References of "Goncalves, Jorge 50001877"
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See detailGlobal Optimality Bounds for ICA Algorithms
Colombo, Nicolo UL; Thunberg, Johan UL; Goncalves, Jorge UL

in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016)

Independent Component Analysis is a popular statistical method for separating a multivariate signal into additive components. It has been shown that the signal separation problem can be reduced to the ... [more ▼]

Independent Component Analysis is a popular statistical method for separating a multivariate signal into additive components. It has been shown that the signal separation problem can be reduced to the joint diagonalization of the matrix slices of some higher-order cumulants of the signal. In this approach, the unknown mixing matrix can be computed directly from the obtained joint diagonalizer. Various iterative algorithms for solving the non-convex joint diagonalization problem exist, but they usually lack global optimality guarantees. In this paper, we introduce a procedure for computing an optimality gap for local optimal solutions. The optimality gap is then used to obtain an empirical error bound for the estimated mixing matrix. Finally, a class of simultaneous matrix decomposition problems that admit such relaxation procedure is identified. [less ▲]

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See detailConsensus and Formation Control on SE(3) for Switching Topologies
Thunberg, Johan UL; Goncalves, Jorge UL; Hu, Xiaoming

in Automatica (2016), 66

This paper addresses the consensus problem and the formation problem on SE(3) in multi-agent systems with directed and switching interconnection topologies. Several control laws are introduced for the ... [more ▼]

This paper addresses the consensus problem and the formation problem on SE(3) in multi-agent systems with directed and switching interconnection topologies. Several control laws are introduced for the consensus problem. By a simple transformation, it is shown that the proposed control laws can be used for the formation problem. The design is first conducted on the kinematic level, where the velocities are the control laws. Then, for rigid bodies in space, the design is conducted on the dynamic level, where the torques and the forces are the control laws. On the kinematic level, first two control laws are introduced that explicitly use Euclidean transformations, then separate control laws are defined for the rotations and the translations. In the special case of purely rotational motion, the consensus problem is referred to as consensus on SO(3) or attitude synchronization. In this problem, for a broad class of local representations or parameterizations of SO(3), including the Axis–Angle Representation, the Rodrigues Parameters and the Modified Rodrigues Parameters, two types of control laws are presented that look structurally the same for any choice of local representation. For these two control laws we provide conditions on the initial rotations and the connectivity of the graph such that the system reaches consensus on SO(3). Among the contributions of this paper, there are conditions for when exponential rate of convergence occurs. A theorem is provided showing that for any choice of local representation for the rotations, there is a change of coordinates such that the transformed system has a well known structure. [less ▲]

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See detailInverse Problems for Matrix Exponential in System Identification: System Aliasing
Yue, Zuogong UL; Thunberg, Johan UL; Goncalves, Jorge UL

in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016)

This note addresses identification of the A-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge ... [more ▼]

This note addresses identification of the A-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge can be used to simplify the identification. We begin by introducing some general conditions for solvability of the inverse problems for matrix exponential. Next, we introduce “system aliasing” as an issue in the identification of slow sampled systems. Such aliasing give rise to nonunique matrix logarithms. As we show, by imposing additional conditions on and prior knowledge about the A-matrix, the issue of system aliasing can, at least partially, be overcome. Under conditions on the sparsity and the norm of the A-matrix, it is identifiable up to a finite equivalence class. [less ▲]

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See detailAn Iterative Projection method for Synchronization of Invertible Matrices Over Graphs
Thunberg, Johan UL; Colombo, Nicolo UL; Yue, Zuogong UL et al

in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016)

This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite ... [more ▼]

This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite transformations over loops are equal to the identity. Given a set of measured matrices that are not synchronized, the synchronization problem amounts to fining new synchronized matrices close to the former. Under the assumption that the measurement noise is zero mean Gaussian with known covariance, we introduce an iterative method based on linear subspace projection. The method is free of step size determination and tuning and numerical simulations show significant improvement of the solution compared to a recently proposed direct method as well as the Gauss-Newton method. [less ▲]

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See detailLinear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Bernard, Florian UL; Gemmar, Peter; Hertel, Frank UL et al

in Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation (2016)

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly ... [more ▼]

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations. [less ▲]

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See detailIdentifying Biochemical Reaction Networks From Heterogeneous Datasets
Goncalves, Jorge UL; Pan, Wei; Yuan, Ye et al

in IEEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December)

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See detailAssessing the effect of unknown widespread perturbations in complex systems using the mu-gap
Goncalves, Jorge UL; Carignano, Alberto; Webb, Alex

in EEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December)

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See detailShape-aware 3D Interpolation using Statistical Shape Models
Bernard, Florian UL; Salamanca Mino, Luis UL; Thunberg, Johan UL et al

in Symposium on Statistical Shape Models and Applications, Delemont, Switzerland, October 2015 (2015, October)

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See detailCritical transitions in chronic disease: transferring concepts from ecology to systems medicine
Trefois, Christophe UL; Antony, Paul UL; Goncalves, Jorge UL et al

in Current Opinion in Biotechnology (2015), 34

Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and ... [more ▼]

Ecosystems and biological systems are known to be inherently complex and to exhibit nonlinear dynamics. Diseases such as microbiome dysregulation or depression can be seen as complex systems as well and were shown to exhibit patterns of nonlinearity in their response to perturbations. These nonlinearities can be revealed by a sudden shift in system states, for instance from health to disease. The identification and characterization of early warning signals which could predict upcoming critical transitions is of primordial interest as prevention of disease onset is a major aim in health care. In this review, we focus on recent evidence for critical transitions in diseases and discuss the potential of such studies for therapeutic applications. [less ▲]

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See detailOn minimal realisations of dynamical structure functions
Yuan, Ye; Glover, Keith; Goncalves, Jorge UL

in Automatica (2015), 55

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in ... [more ▼]

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements. [less ▲]

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See detailOnline Fault Diagnosis for Nonlinear Power Systems
Pan, Wei UL; Yuan, Ye; Sandberg, Henrik et al

in Automatica (2015), 55

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisationbased formulation of the fault identification problem. An iterative reweighted `1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲]

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See detailA solution for Multi-Alignment by Transformation Synchronisation
Bernard, Florian UL; Thunberg, Johan UL; Gemmar, Peter et al

in The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered ... [more ▼]

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations lie in the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are globally consistent. Simulations demonstrate that for a high amount of noise, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results. [less ▲]

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See detailDynamical Structure Function and Granger Causality: Similarities and Differences
Yue, Zuogong UL; Thunberg, Johan UL; Yuan, Ye et al

in 54th IEEE Conference on Decision and Control, Osaka, Japan, December 15-18, 2015 (2015)

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See detailTransitively Consistent and Unbiased Multi-Image Registration Using Numerically Stable Transformation Synchronisation
Bernard, Florian UL; Thunberg, Johan UL; Salamanca Mino, Luis UL et al

in MIDAS Journal (2015)

Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve ... [more ▼]

Abstract. Transitive consistency of pairwise transformations is a desir- able property of groupwise image registration procedures. The transfor- mation synchronisation method [4] is able to retrieve transitively con- sistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisa- tion method for a ne transformations, which can deal with very large translations, such as those occurring in medical images where the coor- dinate origins may be far away from each other. By using this method in conjunction with any pairwise (a ne) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with re- spect to outliers and achieves higher registration accuracy compared to reference-based registration. [less ▲]

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See detailModelling the natural history of Huntingtons disease progression
Kuan, William; Kasis, Andrea; Yuan, Ye et al

in BMJ Open (2014)

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we ... [more ▼]

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. Methods : Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington’s Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. Results : Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89–91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. Conclusions : This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders. [less ▲]

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See detailH2-Based Network Volatility Measures
Goncalves, Jorge UL; Huang, Qingqing; Yuan, Ye et al

in American Control Conference (2014, June)

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See detailUnderstanding and Predicting Biological Networks Using Linear System Identi cation
Carignano, A.; Yuan, Y.; Dalchau, N. et al

in Kulkarni, V.; Stan, G.; Raman, K. (Eds.) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations (2014)

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See detailNetwork Reconstruction from Intrinsic Noise
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the American Control Conference (2014)

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼]

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per measured state. If the transfer matrix from the inputs to manifest states is known to be minimum phase, this problem is shown to have a unique solution irrespective of the network topology. This is equivalent to there being only one spectral factor (up to a choice of signs of the inputs) of the output spectral density that satisfies these assumptions. Hence for this significant class of systems, the network reconstruction problem is well posed. [less ▲]

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See detailAnalysis of synchronizing biochemical networks via incremental dissipativity
Hamadeh, A.; Goncalves, Jorge UL; Stan, G.B.

in Kulkarni, V.; Stan, G.; Raman, K. (Eds.) A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems (2014)

Detailed reference viewed: 104 (3 UL)