17 citations · 81 across the 20 of their papers we have counts for
23 papers
Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings
Hamed Masoumi, Michel Verhaegen, Nitin Jonathan Myers
Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruct…
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control
Jan-Willem van Wingerden, Sebastiaan Mulders, Rogier Dinkla +2
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrum…
Subpixel image reconstruction using nonuniform defocused images
Hieu Thao Nguyen, Oleg Soloviev, Jacques Noom +1
This paper considers the problem of reconstructing an object with high-resolution using several low-resolution images, which are degraded due to nonuniform defocus effects caused b…
Projection methods for high numerical aperture phase retrieval
Nguyen Hieu Thao, Oleg Soloviev, Russell Luke +1
We develop for the first time a mathematical framework in which the class of projection algorithms can be applied to high numerical aperture (NA) phase retrieval. Within this frame…
Automatic Delay Tuning of a Novel Ring Resonator-based Photonic Beamformer for a Transmit Phased Array Antenna
L. Bliek, S. Wahls, I. Visscher +5
We present a novel photonic beamformer for a fully integrated transmit phased array antenna, together with an automatic procedure for tuning the delays in this system. Such an auto…
Sparsity Preserving Optimal Control of Discretized PDE Systems
Aleksandar Haber, Michel Verhaegen
We focus on the problem of optimal control of large-scale systems whose models are obtained by discretization of partial differential equations using the Finite Element (FE) or Fin…
Subspace Identification of Large-Scale 1D Homogeneous Networks
Chengpu Yu, Michel Verhaegen, Anders Hansson
This paper considers the identification of large-scale 1D networks consisting of identical LTI dynamical systems. A new subspace identification method is developed that only uses l…
Gray Box Identification of State-Space Models Using Difference of Convex Programming
Chengpu Yu, Lennart Ljung, Michel Verhaegen
Gray-box identification is prevalent in modeling physical and networked systems. However, due to the non-convex nature of the gray-box identification problem, good initial paramete…
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the obj…
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen +1
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogat…
Mean and variance of the LQG cost function
Hildo Bijl, Jan Willem van Wingerden, Thomas B. Schön +1
Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the r…
System Identification through Online Sparse Gaussian Process Regression with Input Noise
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have…
Identification of Structured LTI MIMO State-Space Models
Chengpu Yu, Michel Verhaegen, Shahar Kovalsky +1
The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved…
Data-Driven Robust Receding Horizon Fault Estimation
Yiming Wan, Tamas Keviczky, Michel Verhaegen +1
This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustnes…
N2SID: Nuclear Norm Subspace Identification
Michel Verhaegen, Anders Hansson
The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimi…
Sparse solution of the Lyapunov equation for large-scale interconnected systems
Aleksandar Haber, Michel Verhaegen
We consider the problem of computing an approximate banded solution of the continuous-time Lyapunov equation $\underline{A}\underline{X}+\underline{X}\underline{A}^{T}=\underline{P…
Sparse approximate inverses of Gramians and impulse response matrices of large-scale interconnected systems
Aleksandar Haber, Michel Verhaegen
In this paper we show that inverses of well-conditioned, finite-time Gramians and impulse response matrices of large-scale interconnected systems described by sparse state-space mo…
Distributed System Identification with ADMM
Anders Hansson, Michel Verhaegen
This paper presents identification of both network connected systems as well as distributed systems governed by PDEs in the framework of distributed optimization via the Alternatin…
Nuclear Norm Subspace Identification (N2SID) for short data batches
Michel Verhaegen, Anders Hansson
Subspace identification is revisited in the scope of nuclear norm minimization methods. It is shown that essential structural knowledge about the unknown data matrices in the data…
Nonlinear Compressive Particle Filtering
Henrik Ohlsson, Michel Verhaegen, S. Shankar Sastry
Many systems for which compressive sensing is used today are dynamical. The common approach is to neglect the dynamics and see the problem as a sequence of independent problems. Th…
Subspace identification of large-scale interconnected systems
Aleksandar Haber, Michel Verhaegen
We propose a decentralized subspace algorithm for identification of large-scale, interconnected systems that are described by sparse (multi) banded state-space matrices. First, we…
High precision wavelength estimation method for integrated optics
R. M. Oldenbeuving, H. Song, G. Schitter +5
A novel and simple approach to optical wavelength measurement is presented in this paper. The working principle is demonstrated using a tunable waveguide micro ring resonator and s…
Quadratic Basis Pursuit
Henrik Ohlsson, Allen Y. Yang, Roy Dong +2
In many compressive sensing problems today, the relationship between the measurements and the unknowns could be nonlinear. Traditional treatment of such nonlinear relationships hav…