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High dose rate brachytherapy affords a frequent reassurance of the precise dwell positions of the radiation source. The current investigation proposes a multi-dimensional scaling transformation of both data sets to estimate dwell positions without any external reference. Furthermore, the related distributions of dwell positions are characterized by uni-or bi-modal heavy-tailed distributions. The latter are well represented by α-stable distributions. The newly proposed data analysis provides dwell position deviations with high accuracy, and, furthermore, offers a convenient visualization of the actual shapes of the catheters which guide the radiation source during the treatment.

In this article we address a novel method for spectrum sensing, based on the Expectation Maximization algorithm applied to the histogram of the moving average signal power. The method enables the estimation of the number of active users in a given frequency band, the power received from each user, the occupied time slots and the front-end noise floor. The proposed approach takes advantage of the statistical properties of the averaging estimator output, which allows to model the received estimated power as a Gaussian mixture. This model represents the distributions of the users transmitted signal power as well as the system noise floor. Moreover, the Gaussian with the lowest mean that is related with the noise floor, can be used to estimate an adaptive threshold for a constant false alarm rate detector. Finally, the method was validated in a Wi-Fi experimental setup, where real-world data was acquired with a software defined radio.

International audience; In this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF algorithms are very restricted in the size of tensors that can be decomposed. Our algorithm overcomes this size restriction by interpreting the tensor as a set of sub-tensors and by proceeding the decomposition of sub-tensor by sub-tensor. This approach requires only one sub-tensor at once to be available in memory.

In this article we propose a subtractive histogram clustering method to estimate the number of LTE users based on the uplink energy probability density distribution sensed by an RF front-end. The energy of the signal is estimated and its histogram is analyzed to determinate the number of different distributions. As the energy estimation of the sensed LTE uplink can be modeled by a Gaussian mixture, this allow us to have a priori information that help us to determine the number of users. The lowest value Gaussian distribution can be used to accurately estimate the noise floor and the remaining distributions allow us to estimate the number of LTE users and their received power.

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.

A new GiT-BEMD algorithm is developed.Based on Green's function with tension.Much faster than canonical BEMD algorithms.Easy to implement and very robust.Superior performance tested on artificial and real images. Bidimensional Empirical Mode Decomposition(BEMD) interprets an image as a superposition of Bidimensional Intrinsic Mode Functions (BIMFs). They are extracted by a process called sifting, which encompasses two-dimensional surface interpolations connecting a set of local maxima or minima to form corresponding envelope surfaces. Existing surface interpolation schemes are computationally very demanding and often induce artifacts in the extracted modes. This paper suggests a novel method of envelope surface interpolation based on Green's functions. Including surface tension greatly improves the stability of the new method which we call Green's function in tension-based BEMD (GiT-BEMD). Simulation results, using toy images with various textures, facial images and functional neuroimages, demonstrate the superior performance of the new method when compared to its canonical BEMD counterpart. GiT-BEMD strongly speeds up computations and achieves a higher quality of the extracted BIMFs. Furthermore, GiT-BEMD can be extended simply to an ensemble-based variant (GiT-BEEMD), if needed. In summary, the study suggests the new variant GiT-BEMD as a highly competitive, fast and stable alternative to existing BEMD techniques for image analysis.

A survey of our recent work on probabilistic NMF is provided. All variants discussed here are illustrated by their application to the analysis of failure patterns emerging from manufacturing and processing silicon wafers. It starts with binNMF, a variant developed to apply NMF to binary data sets. The latter are modeled as a probabilistic superposition of a finite number of intrinsic continuous-valued failure patterns characteristic for the manufacturing process. We further discuss related theoretical work on a semi-non-negative matrix factorization based on the logistic function, which we called logistic NMF. While addressing uniqueness issues, we propose a Bayesian Optimality Criterion for NMF and a determinant criterion to geometrically constrain the solutions of NMF problems, leading to detNMF. This approach also provides an intuitive explanation for the often used multilayer approach. Finally, we present a Variational Bayes NMF (VBNMF) algorithm which represents a generalization of the famous Lee–Seung method. We also demonstrate its ability to estimate the intrinsic dimension (model order) of the NMF method.

This paper proposes a novel formulation of the generalized eigendecomposition (GED) approach to blind source separation (BSS) problems. The generalized eigendecomposition algorithms consider the estimation of a pair of correlation matrices (a matrix pencil) using observed sensor signals. Each of various algorithms proposed in the literature uses a different approach to form the pencil. This study proposes a linear algebra formulation which exploits the definition of congruent matrix pencils and shows that the solution and its constraints are independent of the way the matrix pencil is computed. Also an iterative eigendecomposition algorithm, that updates separation parameters on a sample-by-sample basis, is developed. It comprises of: (1) performing standard eigendecompositions based on power and deflation techniques; (2) computing a transformation matrix using spectral factorization. Another issue discussed in this work is the influence of the length of the data segment used to estimate the pencil. The algorithm is applied to artificially mixed audio data and it is shown that the separation performance depends on the eigenvalue spread. The latter varies with the number of samples used to estimate the eigenvalues.

In this work a generalized version of AMUSE, called dAMUSE is proposed. The main modification consists in embedding the observed mixed signals in a high-dimensional feature space of delayed coordinates. With the embedded signals a matrix pencil is formed and its generalized eigendecomposition is computed similar to the algorithm AMUSE. We show that in this case the uncorrelated output signals are filtered versions of the unknown source signals. Further, denoising the data can be achieved conveniently in parallel with the signal separation. Numerical simulations using artificially mixed signals are presented to show the performance of the method. Further results of a heart rate variability (HRV) study are discussed showing that the output signals are related with LF (low frequency) and HF (high frequency) fluctuations. Finally, an application to separate artifacts from 2D NOESY NMR spectra and to denoise the reconstructed artefact-free spectra is presented also.

NMF is a blind source separation technique decomposing multivariate non-negative data sets into meaningful non-negative basis components and non-negative weights. There are still open problems to be solved: uniqueness and model order selection as well as developing efficient NMF algorithms for large scale problems. Addressing uniqueness issues, we propose a Bayesian optimality criterion (BOC) for NMF solutions which can be derived in the absence of prior knowledge. Furthermore, we present a new Variational Bayes NMF algorithm VBNMF which is a straight forward generalization of the canonical Lee–Seung method for the Euclidean NMF problem and demonstrate its ability to automatically detect the actual number of components in non-negative data.

Biomedical signals are in general non-linear and non-stationary. empirical mode decomposition in conjunction with a Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract intrinsic mode functions. The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis, which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the recently proposed weighted sliding EMD algorithm (wSEMD) and, additionally, proposes a more sophisticated implementation of the weighting process. As an application to biomedical signals we will show that wSEMD in combination with mutual information could be used to detect temporal correlations of arterial blood pressure and intracranial pressure monitored at a neurosurgical intensive care unit. We will demonstrate that the wSEMD technique renders itself much more flexible than the Fourier based method used in Faltermeier et al. (Acta Neurochir Suppl, 114, 35–38, 2012).

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an on-line analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed slidingEMD algorithm and presents some applications to biomedical time series from neuromonitoring.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Intelligent signal processing is crucial to unravel the information content buried in biomedical time series. Empirical Mode Decomposition is ideally suited to extract all pure oscillatory modes which are contained in the signal. These modes, called Intrinsic Mode Functions (IMFs), represent a complete set of locally orthogonal basis functions with time-varying amplitude and frequency. The contribution discusses the application of an online variant, called SEMD, to non-stationary biomedical time series recorded during neuromonitoring.

Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The method is fully adaptive and generates the basis to represent the data solely from these data and based on them. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called Sliding Empirical Mode Decomposition (SEMD), which is shown to perform well on large scale time series.

Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. Gene expression profiles naturally conform to assumptions about data formats raised by NMF. However, it is known not to lead to unique results concerning the component signals extracted. In this paper we consider an extension of the NMF algorithm which provides unique solutions whenever the underlying component signals are sufficiently sparse. A new sparseness measure is proposed most appropriate to suitably transformed gene expression profiles. The resulting fitness function is discontinuous and exhibits many local minima, hence we use a genetic algorithm for its optimization. The algorithm is applied to toy data to investigate its properties as well as to a microarray data set related to Pseudo-Xanthoma Elasticum (PXE).

In this paper, an automatic assignment tool, called BSS-AutoAssign, for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY) spectroscopy signals of proteins dissolved in water.

In this work an automatic assignment tool for estimated independent components within an independent component analysis is presented. The tool is applied to the problem of removing the water resonance and related artifacts from multi-dimensional proton NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind extraction of artifact-related source signals is effected by a recently developed algorithm called dAMUSE.

Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. As the proposed target function has many local minima, we use a genetic algorithm for its minimization.

We present denoising algorithms based on either local independent component analysis (ICA) and a minimum description length (MDL) estimator or a generalized eigenvalue decomposition (GEVD) using a matrix pencil of time-delayed signals. Both methods are applied to signals embedded in delayed coordinates in a high-dim feature space Ω and denoising is achieved by projecting onto a lower dimensional signal subspace. We discuss the algorithms and provide applications to the analysis of 2D NOESY protein NMR spectra.

In this work we propose a generalized eigendecomposition (GEVD) of a matrix pencil computed after embedding the data into a high-dim feature space of delayed coordinates. The matrix pencil is computed like in AMUSE but in the feature space of delayed coordinates. Its GEVD yields filtered versions of the source signals as output signals. The algorithm is implemented in two EVD steps. Numerical simulations study the influence of the number of delays and the noise level on the performance.

Multidimensional 1H nmr spectra of biomolecules dissolved in light water are contaminated by an intense water artefact. We dis- cuss the application of the generalized eigenvalue decomposition (GEVD) method using a matrix pencil to explore the time structure of the signals in order to separate out the water artefacts. Simulated as well as experi- mental 2D NOESY spectra of proteins are studied. Results are compared to those obtained with the FastICA algorithm.

The blind source separation might be addressed as a generalized eigenvalue problem. A complete formulation using linear algebra statements shows that the required matrix pencil might be computed at the input and at the output of a simple linear filter. This method is evaluated against FastICA algorithm.

In this work a modified version of AMUSE, called dAMUSE, is proposed. The main modification consists in increasing the dimension of the data vectors by joining delayed versions of the observed mixed signals. With the new data a matrix pencil is computed and its generalized eigendecomposition is performed as in AMUSE. We will show that in this case the output (or independent) signals are filtered versions of the source signals. Some numerical simulations using artificially mixed signals as well as biological data (RR and QT intervals of Electrocardiogram) are presented.

In this work an automatic assignment tool for estimated independent components within an independent component analysis is presented. The algorithm is applied to the problem of removing the water artifact from 2D NOESY NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind source separation of the water artifact from the remaining protein spectrum is done with the recently developed algorithm dAMUSE.