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Dr. Björn Rüfferビヨン リュッフェル |
Welcome to my homepage! I'm currently a Research Fellow at the University of Melbourne within the Department of Electrical and Electronic Engineering. The address http://bjoern.rueffer.info is intended as a permanent link to this web-page, and should be robust against changes of employer, country, web-hoster etc.
You might occasionally see different spellings of my name: Bjoern Rueffer or Bjorn Ruffer. The first one is officially accepted and should be used when no umlauts are available. The second spelling makes it easier to pronounce my name for English speakers, but it is not preferred in written form.
External: Technical blog | Private blog
Research Interests
My main area of interest and research is mathematical systems theory and nonlinear automatic control. Of particular interest to me are monotone systems and large-scale systems. But I am also interested in applications raging from logistic processes, autonomous vehicle formations, neural networks, and optical communication systems to iterative algorithms. Currently I am working on applications in optical communications. Below are some more details regarding different kinds of large-scale systems and problems that I am interested in:
Optical communication networks
Modern long-distance digital communication is based on optical fibre links. Better design of optical amplifiers can make communication more resource efficient and reliable, hence cost effective. My interests in this area includes robustness of large-scale networks and propagation of transients.
Iterative algorithms and dynamical systems
Message-passing algorithms are widely used, e.g., in error correction coding (FEC). A popular example is the iterative decoding of LDPC or turbo codes. Message passing algorithms can equivalently be formulated as very high-order dynamical systems. Understanding these kind of systems leads to a better understanding of iterative error correction decoding and may result in design methods for LDPC codes. On the other hand, techniques that are now standard in information theory may lead to interesting counterparts on the dynamical systems theory side.
Neural networks
Artificial neural networks are a scientific approach to emulate what the human brain does. The human brain contains on average approximately 100 billion neurons. Each of them linked to, on average, several thousand other neurons. Understandably, it is a hard task to understand a large-scale network like the human brain. This is one of the aims of neuroscience. Yet, artificial neural networks of smaller size have successfully been used in an ever-growing number of applications, e.g. for pattern recognition, decoding, or automatic control. I am interested in understanding artificial neural networks as approximations of the brain, as it is, after all, a large-scale dynamical system.
Vehicle formation control
An interesting problem is how a group of vehicles (e.g., trucks, planes, or autonomous underwater vehicles (AUVs)) should maintain a prescribed formation while they simultaneously track a given trajectory. This problem becomes increasingly difficult to tackle, if communication between vehicles is limited. Robust decentralized control aims to tackle these obstacles. Yet, there are also fundamental limitations, also known as string instability.
Autonomous control in logistic processes
Autonomous logistic processes can describe supply chains, transportation, shop floor logistics and more. I have investigated systems like this together with my former colleagues of the Collaborative Research Centre 637 at the University of Bremen, Germany.
Input-to-state stability, general small gain theorems, and applications in automatic control
Input-state-stability (ISS) is a stability concept for nonlinear control systems that has been introduced by Eduardo D. Sontag in 1989. Since then it has become one of the main tools in nonlinear control theory. General ISS small gain theorems can be used to prove stability properties of large-scale systems by decomposing them into lower-order systems. One then proves stability properties of the lower-order systems first and then aggregates these results for the composite large-scale system.
Monotone maps and monotone dynamical systems
A monotone map is a function from one partially ordered space into itself that preserves order. Such a map induces a discrete-time dynamical system, which is hence called a monotone system. Monotone systems naturally arise in the context of general small gain theorems. But also several message-passing algorithms give rise to at least partially monotone systems.
A brief curriculum vitae
Björn Rüffer received his Master of Science degree from the Department of Mathematics at the University of Warwick, UK, in 2004. In 2007 he completed his PhD thesis in the area of mathematical systems theory at the Center for Applied and Industrial Mathematics (ZeTeM) within the Department of Mathematics and Computer Science at the University of Bremen, Germany. In Bremen, he was also a member of the Collaborative Research Centre 637 “Autonomous Cooperating Logistic Processes — A paradigm shift and its limitations.” From October 2007 to June 2009 he was a member of the Signal Processing Microelectronics (SPM) group and the School of Electrical Engineering and Computer Science at the University of Newcastle, Australia. Since July 2009 he is with the Department of Electrical and Electronic Engineering at the University of Melbourne, Australia. In late 2009, Björn was awarded a fellowship from the Japan Society for the Promotion of Science (JSPS) for a research visit to the Kyushu Institute of Technology, Japan.
Björn’s mathematical ancestors can traced here .
List of Publications
Electronic versions of some of my publications and preprints can be found
in the publication databases of
SPM and
SFB637. Others, indicated by a 
symbol, can be downloaded
directly from this web page. Note, however, that there might be
minor differences between the published versions and the
preprint versions of my papers available here.
There's also a BibTeX file with citation details of all my papers for download below.
Journal papers and book chapters
Submitted to Numer. Math., May 5, 2010.
arXiv:1005.0741v1.
IEEE Trans. Autom. Control 55(7):1732–1736, 2010.
DOI:10.1109/TAC.2010.2048053.
SIAM J. Control Optim. 48(6):4089–4118, 2010.
DOI:10.1137/090746483.
arXiv:0901.1842
Positivity 14(2):257–283, 2010.
DOI:10.1007/s11117-009-0016-5.
The original publication is available at www.springerlink.com.
Systems Control Lett. 59(3–4):241–247, 2010.
DOI:10.1016/j.sysconle.2010.02.001.
Automatica J. IFAC 46(6):1019–1027, 2010.
DOI:10.1016/j.automatica.2010.03.012.
IEEE Trans. Autom. Control 55(7):1722–1725, 2010.
DOI:10.1109/TAC.2010.2048938.
IET Control Theory Appl. 4(7):1188–1200, 2010.
DOI:10.1049/iet-cta.2009.0233.
Logistik Management 9(1):25–36, 2007.
In: M. Hülsmann and K. Windt (Eds.): Understanding Autonomous Cooperation and Control in Logistics, pp. 121–138, Springer, 2007.
Math. Control Signals Syst. 19(2):93–122, 2007.
arXiv:math/0506434v1
Conference articles
In: Proc. 19th Int. Symp. Math. Th. Networks Systems (MTNS), Budapest, Hungary, July, 2010.
(electronic).
In: Proc. 19th Int. Symp. Math. Th. Networks Systems (MTNS), Budapest, Hungary, July, 2010.
(electronic).
In: Proc. 49th IEEE Conf. Decis. Control, 2010.
To appear.
In: Proc. of the 10th SICE Annual Conference on Control Systems, Kumamoto, Japan, March, 2010.
(electronic).
In: Proc. Joint 48th IEEE Conf. Decis. Control and 28th Chinese Contr. Conf., Shanghai, P.R.China, pp. 638–643, 2009.
In: Proc. of the 8th SICE Annual Conference on Control Systems, Kyoto, Japan, pp. 52431–52434, 2008.
In: PAMM, Special Issue: 79th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Bremen, Germany, pp. 10911–10912, March, 2008.
DOI:10.1002/pamm.200810911.
In: Proc. 47th IEEE Conf. Decis. Control, Cancun, Mexico, pp. 25–30, December 9–11, 2008.
In: Proc. 46th IEEE Conf. Decis. Control, New Orleans, LA, USA, pp. 4471–4476, 2007.
In: Proc. 1st Int. Conference Dynamics in Logistics, Bremen, Germany, pp. 359-366, August 28–30, 2007.
In: Proc. 7th IFAC Symp. Nonlinear Control Systems, Pretoria, South Africa, pp. 283–288, August 22–24, 2007.
In: Proc. 17th Int. Symp. Math. Th. Networks Systems (MTNS), Kyoto, Japan, pp. 89–97, 2006.
In: Proc. 17th Int. Symp. Math. Th. Networks Systems (MTNS), Kyoto, Japan, pp. 77–82, 2006.
In: Proc. Int. Scientific Annual Conference on Operations Research, Bremen, Germany, pp. 91–96, 2005.
In: Proc. Joint 44th IEEE Conf. Decis. Control and Europ. Contr. Conf., Seville, Spain, pp. 5633–5638, 2005.
Theses and reports
Technical report, Department of Electrical Engineering and Computer Science, University of Newcastle, Australia, November, 2008.
PhD thesis, Universität Bremen, Germany, October, 2007.
Available online at [external resource].
Technical report, ZeTeM, Universität Bremen, Germany, July 19th, 2006.
Masters thesis, Dept. Mathematics, University of Warwick, UK, 2003.
BibTeX file
You are more than welcome to cite papers of mine in your work. To make that easier for you, here's a BibTeX file that I maintain: [rueffer.bib].
© Björn Rüffer 2009, 2010
email:
Last modified: Sun Jul 18 20:30:33 EST 2010
Disclaimer: This page, its contents and style, are the responsibility of the author and do not represent the views, policies or opinions of The University of Melbourne.