Research at Systems and Control
Modeling, inference and control in complex dynamical systems
Current projects
Dynamical systems, physical and others, can be described by a variety of mathematical models. We develop methods for modeling and estimation of dynamical systems based on measurements of input/output data. The models can be used for analysis, in order to better understand the properties of the system, or for control, to automatically regulate a process without human interaction. Contact person: Alexander MedvedevAutomatic control and System identification
Many interesting phenomena around us are complex, dynamical and stochastic in nature, and the available data are inherently uncertain. We develop theory and tools for learning, reasoning and acting based on probabilistic models and measured data, methods that allow humans and machines to better understand the surrounding world. Contact person: Thomas SchönStatistical Machine Learning
The goal of signal processing is to extract information from measured quantities, or signals. This broad concept ranges from simple linear filtering of time series to reduce noise, to nonlinear parameter estimation based on high-dimensional data using statistical models. Estimation theory, optimization, and statistics, play central roles. Contact person: Thomas SchönSignal Processing
Important application areas
The theory and methods of dynamical systems, control, identification and signal processing have much to offer research and clinical practice in modern medicine. We develop methods used in the diagnosis, assessment, and treatment of medical conditions, based on dynamical models of physiological and biological systems. Currently we have projects on Parkinson's disease, breast cancer, diabetes and balance impairment. Contact person: Alexander MedvedevBiomedical systems
Water quality and treatment of water is a growing concern around the world. Demands on quality and increasing loads call for optimized operation of wastewater treatment plants. Applied research in automatic control is an important tool in improving the performance of treatment plants. We are developing control and estimation strategies for wastewater treatment plants that improve pollutant removal, reduce the need for chemicals and yield energy savings. Contact person: Bengt CarlssonWastewater engineering
Recent publications
More comprehensive list of publications
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The Vanishing and Appearing Sources during a Century of Observations Project. I. USNO Objects Missing in Modern Sky Surveys and Follow-up Observations of a "Missing Star"
. In Astronomical Journal, volume 159, number 1, American Astronomical Society, 2020. (DOI). -
Polynomial nonlinear state space identification of an aero-engine structure. In Computers & structures, volume 238, 2020. (DOI).
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Robust Prediction When Features are Missing. In IEEE Signal Processing Letters, volume 27, pp 720-724, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020. (DOI).
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Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters. In Control Engineering Practice, volume 103, Elsevier BV, 2020. (DOI).
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A multiple motion sensors index for motor state quantification in Parkinson's disease. In Computer Methods and Programs in Biomedicine, volume 189, ELSEVIER IRELAND LTD, 2020. (DOI).
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Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing. In IEEE Transactions on Signal Processing, volume 68, pp 2143-2154, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020. (DOI).
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Machine Learning in Risk Prediction. In Hypertension, volume 75, number 5, pp 1165-1166, 2020. (DOI).
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Automatic diagnosis of the 12-lead ECG using a deep neural network. In Nature Communications, volume 11, number 1, NATURE PUBLISHING GROUP, 2020. (DOI, Fulltext, fulltext:print
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Timed Up-and-Go Dual-Task Testing in the Assessment of Cognitive Function: A Mixed Methods Observational Study for Development of the UDDGait Protocol. In International Journal of Environmental Research and Public Health, volume 17, number 5, MDPI, 2020. (DOI, Fulltext, fulltext:print).
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Frequency domain identification of FIR models in the presence of additive input-output noise. In Automatica, volume 115, PERGAMON-ELSEVIER SCIENCE LTD, 2020. (DOI).