Results 1  10
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158
Recent Developments in Blind Channel Equalization: From Cyclostationarity to Subspaces
 Signal Processing
, 1996
"... Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR chan ..."
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Cited by 49 (1 self)
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Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR channels. Many of the recently developed approaches invoke, either explicitly or implicitly, the algebraic structure of the data model, while some others resort to the use of cyclic correlation/spectral fitting techniques. The objective of this paper is to establish insightful connections among these studies and present recent developments of blind channel equalization. We also unify various representative algorithms into a common theoretical framework. 1 1
System Identification of Nonlinear StateSpace Models
, 2009
"... This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient i ..."
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Cited by 39 (18 self)
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This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of socalled “particle smoothing” methods to compute required conditional expectations via a sequential Monte Carlo approach. Simulation examples demonstrate the efficacy of these techniques.
Experimental design in systems biology, based on parameter sensitivity analysis using a Monte Carlo method: a case study for the TNFalphamediated NFkappaB signal transduction pathway
 SIMULATION
"... Mathematical modeling and dynamic simulation of signal transduction pathways is a central theme in systems biology and is increasingly attracting attention in the postgenomic era. The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in this area. Th ..."
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Cited by 34 (0 self)
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Mathematical modeling and dynamic simulation of signal transduction pathways is a central theme in systems biology and is increasingly attracting attention in the postgenomic era. The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in this area. This study’s aim is to introduce a new strategy for experimental design based on parameter sensitivity analysis. The approach identifies key parameters/variables in a signal transduction pathway model and can thereby provide experimental biologists with guidance on which proteins to consider for measurement. The article focuses on applying this approach to the TNFαmediated NFκB pathway, which plays an important role in immunity and inflammation and in the control of cell proliferation, differentiation, and apoptosis. A mathematical model of this pathway is proposed, and the sensitivity analysis of model parameters is illustrated for this model by employing the Monte Carlo method over a broad range of parameter values.
Image Processing with Multiscale Stochastic Models
, 1993
"... In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A ..."
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Cited by 34 (4 self)
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In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A multiscale model for the error process associated with this algorithm is derived. Next, we illustrate how the multiscale models can be used in the context of regularizing illposed inverse problems and demonstrate the substantial computational savings that such an approach offers. Several novel features of the approach are developed including a technique for choosing the optimal resolution at which to recover the object of interest. Next, we show that this class of models contains other widely used classes of statistical models including 1D Markov processes and 2D Markov random fields, and we propose a class of multiscale models for approximately representing Gaussian Markov random fields...
Simulation methods for optimal experimental design in systems biology
 Simulation
, 2003
"... To obtain a systemslevel understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often ro ..."
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Cited by 31 (2 self)
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To obtain a systemslevel understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often robust with respect to variations in some of the parameters. Hence, it is important to use optimal experimental design considerations in advance of the experiments to improve the information content of the measurements. Using the MAP–Kinase pathway as an example, the authors present a simulation study investigating the application of different optimality criteria. It is demonstrated that experimental design significantly improves the parameter estimation accuracy and also reveals difficulties in parameter estimation due to robustness.
Learning GPBayesFilters via Gaussian process latent variable models
 In Proceedings of robotics: science and systems (RSS
, 2009
"... Abstract — GPBayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GPBayesFilters learn nonparametric filter models from training data contain ..."
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Cited by 24 (4 self)
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Abstract — GPBayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GPBayesFilters learn nonparametric filter models from training data containing sequences of control inputs, observations, and ground truth states. The need for ground truth states limits the applicability of GPBayesFilters to systems for which the ground truth can be estimated without prohibitive overhead. In this paper we introduce GPBFLEARN, a framework for training GPBayesFilters without any ground truth states. Our approach extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems. We show how weak labels for the ground truth states can be incorporated into the GPBFLEARN framework. The approach is evaluated using a difficult tracking task, namely tracking a slotcar based on IMU measurements only. I.
Giannakis, “Deterministic approaches for blind equalization of timevarying channels with antenna arrays
 IEEE Trans. Signal Processing
, 1998
"... Abstract—In this paper, we study the blind equalization problem of timevarying (TV) systems where the channel variations are too rapid to be tracked with conventional adaptive equalizers. We show that using a finite Fourier basis expansion, a TV antenna array system can be cast into a timeinvarian ..."
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Cited by 21 (0 self)
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Abstract—In this paper, we study the blind equalization problem of timevarying (TV) systems where the channel variations are too rapid to be tracked with conventional adaptive equalizers. We show that using a finite Fourier basis expansion, a TV antenna array system can be cast into a timeinvariant multiinput, multioutput (MIMO) framework. The multiple inputs are related through the bases, thereby allowing blind equalization to be accomplished without the use of higher order statistics. Two deterministic blind equalization approaches are presented: One determines the channels first and then the equalizers, whereas the other estimates the equalizers directly. Related issues such as order determination are addressed briefly. The proposed algorithms are illustrated using simulations. I.
Decomposition of human motion into dynamic based primitives with application to drawing tasks
, 2002
"... Using tools from dynamical systems and systems identification we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet ..."
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Cited by 18 (3 self)
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Using tools from dynamical systems and systems identification we develop a framework for the study of primitives for human motion, which we refer to as movemes. The objective is understanding human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. In this work we define conditions under which a class of dynamical models is able to represent a given collection of trajectories as different movemes, and we refer to these conditions as wellposedness. Based on the assumption of wellposedness, we develop segmentation and classification algorithms in order to reduce a complex activity into the sequence of movemes that have generated it. Using examples we show that the definition of wellposedness can be applied in practice and show analytically that the proposed algorithms are robust with respect to noise and model uncertainty. We test our ideas on data sampled from five human subjects who were drawing figures using a computer mouse. Our experiments show that we are able to distinguish between movemes and recognize them even when they take place in activities containing more than one moveme at a time.
System Identification for Limit Cycling Systems: A Case Study for Combustion Instabilities
, 1997
"... This paper presents a case study in system identification for limit cycling systems. The focus of the paper is on (a) the use of a model structure derived from physical considerations and (b) the use of algorithms for the identification of component subsystems of this model structure. The physica ..."
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Cited by 17 (5 self)
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This paper presents a case study in system identification for limit cycling systems. The focus of the paper is on (a) the use of a model structure derived from physical considerations and (b) the use of algorithms for the identification of component subsystems of this model structure. The physical process used in this case study is that of a reduced order model for combustion instabilities for lean premixed systems. The identification techniques applied in this paper are the use of linear system identification tools (prediction error methods), time delay estimation (based on Kalman filter harmonic estimation methods) and qualitative validation of model properties using harmonic balance and describing function methods. The novelty of the paper, apart from its practical application, is that closed loop limit cycle data is used together with a priori process structural knowledge to identify both linear dynamic forward and nonlinear feedback paths. Future work will address the re...