Plenary Speakers

6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009)

September 16-18, 2009, Salzburg, Austria

It is our great pleasure to announce that the following distinguished researchers will be the plenary speakers at ISPA 2009:

• Professor H. Vincent Poor
  Dean, School of Engineering and Applied Science
  Michael Henry Strater University Professor of Electrical Engineering
  Department of Electrical Engineering, Princeton University, New Jersey, USA
  Plenary lecture title: Collaborative Signal Processing in Wireless Sensor Networks
• Dr. Henning Puder
  Head of the Siemens Audiology Group
  Siemens, Erlangen, Germany
  Plenary lecture title: Hearing aids: An overview of the state-of-the-art, challenges, and future trends of an interesting audio signal processing application.
• Professor Stefan Katzenbeisser
  Security Engineering Group, TU Darmstadt, Germany
  Plenary lecture title: Signal Processing in the Encrypted Domain: Analyzing signals privately
• Professor John Daugman
  The Computer Laboratory, University of Cambridge, UK
  Plenary lecture title: Recognising persons by their iris patterns

Professor H. Vincent Poor Dean, School of Engineering and Applied Science
Michael Henry Strater University Professor of Electrical Engineering
Department of Electrical Engineering, Princeton University, New Jersey, USA

Plenary lecture title:Collaborative Signal Processing in Wireless Sensor Networks

Abstract
Wireless sensor networks (WSNs) can be distinguished from other types of wireless communication networks by three salient features: the primary application for which WSNs are used is inference (i.e., detection, estimation, mapping, etc.); the information sources generated at different terminals in WSNs are often correlated with one another due to the fact that the sensors are typically measuring a common underlying physical phenomenon; and the energy limitations are often more severe than in other networks due to the circumstances of deployment in hard-to-access locations. These features drive the design choices made in WSNs, and in particular they provide a natural setting for the development of novel collaborative signal processing techniques. This talk will discuss several issues relating to these considerations, and in particular the use of distributed and collaborative algorithms for resource-efficient beam-forming and inference in WSNs will be considered as paradigms for this general area. Time permitting, other recent results and some ideas for future research in this area will also be discussed briefly.

Short Biography
H. Vincent Poor is Dean of the School of Engineering and Applied Science at Princeton University, where he is also the Michael Henry Strater University Professor of Electrical Engineering. His primary research interests are in the areas of stochastic analysis and statistical signal processing, with applications in wireless networks and related fields. Among his publications in these areas is the recent book, Quickest Detection (Cambridge University Press, 2009). Dr. Poor is a member of the U. S. National Academy of Engineering, a Fellow of the American Academy of Arts & Sciences, and a former Guggenheim Fellow. He is also a Fellow of the IEEE, the Institute of Mathematical Statistics, and other scientific and technical organizations. Recent recognition of his work includes the 2007 IEEE Marconi Prize Paper Award, the 2007 Technical Achievement Award of the IEEE Signal Processing Society, and the 2008 Aaron D. Wyner Award of the IEEE Information Theory Society.

Dr. Henning Puder
Head of the Siemens Audiology Group
Siemens, Erlangen, Germany

Plenary lecture title: Hearing aids: An overview of the state-of-the-art, challenges, and future trends of an interesting audio signal processing application.

Abstract
Hearing impaired people mostly suffer from a non-reversible damage of the inner ear. The consequence is a reduced dynamic perception range: soft signals are hardly noticed, however, loud sounds are perceived as loud as by normal hearing people. Additionally, hearing impaired people suffer from a stronger frequency masking than normal hearing people which causes a reduced ability to separate desired signals from non-desired signals in typical noisy acoustic environments. The consequence is a reduced speech intelligibility in these noisy situations. In this contribution, first, the mentioned problems are explained. Based on these physiological effects, audio signal processing methods are shown which allow to considerable enhance the audibility for hearing impaired people. A special focus is dedicated to directional processing, noise reduction, level dependent amplification, and feedback cancellation. New additional components are also explained such as the wireless audio connection to electronic devices such as telephones, TV sets, MP3 players via a wireless body area network. Finally, two future trends will be highlighted such as learning procedures in hearing aids and binaural signal processing in order to further enhance the signal quality by combining the audio signals from both sides of the head.

Short Biography
Henning Puder was born in Bensheim, Germany in 1970. He received his diploma degrees in electrical engineering from Darmstadt University of Technology (Germany) and Grande Ecole Supérieure d’Electricité, Paris (France) in 1997, respectively, and his Ph.D. degrees in electrical engineering from Darmstadt University of Technology in 2003. From 1997 to 2002 he was member of the Signal Theory research group of Professor Hänsler at Darmstadt University of Technology with the focus on digital audio signal processing. Here, he was concerned with hands-free car phones, especially with procedures for echo and noise cancellation. The topic of his PhD. thesis was a model based noise reduction approach for hands-free car phones. His thesis was honored with the German ITG Johann-Philipp-Reis award in 2003, shared with Peter Jax from RWTH Aachen. Since 2002, Henning Puder is with Siemens Audiology Group, Erlangen, Germany, where his main research area is within audio signal processing applications for hearing aids, such as noise reduction, beamforming, and feedback cancellation. In 2006 he took over the responsibility for the Signal Processing Group within the R&D department of the Siemens Audiology Group where he is currently responsible for a group of 15 people.

Professor Stefan Katzenbeisser
Security Engineering Group, TU Darmstadt, Germany

Plenary lecture title: Signal Processing in the Encrypted Domain: Analyzing signals privately

Abstract
Signal Processing techniques are increasingly used to analyze signals that are privacy sensitive: examples range from biometric identification over processing of surveillance camera images to medical statistical databases. In these scenarios, signal processing tools are used to analyze data which can be directly related to a person and which may have severe consequences if revealed to unauthorized parties. For example, biometric information may be copied and misused to perform impersonation attacks, surveillance camera images may be used to track movements of people or medical databases might, despite anonymization, reveal an individual diagnosis. Traditional cryptographic methods can only partly solve this privacy problem: even though conventional encryption may be used to protect data that is stored in a computer system, it does not secure data during processing in untrusted environments, since the signals usually need to be decrypted before they can be used. To alleviate the problem, "signal processing in the encrypted domain" attempts to apply novel cryptographic mechanisms that support processing operations on encrypted data to the domain of signal processing: Sensitive signals are encrypted and signal processing operations are directly performed on encrypted samples; the schemes are constructed in such a way that only the result of the processing operation is accessible, but not the privacy-sensitive input signal. In this talk we survey the state of the field, discuss novel applications and identify open research problems.

Short Biography
Stefan Katzenbeisser received the PhD degree from the Vienna University of Technology, Austria. After working as a research scientist at the Technical University in Munich, Germany, and as a senior scientist at Philips Research, he joined the Technical University of Darmstadt as assistant professor. His current research interests include Digital Rights Management, security aspects of digital watermarking, data privacy, software security and cryptographic protocol design. He has authored more than 40 scientific publications and served on the program committees of several workshops and conferences devoted to watermarking and applied cryptography. Among others, he was the program chair of the Information Hiding Workshop (2005), the IFIP Communications and Multimedia Security Conference (2005) and the International Workshop on Digital Watermarking (2007). Currently, he is an associate editor of the IEEE Transactions on Dependable and Secure Computing and the EURASIP Journal on Information Security. He is a member of the IEEE, ACM and IACR.

Professor John Daugman
The Computer Laboratory, University of Cambridge, UK

Plenary lecture title: Recognising persons by their iris patterns

Abstract
Iris recognition provides real-time, high confidence identification of persons by encoding and analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected, internal, organ whose random texture is epigenetic and stable over life, it can serve as a living password. Recognition decisions are made with confidence levels high enough to support rapid exhaustive searches through national-sized databases. The principle that underlies these algorithms is the failure of an efficient test of statistical independence involving more than 200 degrees-of-freedom, based on phase sequencing each iris pattern with quadrature 2D wavelets. Different persons always pass this test of statistical independence, but images from the same iris almost always fail this test of independence. Database search speeds are about 1 million persons per second per CPU. Data included in this talk come from 200 billion iris cross-comparisons between different eyes, from a database consisting of 632,500 iris images acquired in the United Arab Emirates in a networked national border-crossing security programme that every day performs about 9 billion iris comparisons using these algorithms. Current research efforts with this technology aim to make it more tolerant of difficult conditions of iris capture, such as "iris on the move; at a distance; off-axis;" and robust against spoofing.

Short Biography
John Daugman received his degrees at Harvard University and then taught at Harvard before coming to Cambridge University (UK). He has held the Johann Bernoulli Chair of Mathematics and Informatics at the University of Groningen (NL), and the Toshiba Endowed Chair at the Tokyo Institute of Technology (Japan). His areas of research and teaching at Cambridge include computer vision, information theory, and statistical pattern recognition. Daugman is the inventor of iris recognition, and his algorithms currently underlie all public deployments of this technology; worldwide some 50 million persons have been enrolled by these algorithms. Awards for his scientific and technical work include the US Presidential Young Investigator Award, the Information Technology Award and Medal of the British Computer Society, the "Millennium Product" Award of the UK Design Council, the "Time 100" Innovators Award, and the OBE, Order of the British Empire.

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