhou government has provided household solid waste (HSW source separation since 2000, the program remains largely ineffective. Between January and March 2014, the authors conducted an intercept survey in five different community groups in Suzhou, and 505 valid surveys were completed. Based on the survey, the authors used an ordered probit regression to study residents’ HSW source separation activities for both Suzhou and for the five community groups. Results showed that 43% of the respondents in Suzhou thought they knew how to source separate HSW, and 29% of them have source separated HSW accurately. The results also found that the current HSW source separation pilot program in Suzhou is valid, as HSW source separation facilities and residents’ separation behavior both became better and better along with the program implementation. The main determinants of residents’ HSW source separation behavior are residents’ age, HSW source separation facilities and government preferential policies. The accessibility to waste management service is particularly important. Attitudes and willingness do not have significant impacts on residents’ HSW source separation behavior.
Wood ash as a magnesium source for phosphorus recovery from source-separated urine.
Sakthivel, S Ramesh; Tilley, Elizabeth; Udert, Kai M
2012-03-01
Struvite precipitation is a simple technology for phosphorus recovery from source-separated urine. However, production costs can be high if expensive magnesium salts are used as precipitants. Therefore, waste products can be interesting alternatives to industrially-produced magnesium salts. We investigated the technical and financial feasibility of wood ash as a magnesium source in India. In batch experiments with source-separated urine, we could precipitate 99% of the phosphate with a magnesium dosage of 2.7 mol Mg mol P(-1). The availability of the magnesium from the wood ash used in our experiment was only about 50% but this could be increased by burning the wood at temperatures well above 600 °C. Depending on the wood ash used, the precipitate can contain high concentrations of heavy metals. This could be problematic if the precipitate were used as fertilizer depending on the applicable fertilizer regulations. The financial study revealed that wood ash is considerably cheaper than industrially-produced magnesium sources and even cheaper than bittern. However, the solid precipitated with wood ash is not pure struvite. Due to the high calcite and the low phosphorus content (3%), the precipitate would be better used as a phosphorus-enhanced conditioner for acidic soils. The estimated fertilizer value of the precipitate was actually slightly lower than wood ash, because 60% of the potassium dissolved into solution during precipitation and was not present in the final product. From a financial point of view and due to the high heavy metal content, wood ash is not a very suitable precipitant for struvite production. Phosphate precipitation from urine with wood ash can be useful if (1) a strong need for a soil conditioner that also contains phosphate exists, (2) potassium is abundant in the soil and (3) no other cheap precipitant, such as bittern or magnesium oxide, is available. PMID:22297249
The Effects of Environmental Management Systems on Source Separation in the Work and Home Settings
Chris von Borgstede; Maria Andersson; Ola Eriksson
2012-01-01
Measures that challenge the generation of waste are needed to address the global problem of the increasing volumes of waste that are generated in both private homes and workplaces. Source separation at the workplace is commonly implemented by environmental management systems (EMS). In the present study, the relationship between source separation at work and at home was investigated. A questionnaire that maps psychological and behavioural predictors of source separation was distributed to empl...
The use of the non-invasively obtained fetal electrocardiogram (ECG) in fetal monitoring is complicated by the low signal-to-noise ratio (SNR) of ECG signals. Even after removal of the predominant interference (i.e. the maternal ECG), the SNR is generally too low for medical diagnostics, and hence additional signal processing is still required. To this end, several methods for exploiting the spatial correlation of multi-channel fetal ECG recordings from the maternal abdomen have been proposed in the literature, of which principal component analysis (PCA) and independent component analysis (ICA) are the most prominent. Both PCA and ICA, however, suffer from the drawback that they are blind source separation (BSS) techniques and as such suboptimum in that they do not consider a priori knowledge on the abdominal electrode configuration and fetal heart activity. In this paper we propose a source separation technique that is based on the physiology of the fetal heart and on the knowledge of the electrode configuration. This technique operates by calculating the spatial fetal vectorcardiogram (VCG) and approximating the VCG for several overlayed heartbeats by an ellipse. By subsequently projecting the VCG onto the long axis of this ellipse, a source signal of the fetal ECG can be obtained. To evaluate the developed technique, its performance is compared to that of both PCA and ICA and to that of augmented versions of these techniques (aPCA and aICA; PCA and ICA applied on preprocessed signals) in generating a fetal ECG source signal with enhanced SNR that can be used to detect fetal QRS complexes. The evaluation shows that the developed source separation technique performs slightly better than aPCA and aICA and outperforms PCA and ICA and has the main advantage that, with respect to aPCA/PCA and aICA/ICA, it performs more robustly. This advantage renders it favorable for employment in automated, real-time fetal monitoring applications
Source Separation and Composting of Organic Municipal Solid Waste.
Gould, Mark; And Others
1992-01-01
Describes a variety of composting techniques that may be utilized in a municipal level solid waste management program. Suggests how composting system designers should determine the amount and type of organics in the waste stream, evaluate separation approaches and assess collection techniques. Outlines the advantages of mixed waste composting and…
Sparse Reverberant Audio Source Separation via Reweighted Analysis
Arberet, Simon; Vandergheynst, Pierre; Carrillo, Rafael; Thiran, Jean-Philippe; Wiaux, Yves
2013-01-01
We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the convolutive mixing process. In this paper, we address the source estimation of convolutive mixtures with a new algorithm based on i) an analysis sparse prior, ii) a rewe...
Semi-Blind Cancellation of IQ-Imbalances
Hesse, Matthias; Mailand, Marko; Jentschel, Hans-Joachim; Deneire, Luc; Lebrun, Jerome
2008-01-01
The technical realization of modern wireless receivers yields significant interfering IQ-imbalances, which have to be compensated digitally. To cancel these IQ-imbalances, we propose an algorithm using iterative blind source separation (IBSS) as well as information about the modulation scheme used (hence the term semi-blind). The novelty of our approach lies in the fact that we match the nonlinearity involved in the IBSS algorithm to the probability density function of the source signals. Mor...
Single-channel source separation using non-negative matrix factorization
Schmidt, Mikkel Nørgaard
which a number of methods for single-channel source separation based on non-negative matrix factorization are presented. In the papers, the methods are applied to separating audio signals such as speech and musical instruments and separating different types of tissue in chemical shift imaging....
30 CFR 57.6404 - Separation of blasting circuits from power source.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Separation of blasting circuits from power... NONMETAL MINES Explosives Electric Blasting-Surface and Underground § 57.6404 Separation of blasting circuits from power source. (a) Switches used to connect the power source to a blasting circuit shall...
30 CFR 56.6404 - Separation of blasting circuits from power source.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Separation of blasting circuits from power... MINES Explosives Electric Blasting § 56.6404 Separation of blasting circuits from power source. (a) Switches used to connect the power source to a blasting circuit shall be locked in the open position...
Sound fields separation and reconstruction of irregularly shaped sources
Totaro, N.; Vigoureux, D.; Leclère, Q.; Lagneaux, J.; Guyader, J. L.
2015-02-01
Nowadays, the need of source identification methods is still growing and application cases are more and more complex. As a consequence, it is necessary to develop methods allowing us to reconstruct sound fields on irregularly shaped sources in reverberant or confined acoustic environment. The inverse Patch Transfer Functions (iPTF) method is suitable to achieve these objectives. Indeed, as the iPTF method is based on Green's identity and double measurements of pressure and particle velocity on a surface surrounding the source, it is independent of the acoustic environment. In addition, the finite element solver used to compute the patch transfer functions permits us to handle sources with 3D irregular shapes. In the present paper, two experimental applications on a flat plate and an oil pan have been carried out to show the performances of the method on real applications. As for all ill-posed problem, it is shown that the crucial point of this method is the choice of the parameter of the Tikhonov regularization, one of the most widely used in the literature. The classical L-curve strategy sometimes fails to choose the best solution. This issue is clearly explained and an adapted strategy combining L-curve and acoustic power conservation is proposed. The efficiency of this strategy is demonstrated on both applications and compared to results obtained with Generalized Cross Validation (GCV) technique.
Quantum Rate Distortion, Reverse Shannon Theorems, and Source-Channel Separation
Datta, Nilanjana; Hsieh, Min-Hsiu; Wilde, Mark M.
2013-01-01
We derive quantum counterparts of two key theorems of classical information theory, namely, the rate distortion theorem and the source-channel separation theorem. The rate-distortion theorem gives the ultimate limits on lossy data compression, and the source-channel separation theorem implies that a two-stage protocol consisting of compression and channel coding is optimal for transmitting a memoryless source over a memoryless channel. In spite of their importance in the classical domain, the...
Student support and perceptions of urine source separation in a university community.
Ishii, Stephanie K L; Boyer, Treavor H
2016-09-01
Urine source separation, i.e., the collection and treatment of human urine as a separate waste stream, has the potential to improve many aspects of water resource management and wastewater treatment. However, social considerations must be taken into consideration for successful implementation of this alternative wastewater system. This work evaluated the perceptions of urine source separation held by students living on-campus at a major university in the Southeastern region of the United States. Perceptions were evaluated in the context of the Theory of Planned Behavior. The survey population represents one group within a community type (universities) that is expected to be an excellent testbed for urine source separation. Overall, respondents reported high levels of support for urine source separation after watching a video on expected benefits and risks, e.g., 84% indicated that they would vote in favor of urine source separation in residence halls. Support was less apparent when measured by willingness to pay, as 33% of respondents were unwilling to pay for the implementation of urine source separation and 40% were only willing to pay $1 to $10 per semester. Water conservation was largely identified as the most important benefit of urine source separation and there was little concern reported about the use of urine-based fertilizers. Statistical analyses showed that one's environmental attitude, environmental behavior, perceptions of support within the university community, and belief that student opinions have an impact on university decision makers were significantly correlated with one's support for urine source separation. This work helps identify community characteristics that lend themselves to acceptance of urine source separation, such as those related to environmental attitudes/behaviors and perceptions of behavioral control and subjective norm. Critical aspects of these alternative wastewater systems that require attention in order to foster public
Empirical Study on Factors Influencing Residents' Behavior of Separating Household Wastes at Source
Qu Ying; Zhu Qinghua; Murray Haight
2007-01-01
Source separation is the basic premise for making effective use of household wastes. In eight cities of China, however, several pilot projects of source separation finally failed because of the poor participation rate of residents. In order to solve this problem, identifying those factors that influence residents' behavior of source separation becomes crucial. By means of questionnaire survey, we conducted descriptive analysis and exploratory factor analysis. The results show that trouble-feeling, moral notion, environment protection, public education, environment value and knowledge deficiency are the main factors that play an important role for residents in deciding to separate their household wastes. Also, according to the contribution percentage of the six main factors to the total behavior of source separation, their influencing power is analyzed, which will provide suggestions on household waste management for policy makers and decision makers in China.
Source Separation and Higher-Order Causal Analysis of MEG and EEG
Zhang, Kun
2012-01-01
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.
Monaural separation of dependent audio sources based on a generalized Wiener filter
Ma, Guilin; Agerkvist, Finn T.; Luther, J.B.
2007-01-01
) coefficients of the dependent sources is modeled by complex Gaussian mixture models in the frequency domain from samples of individual sources to capture the properties of the sources and their correlation. During the second stage, the mixture is separated through a generalized Wiener filter, which takes...
Hosseinzadeh Nik, Tahereh; Shahsavari, Negin; Ghadirian, Hannaneh; Ostad, Seyed Nasser
2016-07-01
The aim of this randomized clinical study was to investigate the effectiveness of acetaminophen 650 mg or liquefied ibuprofen 400 mg in pain control of orthodontic patients during separation with an elastic separator. A total of 101 patients with specific inclusion criteria were divided randomly into three groups (acetaminophen, liquefied ibuprofen, and placebo). They were instructed to take their drugs one hour before separator placement and every six hours afterward (five doses in total). They recorded their discomfort on visual analog scales immediately after separator placement, 2 hours later, 6 hours later, at bedtime, and 24 hours after separator placement. Repeated measure analysis of variance (ANOVA) was used to compare the mean pain scores between the three groups. Data were collected from 89 patients. The pain increased with time in all groups. Pain scores were statistically lower in the analgesic groups compared with the placebo group (P.valueacetaminophen and liquefied ibuprofen) (P.value=1). Acetaminophen and liquefied ibuprofen have similar potential in pain reduction during separation. PMID:27424011
Xu, Wanying; Zhou, Chuanbin; Lan, Yajun; Jin, Jiasheng; Cao, Aixin
2015-05-01
Municipal solid waste (MSW) management (MSWM) is most important and challenging in large urban communities. Sound community-based waste management systems normally include waste reduction and material recycling elements, often entailing the separation of recyclable materials by the residents. To increase the efficiency of source separation and recycling, an incentive-based source separation model was designed and this model was tested in 76 households in Guiyang, a city of almost three million people in southwest China. This model embraced the concepts of rewarding households for sorting organic waste, government funds for waste reduction, and introducing small recycling enterprises for promoting source separation. Results show that after one year of operation, the waste reduction rate was 87.3%, and the comprehensive net benefit under the incentive-based source separation model increased by 18.3 CNY tonne(-1) (2.4 Euros tonne(-1)), compared to that under the normal model. The stakeholder analysis (SA) shows that the centralized MSW disposal enterprises had minimum interest and may oppose the start-up of a new recycling system, while small recycling enterprises had a primary interest in promoting the incentive-based source separation model, but they had the least ability to make any change to the current recycling system. The strategies for promoting this incentive-based source separation model are also discussed in this study. PMID:25819930
Bayesian Source Separation Applied to Identifying Complex Organic Molecules in Space
Knuth, Kevin H; Choinsky, Joshua; Maunu, Haley A; Carbon, Duane F
2014-01-01
Emission from a class of benzene-based molecules known as Polycyclic Aromatic Hydrocarbons (PAHs) dominates the infrared spectrum of star-forming regions. The observed emission appears to arise from the combined emission of numerous PAH species, each with its unique spectrum. Linear superposition of the PAH spectra identifies this problem as a source separation problem. It is, however, of a formidable class of source separation problems given that different PAH sources potentially number in the hundreds, even thousands, and there is only one measured spectral signal for a given astrophysical site. Fortunately, the source spectra of the PAHs are known, but the signal is also contaminated by other spectral sources. We describe our ongoing work in developing Bayesian source separation techniques relying on nested sampling in conjunction with an ON/OFF mechanism enabling simultaneous estimation of the probability that a particular PAH species is present and its contribution to the spectrum.
Duarte L.T.
2014-03-01
Full Text Available The development of chemical sensor arrays based on Blind Source Separation (BSS provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE. The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.
Source Separation and Clustering of Phase-Locked Subspaces: Derivations and Proofs
Almeida, Miguel; Schleimer, Jan-Hendrik; Bioucas-Dias, José; Vigário, Ricardo
2011-01-01
Due to space limitations, our submission "Source Separation and Clustering of Phase-Locked Subspaces", accepted for publication on the IEEE Transactions on Neural Networks in 2011, presented some results without proof. Those proofs are provided in this paper.
Fate of pharmaceuticals in full-scale source separated sanitation system
Butkovskyi, A.; Hernandez Leal, L.; Rijnaarts, H.H.M.; Zeeman, G.
2015-01-01
Removal of 14 pharmaceuticals and 3 of their transformation products was studied in a full-scale source separated sanitation system with separate collection and treatment of black water and grey water. Black water is treated in an up-flow anaerobic sludge blanket (UASB) reactor followed by oxygen
A cost evaluation method for transferring municipalities to solid waste source-separated system.
Lavee, Doron; Nardiya, Shlomit
2013-05-01
Most of Israel's waste is disposed in landfills, threatening scarce land resources and posing environmental and health risks. The aim of this study is to estimate the expected costs of transferring municipalities to solid waste source separation in Israel, aimed at reducing the amount of waste directed to landfills and increasing the efficiency and amount of recycled waste. Information on the expected costs of operating a solid waste source separation system was gathered from 47 municipalities and compiled onto a database, taking into consideration various factors such as costs of equipment, construction adjustments and waste collection and disposal. This database may serve as a model for estimating the costs of entering the waste source separation system for any municipality in Israel, while taking into consideration its specific characteristics, such as size and region. The model was used in Israel for determining municipalities' eligibility to receive a governmental grant for entering an accelerated process of solid waste source separation. This study displays a user-friendly and simple operational tool for assessing municipalities' costs of entering a process of waste source separation, providing policy makers a powerful tool for diverting funds effectively in promoting solid waste source separation. PMID:23465315
A Blind Angle? News Sources, Gender and Ethnicity in Danish TV News
Fiig, Christina
The paper will present and discuss a framework for grasping some of the democratic consequences of biased TV news programs. In line with Jürgen Habermas, one can ask what consequences it has for a democratic public sphere that the national TV news landscape is biased in term of source diversity...
Y. Yokoo
2014-01-01
This study compared a time source hydrograph separation method to a geographic source separation method, to assess if the two methods produced similar results. The time source separation of a hydrograph was performed using a numerical filter method and the geographic source separation was performed using an end-member mixing analysis employing hourly discharge, electric conductivity, and turbidity data. These data were collected in 2006 at the Kuroiwa monitoring ...
Prospects of Source-Separation-Based Sanitation Concepts: A Model-Based Study
Cees Buisman
2013-07-01
Full Text Available Separation of different domestic wastewater streams and targeted on-site treatment for resource recovery has been recognized as one of the most promising sanitation concepts to re-establish the balance in carbon, nutrient and water cycles. In this study a model was developed based on literature data to compare energy and water balance, nutrient recovery, chemical use, effluent quality and land area requirement in four different sanitation concepts: (1 centralized; (2 centralized with source-separation of urine; (3 source-separation of black water, kitchen refuse and grey water; and (4 source-separation of urine, feces, kitchen refuse and grey water. The highest primary energy consumption of 914 MJ/capita(cap/year was attained within the centralized sanitation concept, and the lowest primary energy consumption of 437 MJ/cap/year was attained within source-separation of urine, feces, kitchen refuse and grey water. Grey water bio-flocculation and subsequent grey water sludge co-digestion decreased the primary energy consumption, but was not energetically favorable to couple with grey water effluent reuse. Source-separation of urine improved the energy balance, nutrient recovery and effluent quality, but required larger land area and higher chemical use in the centralized concept.
The single staged ECR source at the TRIUMF isotope separator TISOL
With its installation at the isotope separator TISOL the single staged ECR source has now become operational in delivering radioactive species extracted from the production target which is bombarded by 500 MeV protons. Among the radioactive species detected so far are He, C, N, Ne, Cl, Ar, Kr and Xe. The dependency of the ion currents/efficiencies on several source parameters is discussed as well as the technical difficulties in connecting ECR sources to on-line isotope separators. (Author) (5 refs., tab., 6 figs.)
Applying the Background-Source separation algorithm to Chandra Deep Field South data
Guglielmetti, F; Fischer, R; Rosati, P; Tozzi, P
2012-01-01
A probabilistic two-component mixture model allows one to separate the diffuse background from the celestial sources within a one-step algorithm without data censoring. The background is modeled with a thin-plate spline combined with the satellite's exposure time. Source probability maps are created in a multi-resolution analysis for revealing faint and extended sources. All detected sources are automatically parametrized to produce a list of source positions, fluxes and morphological parameters. The present analysis is applied to the Chandra Deep Field South 2 Ms public released data. Within its 1.884 ks of exposure time and its angular resolution (0.984 arcsec), the Chandra Deep Field South data are particularly suited for testing the Background-Source separation algorithm.
Reverberant Audio Source Separation via Sparse and Low-Rank Modeling
Arberet, Simon; Vandergheynst, Pierre
2013-01-01
The performance of audio source separation from underde- termined convolutive mixture assuming known mixing filters can be significantly improved by using an analysis sparse prior optimized by a reweighting l1 scheme and a wideband data- fidelity term, as demonstrated by a recent article. In this letter, we show that the performance can be improved even more significantly by exploiting a low-rank prior on the source spectrograms. We present a new algorithm to estimate the sources based on i) ...
Municipal solid waste source-separated collection in China: A comparative analysis
A pilot program focusing on municipal solid waste (MSW) source-separated collection was launched in eight major cities throughout China in 2000. Detailed investigations were carried out and a comprehensive system was constructed to evaluate the effects of the eight-year implementation in those cities. This paper provides an overview of different methods of collection, transportation, and treatment of MSW in the eight cities; as well as making a comparative analysis of MSW source-separated collection in China. Information about the quantity and composition of MSW shows that the characteristics of MSW are similar, which are low calorific value, high moisture content and high proportion of organisms. Differences which exist among the eight cities in municipal solid waste management (MSWM) are presented in this paper. Only Beijing and Shanghai demonstrated a relatively effective result in the implementation of MSW source-separated collection. While the six remaining cities result in poor performance. Considering the current status of MSWM, source-separated collection should be a key priority. Thus, a wider range of cities should participate in this program instead of merely the eight pilot cities. It is evident that an integrated MSWM system is urgently needed. Kitchen waste and recyclables are encouraged to be separated at the source. Stakeholders involved play an important role in MSWM, thus their responsibilities should be clearly identified. Improvement in legislation, coordination mechanisms and public education are problematic issues that need to be addressed.
Rifai Chai; Naik, Ganesh R; Tran, Yvonne; Sai Ho Ling; Craig, Ashley; Nguyen, Hung T
2015-08-01
An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05). PMID:26736312
Yunhan Luo; Houxin Cui; Xiaoyu Gu; Rong Liu; Kexin Xu
2005-01-01
Based on analysis of the relation between mean penetration depth and source-detector separation in a threelayer model with the method of Monte-Carlo simulation, an optimal source-detector separation is derived from the mean penetration depth referring to monitoring the change of chromophores concentration of the sandwiched layer. In order to verify the separation, we perform Monte-Carlo simulations with varied absorption coefficient of the sandwiched layer. All these diffuse reflectances are used to construct a calibration model with the method of partial least square (PLS). High correlation coefficients and low root mean square error of prediction (RMSEP) at the optimal separation have confirmed correctness of the selection. This technique is expected to show light on noninvasive diagnosis of near-infrared spectroscopy.
Life cycle assessment of grain production using source-separated human urine and mineral fertiliser
Tidåker, Pernilla
2003-01-01
Source-separation of human urine is one promising technique for closing the nutrient cycle, reducing nutrient discharge and increasing energy efficiency. Separated urine can be used as a valuable fertiliser in agriculture, replacing mineral fertiliser. However, a proper handling of the urine at farm level is crucial for the environmental performance of the whole system. This study started from an agricultural point of view, demonstrating how grain production systems using human urine might be...
Subband-based Single-channel Source Separation of Instantaneous Audio Mixtures
Taghia, Jalil; Doostari, Mohammad Ali
2009-01-01
In this paper, a new algorithm is developed to separate the audio sources from a single instantaneous mixture. The algorithm is based on subband decomposition and uses a hybrid system of Empirical Mode Decomposition (EMD) and Principle Component Analysis (PCA) to construct artificial observations from the single mixture. In the separation stage of algorithm, we use Independent Component Analysis (ICA) to find independent components. At first the observed mixture is divided into a finite numbe...
Larsen, Anna Warberg; Astrup, Thomas
2011-01-01
variations between emission factors for different incinerators, but the background for these variations has not been thoroughly examined. One important reason may be variations in collection of recyclable materials as source separation alters the composition of the residual waste incinerated. The objective......CO2-loads from combustible waste are important inputs for national CO2 inventories and life-cycle assessments (LCA). CO2 emissions from waste incinerators are often expressed by emission factors in kg fossil CO2 emitted per GJ energy content of the waste. Various studies have shown considerable...... of this study was to quantify the importance of source separation for determination of emission factors for incineration of residual household waste. This was done by mimicking various source separation scenarios and based on waste composition data calculating resulting emission factors for residual...
Separation of beam and electrons in the spallation neutron source H- ion source
The Spallation Neutron Source (SNS) requires an ion source producing an H- beam with a peak current of 35 mA at a 6.2% duty factor. For the design of this ion source, extracted electrons must be transported and dumped without adversely affecting the H- beam optics. Two issues are considered: (1) electron containment transport and controlled removal; and (2) first-order H- beam steering. For electron containment, various magnetic, geometric and electrode biasing configurations are analyzed. A kinetic description for the negative ions and electrons is employed with self-consistent fields obtained from a steady-state solution to Poisson's equation. Guiding center electron trajectories are used when the gyroradius is sufficiently small. The magnetic fields used to control the transport of the electrons and the asymmetric sheath produced by the gyrating electrons steer the ion beam. Scenarios for correcting this steering by split acceleration and focusing electrodes will be considered in some detail
Nielsen, Christoffer Rosenkilde; Nielson, Hanne Riis
2006-01-01
operation blinding. In this paper we study the theoretical foundations for one of the successful approaches to validating cryptographic protocols and we extend it to handle the blinding primitive. Our static analysis approach is based on Flow Logic; this gives us a clean separation between the specification...... of the analysis and its realisation in an automatic tool. We concentrate on the former in the present paper and provide the semantic foundation for our analysis of protocols using blinding - also in the presence of malicious attackers....
On-line isotope separation. Tests for targets and ion sources compatibility
We have performed a compilation of the influence of various parameters on suitable targets (composition, structure and nuclear constraint) for fission and spallation reactions induced by charged particles. In that case, targets are generally located near or inside the ionization chamber. A survey of typical ions sources and separators particularly used with heavy ion beams is given. These sources are often feeded either by a helium jet transport system or by a catcher foil
Carabias Orti, Julio J; Cobos, M??ximo; Vera Candeas, Pedro; Rodr??guez Serrano, Francisco J
2013-01-01
Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge am...
Ahuja, Chaitanya; Nathwani, Karan; Rajesh M. Hegde
2014-01-01
Conventional NMF methods for source separation factorize the matrix of spectral magnitudes. Spectral Phase is not included in the decomposition process of these methods. However, phase of the speech mixture is generally used in reconstructing the target speech signal. This results in undesired traces of interfering sources in the target signal. In this paper the spectral phase is incorporated in the decomposition process itself. Additionally, the complex matrix factorization problem is reduce...
Yuan, Yalin; Yabe, Mitsuyasu
2014-01-01
A source separation program for household kitchen waste has been in place in Beijing since 2010. However, the participation rate of residents is far from satisfactory. This study was carried out to identify residents’ preferences based on an improved management strategy for household kitchen waste source separation. We determine the preferences of residents in an ad hoc sample, according to their age level, for source separation services and their marginal willingness to accept compensation for the service attributes. We used a multinomial logit model to analyze the data, collected from 394 residents in Haidian and Dongcheng districts of Beijing City through a choice experiment. The results show there are differences of preferences on the services attributes between young, middle, and old age residents. Low compensation is not a major factor to promote young and middle age residents accept the proposed separation services. However, on average, most of them prefer services with frequent, evening, plastic bag attributes and without instructor. This study indicates that there is a potential for local government to improve the current separation services accordingly. PMID:25546279
Yalin Yuan
2014-12-01
Full Text Available A source separation program for household kitchen waste has been in place in Beijing since 2010. However, the participation rate of residents is far from satisfactory. This study was carried out to identify residents’ preferences based on an improved management strategy for household kitchen waste source separation. We determine the preferences of residents in an ad hoc sample, according to their age level, for source separation services and their marginal willingness to accept compensation for the service attributes. We used a multinomial logit model to analyze the data, collected from 394 residents in Haidian and Dongcheng districts of Beijing City through a choice experiment. The results show there are differences of preferences on the services attributes between young, middle, and old age residents. Low compensation is not a major factor to promote young and middle age residents accept the proposed separation services. However, on average, most of them prefer services with frequent, evening, plastic bag attributes and without instructor. This study indicates that there is a potential for local government to improve the current separation services accordingly.
Gjerris, Mickey
2015-01-01
This article describes how we seem to live in a willed blindness towards the effects that our meat production and consumption have on animals, the environment and the climate. A willed blindness that cannot be explained by either lack of knowledge or scientific uncertainty. The blindness enables us...... willed blindness focusing on the development of either a new moral vision of our obligations or new visions of what a good life is....