Aðalfundurinn fer fram í húsakynnum Símans,
Ármúla 25, Reykjavík.

Fundurinn hefst á kynningu á rannsóknum Hrefnu Marín Gunnarsdóttur sem ber heitið:
„Mat á hrjúfleika yfirborðs Mars með tvístæðum radar“,
en að því loknu hefst formleg dagskrá aðalfundar, sem hér segir:

1. Formaður flytur skýrslu stjórnar.
2. Reikningar félagsins lagðir fram til samþykktar.
3. Tilkynning um stjórnarkjör í janúar 2011.
4. Önnur mál.

Bornar verða fram veitingar.

Allir félagar í IEEE og nemendadeild IEEE
eru hvattir til að mæta á aðalfundinn.

Stjórn IEEE á Íslandi

Current massive computer power in the hands of people goes mostly untapped, since most users, most of the time, make use of just a tiny amount of it. In this talk we will show how this is currently used and how it can be used in massively parallel computation experiments, in which social factors play an important role.

Dr. Klaus-Robert Müller, Professor for machine learning at Technical University of Berlin and department head IDA group at FhG FIRST
http://www.ml.tu-berlin.de/menue/mitglieder/klaus-robert_mueller/

Brain Computer Interfacing (BCI) – a modern instantiation of Neurotechnology – aims at making use of brain signals for e.g. the control of objects, spelling, gaming and so on. This talk will first provide a brief overview of Brain Computer Interfaces from a machine learning and signal processing perspective. In particular it shows the wealth, the complexity and the difficulties of the data available, a truely enormous challenge: In real-time a multi-variate very strongly noise contaminated data stream is to be processed and neuroelectric activities are to be accurately decoded in real time. Emphasis is put on a novel computational method for alleviating non-stationarity in data, namely stationary subspace analysis (SSA). Finally, I report in more detail about the Berlin Brain Computer Interface (BBCI) that is based on EEG signals and take the audience all the way from the measured signal, the preprocessing and filtering, the classification to the respective application. BCI as a new channel for man-machine communication is discussed in a clinical setting and for gaming.

This is joint work with Benjamin Blankertz, Michael Tangermann, Claudia Sanelli, Carmen Vidaurre, Thorsten Dickhaus, Steven Lemm, Paul von Bünau, Frank Meinecke, Wojciech Wojcikiewicz (TU Berlin), Guido Nolte, Andreas Ziehe, (Fraunhofer FIRST, Berlin) Gabriel Curio, Vadim Nikulin (Charite, Berlin) and further members of the Berlin Brain Computer Interface team, see www.bbci.de.

Biographical information: Professor Manos M. Tentzeris received the Diploma Degree in Electrical and Computer Engineering from the National Technical University of Athens (”Magna Cum Laude”) in Greece and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from the University of Michigan, Ann Arbor, MI and he is currently a Professor with School of ECE, Georgia Tech, Atlanta, GA. He has published more than 320 papers in refereed Journals and Conference Proceedings, 3 books and 17 book chapters. Dr. Tentzeris has helped develop academic programs in Highly Integrated/Multilayer Packaging for RF and Wireless Applications using ceramic and organic flexible materials, paper-based RFID’s and sensors, Microwave MEM’s, SOP-integrated (UWB, mutliband, conformal) antennas and Adaptive Numerical Electromagnetics (FDTD, MultiResolution Algorithms) and heads the ATHENA research group (20 researchers). He is the Georgia Electronic Design Center Associate Director for RFID/Sensors research, and he has been the Georgia Tech NSF-Packaging Research Center Associate Director for RF Research and the RF Alliance Leader from 2003-2006.  He was the recipient/co-recipient of the 2009 E.T.S.Walton Award from the Irish Science Foundation, the 2007 IEEE APS Symposium Best Student Paper Award, the 2007 IEEE IMS Third Best Student Paper Award, the 2007 ISAP 2007 Poster Presentation Award, the 2006 IEEE MTT Outstanding Young Engineer Award, the 2006 Asian-Pacific Microwave Conference Award, the 2004 IEEE Transactions on Advanced Packaging Commendable Paper Award, the 2003 NASA Godfrey “Art” Anzic Collaborative Distinguished Publication Award, the 2003 IBC International Educator of the Year Award, the 2003 IEEE CPMT Outstanding Young Engineer Award, the 2002 International Conference on Microwave and Millimeter-Wave Technology Best Paper Award (Beijing, CHINA), the 2002 Georgia Tech-ECE Outstanding Junior Faculty Award, the 2001 ACES Conference Best Paper Award and the 2000 NSF CAREER Award and the 1997 Best Paper Award of the International Hybrid Microelectronics and Packaging Society. He was the TPC Chair for IEEE IMS 2008 Symposium and the Chair of the 2005 IEEE CEM-TD Workshop and he is the Vice-Chair of the RF Technical Committee (TC16) of the IEEE CPMT Society. He is the founder and chair of the RFID Technical Committee (TC24) of the IEEE MTT Society and the Secretary/Treasurer of the IEEE C-RFID. He has organized various sessions and workshops on RF/Wireless Packaging and Integration, RFID’s, Numerical Techniques/Wavelets, in IEEE ECTC, IMS, VTC and APS Symposia in all of which he is a member of the Technical Program Committee in the area of “Components and RF”. He is the Associate Editor of IEEE Transactions on Microwave Theory and Techniques, IEEE Transactions on Advanced Packaging and International Journal on Antennas and Propagation. Dr.Tentzeris was a Visiting Professor with the Technical University of Munich, Germany for the summer of 2002, where he introduced a course in the area of High-Frequency Packaging. He has given more than 80 invited talks in the same area to various universities and companies in Europe, Asia and America. He is a Senior Member of IEEE, a member of URSI-Commission D,  a member of MTT-15 committee,  an Associate Member of EuMA, a Fellow of the Electromagnetic Academy and a member of the Technical Chamber of Greece.
Prof. Tentzeris will be the IEEE MTT-S Distinguished Microwave Lecturer from 2010-2012.

The workshop topics include but are not limited to:
• High-frequency electromagnetic simulation
• Surrogate-based optimization methods and space mapping
• Knowledge-based design and tuning methodologies
• Automated design optimization using electromagnetic simulators
• Inverse problems in electromagnetic
• Approximation, interpolation and response surface methodologies
• Applications of adjoint sensitivity for EM-driven optimization
• Artificial neural network modeling of devices and systems
• Software architectures for optimization-oriented design
• Co-simulation and related approaches
• Bioelectromagnetism
• CAD of photonic devices

The list of confirmed speakers include researchers from leading groups working on microwave/RF CAD as well as representatives of major microwave software manufacturers (CST AG, Sonnet Software Inc., EM Software and Systems Inc.).

Prospective participants/speakers are encouraged to contact organizers (Slawomir Koziel and/or Mohamed Bakr, see contact information below). More information about the workshop can be found on the official workshop website at http://ru.is/kennarar/koziel/Workshop_Reykjavik_2010.html

Workshop Chairs:

Slawomir Koziel
Engineering Optimization & Modeling Center
School of Science and Engineering
Reykjavik University
Menntavegur 1
109 Reykjavik, Iceland
E-mail: koziel@ru.is

Mohamed Bakr
Department of Electrical and Computer Eng.
McMaster University
1280 Main Street West
Hamilton, Ontario
Canada L8S 4K1
E-mail: mbakr@mail.ece.mcmaster.ca

Markmiðið viðurkenningarinnar er m.a. að vekja athygli á árangri rafmagnsverkfræðinga í starfi og afrekum þeirra sem vert er að halda á lofti. Viðurkenningin er veitt þeim rafmagnsverkfræðingi sem hverju sinni þykir hafa skarað fram úr í störfum sínum á sviði rafmagnsverkfræði eða fyrir íslenskt samfélag. Auglýst var eftir tilnefningum og voru skilyrði þau að viðkomandi væri Íslendingur eða starfandi á Íslandi. Sameiginleg dómnefnd VFÍ og IEEE á Íslandi valdi síðan viðurkenningarhafa úr 6 tilnefningum.

Í tilefni af viðurkenningarafhendingunni kemur hingað til lands fulltrúi IEEE, Brian Harrington gjaldkeri IEEE á svæði 8, þ.e. Evrópa, Afríka og Mið-Austurlönd.
Boðið verður upp á léttar veitingar.

Egill er nemi á þriðja ári við Rafmagns- og tölvuverkfræðideild HÍ.  Fyrirlesturinn er um rannsóknir sem Egill stundaði við California Institude of Technology (CalTech) sumarið 2009.     Hann var einn af þremur nemendum Háskóla Íslands sem tóku þátt í SURF (summer undergraduate research fellowship) við skólann og vann á lífverkfræðirannsóknarstofu undir leiðsögn Dr. Michael Dickinson.

Útdráttur/abstract

Feed forward neural networks are well established in several fields of science for regression and classfication applications. However, in most cases, a poor choice of the training algorithm has been the biggest hindrance for utilizing the complete potential of neural networks. Traditionally, backpropogation
algorithm is used to train the neural networks. It is notoriously slow and has the problem that it can converge at the local minima of the cost function. This talk will focus on applications of neural networks in remote sensing image analysis with an emphasis on the results of two advanced training algorithms namely, scaled conjugate gradients and Kalman filter which do not converge at the local minima. Also, neural networks can approximate any functional continuous mapping from one finite dimensional space to another. So, neural network regression can be used for normalization of multi-temporal images even if the pixel intensities have non-linear variations. A new method
for automatic change detection using iterative neural network regression will be presented.

Útdráttur/abstract:

Hyperspectral imaging provides rich spectral information for every
pixel in a particular scene, hence increasing the ability to
distinguish physical structures in the scene. However, a large number
of spectral channels presents challenges for image classification.
While pixel-wise classification techniques process each pixel
independently without considering information about spatial
structures, further improvements can be achieved by the incorporation
of spatial information in a classifier, especially in areas where
structural information is important to distinguish between classes.

In the talk, novel strategies for spectral-spatial classification of
hyperspectral images are introduced. One of the recently proposed
approaches consists of performing image segmentation in order to use
every region from the segmentation map as an adaptive spatial
neighborhood for all the pixels within this region. Furthermore, this
spatial information must be combined with the available spectral
information with a classifier. A simple approach leading to good
classification performances consists of performing pixel-wise
classification and segmentation independently, and then assigning all
the pixels of every region to the majority class within this region
(majority vote rule). A Support Vectors Machine (SVM) classifier has
shown to be very suitable for classification of hyperspectral data.
Therefore, the SVM can be used as a pixel-wise classifier in the
described spectral-spatial classification scheme.

In the talk we also concentrate on approaches to reduce
oversegmentation in an image, which is achieved by introducing
“markers” in the segmentation procedure. We propose to perform a
probabilistic pixel-wise classification first, in order to choose the
most reliable pixels as markers of spatial regions. Furthermore,
marker-controlled region growing is performed, using either watershed
or Minimum Spanning Forest methods. The developed
techniques significantly decrease oversegmentation, improve
classification accuracies and provide classification maps with more
homogeneous regions, when compared to previously proposed
classification methods.

—–
Yuliya Tarabalka is a Phd candidate at Faculty of Electrical and Computer Engineering, University of Iceland, and Gipsa-Lab, Grenoble Institute of Technology, France.