Publications (134) View all
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Article: High precision wavelength estimation method for integrated optics
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ABSTRACT: 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 single photodiode. The initial calibration is done with a set of known wavelengths and resonator tunings. The combined spectral sensitivity function of the resonator and photodiode at each tuning voltage was modeled by a neural network. For determining the unknown wavelengths, the resonator was tuned with a set of heating voltages and the corresponding photodiode signals are collected. The unknown wavelength was estimated, based on the collected photodiode signals, the calibrated neural networks, and an optimization algorithm. The wavelength estimate method provides a high spectral precision of about 8 pm (5*10^(-6) at 1550 nm) in the wavelength range between 1549 nm to 1553 nm. A higher precision of 5 pm (3*10^(-6)) is achieved in the range between 1550.3 nm to 1550.8 nm, which is a factor of five improved compared to a simple lookup of data. The importance of our approach is that it strongly simplifies the optical system and enables optical integration. The approach is also of general importance, because it may be applicable to all wavelength monitoring devices which show an adjustable wavelength response.04/2013; -
Article: Global data-driven modeling of wind turbines in the presence of turbulence
Control Engineering Practice 04/2013; 21(4):441-454. · 1.48 Impact Factor -
Article: Robust Fault Isolation with Statistical Uncertainty in Identified Parameters
IEEE Transactions on Signal Processing. 01/2012; 60:5556-5561. -
Article: Identification of Fault Estimation Filter from I/O Data for Systems with Stable Inversion
IEEE Transactions on Automatic Control 01/2012; 57:1347-1361. · 2.11 Impact Factor -
Conference Proceeding: LPV subspace identification using a novel nuclear norm regularization method
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ABSTRACT: It is well-known that recently proposed Linear Parameter-Varying (LPV) subspace identification techniques suffer from a curse of dimensionality leading to an ill-posed parameter estimation problem. In this paper we will focus on regularization methods to solve the parameter estimation problem. Tikhonov and TSVD regularization are conventional general-purpose regularization methods. These general-purpose regularization methods give preference to a solution with a small 2-norm. In principle many other types of additional information about the desired solution can be incorporated in order to stabilize the ill-posed problem. The main contribution of this paper is that we propose a novel regularization strategy for LPV subspace methods: the nuclear norm regularization method. By applying state-of-the-art convex optimization techniques, the method stabilizes the parameter estimation problem by including information on the desired solution that is specific to the (LPV) subspace identification scheme. We will conclude the paper with a summarizing comparison between the different regularization techniques.American Control Conference (ACC), 2011; 08/2011
