The first day of the Conference will include a Tutorial Session and a Demo Session. The Tutorial Session is entitled:


 which is endorsed by the IEEE Circuits and Systems Society Technical Committee on Cellular Neural Networks and Array Computing. The presenters are:

 Marco Gilli
 Csaba Rekeczky
 Bertram Shi

Tutorial Summary:

A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally-coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some prescribed dynamical laws.  Since the CNN was first introduced in 1988, research in this field has developed rapidly. The goal of this tutorial is to provide participants with a snapshot of the current state of the art and research trends.  It covers the broad multi-disciplinary areas of CNN research, from theoretical aspects to applications. The presenters have experience ranging from theoretical analysis of CNN dynamics, VLSI implementation and system level applications.

Table of Contents

CNN Architectures
            Single layer CNNs
            Multi-layer CNNs
            Uncoupled CNNs – driving point analysis
            Coupled CNNs: stationary behavior and stability analysis; periodic and non- periodic behavior.
            Complex dynamics in multi-layer CNNs
Image processing operations on CNN architectures

            Nonlinear filtering and binary/gray-scale morphological processing
            Pattern creation-interpretation and usage for meaningful computing
            Computing with diffusion and waves
VLSI Implementations
            Analog VLSI implementation of CNN analog processing primitives
            Digital interfaces to CNN chips
            Low power design
            Multi-layer CNN implementations
            Comparison with other neuromorphic array computing architectures
Vision Systems Based on CNN Chips
            Eye-Ris System
            Bi-i System
Applications using CNN Technology
            High-speed Industrial Process Control
            High-speed Industrial Quality Assurance
            Indoor/outdoor Surveillance
            UGV/UAV Platform based Surveillance and Reconnaissance
            Collision Avoidance
            Biological Modeling
                        Retinal Modeling
                        Cortical Modeling
            / R&D Toward CNN type Retinal and Cortical Implants /

The LIVE CNN TECHNOLOGY DEMO SESSION will consist of following demos:

1.  “Bioinspired Robotics: Application of a CNN-based CPG VLSI Chip to Control an Autonomous Mini-hexapod Robot”, P. Arena, L. Fortuna, M. Frasca, L. Patané, and M. Pollino, DIEES University of Catania, ITALY. 

2.  “Cellular Wave Computing the Multi-channel Mammalian Retina Model by the Bi-i Camera-computer”,
D. Balya and B. Roska, Friedrich Miescher Institute for Biomedical Research, SWITZERLAND

3.  “Demonstration of Real-time Image Processing on the SCAMP-3 Vision System”,
P. Dudek, D. R.W. Barr, A. Lopich, and S. J. Carey, University of Manchester, UK

4.  “CNNOPT: Learning Dynamics and CNN Chip-specific Robustness”,
D. Hillier, S. X. de Souza, J. A. K. Suykens, and J. Vandewalle, Péter Pázmány Catholic University, HUNGARY,
Katholieke Universiteit Leuven, BELGIUM

5.  “Computing and Combining the Outputs of Cortically Inspired Feature Maps”,
B. E. Shi, E. K. C. Tsang, S. Y. M. Lam, and Y. Meng, Hong Kong University of Science and Technology,     HONG KONG

6.  “A Real-time Mammalian Retina Model Implementation on FPGA”,
Z. Nagy, Zs. Vörösházi, and P. Szolgay, Pannon University, Department of Image Processing and Neurocomputing, HUNGARY, Analogic and Neural Computing Laboratory, Computer and Automation Institute of HAS, HUNGARY

7.  ”Implementation of Cellular Wave Computing Methods by Hardware Learning: Ion Beam Analysis”,
G Geis, V. Senger, and R. Tetzlaff, Institute of Applied Pyhsics, JWG University Frankfurt a. M., GERMANY

8.  “Multitarget Tracking Applications of the Bi-i Platform: Attention-selection, Tracking and Control”,
G. Tímar and Cs. Rekeczky, Péter Pázmány Catholic University, Faculty of Information Technology, HUNGARY

9.  “Pixel Level Snakes Execution on the SIMD Processor Array Vision Chip SCAMP-3”,
D. L. Vilarińo and P. Dudek, University of Santiago de Compostela, SPAIN, University of Manchester, UK

10.“Fast and robust face tracking applied to wheelchair driving”,
S. Xavier-de-Souza, M. V. Dyck, J. A. K. Suykens, and J. Vandewalle, Katholieke Universiteit Leuven, BELGIUM

The organizers are:

Paolo Arena
Csaba Rekeczky