This is archived site of SPSympo 2017 conference.
We cordially invite our prospective participants for tutorial sessions held during the Signal Processing Symposium 2017.
The tutorial sessions are co-organized by SPSympo-2017 Organizing Committee and IEEE Polish Chapter of Signal Processing Society
We describe various applications in sensing and associated signal processing, cognition and reasoning, as motivation and justification for multi-resolution analysis. We also include abstractions of the cortex architectures for sound and vision in ferrets, mice, and humans. These considerations inspire the class of generic architectures we propose. We next describe a rigorous mathematical framework we have developed that provides a hierarchical architecture for learning and cognition. The resulting architecture combines a wavelet preprocessor, a group invariant feature extractor and an aligned hierarchical (layered) learning algorithm. There are two feedback loops one back from the learning output to the feature extractor and one all the way back to the wavelet preprocessor. We show that the scheme can incorporate all metric differences but also non-metric dissimilarity measures like Bregman divergences. The learning module incorporates two universal learning algorithms in their hierarchical tree-structured form, both due to Kohonen. Learning Vector Quantization (LVQ) for supervised learning and Self-Organizing Map (SOM) for unsupervised learning. We demonstrate convergence of the resulting algorithms. We demonstrate the superior performance of the resulting algorithms and architecture on a variety of practical problems including: speaker and sound identification, simultaneous direction of arrival speaker ID and vowel ID, radar classification, ISAR classification, face recognition based on photographs. We describe how the resulting architecture and analytics capture the architecture abstractions of the cortex of higher-level animals and humans w.r.t. sound and vision sensing and understanding. We describe multi-resolution aspect graphs and their use in understanding and explaining the framework, and associated descriptions and importance of group invariance and representation of sound and vision objects that are non-traditional, including and emerging framework for shape recognition. We provide an interpretation of the algorithms as data-driven multi-resolution partition based classifiers and associated geometric constructions. We describe the implications on complexity reduction, and why these results explain known performance in higher-level animals and humans. We demonstrate how the underlying mathematics can be used to provide systematic models for design, analysis and evaluation of deep neural networks. We describe how the underlying mathematical framework is related to recent work by Mallat and others on a mathematical foundation for deep convolutional neural networks and learning. We close with a description of current work and future plans on mixed signal (digital and analog) micro-electronic implementations that exploit known architectural abstractions of the cortex of higher-level animals and humans w.r.t. to sound and vision sensing and cognition. We call the resulting chip class “Cortex-on-a-Chip”.
John S. Baras, Lockheed Martin Chair in Systems Engineering
Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens, Greece, 1970; M.S., Ph.D. in Applied Mathematics from Harvard University 1971, 1973. Since 1973, faculty member in the Electrical and Computer Engineering Department, and in the Applied Mathematics, Statistics and Scientific Computation Program, at the University of Maryland College Park. Since 2000, faculty member in the Fischell Department of Bioengineering. Since 2014, faculty member in the Mechanical Engineering Department. Founding Director of the Institute for Systems Research (ISR), 1985 to 1991. Since 1991, Founding Director of the Maryland Center for Hybrid Networks (HYNET). Since 2013, Guest Professor at the Royal Institute of Technology (KTH), Sweden. IEEE Life Fellow, SIAM Fellow, AAAS Fellow, NAI Fellow, IFAC Fellow, and a Foreign Member of the Royal Swedish Academy of Engineering Sciences (IVA). Received the 1980 George Axelby Prize from the IEEE Control Systems Society, the 2006 Leonard Abraham Prize from the IEEE Communications Society, the 2014 Tage Erlander Guest Professorship from the Swedish Research Council, and a three year (2014-2017) Senior Hans Fischer Fellowship from the Institute for Advanced Study of the Technical University of Munich, Germany. He was inducted in the A. J. Clark School of Engineering Innovation Hall of Fame (2016) of the University of Maryland and he was selected to receive the 2017 IEEE Simon Ramo Medal, and the 2017 AACC Richard E. Bellman Control Heritage Award. Professor Baras' research interests include systems and control, optimization, communication networks, signal processing and understanding, robotics, computing systems and networks, network security and trust, systems biology, healthcare management systems, model-based systems engineering.
Web page: http://dev-baras.pantheonsite.io/
In this tutorial Subspace Techniques (ST) for identifying linear time invariant state space models from input-output data are revised. ST do not require a parametrization of the system matrices and therefore are less prone to problems related to local minima that often hamper succesful application of parametric optimization based identification methods. The overview follows the historic line of development. It starts from Kronecker’s result on the representation of an infinite power series by a rational function and then addresses respectively the deterministic realization problem, its stochastic variant and finally the identification of a state space model given in innovation form. The tutorial summarizes the fundamental algorithmic principles of key methods over 3 decades of research in this field and gives a glimps on potential future research directions.
Michel Verhaegen received an engineering degree in aeronautics from the Delft University of Technology, The Netherlands, in August 1982, and the doctoral degree in applied sciences from the Catholic University Leuven, Belgium, in November 1985. From 1985 on he held different research positions at NASA, TU Delft, Uppsala, McGill, Lund and the German Aerospace Laboratory. In 1999 he became professor in Systems and Control Engineering at the faculty of Applied Physics of the university of Twente in the Netherlands in 1999. From 2001 on Prof. Verhaegen has been appointed full time professor at the the Delft University of Technology where he was one of the founders of the new Delft Center for Systems and Control. Prof. Verhaegen received a Best Presentation Award at the American Control Conference, Seattle, WA, 1986 and a Recognition Award from NASA in 1989. He is currently holding an ERC Advanced grant. As stated here such "grants are designed to support excellent Principal Investigators at the career stage at which they are already established research leaders with a recognised track record of research achievements. Applicant Principal Investigators must demonstrate the ground-breaking nature, ambition and feasibility of their scientific proposal."
Topic list (preliminary):
Estimated duration: ~ 1h – 2h
A constant interest growth is observed in the area of exploring the data from human body. Wide range of tools is available for registering biosignal in various modalities, thus analyzing such data using various mathematical techniques is of great importance. In the tutorial, the general overview of the biomedical systems and signal types will be presented, with emphasize on the origin and characteristics of each particular signal and the concepts of biosignal treatment in the medical IoT framework.
The overview of mathematical tools used for biosignal analysis will be presented. Four classes of methods, i.e. linear, nonlinear, uni- and multivariate techniques will be discussed with examples. Applications of biosignal analysis will be presented. Among the topics, analysis of brain electrical activity, epileptic seizure prediction using machine learning, development of fuzzy inference system for early diagnostics of Alzheimer’s Disease, and contactless registration of human respiration from video in visible range will be described in details.
Dr. Anton Popov (MSc 2003, PhD 2007) is the Associate Professor of Physical and Biomedical Electronics Department, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (Kyiv, Ukraine). He is the chair of Biomedical Electronics and Signal Analysis Group, and has over fifteen years experience in applications of mathematical methods to biosignal analysis. Research interests include analysis of brain and heart activity.
Topic list (preliminary):
Estimated duration: ~ 1,5h
Radar system allows detection and tracking of non-coopertive target making use of ElectroMagnetic (e.m.) reflection of target illuminated by the transmitted signal.
In the last 20 years, the radar has shown rapidly technological progresses and a consequent improvement of the performance. Current wide band radars are now able to reconstruct microwave images of the targets thanks to their high spatial resolution capabilities.
The tutorial focuses on fundamentals of radar imaging aiming at demonstrating that the radar behaves as a camera producing e.m. image in any meteo and day and night conditions.
The main common Range-Doppler technique will be explained and some critical aspects related to image focusing and cross-range scaling mentioned.
Which are the differences between an ElctroOptical camera and and imaging radar? Answer to this simple question will be addressed.
The tutorial will conclude with an excursus of the advanced and recent imaging techniques with application to real data.
Prof. Fabrizio Berizzi was born in Piombino (Italy) on 25-th of November 1965.
He received the electronic engineering and Ph.D. degrees from the University of Pisa (Italy) in June 1990 and September 1994 respectively. He became an University researcher in 1992 and he was promoted to Associate Professor in October 2000. Since December 2009, he has been a full professor on Electronic and Telecommunication Engineering at the University of Pisa. He has served the Italian Navy as a junior Lieutenant from April 1991 to July 1992. Now his Navy officer activity is suspended but he is still in force with the Italian Navy as Lieutenant degree.
He has been an IEEE senior member since 2006. He has been the Italian Academic national representative of the NATO SET panel member since 30th of April 2014. He is co-chair of the NATO SET 196 task group and member of the NATO SET 195, 207, 227, 215. He is one of the lectures of the NATO Lecture Series 243 on “ Passive radar technologies”. He was the Local Host organizer of the NATO SET Fall 2015 Panel Business meeting that was held in Pisa on October 2015.
He is the Italian academic member in the EDA Captech RADAR. Since September 2014, he has been the Italian academic representative of the Scalable Multifunction Radio Frequency (SMRF) Umbrella Management Group (UMG) and Technical Support Group) in EDA.
He is the head of the Radar Laboratory of the University of Pisa (Italy) an the Deputy Director of the Radar and Surveillance System (RaSS) laboratory of the CNIT (National Inter-university consortium for Telecommunication) in Italy.
His main research interests are in the field of radar system design and signal processing and specifically in radar imaging (SAR/ISAR/InSAR,3D imaging), polarimetric, passive, Over the Horizon, multichannel/multistatic, cognitive radars.
He is the author of more than 100 scientific papers in the most prestigious international Journal (IEEE AES, SP, IP, GRSS, IET RSN, etc.) and of several books the last of which is the “Radar imaging for maritime observation” published by CRC press (USA) on June 2016 and awarded as the “outstanding IRF book of the year” by the Information Research Foundation (IRF) (USA).
He have been giving short courses and tutorials around the world at several international Universities and institutes (Univ. of Melbourne, Adelaide, Cape Town, Warsaw, Southampton, KAST (Saudi Arabia), DSTO (AUS), IEEE RADAR conferences, IRS and EURAD international conference and many others.
Super Massive Black Hole (SMBH) in the center of Milky Way galaxy (Sgr A*) is the closest one to the Earth. A unique feature of any black hole is the existence of an Event Horizon (EH). The EH represents the boundary of a space-time domain from which nothing, not even photons, may ever leave and reach an external observer. At the same time, accreting matter produces electromagnetic radiation in a wide frequency range which may be readily detected with nowadays radio receiving technology, which gives a chance to estimate experimentally the EH shadow. The presently available angular resolution allows us to measure even the shape of the shadow of the EH of the Sgr A* SMBH. Implementation of such an experiment will be Experimentum Crucis for proving the validity of General Relativity. These experiments are based on the use of multi-position Interferometers with a Very Large Base (VLBI).
Our lecture briefly describes the above and related issues, such as: formation of the EH; existing Projects on the EH measuring; Event Horizon Telescope (EHT) Project, and, in some more detail, a new Project on ‘Event Horizon Imaging Experiment’ (EHIE) suggested and elaborated recently by European Space Agency (ESA) – M.Martin-Neira, Vololimyr Kudryashev, et al.
The concept of the horizon plays an increasing role not only in gravitation, but also in physics in general. The existence of the event horizon manifests itself both at macro and micro scales. In the latter case, the event horizon, generating a minimum length, may determine the discrete structure of space.
Prof. Konstantin Lukin, Fellow IEEE, received his diploma in Radiophysics & Electronics from Kharkov State University, Ukraine, in 1973. He is Head of the Laboratory for Nonlinear Dynamics of Electronic Systems, LNDES, at IRE NASU. He completed his Candidate of Sciences thesis in IRE NASU and defended it at Moscow State University (MSU) in 1980. He completed his Doctor of Sciences dissertation in Physical Electronics in IRE NASU and defended it at Kharkov State University in 1989. He has been a visiting scientist at the International Center for Theoretical Physics (ICTP, Trieste, Italy) in 1996-1997 and a visiting professor at the Joint Research Center (JRC, Ispra, Italy) in 1997-1998. His current research interests are as follows: digital and analogue generation and processing of random/chaotic/noise signals and their applications in Noise Radar for SAR imaging, differential interferometry; microwave monitoring of urban areas and detection of pre-catastrophic states of large natural and manmade objects, such as landslides, bridges, TV towers, dams, large building, hangars, etc. He is Co-Chairman of RTO/NATO Task Group on 'Space and Frequency Diverse Noise Radar'. Dr.Lukin is author or coauthor of more than 260 journal publications and 2 monograph. He is a TPC member of the EUSAR, IRS, SPSympo, IRMMW-THz, MSMW, IEEE IVEC and Chairman of NRT-2002, 2003, 2012 International Conferences. He was leader of many international R&D projects on Noise Radar systems and sensors; on SAR imaging and microwave monitoring of environment. Currently he is leader of Ukrainian part of the SCOUT Project under FP-7 Program and 2 Projects under NATO SfP Program.