Cellular neural network: Difference between revisions

Revision as of 08:32, 27 December 2020

Revision as of 08:37, 27 December 2020

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==Model of computation==

{{Section citations needed|date=December 2020}}

The dynamical behavior of CNN processors can be expressed using [[differential equations]], where each equation represents the state of an individual processing unit. The behavior of the entire CNN processor is defined by its initial conditions, the inputs, the cell interconnections (topology and weights), and the cells themselves. One possible use of CNN processors is to generate and respond to signals of specific dynamical properties. For example, CNN processors have been used to generate multi-scroll[[Multiscroll attractor|multiscroll chaos]] (like the [[Chen attractor]]),<ref>M. Yalcin, J. Suykens, and J. Vandewalle, Cellular Neural Networks, Multi-Scroll Chaos And Synchronization, 2005.</ref> [[Synchronization|synchronize]] with chaotic systems, and exhibit multi-level [[hysteresis]].<ref>A. Slavova and M. Markovat, "Receptor Based CNN Model with Hysteresis for Pattern Generation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>K. Yokosawa, Y. Tanji and M. Tanaka, "CNN with Multi-Level Hysteresis Quantization Output" Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>T. Nakaguchi, K. Omiya and M. Tanaka, "Hysteresis Cellular Neural Networks for Solving Combinatorial Optimization Problems", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> CNN processors are designed specifically to solve local, low-level, processor intensive problems expressed as a function of space and time. For example, CNN processors can be used to implement high-pass and low-pass filters and [[Mathematical morphology|morphological]] operators. They can also be used to approximate a wide range of [[Partial differential equations]] (PDE) such as heat dissipation and wave propagation.<ref>P. Sonkolyt, P. Kozmat, Z. Nagyt and P. Szolgay, "Acoustic Wave Propagation Modeling on CNN-UM Architecture", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>

=== Reaction-Diffusion ===

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=== Image processing ===

CNN processors were designed to perform image processing; specifically, real-time ultra-high frame-rate (>10,000 frame/s) processing for applications like particle detection in jet engine fluids and spark-plug detection. Currently, CNN processors can achieve up to 50,000 frames per second, and for certain applications such as missile tracking, flash detection, and spark-plug diagnostics these microprocessors have outperformed a conventional [[supercomputer]]. CNN processors lend themselves to local, low-level, processor intensive operations and have been used in feature extraction, level and gain adjustments, color constancy detection,<ref>L. Torok and A. Zarandy, "CNN Based Color Constancy Algorithm", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> contrast enhancement, [[deconvolution]], [[image compression]],<ref>P. Venetianer and T. Roska, "Image Compression by Cellular Neural Networks," IEEE Trans. Circuits Syst., 45(3): 205-215, 1998.</ref><ref>R. Dogarut, R. Tetzlaffl and M. Glesner, "Semi-Totalistic CNN Genes for Compact Image Compression", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> motion estimation,<ref>Y. Cheng, J. Chung, C. Lin and S. Hsu, "Local Motion Estimation Based On Cellular Neural Network Technology for Image Stabilization Processing", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>A. Gacsadi, C. Grava, V. Tiponut, and P. Szolgay, "A CNN Implementation of the Horn & Schunck Motion Estimation Method", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image encoding, image decoding, image segmentation,<ref>S. Chen, M. Kuo and J. Wang, "Image Segmentation Based on Consensus Voting", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>G. Grassi, E. Sciascio, A. Grieco and P. Vecchio, "A New Object-oriented Segmentation Algorithm based on CNNs - Part II: Performance Evaluation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> orientation preference maps, pattern learning/recognition, multi-target tracking, image stabilization, resolution enhancement, image deformations and mapping, image inpainting, optical flow, contouring, [[moving object detection]],<ref>G. Costantini, D. Casali, and R. Perfetti, "Detection of Moving Objects in a Binocular Video Sequence", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> axis of symmetry detection,<ref>G Costantini, D. Casafi., and R. Perfetti, "A New CNN-based Method for Detection of the Axis of Symmetry.", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and [[image fusion]].<ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image Processing Library for the Aladdin Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image processing library for the Aladdin Visual Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref><ref>K. Wiehler, M. Perezowsky, R. Grigat, "A Detailed Analysis of Different CNN Implementations for a Real-Time Image Processing System", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref>

Due to their processing capabilities and flexibility, CNN processors have been used and [[Prototype|prototyped]] for novel field applications such as flame analysis for monitoring combustion at a waste [[Incineration|incinerator]],<ref>L. Bertucco, A. Fichaa, G. Nmari and A. Pagano, "A Cellular Neural Networks Approach to Flame Image Analysis for Combustion Monitoring", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> mine-detection using [[infrared]] imagery, [[calorimeter]] cluster peak for [[high energy physics]],<ref>C. Baldanza, F. Bisi, M. Bruschi, I. D’Antone, S. Meneghini, M. Riui, M. Zufa, "A Cellular Neural Network For Peak Finding In High-Energy Physics", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> anomaly detection in potential field maps for geophysics, laser dot detection, metal inspection for detecting manufacturing defects, and [[Seismology|seismic]] horizon picking. They have also been used to perform [[Biometrics|biometric]] functions such as [[fingerprint recognition]],<ref>T. Su, Y. Du, Y. Cheng, and Y. Su, "A Fingerprint Recognition System Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> vein feature extraction, face tracking,<ref>S. Xavier-de-Souza, M. Van Dyck, J. Suykens and J. Vandewalle, "Fast and Robust Face Tracking for CNN Chips: Application to Wheelchair Driving", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and generating visual stimuli via emergent patterns to gauge perceptual [[Resonance|resonances]].

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*T. Roska, L. Chua, "The CNN Universal Machine: An Analogic Array Computer", IEEE Trans. on Circuits and Systems-II, 40(3): 163-172, 1993.

*T. Roska and L. Chua, "Cellular Neural Networks with Non-Linear and Delay-Type Template Elements and Non-Uniform Grids", Int’l Journal of Circuit Theory and Applications, 20:469-481, 1992.

*M. Yalcin, J. Suykens, and J. Vandewalle, Cellular Neural Networks, Multi-Scroll Chaos And Synchronization, 2005.

*K. Yokosawa, Y. Tanji and M. Tanaka, "CNN with Multi-Level Hysteresis Quantization Output" Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.

*T. Nakaguchi, K. Omiya and M. Tanaka, "Hysteresis Cellular Neural Networks for Solving Combinatorial Optimization Problems", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.

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*B. Shi, T. Roska and L. Chua, "Estimating Optical Flow with Cellular Neural Networks," Int’l Journal of Circuit Theory and Applications, 26: 344-364, 1998.

*D. Vilarino and C. Rekeczky, "Implementation of a Pixel-Level Snake Algorithm on a CNNUM-Based Chip Set Architecture", IEEE Trans. On Circuits And Systems - I, 51(5): 885-891, 2004.

*G Costantini, D. Casafi., and R. Perfetti, "A New CNN-based Method for Detection of the Axis of Symmetry.", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.

*C. Amenta, P. Arena, S. Baglio, L. Fortuna, D. Richiura, M.Xibilia and L. Vu1, "SC-CNNs for Sensors Data Fusion and Control in Space Distributed Structures", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.

*E. Bilgili, O. Ucan, A. Albora and I. Goknar, "Potential Anomaly Separation Using Genetically Trained Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.