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Dissertations / Theses on the topic 'Statistical Signal Processing'
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Zhao, Wentao. "Genomic applications of statistical signal processing." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2952.
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Vollgraf, Roland. "Unsupervised learning methods for statistical signal processing." [S.l.] : [s.n.], 2006. http://opus.kobv.de/tuberlin/volltexte/2007/1488.
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Eng, Frida. "Non-Uniform Sampling in Statistical Signal Processing." Doctoral thesis, Linköping : Department of Electrical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8480.
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Bornn, Luke. "Statistical solutions for and from signal processing." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/5345.
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With the wide range of fields engaging in signal processing research, many
methods do not receive adequate dissemination across disciplines due to
differences in jargon, notation, and level of rigor. In this thesis, I attempt to
bridge this gap by applying two statistical techniques originating in signal
processing to fields for which they were not originally intended. Firstly,
I employ particle filters, a tool used for state estimation in the physics
signal processing world, for the task of prior sensitivity analysis and cross
validation in Bayesian statistics. Secondly, I demonstrate the application of
support vector forecasters, a tool used for forecasting in the machine learning
signal processing world, to the field of structural health monitoring.
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Sallee, Philip Andrew. "Statistical methods for image and signal processing /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2004. http://uclibs.org/PID/11984.
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Xu, Cuichun. "Statistical processing on radar, sonar, and optical signals /." View online ; access limited to URI, 2008. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3328735.
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Kuchler, Ryan J. "Theory of multirate statistical signal processing and applications." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FKuchler%5FPhD.pdf.
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Vigoda, Benjamin William 1973. "Continuous-time analog circuits for statistical signal processing." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/62962.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Vita.
Includes bibliographical references (p. 205-209).
This thesis proposes an alternate paradigm for designing computers using continuous-time analog circuits. Digital computation sacrifices continuous degrees of freedom. A principled approach to recovering them is to view analog circuits as propagating probabilities in a message passing algorithm. Within this framework, analog continuous-time circuits can perform robust, programmable, high-speed, low-power, cost-effective, statistical signal processing. This methodology will have broad application to systems which can benefit from low-power, high-speed signal processing and offers the possibility of adaptable/programmable high-speed circuitry at frequencies where digital circuitry would be cost and power prohibitive. Many problems must be solved before the new design methodology can be shown to be useful in practice: Continuous-time signal processing is not well understood. Analog computational circuits known as "soft-gates" have been previously proposed, but a complementary set of analog memory circuits is still lacking. Analog circuits are usually tunable, rarely reconfigurable, but never programmable. The thesis develops an understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time, and explores the use of linear and nonlinear circuits for analog memory. An exemplary embodiment called the Noise Lock Loop (NLL) using these design primitives is demonstrated to perform direct-sequence spread-spectrum acquisition and tracking functionality and promises order-of-magnitude wins over digital implementations. A building block for the construction of programmable analog gate arrays, the "soft-multiplexer" is also proposed.
by Benjamin Vigoda.
Ph.D.
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Vallet, Pascal. "Random matrices and applications to statistical signal processing." Thesis, Paris Est, 2011. http://www.theses.fr/2011PEST1055/document.
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Dans cette thèse, nous considérons le problème de la localisation de source dans les grands réseaux de capteurs, quand le nombre d'antennes du réseau et le nombre d'échantillons du signal observé sont grands et du même ordre de grandeur. Nous considérons le cas où les signaux source émis sont déterministes, et nous développons un algorithme de localisation amélioré, basé sur la méthode MUSIC. Pour ce faire, nous montrons de nouveaux résultats concernant la localisation des valeurs propres des grandes matrices aléatoires gaussiennes complexes de type information plus bruitIn this thesis, we consider the problem of source localization in large sensor networks, when the number of antennas of the network and the number of samples of the observed signal are large and of the same order of magnitude. We also consider the case where the source signals are deterministic, and we develop an improved algorithm for source localization, based on the MUSIC method. For this, we fist show new results concerning the position of the eigen values of large information plus noise complex gaussian random matrices
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Palladini, Alessandro <1981>. "Statistical methods for biomedical signal analysis and processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1358/1/palladini_alessandro_tesi.pdf.
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Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling.
In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans.
In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution.
In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests.
In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.
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Palladini, Alessandro <1981>. "Statistical methods for biomedical signal analysis and processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1358/.
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Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling.
In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans.
In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution.
In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests.
In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.
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Yan, Yan. "Statistical signal processing for echo signals from ultrasound linear and nonlinear scatterers." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/11634.
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Noor, Fazal. "Inverse and Eigenspace decomposition algorithms for statistical signal processing." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=39489.
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In this work, a number of advances are described which we feel lead to better understanding and solution of the eigenvalue and inverse eigenvalue problems for Hermitian Toeplitz matrices. First, a unitary matrix is derived which transforms a Hermitian Toeplitz matrix into a real Toeplitz plus Hankel matrix. Some properties of this transformation are also presented. Second, we solve the inverse eigenvalue problem for Hermitian Toeplitz matrices. Specifically, we present a method for the construction of a Hermitian Toeplitz matrix from an arbitrary set of real eigenvalues. The procedure utilizes the discrete Fourier transform to first construct a real symmetric negacyclic matrix from the specified eigenvalues. The algorithm presented is computationally efficient. Finally, we derive a new order recursive algorithm and modify Trench's algorithm, both for eigenvalue decomposition. The former development is of mathematical interest; whereas, the latter is clearly of practical interest. The modifications proposed to Trench's algorithm are to employ noncontiguous intervals and to include a procedure to detect multiple eigenvalues. The goals of the modification are to improve the rate of convergence. The modified algorithm presented utilizes three root searching techniques: the Pegasus method, the modified Rayleigh quotient iteration with bisection shifts (MRQI-B), and the MRQI with Pegasus shifts (MRQI-P). Simulation results are provided for large matrices of orders 50, 100, 200, and 500. Application of the algorithms to Pisarenko's harmonic decomposition, an important signal processing problem, is presented. Fortran programs of the modified method are also provided.
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Hill, S. "Applications of statistical learning theory to signal processing problems." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604048.
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The dissertation focuses on the applicability of Support Vector Regression (SVR) in signal processing contexts. This is shown to be particularly well-suited to filtering in alpha-stable noise environments, and a further slight modification is proposed to this end. The main work in this dissertation on SVR is on the application to audio filtering based on perceptual criteria. This appears an ideal solution to the problem due to the fact that the loss function typically used by perceptual audio filtering practitioners incorporates a region of zero loss, as does SVR. SVR is extended to the problem of complex-valued regression, for application in the audio filtering problem to the frequency domain. This is with regions of zero loss that are both square and circular, and the circular case is extended to the problem of vector-valued regression. Three experiments are detailed with a mix of both good and poor results, and further refinements are proposed. Polychotomous, or multi-category classification is then studied. Many previous attempts are reviewed, and compared. A new approach is proposed, based on a geometrical structure. This is shown to overcome many of the problems identified with previous methods in addition to being very flexible and efficient in its implementation. This architecture is also derived, for just the three-class case, using a complex-valued kernel function. The general architecture is used experimentally in three separate implementations to give a demonstration of the overall approach. The methodology is shown to achieve results comparable to those of many other methods, and to include many of them as special cases. Further possible refinements are proposed which should drastically reduce optimisation times for so-called 'all-together' methods.
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Martinsson, Jesper. "Statistical tools for ultrasonic analysis of dispersive fluids." Licentiate thesis, Luleå : Luleå University of Technology, 2006. http://epubl.ltu.se/1402-1757/2006/17/.
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Nguyen, Quang-Thang. "Contributions to Statistical Signal Processing with Applications in Biomedical Engineering." Télécom Bretagne, 2012. http://www.telecom-bretagne.eu/publications/publication.php?idpublication=13290.
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Cette étude présente des contributions en traitement statistique du signal avec des applications biomédicales. La thèse est divisée en deux parties. La première partie traite de la détection des hotspots à l'interface des protéines. Les hotspots sont les résidus dont les contributions énergétiques sont les plus importantes dans l'interaction entre protéines. Les forêts aléatoires (Random Forests) sont utilisées pour la classification. Une nouvelle famille de descripteurs de hotspot est également introduite. Ces descripteurs sont basés seulement sur la séquence primaire unidimensionnelle d'acides aminés constituant la protéine. Aucune information sur la structure tridimensionnelle de la protéine ou le complexe n'est nécessaire. Ces descripteurs, capitalisant les caractéristiques fréquentielle des protéines, nous permettent de savoir la façon dont la séquence primaire d'une protéine peut déterminer sa structure tridimensionnelle et sa fonction. Dans la deuxième partie, le RDT (Random Distortion Testing), un test robuste d'hypothèse, est considéré. Son application en détection du signal a montré que le RDT peut résister aux imperfections du modèle d'observation. Nous avons également proposé une extension séquentielle du RDT. Cette extension s'appelle le RDT Séquentiel. Trois problèmes classiques de détection d'écart/distorsion du signal sont reformulés et résolus dans le cadre du RDT. En utilisant le RDT et le RDT Séquentiel, nous étudions la détection d'AutoPEEP (auto-Positive End Expiratory Pressure), une anomalie fréquente en ventilation mécanique. C'est la première étude de ce type dans la littérature. L'extension à la détection d'autres types d'asynchronie est également étudiée et discutée. Ces détecteurs d'AutoPEEP et d'asynchronies sont les éléments principaux de la plateforme de suivi de manière automatique et continue l'interface patient-ventilateur en ventilation mécaniqueThis PhD thesis presents some contributions to Statistical Signal Processing with applications in biomedical engineering. The thesis is separated into two parts. In the first part, the detection of protein interface hotspots ¿ the residues that play the most important role in protein interaction ¿ is considered in the Machine Learning framework. The Random Forests is used as the classifier. A new family of protein hotspot descriptors is also introduced. These descriptors are based exclusively on the primary one-dimensional amino acid sequence. No information on the three dimensional structure of the protein or the complex is required. These descriptors, capturing the protein frequency characteristics, make it possible to get an insight into how the protein primary sequence can determine its higher structure and its function. In the second part, the RDT (Random Distortion Testing) robust hypothesis testing is considered. Its application to signal detection is shown to be resilient to model mismatch. We propose an extension of RDT in the sequential decision framework, namely Sequential RDT. Three classical signal deviation/distortion detection problems are reformulated and cast into the RDT framework. Using RDT and Sequential RDT, we investigate the detection of AutoPEEP (auto-Positive End Expiratory Pressure), a common ventilatory abnormality during mechanical ventilation. This is the first work of that kind in the state-of-the-art. Extension to the detection of other types of asynchrony is also studied and discussed. These early detectors of AutoPEEP and asynchrony are key elements of an automatic and continuous patient-ventilator interface monitoring framework
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Elzanaty, Ahmed Mohamed Aly <1986>. "Sparse Signal Processing and Statistical Inference for Internet of Things." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8613/1/Elzanaty_Thesis_30_ETIT_March18.pdf.
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Data originating from many devices within the Internet of Things (IoT) framework can be modeled as sparse signals. Efficient compression techniques of such data are essential to reduce the memory storage, bandwidth, and transmission power. In this thesis, I develop some theory and propose practical schemes for IoT applications to exploit the signal sparsity for efficient data acquisition and compression under the frameworks of compressed sensing (CS) and transform coding.
In the context of CS, the restricted isometry constant of finite Gaussian measurement matrices is investigated, based on the exact distributions of the extreme eigenvalues of Wishart matrices. The analysis determines how aggressively the signal can be sub-sampled and recovered from a small number of linear measurements. The signal reconstruction is guaranteed, with a predefined probability, via various recovery algorithms.
Moreover, the measurement matrix design for simultaneously acquiring multiple signals is considered. This problem is important for IoT networks, where a huge number of nodes are involved. In this scenario, the presented analytical methods provide limits on the compression of joint sparse sources by analyzing the weak restricted isometry constant of Gaussian measurement matrices.
Regarding transform coding, two efficient source encoders for noisy sparse sources are proposed, based on channel coding theory. The analytical performance is derived in terms of the operational rate-distortion and energy-distortion. Furthermore, a case study for the compression of real signals from a wireless sensor network using the proposed encoders is considered. These techniques can reduce the power consumption and increase the lifetime of IoT networks.
Finally, a frame synchronization mechanism has been designed to achieve reliable radio links for IoT devices, where optimal and suboptimal metrics for noncoherent frame synchronization are derived. The proposed tests outperform the commonly used correlation detector, leading to accurate data extraction and reduced power consumption.
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Gabriel, Joseph R. "Invariant hypothesis testing with applications in signal processing /." View online ; access limited to URI, 2004. http://0-wwwlib.umi.com.helin.uri.edu/dissertations/dlnow/3135904.
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Larocque, Jean-René. "Advanced bayesian methods for array signal processing /." *McMaster only, 2001.
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Hamdi, Maziyar. "Statistical signal processing on dynamic graphs with applications in social networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56256.
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Due to the proliferation of social networks and their significant effects on our day-to-day activities, there has been a growing interest in modeling and analyzing behavior of agents in social networks over the past decade. The unifying theme of this thesis is to develop a set of mathematical theories and algorithmic tools for different estimation and sensing problems over graphs with applications to social networks. The first part of this dissertation is devoted to multi-agent Bayesian estimation and learning problem in social networks. We consider a set of agents that interact over a network to estimate an unknown parameter called state of nature. As a result of the recursive nature of Bayesian models and the correlation introduced by the structure of the underlying communication graph, information collected by one agent can be mistakenly considered independent, that is, mis-information prop- agation, also known as data incest arises. This part presents data incest removal algorithms that ensure complete mitigation of the mis-information associated with the estimates of agents in two different information exchange patterns: First, a scenario where beliefs (posterior distribution of state of nature) are transmitted over the network. Second, a social learning context where agents map their local beliefs into a finite set of actions and broadcast their actions to other agents. We also present a necessary and sufficient condition on the structure of information flow graph to mitigate mis-information propagation. The second part of the thesis considers a Markov-modulated duplication-deletion random graph where at each time instant, one node can either join or leave the network; the probabilities of joining or leaving evolve according to the realization of a finite state Markov chain. This part presents two results. First, motivated by social network applications, the asymptotic behavior of the degree distribution is analyzed. Second, a stochastic approximation algorithm is presented to track empirical degree distribution as it evolves over time. The tracking performance of the algorithm is analyzed in terms of mean square error and a functional central limit theorem is presented for the asymptotic tracking error.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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NETO, MARIO HENRIQUE ALVES SOUTO. "SPARSE STATISTICAL MODELLING WITH APPLICATIONS TO RENEWABLE ENERGY AND SIGNAL PROCESSING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24980@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Motivado pelos desafios de processar a grande quantidade de dados disponíveis, pesquisas recentes em estatística tem sugerido novas técnicas de modelagem e inferência. Paralelamente, outros campos como processamento de sinais e otimização também estão produzindo métodos para lidar problemas em larga escala. Em particular, este trabalho é focado nas teorias e métodos baseados na regularização l1. Após uma revisão compreensiva da norma l1 como uma ferramenta para definir soluções esparsas, estudaremos mais a fundo o método LASSO. Para exemplificar como o LASSO possui uma ampla gama de aplicações, exibimos um estudo de caso em processamento de sinal esparso. Baseado nesta idea, apresentamos o l1 level-slope filter. Resultados experimentais são apresentados para uma aplicação em transmissão de dados via fibra óptica. Para a parte final da dissertação, um novo método de estimação é proposto para modelos em alta dimensão com variância periódica. A principal ideia desta nova metodologia é combinar esparsidade, induzida pela regularização l1, com o método de máxima verossimilhança. Adicionalmente, esta metodologia é utilizada para estimar os parâmetros de um modelo mensal estocástico de geração de energia eólica e hídrica. Simulações e resultados de previsão são apresentados para um estudo real envolvendo cinquenta geradores de energia renovável do sistema Brasileiro.
Motivated by the challenges of processing the vast amount of available data, recent research on the ourishing field of high-dimensional statistics is bringing new techniques for modeling and drawing inferences over large amounts of data. Simultaneously, other fields like signal processing and optimization are also producing new methods to deal with large scale problems. More particularly, this work is focused on the theories and methods based on l1-regularization. After a comprehensive review of the l1-norm as tool for finding sparse solutions, we study more deeply the LASSO shrinkage method. In order to show how the LASSO can be used for a wide range of applications, we exhibit a case study on sparse signal processing. Based on this idea, we present the l1 level-slope filter. Experimental results are given for an application on the field of fiber optics communication. For the final part of the thesis, a new estimation method is proposed for high-dimensional models with periodic variance. The main idea of this novel methodology is to combine sparsity, induced by the l1-regularization, with the maximum likelihood criteria. Additionally, this novel methodology is used for building a monthly stochastic model for wind and hydro inow. Simulations and forecasting results for a real case study involving fifty Brazilian renewable power plants are presented.
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Ginzberg, Paul. "Quaternion matrices : statistical properties and applications to signal processing and wavelets." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/18975.
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Similarly to how complex numbers provide a possible framework for extending scalar signal processing techniques to 2-channel signals, the 4-dimensional hypercomplex algebra of quaternions can be used to represent signals with 3 or 4 components. For a quaternion random vector to be suited for quaternion linear processing, it must be (second-order) proper. We consider the likelihood ratio test (LRT) for propriety, and compute the exact distribution for statistics of Box type, which include this LRT. Various approximate distributions are compared. The Wishart distribution of a quaternion sample covariance matrix is derived from first principles. Quaternions are isomorphic to an algebra of structured 4x4 real matrices. This mapping is our main tool, and suggests considering more general real matrix problems as a way of investigating quaternion linear algorithms. A quaternion vector autoregressive (VAR) time-series model is equivalent to a structured real VAR model. We show that generalised least squares (and Gaussian maximum likelihood) estimation of the parameters reduces to ordinary least squares, but only if the innovations are proper. A LRT is suggested to simultaneously test for quaternion structure in the regression coefficients and innovation covariance. Matrix-valued wavelets (MVWs) are generalised (multi)wavelets for vector-valued signals. Quaternion wavelets are equivalent to structured MVWs. Taking into account orthogonal similarity, all MVWs can be constructed from non-trivial MVWs. We show that there are no non-scalar non-trivial MVWs with short support [0,3]. Through symbolic computation we construct the families of shortest non-trivial 2x2 Daubechies MVWs and quaternion Daubechies wavelets.
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Al-Qaisi, Aws K. "Statistical blind signal processing for single trace and 2D multicomponent seismic wavefield." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.545782.
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Ghodsi, Zara. "A novel statistical signal processing approach for analysing high volatile expression profiles." Thesis, Bournemouth University, 2017. http://eprints.bournemouth.ac.uk/29108/.
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The aim of this research is to introduce new advanced statistical methods for analysing gene expression profiles to consequently enhance our understanding of the spatial gradients of the proteins produced by genes in a gene regulatory network (GRN). To that end, this research has three main contributions. In this thesis, the segmentation Network (SN) in Drosophila melanogaster and the bicoid gene (bcd) as the critical input of this network are targeted to study. The first contribution of this research is to introduce a new noise filtering and signal processing algorithm based on Singular Spectrum Analysis (SSA) for extracting the signal of bicoid gene. Using the proposed SSA algorithm which is based on the minimum variance estimator, the extraction of bcd signal from its noisy profile is considerably improved compared to the most widely accepted model, Synthesis Diffusion Degradation (SDD). The achieved results are evaluated via both simulation studies and empirical results. Given the reliance of this research towards introducing an improved signal extraction approach, it is mandatory to compare the proposed method with the other well-known and widely used signal processing models. Therefore, the results are compared with a range of parametric and non-parametric signal processing methods. The conducted comparison study confirmed the outperformance of the SSA technique. Having the superior performance of SSA, in the second contribution, the SSA signal extraction performance is optimised using several novel computational methods including window length and eigenvalue identification approaches, Sequential and Hybrid SSA and SSA based on Colonial Theory. Each introduced method successfully improves a particular aspect of the SSA signal extraction procedure. The third and final contribution of this research aims at extracting the regulatory role of the maternal effect genes in SN using a variety of causality detection techniques. The hybrid algorithm developed here successfully portrays the interactions which have been previously accredited via laboratory experiments and therefore, suggests a new analytical view to the GRNs.
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Faubel, Friedrich [Verfasser], and Dietrich [Akademischer Betreuer] Klakow. "Statistical signal processing techniques for robust speech recognition / Friedrich Faubel. Betreuer: Dietrich Klakow." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1090875703/34.
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Nevat, Ido Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Topics in statistical signal processing for estimation and detection in wireless communication systems." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44664.
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During the last decade there has been a steady increase in the demand for incorporation of high data rate and strong reliability within wireless communication applications. Among the different solutions that have been proposed to cope with this new demand, the utilization of multiple antennas arises as one of the best candidates due to the fact that it provides both an increase in reliability and also in information transmission rate. A Multiple Input Multiple Output (MIMO) structure usually assumes a frequency non-selective characteristic at each channel. However, when the transmission rate is high, the whole channel can become frequency selective. Therefore, the use of Orthogonal Frequency Division Multiplexing (OFDM) that transforms a frequency selective channel into a large set of individual frequency on-selective narrowband channels, is well suited to be used in conjunction with MIMO systems. A MIMO system employing OFDM, denoted MIMO-OFDM, is able to achieve high spectral efficiency. However, the adoption of multiple antenna elements at the transmitter for spatial transmission results in a superposition of multiple transmitted signals at the receiver, weighted by their corresponding multipath channels. This in turn results in difficulties with reception, and imposes a real challenge on how to design a practical system that can offer a true spectral efficiency improvement. In addition, as wireless networks continue to expend in geographical size, the distance between the source and the destination precludes direct communication between them. In such scenarios, a repeater is placed between the source and the destination to achieve end-to-end communication. New advances in electronics and semiconductor technologies have enabled and made relay based systems feasible. As a result, these systems have become a hot research topic in the wireless research community in recent years. Potential application areas of cooperation diversity are the next generation cellular networks, mobile wireless ad-hoc networks, and mesh networks for wireless broadband access. Besides increasing the network coverage, relays can provide additional diversity to combat the effects of the wireless fading channel. This thesis is concerned with methods to facilitate the use of MIMO, OFDM and relay based systems. In the first part of this thesis, we concentrate on low complexity algorithms for detection of symbols in MIMO systems, with various degrees of quality of channel state information. First, we design algorithms for the case that perfect Channel State Information (CSI) is available at the receiver. Next, we design algorithms for the detection of non-uniform symbols constellations where only partial CSI is given at the receiver. These will be based on non-convex and stochastic optimisation techniques. The second part of this thesis addresses primary issues in OFDM systems. We first concentrate on a design of an OFDM receiver. First we design an iterative receiver for OFDM systems which performs detection, decoding and channel tracking that aims at minimising the error propagation effect due to erroneous detection of data symbols. Next we focus our attention to channel estimation in OFDM systems where the number of channel taps and the power delay profile are both unknown a priori. Using Trans Dimensional Markov Chain Monte Carlo (TDMCMC) methodology we design algorithms to perform joint model order selection and channel estimation. The third part of this thesis is dedicated to detection of data symbols in relay systems with non-linear relay functions and where only partial CSI is available at the receiver. In order to design the optimal data detector, the likelihood function needs to be evaluated at the receiver. Since the likelihood function cannot be obtained analytically or not even in a closed form in this case, we shall utilse a ???Likelihood Free??? inference methodology. This will be based on the Approximate Bayesian Computation (ABC) theory to enable the design of novel data sequence detectors.
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Alterovitz, Gil 1975. "A Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34479.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2006.Includes bibliographical references (leaves 73-85).
Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a multi-stage process which compares probabilistic networks generated from mass spectrometry-based data with a mass-based network of protein interactions. In addition, such models can provide insight on features of existing models by identifying relevant proteins. This work finds that the search space of potential proteins can be reduced such that simple antibody-based tests can be used to validate protein identity. This is done with real proteins as a proof of concept. Regarding protein interaction networks, the largest human protein interaction meta-database was created as part of this project, containing over 162,000 interactions. A further contribution is the implementation of the massome network database of mass-based interactions- which is used in the protein identification process.
(cont.) This network is explored in terms potential usefulness for protein identification. The framework provides an approach to a number of core issues in proteomics. Besides providing these tools, it yields a novel way to approach statistical signal processing problems in this domain in a way that can be adapted as proteomics-based technologies mature.
by Gil Alterovitz.
Ph.D.
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Lobos, Morales Rodrigo Alejandro. "Application of statistical signal processing techniques in natural rock textures characterization and astrometry." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/135080.
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Magíster en Ciencias de la Ingeniería, Mención EléctricaIngeniero Civil Eléctrico
Tanto en ingeniería en minas como en astronomía existen problemas inversos en los cuales técnicas del procesamiento de señales juegan un rol importante al momento de procesar la información existente de manera óptima. En este trabajo de Tesis los problemas de clasifi- cación de texturas de roca y astronometría son abordados usando técnicas del procesamiento estadístico de señales. En ingeniería en minas es de gran importancia contar con una buena caracterización del subsuelo. Para ello diversas fuentes de información son utilizadas, encontrándose entre ellas la información visual de la textura de las rocas. Pese al amplio uso de estas fuentes para hacer inferencia del tipo de roca, no se ha logrado el desarrollo de técnicas de procesamiento computacional y automático que las implementen de manera exitosa. En este trabajo de tesis, seis clases de textura de roca son analizadas utilizando técnicas avanzadas del procesamiento de imágenes. Específicamente, para cada clase se propone la extracción de características especialmente diseñadas para esa clase. Las características propuestas ofrecen un alto poder discriminador y baja dimensionalidad. Adicionalmente, se propone un esquema de banco de detectores binarios con el fin de poner a prueba las características diseñadas. Finalmente, el desempeño de clasificación del método propuesto es comparado con métodos en el estado del arte de clasificación de texturas, mostrando ganancias importantes en cuanto a error de clasificación. El problema de astrometría corresponde a la determinación de la posición de astros me- diante dispositivos detectores, comúnmente CCDs (Charged Coupled Devices). Dichos dis- positivos presentan fuentes de ruido que afectan negativamente los métodos de localización. En este trabajo de tesis el método de mínimos cuadrados es analizado en detalle. En este contexto dicho método corresponde a un problema de regresión no lineal, por lo cual el desempeño o varianza del estimador resultante no puede ser caracterizado de manera aná- litica. Para ello se propone un método de aproximación de la varianza del estimador, que permite la comparación analítica con la cota de Cramér-Rao. Finalmente, análisis empíricos son desarrollados utilizando diversas configuraciones experimentales, encontrándose que, en determinadas condiciones de medición, el estimador es eficiente con respecto a la cota de Cramér-Rao.
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GRASSI, FRANCESCO. "Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710580.
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The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem.
Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids.
However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data.
To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions.
The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals.
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Giboulot, Quentin. "Statistical Steganography based on a Sensor Noise Model using the Processing Pipeline." Thesis, Troyes, 2022. http://www.theses.fr/2022TROY0003.
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La stéganographie est la discipline traitant des techniques visant à dissimuler de l'information dans un média de couverture jugé inoffensif. Dans le cadre de ce manuscrit, les médias de couvertures choisis sont des images JPEG. Les schémas stéganographiques basés sur un modèle statistique d'images naturelles présentent un avantage certain par rapport aux schémas basés sur des heuristiques. En effet, ils fournissent un lien direct entre détectabilité théorique et performances empiriques. Cependant, cet avantage dépend de la précision des modèles sous-jacents. Cette précision était insuffisante dans les travaux précédants ce manuscrit. Nous proposons deux contributions principales pour résoudre ce problème. Premièrement, nous dérivons un modèle de bruit prenant en compte le capteur, le réglage ISO et la chaîne de traitement. Cela conduit à un modèle gaussien multivarié du bruit modélisant les dépendances intra et inter-blocs dans les images JPEG. Ensuite, nous concevons une série de schémas stéganographiques exploitant ce modèle de bruit. Ils minimisent ou bornent la puissance du détecteur le plus puissant pour fournir des garanties de sécurité lorque les hypothèses du modèle sont respectées. En particulier, nous montrons que la covariance optimale du signal stégo est proportionnelle à la covariance du bruit de l'image cover. Enfin, nous montrons que ces algorithmes atteignent des performances à l'état de l'art, dépassant largement les algorithmes standard de la stéganographie JPEGSteganography is the discipline concerned with techniques designed to embed hidden data into an innocuous cover media. In the case of this manuscript, the cover media of choice are JPEG images. Steganography schemes based on a statistical model of natural images possess a clear advantage against schemes based on heuristics. Indeed, they provide a direct link between theoretical detectability and empirical performance. However, this advantage is dependent on the accuracy of the underlying cover and the stego model. Until the work presented in this manuscript, the available models were not accurate enough for statistical steganography schemes to attain competitive performances in the JPEG domain or to provide security guarantees for natural images. In this manuscript, we propose two main contributions to solve this problem. First, we derive a model of noise in the developed domain which takes into account the camera sensor, ISO setting and the full processing pipeline. This leads to a multivariate Gaussian model of the noise which models intra and inter-block dependencies in JPEG images. Secondly, we design a series of steganographic algorithms leveraging this noise model. They minimize or bound the power of the most powerful detector to provide security guarantees when meeting the model assumptions. In particular, we show that the optimal covariance of the stego signal is proportional to the covariance of the cover noise. Finally, these algorithms are shown to attain state-of-the-art performance, greatly outperforming the standard algorithms in side-informed JPEG steganography
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Wu, Tsan-Ming. "Statistical impulse reponse modeling and dereverberation for room acoustics." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/14932.
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Sekiguchi, Kouhei. "A Unified Statistical Approach to Fast and Robust Multichannel Speech Separation and Dereverberation." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263770.
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Ng, William Reilly James P. "Advances in wideband array signal processing using numerical Bayesian methods /." *McMaster only, 2003.
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wang, xiaoni. "A STUDY OF EQUATORIAL IONOPSHERIC VARIABILITY USING SIGNAL PROCESSING TECHNIQUES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2415.
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The dependence of equatorial ionosphere on solar irradiances and geomagnetic activity are studied in this dissertation using signal processing techniques. The statistical time series, digital signal processing and wavelet methods are applied to study the ionospheric variations. The ionospheric data used are the Total Electron Content (TEC) and the critical frequency of the F2 layer (foF2). Solar irradiance data are from recent satellites, the Student Nitric Oxide Explorer (SNOE) satellite and the Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellite. The Disturbance Storm-Time (Dst) index is used as a proxy of geomagnetic activity in the equatorial region. The results are summarized as follows. (1) In the short-term variations < 27-days, the previous three days solar irradiances have significant correlation with the present day ionospheric data using TEC, which may contribute 18% of the total variations in the TEC. The 3-day delay between solar irradiances and TEC suggests the effects of neutral densities on the ionosphere. The correlations between solar irradiances and TEC are significantly higher than those using the F10.7 flux, a conventional proxy for short wavelength band of solar irradiances. (2) For variations < 27 days, solar soft X-rays show similar or higher correlations with the ionosphere electron densities than the Extreme Ultraviolet (EUV). The correlations between solar irradiances and foF2 decrease from morning (0.5) to the afternoon (0.1). (3) Geomagnetic activity plays an important role in the ionosphere in short-term variations < 10 days. The average correlation between TEC and Dst is 0.4 at 2-3, 3-5, 5-9 and 9-11 day scales, which is higher than those between foF2 and Dst. The correlations between TEC and Dst increase from morning to afternoon. The moderate/quiet geomagnetic activity plays a distinct role in these short-term variations of the ionosphere (~0.3 correlation).Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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Florêncio, Dinei Alfonso Ferreira. "A new sampling theory and a framework for nonlinear filter banks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15792.
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Zou, Yuexian, and 鄒月嫻. "Robust statistics based adaptive filtering algorithms for impulsive noise suppression." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B22823736.
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(Uncorrected OCR)
Abstract
Abstract of thesis entitled
Robust Statistics Based Adaptive Filtering Algorithms
For Impulsive Noise Suppression
Submitted by Yuexian Zou
for the degree of Doctor of Philosophy at The University of Hong Kong in May 2000
The behavior of an adaptive filter is inherently decided by how its estimation error and the cost function are formulated under certain assumption of the involving signal statistics. This dissertation is concerned with the development of robust adaptive filtering in an impulsive noise environment based on the linear transversal filter (LTF) and the lattice-ladder filer (LLF) structures. Combining the linear adaptive filtering theory and robust statistics estimation techniques, two new cost functions, called the mean M -estimate error (MME) and the sum of weighted M -estimate error (SWME), are proposed. They can be taken as the generalizations of the well-known mean squared error (MSE) and the sum of weighted squares error (SWSE) cost functions when the
involving signals are Gaussian.
Based on the SWME cost function, the resulting optimal weight vector is governed by an M-estimate normal equation and a recursive least M -estimate (RLM) algorithm is derived. The RLM algorithm preserves the fast initial convergence, lower steady-state 11
Abstract
derived. The RLM algorithm preserves the fast initial convergence, lower steady-state error and the robustness to the sudden system change of the recursive least squares (RLS) algorithm under Gaussian noise alone. Meanwhile, it has the ability to suppress impulse noise both in the desired and input signals. In addition, using the MME cost function, stochastic gradient based adaptive algorithms, named the least mean Mestimate (LMM) and its transform dOlnain version, the transform domain least mean Mestimate (TLMM) algorithms have been developed. The LMM and TLMM algorithms can be taken as the generalizations of the least-mean square (LMS) and transform domain normalized LMS (TLMS) algorithms, respectively. These two robust algorithms give similar performance as the LMS and TLMS algorithms under Gaussian noise alone and are able to suppress impulse noise appearing in the desired and input signals. It is noted that the performance and the computational complexity of the RLM, LMM and TLMM algorithms have a close relationship with the estimate of the threshold parameters for the M-estimate functions. A robust and effective recursive method has been suggested in this dissertation to estimate the variance of the estimation error and the required threshold parameters with certain confidence to suppress the impulsive noise. The mean and mean square convergence performances of the RLM and the LMM algorithms are evaluated, respectively, when the impulse noise is assumed to be contaminated Gaussian distribution.
Motivated by the desirable features of the lattice-ladder filter, a new robust adaptive gradient lattice-ladder filtering algorithm is developed by minimizing an MME cost function together with an embedded robust impulse suppressing process, especially for impulses appearing in the filter input. The resultant robust gradient lattice-robust
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Abstract
normalized LMS (RGAL-RNLMS) algorithm perfonns comparably to the conventional GAL-NLMS algorithm under Gaussian noise alone; meanwhile, it has the capability of suppressing the adverse effects due to impulses in the input and the desired signals. The additional computational complexity compared to the GAL-NLMS algorithm is of
O(Nw log Nw) + O(NfI log N,J .
Extensive computer simulation studies are undertaken to evaluate the performance of the RLM, LMM, TLMM and the RGAL-RNLMS algorithms under the additive noise with either a contaminated Gaussian distribution or the symmetric alpha-stable (SaS ) distributions. The results substantiate the analysis and demonstrate the effectiveness and robustness of the developed robust adaptive filtering algorithms in suppressing impulsive noise both in the input and the desired signals of the adaptive filter. In conclusion, the proposed approaches in this dissertation present an attempt for developing robust adaptive filtering algorithms in impulsive noise environments and can be viewed as an extension of the linear adaptive filter theory. They may become reasonable and effective tools to solve adaptive filtering problems in a non-Gaussian environment in practice.
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Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Le, Pennec Erwan. "Some (statistical) applications of Ockham's principle." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00802653.
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Ce manuscrit présente mes contributions scientifiques de ces dix dernières années à l'interface entre traitement d'image et statistique. Il débute par l'étude d'un exemple jouet, l'estimation de la moyenne d'un vecteur gaussien, qui permet de présenter le type de question statistique auquel je me suis intéressé, de souligner l'importance de la théorie de l'approximation et de présenter le principe de parcimonie d'Ockham. Après une brève description de l'ensemble des contributions, le manuscrit s'organise autour des modèles statistiques que j'ai pu rencontrés: modèle de bruit blanc, modèle de densité et modèle de densité conditionnelle.
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Randolph, Tami Rochele. "Image compression and classification using nonlinear filter banks." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13439.
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Darwich, Tarek D. A. "A statistical technique for two-phase flow metering." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/7482.
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Ben, Abdallah Rayen. "Statistical signal processing exploiting low-rank priors with applications to detection in Heterogeneous Environment." Thesis, Paris 10, 2019. http://www.theses.fr/2019PA100076.
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Dans un premier lieu, nous considérons le problème de l'estimation de sous-espace d'un signal d'intérêt à partir d'un jeu de données bruité. Pour ce faire, nous adoptons une approche Bayésienne afin d'obtenir un estimateur minimisant la distance moyenne entre la vraie matrice de projection et son estimée. Plus particulièrement, nous étendons les estimateurs au contexte Gaussien composé pour les sources où l'a priori sur la base sera une loi complexe generalized Bingham Langevin. Enfin, nous étudions numériquement les performances de l'estimateur proposé sur une application de type space time adaptive processing pour un radar aéroporté au travers de données réelles.Dans un second lieu, nous nous intéressons au test de propriété communes entre les matrices de covariance. Nous proposons des nouveaux tests statistiques dans le contexte de matrices de covariance structurées. Plus précisément, nous considérons un signal de rang faible corrompu par un bruit blanc Gaussien additif. Notre objectif est de tester la similarité des composantes principales à rang faible communes à un ensemble de matrices de covariance. Dans un premier temps, une statistique de décision est dérivée en utilisant le rapport de vraisemblance généralisée. Le maximum de vraisemblance n'ayant pas d'expression analytique dans ce cas, nous proposons un algorithme d'estimation itératif de type majoration-minimisation pour pouvoir évaluer les tests proposés. Enfin, nous étudions les propriétés des détecteurs proposés à l'aide de simulations numériquesIn this thesis, we consider first the problem of low dimensional signal subspace estimation in a Bayesian context. We focus on compound Gaussian signals embedded in white Gaussian noise, which is a realistic modeling for various array processing applications. Following the Bayesian framework, we derive algorithms to compute both the maximum a posteriori and the so-called minimum mean square distance estimator, which minimizes the average natural distance between the true range space of interest and its estimate. Such approaches have shown their interests for signal subspace estimation in the small sample support and/or low signal to noise ratio contexts. As a byproduct, we also introduce a generalized version of the complex Bingham Langevin distribution in order to model the prior on the subspace orthonormal basis. Numerical simulations illustrate the performance of the proposed algorithms. Then, a practical example of Bayesian prior design is presented for the purpose of radar detection.Second, we aim to test common properties between low rank structured covariance matrices.Indeed, this hypothesis testing has been shown to be a relevant approach for change and/oranomaly detection in synthetic aperture radar images. While the term similarity usually refersto equality or proportionality, we explore the testing of shared properties in the structure oflow rank plus identity covariance matrices, which are appropriate for radar processing. Specifically,we derive generalized likelihood ratio tests to infer i) on the equality/proportionality ofthe low rank signal component of covariance matrices, and ii) on the equality of the signalsubspace component of covariance matrices. The formulation of the second test involves nontrivialoptimization problems for which we tailor ecient Majorization-Minimization algorithms.Eventually, the proposed detection methods enjoy interesting properties, that are illustrated on simulations and on an application to real data for change detection
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Tiwari, Ayush. "Comparison of Statistical Signal Processing and Machine Learning Algorithms as Applied to Cognitive Radios." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1533218513862248.
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Rehr, Robert [Verfasser], and Timo [Akademischer Betreuer] Gerkmann. "Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning / Robert Rehr ; Betreuer: Timo Gerkmann." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019. http://d-nb.info/1175584630/34.
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Hong, Jung. "Statistical Parametric Models and Inference for Biomedical Signal Processing: Applications in Speech and Magnetic Resonance Imaging." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10074.
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In this thesis, we develop statistical methods for extracting significant information from biomedical signals. Biomedical signals are not only generated from a complex system but also affected by various random factors during their measurement. The biomedical signals may then be studied in two aspects: observational noise that biomedical signals experience and intrinsic nature that noise-free signals possess. We study Magnetic Resonance (MR) images and speech signals as applications in the one- and two-dimensional signal representation. In MR imaging, we study how observational noise can be effectively modeled and then removed. Magnitude MR images suffer from Rician-distributed signal-dependent noise. Observing that the squared-magnitude MR image follows a scaled non-central Chi-square distribution on two degrees of freedom, we optimize the parameters involved in the proposed Rician-adapted Non-local Mean (RNLM) estimator by minimizing the Chi-square unbiased risk estimate in the minimum mean square error sense. A linear expansion of RNLM's is considered in order to achieve the global optimality of the parameters without data-dependency. Parallel computations and convolution operations are considered as acceleration techniques. Experiments show the proposed method favorably compares with benchmark denoising algorithms. Parametric modelings of noise-free signals are studied for robust speech applications. The voiced speech signals are often modeled as the harmonic model with the fundamental frequency, commonly assumed to be a smooth function of time. As an important feature in various speech applications, pitch, the perceived tone, is obtained by way of estimating the fundamental frequency. In this thesis, two model-based pitch estimation schemes are introduced. In the first, an iterative Auto Regressive Moving Average technique estimates harmonically tied sinusoidal components in noisy speech signals. Dynamic programming implements the smoothness of the fundamental frequency. The second introduces the Continuous-time Voiced Speech (CVS) model, which models the smooth fundamental frequency as a linear combination of block-wise continuous polynomial bases. The model parameters are obtained via a convex optimization with constraints, providing an estimate of the instantaneous fundamental frequency. Experiments validate robustness and accuracy of the proposed methods compared with some current state-of-the-art pitch estimation algorithms.Engineering and Applied Sciences
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Tsanas, Athanasios. "Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572585.
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This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.
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Lundbäck, Jonas. "On signal processing and electromagnetic modelling : applications in antennas and transmission lines." Doctoral thesis, Ronneby : Blekinge Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00363.
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This doctoral thesis is comprised of five parts. The first three parts concern signal processing and electromagnetic modelling of multiport antennas. The last two parts concern signal processing and transmission line theory applied to wave splitting on transmission lines. In Part I, the spherical vector wave expansion of the electromagnetic field is used to completely characterize a multiport antenna. A general framework for modelling an antenna configuration based on measurement data and numerical computation is obtained. The generic electromagnetic model for arbitrary multiport antennas or vector sensors is applied in direction of arrival (DOA) estimation. Next, in Part II using the generic electromagnetic model (from Part I), we obtain the Cramér–Rao bound (CRB) for DOA and polarization estimation using arbitrary multiport antennas. In the Gaussian case, the CRB is given in terms of the transmission matrix, the spherical vector harmonics and its spatial derivatives. Numerical examples using an ideal Tripole antenna array and a non-ideal Tetrahedron antenna array are included. In Part III, the theory of optimal experiments is applied to a cylindrical antenna near-field measurement setup. The D-optimal (determinant) formulation using the Fisher information matrix of the multipole coefficients in the spherical wave expansion of the electrical field result in the optimal measurement positions. The estimation of the multipole coefficients and corresponding electric field using the optimal measurement points is studied using numerical examples and singular value analysis. Further, Part IV describes a Digital Directional Coupler (DDC), a device for wave splitting on a transmission line. The DDC is a frequency domain digital wave splitter based on two independent wide-band measurements of the voltage and the current. A calibration of the digital processor is included to account for the particular transmission line and the sensors that are employed. Properties of the DDC are analyzed using the CRB and an experiment where wave splitting was conducted on a coaxial–cable is accounted for. Finally, in Part V the DDC has been designed and implemented for wave splitting on a medium voltage power cable in a power distribution station using low cost wide–band sensors. Partial discharge measurements are conducted on cross–linked polyethylene insulated power cables. The directional separation capabilities of the DDC are visualized and utilized to separate multiple reflections from partial discharges based on the direction of travel.
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Chen, Li. "Statistical Machine Learning for Multi-platform Biomedical Data Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77188.
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Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opportunities and roles. In this dissertation thesis, we propose to develop, test and apply novel statistical machine learning methods to analyze the data mainly acquired by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and single nucleotide polymorphism (SNP) microarrays. The research work focuses on: (1) tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors; (2) computational Analysis for detecting DNA SNP interactions in genome-wide association studies.
DCE-MRI provides a noninvasive method for evaluating tumor vasculature patterns based on contrast accumulation and washout. Compartmental analysis is a widely used mathematical tool to model dynamic imaging data and can provide accurate pharmacokinetics parameter estimates. However partial volume effect (PVE) existing in imaging data would have profound effect on the accuracy of pharmacokinetics studies. We therefore propose a convex analysis of mixtures (CAM) algorithm to explicitly eliminate PVE by expressing the kinetics in each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure volume pixels at the corners of the clustered pixel time series scatter plot. The algorithm is supported by a series of newly proved theorems and additional noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of the CAM approach together with compartmental modeling on realistic synthetic data, and compare the accuracy of parameter estimates obtained using CAM or other relevant techniques. Experimental results show a significant improvement in the accuracy of kinetic parameter estimation. We then apply the algorithm to real DCE-MRI data of breast cancer and observe improved pharmacokinetics parameter estimation that separates tumor tissue into sub-regions with differential tracer kinetics on a pixel-by-pixel basis and reveals biologically plausible tumor tissue heterogeneity patterns. This method has combined the advantages of multivariate clustering, convex optimization and compartmental modeling approaches.
Interactions among genetic loci are believed to play an important role in disease risk. Due to the huge dimension of SNP data (normally several millions in genome-wide association studies), the combinatorial search and statistical evaluation required to detect multi-locus interactions constitute a significantly challenging computational task. While many approaches have been proposed for detecting such interactions, their relative performance remains largely unclear, due to the fact that performance was evaluated on different data sources, using different performance measures, and under different experimental protocols. Given the importance of detecting gene-gene interactions, a thorough evaluation of the performance and limitations of available methods, a theoretical analysis of the interaction effect and the genetic factors it depends on, and the development of more efficient methods are warranted. Therefore, we perform a computational analysis for detect interactions among SNPs. The contributions are four-fold: (1) developed simulation tools for evaluating performance of any technique designed to detect interactions among genetic variants in case-control studies; (2) used these tools to compare performance of five popular SNP detection methods; and (3) derived analytic relationships between power and the genetic factors, which not only support the experimental results but also gives a quantitative linkage between interaction effect and these factors; (4) based on the novel insights gained by comparative and theoretical analysis, developed an efficient statistically-principled method, namely the hybrid correlation-based association (HCA) to detect interacting SNPs. The HCA algorithm is based on three correlation-based statistics, which are designed to measure the strength of multi-locus interaction with three different interaction types, covering a large portion of possible interactions. Moreover, to maximize the detection power (sensitivity) while suppressing false positive rate (or retaining moderate specificity), we also devised a strategy to hybridize these three statistics in a case-by-case way. A heuristic search strategy is also proposed to largely decrease the computational complexity, especially for high-order interaction detection. We have tested HCA in both simulation study and real disease study. HCA and the selected peer methods were compared on a large number of simulated datasets, each including multiple sets of interaction models. The assessment criteria included several power measures, family-wise type I error rate, and computational complexity. The experimental results of HCA on the simulation data indicate its promising performance in terms of a good balance between detection accuracy and computational complexity. By running on multiple real datasets, HCA also replicates plausible biomarkers reported in previous literatures.Ph. D.
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Mahata, Kaushik. "Estimation Using Low Rank Signal Models." Doctoral thesis, Uppsala University, Department of Information Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3844.
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Designing estimators based on low rank signal models is a common practice in signal processing. Some of these estimators are designed to use a single low rank snapshot vector, while others employ multiple snapshots. This dissertation deals with both these cases in different contexts.
Separable nonlinear least squares is a popular tool to extract parameter estimates from a single snapshot vector. Asymptotic statistical properties of the separable non-linear least squares estimates are explored in the first part of the thesis. The assumptions imposed on the noise process and the data model are general. Therefore, the results are useful in a wide range of applications. Sufficient conditions are established for consistency, asymptotic normality and statistical efficiency of the estimates. An expression for the asymptotic covariance matrix is derived and it is shown that the estimates are circular. The analysis is extended also to the constrained separable nonlinear least squares problems.
Nonparametric estimation of the material functions from wave propagation experiments is the topic of the second part. This is a typical application where a single snapshot vector is employed. Numerical and statistical properties of the least squares algorithm are explored in this context. Boundary conditions in the experiments are used to achieve superior estimation performance. Subsequently, a subspace based estimation algorithm is proposed. The subspace algorithm is not only computationally efficient, but is also equivalent to the least squares method in accuracy.
Estimation of the frequencies of multiple real valued sine waves is the topic in the third part, where multiple snapshots are employed. A new low rank signal model is introduced. Subsequently, an ESPRIT like method named R-Esprit and a weighted subspace fitting approach are developed based on the proposed model. When compared to ESPRIT, R-Esprit is not only computationally more economical but is also equivalent in performance. The weighted subspace fitting approach shows significant improvement in the resolution threshold. It is also robust to additive noise.
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Ma, Liang Suo. "Multichannel blind deconvolution." Department of Electrical, Computer and Telecommunications Engineering - Faculty of Engineering, 2004. http://ro.uow.edu.au/theses/398.
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This thesis studies some issues in the multichannel blind deconvolution (MBD) problem. MBD studies the problem of recovering the original latent source signals from a set of observation data, which is the convolutive mixture of the latent sources and an unknown dynamical system. Assumptions are usually adopted in deriving MBD algorithms to simplify the problem. Common assumptions include: the dynamical system is assumed to be linear and time invariant; the latent sources are assumed to contain at most one Gaussian distributed signal; the latent sources are statistically independent. There are, however, a number of additional assumptions introduced because a particular approach is followed. In our case, we follow the state space approach in representing the unknown dynamical system. This introduces a number of additional assumptions: (I) the mixing environment is assumed to be noise free; (II) the number of sources is assumed to be known; (III) the number of sources is assumed to be equal to the number of sensor measurements; (IV) the number of the states of the mixer is assumed to be known; (V) the latent sources are assumed to be super-Gaussian distributed. Assumption (IV) is specific to the state space approach, while the other assumptions also occur in other approaches. Obviously, the above assumptions are not necessarily true in practice. Our main aim in this thesis is to relax these assumptions so that the unknown dynamical system will be more accurately modelled and the MBD algorithms will be more suitable for practical applications. We propose to relax these five assumptions, one by one, through a number of novel algorithms. Balanced parametrization of linear time invariant systems originates in the field of system identification, system reduction and H1 control. Using a balanced canonical realization of the linear time invariant system, we will derive three versions of a balanced MBD algorithm in discrete time domain, continuous time domain, and unified discrete time and continuous time domains respectively. All these three versions of balanced algorithms can estimate the number of states in the mixer by considering the identified singular values in the balanced parametrization, thus relaxing assumption (IV). It is relatively easy to extend this formulation to include situations when the number of sensor measurements is greater than the number of latent sources, thus relaxing assumption (III) partially. The more difficult situation when the number of sensor measurements is less than the number of latent sources is not considered in this thesis. Most parameter estimation algorithms for the MBD problem include a nonlinear activation function in the algorithm. Dependent on the approach used in the derivation of the parameter estimation algorithm, the nonlinearity can take various forms, e.g., hyperbolic tangent function. However, normally in the derivation, it is implicitly assumed that the latent sources are super- Gaussian distributed, thus the hyperbolic tangent function is implicitly used as the nonlinearity. Unfortunately, in practice, it is seldom known in advance that the latent sources are super-Gaussian distributed. We will investigate a number of flexible source models, which will allow to separate both super-Gaussian and sub-Gaussian distributed sources. Through our empirical studies, we conjecture that the recovery of the latent sources is relatively insensitive to the probability distribution of the source signals, as long as some common nonlinearity is used in the parameter estimation algorithm in the MBD problem. We have empirically verified this conjecture for a set of commonly used nonlinear functions. Hence, assumption (V) is relaxed to an extent that the nonlinearity can be designed to be adaptive, according to the mixture of probability distribution of the latent sources, provided that the latent sources stay either super-Gaussian or sub-Gaussian for sufficiently long for the parameter estimation algorithm to converge sufficiently. The number of sources estimation problem can be formulated as a model comparison problem, which may be solved by evaluating marginal likelihood. However, it usually involves the evaluation of multiple variable integral expressions, which is well known to be difficult to evaluate computationally. Following a variational Bayesian (VB) approach, we overcome this difficulty in MBD problem by deriving a VB MBD algorithm, which has the following features: first, it allows to enclose noises in the system model; secondly, it allows to employ model comparison and automatic relevance determination to estimate the number of sources. Hence assumptions (I) and (II) are relaxed using this approach. This approach is applied to the estimation of the number of sources in artificially mixed speech signals, and then to electroencephalograph signals, the number of sources of which is not known a priori.
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Lau, Wing-yi, and 劉穎兒. "New recursive parameter estimation algorithms in impulsive noise environment with application to frequency estimation and systemidentification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37595866.
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Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.
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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as random finite sets provides an elegant and rigorous framework for the handling of the multi-target tracking problem. With a random finite sets formulation, both the multi-target states and multi-target observations are modelled as finite set valued random variables, that is, random variables which are random in both the number of elements and the values of the elements themselves. Furthermore, compared to other approaches, the random finite sets approach possesses a desirable characteristic of being free of explicit data association prior to tracking. In addition, a framework is available for dealing with random finite sets and is known as finite sets statistics. In this thesis, advanced signal processing techniques are employed to provide enhancements to and develop new random finite sets based multi-target tracking algorithms for the tracking of both point and extended targets with the aim to improve tracking performance in cluttered environments. To this end, firstly, a new and efficient Kalman-gain aided sequential Monte Carlo probability hypothesis density (KG-SMC-PHD) filter and a cardinalised particle probability hypothesis density (KG-SMC-CPHD) filter are proposed. These filters employ the Kalman- gain approach during weight update to correct predicted particle states by minimising the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. The proposed SMC-CPHD filter provides a better estimate of the number of targets. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures. Secondly, the KG-SMC-(C)PHD filters are particle filter (PF) based and as with PFs, they require a process known as resampling to avoid the problem of degeneracy. This thesis proposes a new resampling scheme to address a problem with the systematic resampling method which causes a high tendency of resampling very low weight particles especially when a large number of resampled particles are required; which in turn affect state estimation. Thirdly, the KG-SMC-(C)PHD filters proposed in this thesis perform filtering and not tracking , that is, they provide only point estimates of target states but do not provide connected estimates of target trajectories from one time step to the next. A new post processing step using game theory as a solution to this filtering - tracking problem is proposed. This approach was named the GTDA method. This method was employed in the KG-SMC-(C)PHD filter as a post processing technique and was evaluated using both simulated and real data obtained using the NI-USRP software defined radio platform in a passive bi-static radar system. Lastly, a new technique for the joint tracking and labelling of multiple extended targets is proposed. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. The GLMB filter is a random finite sets-based filter. In particular, a Poisson mixture variational Bayesian (PMVB) model is developed to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. The proposed method was evaluated with various performance metrics in order to demonstrate its effectiveness in tracking multiple extended targets.