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Institutionen för informationsteknologi

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My Research

My research concerns parallellization of (recursive) estimation and control algorithms, with special emphasis to implementation on multi core architectures. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in many applications of signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is however known to be computationally expensive, which often makes the methods infeasible in real-time applications and problems of high dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is the most powerful way of improving the execution time of an algorithm.

Publications

Journal

O Rosén, A Medvedev,
Efficient Parallel Implementation of State Estimation Algorithms on Multicore Platforms,
IEEE Transactions on Control Systems Technology, 2011.

O Rosén, A Medvedev, T Wigren,
Parallelization of the Kalman Filter for Banded Systems on Multicore Computational Platforms,
Control Engineering Practice, 2013.

D. Jansson, O. Rosén, A. Medvedev,
Parametric and Nonparametric Analysis of Eye-Tracking Data by Anomaly Detection
IEEE Transactions on Control Systems Technology, 2014.

Conference

O Rosén, A Medvedev
Parallel Recursive Estimation Using Monte Carlo and Orthogonal Series
Expansions
To appear in proceedings of American Control Conference, Chicago, USA, July 2015.

O Rosén, M Silva, A Medvedev
Particle filter algorithms for identification of minimally
parametrized Wiener models of drug administration effect
To appear in proceedings of IFAC Symposium on Biological and Medical Systems, Berlin, Germany, Aug. 2015.

O Rosén, A Medvedev
The Recursive Bayesian Estimation Problem via Orthogonal Expansions: an Error Bound
IFAC World Congress, Cape town, South Africa, Aug 2014.

O Rosén, MM Silva, A Medvedev
Nonlinear Estimation of a Parsimonious Wiener Model for the Neuromuscular Blockade in Closed-loop Anesthesia
IFAC WC, South Africa, Cape town, Aug, 2014.

O Rosén, A Medvedev
Parallel recursive Bayesian estimation on multicore computational platforms using orthogonal basis functions
American Control Conference (ACC), Portland, USA, 2014.

O Rosén, A Medvedev
Parallel Kalman filtering on Multicore Computational Platforms
Conference on Decision and Control (CDC), 2012, 50th IEEE Conference on Maui,
Hawaii, USA, 2012.

O Rosén, A Medvedev, M Ekman,
Speedup and Tracking Accuracy Evaluation of Parallel Particle Filter Algorithms Implemented on a Multicore Architecture,
Control Applications (CCA), 2010 IEEE International Conference on,
Tokyo, Japan, 2010.

O Rosén, A Medvedev
Efficient parallel implementation of a Kalman filter for single output systems on multicore computational platforms,
Decision and Control and European Control Conference (CDC-ECC), 2011, 50th IEEE Conference on,
Orlando, Florida, USA, 2011.

O Rosén, A Medvedev
An On-line Algorithm For Anomaly Detection in Trajectory Data,
2012 American Control Conference (ACC),
Montreal, Canada, 2012.

F Wahlberg, A Medvedev, O Rosén
A LEGO-based mobile robotic platform for evaluation of parallel control and estimation algorithms,
Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on
Orlando, Florida, USA. 2011.

Jansson D., Rosen O., Medvedev A.,
Non-parametric analysis of eye-tracking data by anomaly detection
European Control Conference (ECC),
Zurich, Schweiz, 2013.

Contributions to the divisions lab courses

Lab platform

I have together with Per Jonsson developed a platform for laborations in control theory. Using the software the Lego NXT can be controlled from Matlab and it provides a simple interface for uploading of controllers to the NXT brick. When the controller is running on the NXT, live plots of the signals can be shown in matlab, and reference signals can be sent to the robot. The control loop is executed on the NXT brick, which allows the robots to move autonomously, and the communication occurs over a Bluetooth link.

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Selfbalancing robot

The course Automatic Control II uses the platform for LQG control of two wheeled self balancing robots. I have been responsible for construction of the lab, which includes modeling, simulation and controller design, experiment design and writing of lab instructions.

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Line tracker robot

The course Introduction to computer control systems uses the platform for PID control of line tracking robots. I have been responsible for construction of the lab, which includes modeling, simulation and controller design, experiment design and writing of lab instructions.

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Teaching

To be filled in...

Uppdaterad  2015-06-10 11:46:41 av Olov Rosén.