## Second-Order Multidimensional Independent Component Analysis: Theory and Methods (2013)

Data-Driven Multimodal Signal Processing With Applications To EEG-fMRI Fusion

Most cognitive processes in the brain are reflected through several aspects simultaneously, allowing us to observe the same process from different biological phenomena. The diverse nature of these biological processes suggests that a better understanding of cerebral activity may be achieved through multimodal measurements. One of the possible multimodal brain recording settings is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which is one of the main topics of this thesis. Two groups of EEG-fMRI integration approaches are possible. The first group, commonly called model-based techniques, are very popular due to the fact that the results from such analyses confirm or disprove a specific hypothesis. However, such hypotheses are not always available, requiring a more explorative approach to analyze the data. This exploration is possible with the second group of approaches, the so-called data-driven methods. The data-driven ...

Mijović, Bogdan — KU Leuven

Inverse Scattering Procedures for the Reconstruction of One-Dimensional Permittivity Range Profiles

Inverse scattering is relevant to a very large class of problems, where the unknown structure of a scattering object is estimated by measuring the scattered field produced by known probing waves. Therefore, for more than three decades, the promises of non-invasive imaging inspection by electromagnetic probing radiations have been justifying a research interest on these techniques. Several application areas are involved, such as civil and industrial engineering, non-destructive testing and medical imaging as well as subsurface inspection for oil exploration or unexploded devices. In spite of this relevance, most scattering tomography techniques are not reliable enough to solve practical problems. Indeed, the nonlinear relationship between the scattered field and the object function and the robustness of the inversion algorithms are still open issues. In particular, microwave tomography presents a number of specific difficulties that make it much more involved to ...

Genovesi, Simone — University of Pisa

Learning from structured EEG and fMRI data supporting the diagnosis of epilepsy

Epilepsy is a neurological condition that manifests in epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. Epilepsy cannot be cured and in many cases is not controlled by medication either. Surgical resection of the region responsible for generating the epileptic seizures might offer remedy for these patients. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measure the changes of brain activity in time over different locations of the brain. As such, they provide valuable information on the nature, the timing and the spatial origin of the epileptic activity. Unfortunately, both techniques record activity of different brain and artefact sources as well. Hence, EEG and fMRI signals are characterised by low signal to noise ratio. Data quality and the vast amount ...

Hunyadi, Borbála — KU Leuven

The problem of signal separation is a very broad and fundamental one. A powerful paradigm within which signal separation can be achieved is the assumption that the signals/sources are statistically independent of one another. This is known as Independent Component Analysis (ICA). In this thesis, the theoretical aspects and derivation of ICA are examined, from which disparate approaches to signal separation are drawn together in a unifying framework. This is followed by a review of signal separation techniques based on ICA. Second order statistics based output decorrelation methods are employed to try to solve the challenging problem of separating convolutively mixed signals, in the context of mainly audio source separation and the Cocktail Party Problem. Various optimisation techniques are devised to implement second order signal separation of both artificially mixed signals and real mixtures. A study of the advantages and ...

Ahmed, Alijah — University of Cambridge

Bayesian Approaches in Image Source Seperation

In this thesis, a general solution to the component separation problem in images is introduced. Unlike most existing works, the spatial dependencies of images are modelled in the separation process with the use of Markov random fields (MRFs). In the MRFs model, Cauchy density is used for the gradient images. We provide a general Bayesian framework for the estimation of the parameters of this model. Due to the intractability of the problem we resort to numerical solutions for the joint maximization of the a posteriori distribution of the sources, the mixing matrix and the noise variances. For numerical solution, four different methods are proposed. In first method, the difficulty of working analytically with general Gibbs distributions of MRF is overcome by using an approximate density. In this approach, the Gibbs distribution is modelled by the product of directional Gaussians. The ...

Kayabol, Koray — Istanbul University

Extraction of efficient and characteristic features of multidimensional time series

In numerous signal processing applications one disposes of multiple probes, delivering simultaneously information about one or multiple observed processes. The resulting multidimensional time series are often highly redundant and may contain stochastic contributions. The perception of the useful information becomes therefore very difficult and sometimes impossible. Thus, the major issue of concern of this thesis resides in the development of novel algorithms for the extraction of the salient and characteristic features of multidimensional time series. The proposed algorithms are based on parametric signal processing, namely we assume that the features of the experimental data can be represented efficiently by a specific model. We present a global framework for the selection of a specific model out of the large span of techniques proposed in the literature. For the selection of the model classes we use, in addition to prior knowledge about ...

Vetter, Rolf — Swiss Federal Institute of Technology

Explicit and implicit tensor decomposition-based algorithms and applications

Various real-life data such as time series and multi-sensor recordings can be represented by vectors and matrices, which are one-way and two-way arrays of numerical values, respectively. Valuable information can be extracted from these measured data matrices by means of matrix factorizations in a broad range of applications within signal processing, data mining, and machine learning. While matrix-based methods are powerful and well-known tools for various applications, they are limited to single-mode variations, making them ill-suited to tackle multi-way data without loss of information. Higher-order tensors are a natural extension of vectors (first order) and matrices (second order), enabling us to represent multi-way arrays of numerical values, which have become ubiquitous in signal processing and data mining applications. By leveraging the powerful utitilies offered by tensor decompositions such as compression and uniqueness properties, we can extract more information from multi-way ...

Boussé, Martijn — KU Leuven

Group-Sparse Regression - With Applications in Spectral Analysis and Audio Signal Processing

This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e., where only a few of the elements in the response variable have non-zero values. The thesis collects six papers which, to a varying extent, deals with the applications, implementations, modifications, translations, and other analysis of such problems. Sparse regression is often used to approximate additive models with intricate, non-linear, non-smooth or otherwise problematic functions, by creating an underdetermined model consisting of candidate values for these functions, and linear response variables which selects among the candidates. Sparse regression is therefore a widely used tool in applications such as, e.g., image processing, audio processing, seismological and biomedical modeling, but is ...

Kronvall, Ted — Lund University

Decomposition methods with applications in neuroscience

The brain is the most important signal processing unit in the human body. It is responsible for receiving, processing and storing information. One of the possibilities to study brain functioning is by placing electrodes on the scalp and recording the synchronous neuronal activity of the brain. Such a recording measures a combination of active processes in the whole brain. Unfortunately, it is also contaminated by artifacts. By extracting the artifacts and removing them, cleaned recordings can be investigated. Furthermore, it is easier to look at speciﬁc brain activities, like an epileptic seizure, than at a combination. In this thesis, we present diﬀerent mathematical techniques that can be used to extract individual contributing sources from the measured signals for this purpose. We focused on Canonical Correlation Analysis (CCA), Independent Component Analysis (ICA) and Canonical/ Parallel Factor Analysis (CPA). We show that ...

De Vos, Maarten — Katholieke Universiteit Leuven

Advanced Signal Processing Concepts for Multi-Dimensional Communication Systems

The widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic. With the rise of smart homes, smart buildings, and smart cities, this demand is ever growing since future communication systems will require the integration of multiple networks serving diverse sectors, domains and applications, such as multimedia, virtual or augmented reality, machine-to-machine (M2M) communication / the Internet of things (IoT), automotive applications, and many more. Therefore, in the future, the communication systems will not only be required to provide Gbps wireless connectivity but also fulfill other requirements such as low latency and massive machine type connectivity while ensuring the quality of service. Without significant technological advances to increase the system capacity, the existing telecommunications infrastructure will be unable to support these multi-dimensional requirements. This poses an important demand for suitable waveforms with ...

Cheema, Sher Ali — Technische Universität Ilmenau

Functional Neuroimaging Data Characterisation Via Tensor Representations

The growing interest in neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has by now been recognized as an effective approach exploiting its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization; the identification of the regions of the brain which are activated at specific time instances. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data. In the first part of this thesis, we aimed at investigating the possible gains from exploiting the 3-dimensional nature of the brain images, through a higher-order tensorization ...

Christos Chatzichristos — National and Kapodistrian University of Athens

The subject of the thesis is the emergence and analysis of visual texture microstructure for efficient modeling, descriptive feature extraction and image representation. Main objectives are the problems of image texture modeling and analysis in Computer Vision systems, with emphasis on the subproblems of texture detection, segmentation and separation in images. Advanced modeling and analysis methods are developed in parallel directions: a) Multiband models of narrowband components and spatial modulations, b) Energy methods for texture feature extraction, c) Variational techniques of image decomposition and texture separation. The proposed methods are applied on a database of digitized soilsection images to quantify and evaluate the biological quality of soils and in different types and collections of natural images. The developed model is the common ground to approach texture in its different forms and applications. In total, a complete system for texture processing ...

Evangelopoulos, Georgios — National Technical University of Athens

Contributions to Analysis and DSP-based Mitigation of Nonlinear Distortion in Radio Transceivers

This thesis focuses on different nonlinear distortion aspects in radio transmitter and receivers. Such nonlinear distortion aspects are generally becoming more and more important as the communication waveforms themselves get more complex and thus more sensitive to any distortion. Also balancing between the implementation costs, size, power consumption and radio performance, especially in multiradio devices, creates tendency towards using lower cost, and thus lower quality, radio electronics. Furthermore, increasing requirements on radio flexibility, especially on receiver side, reduces receiver radio frequency (RF) selectivity and thus increases the dynamic range and linearity requirements. Thus overall, proper understanding of nonlinear distortion in radio devices is essential, and also opens the door for clever use of digital signal processing (DSP) in mitigating and suppressing such distortion effects. On the receiver side, the emphasis in this thesis is mainly on the analysis and DSP ...

Shahed hagh ghadam, Ali — Tampere University of Technology

Oscillator-plus-Noise Modeling of Speech Signals

In this thesis we examine the autonomous oscillator model for synthesis of speech signals. The contributions comprise an analysis of realizations and training methods for the nonlinear function used in the oscillator model, the combination of the oscillator model with inverse filtering, both significantly increasing the number of `successfully' re-synthesized speech signals, and the introduction of a new technique suitable for the re-generation of the noise-like signal component in speech signals. Nonlinear function models are compared in a one-dimensional modeling task regarding their presupposition for adequate re-synthesis of speech signals, in particular considering stability. The considerations also comprise the structure of the nonlinear functions, with the aspect of the possible interpolation between models for different speech sounds. Both regarding stability of the oscillator and the premiss of a nonlinear function structure that may be pre-defined, RBF networks are found a ...

Rank, Erhard — Vienna University of Technology

This study proposes a new spectral representation called the Zeros of Z-Transform (ZZT), which is an all-zero representation of the z-transform of the signal. In addition, new chirp group delay processing techniques are developed for analysis of resonances of a signal. The combination of the ZZT representation with the chirp group delay processing algorithms provides a useful domain to study resonance characteristics of source and filter components of speech. Using the two representations, effective algorithms are developed for: source-tract decomposition of speech, glottal flow parameter estimation, formant tracking and feature extraction for speech recognition. The ZZT representation is mainly important for theoretical studies. Studying the ZZT of a signal is essential to be able to develop effective chirp group delay processing methods. Therefore, first the ZZT representation of the source-filter model of speech is studied for providing a theoretical background. ...

Bozkurt, Baris — Universite de Mons

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