Content deleted Content added
citations |
refs |
||
Line 1:
In [[computer science]] and [[machine learning]], '''cellular neural networks (CNN)''' or '''cellular nonlinear networks (CNN)''' are a [[parallel computing]] paradigm similar to [[neural networks]], with the difference that communication is allowed between neighbouring units only. Typical applications include [[image processing]], analyzing 3D surfaces, solving [[partial differential equation]]s, reducing non-visual problems to [[Geometry|geometric]] maps, modelling biological [[visual system|vision]] and other [[Sensory-motor coupling|sensory-motor]] organs.<ref>{{Cite book|last=Slavova|first=A.|url=https://books.google.com/books?id=bt4PUx8CZXIC&q=Cellular+neural+network|title=Cellular Neural Networks: Dynamics and Modelling|date=2003-03-31|publisher=Springer Science & Business Media|isbn=978-1-4020-1192-4|language=en}}</ref>
Line 61 ⟶ 59:
==Model of computation==
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, inputs, 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 [[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)<ref>G. Cserey, C. Rekeczky and P. Foldesy, "PDE Based Histogram Modification with Embedded Morphological Processing of the Level Sets", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> 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 ===
Line 79 ⟶ 77:
==Physical implementations==
There are toy models simulating CNN processors using [[Billiard ball|billiard balls]], but these are used for theoretical studies. In practice, CNN are physically implemented on hardware and current technologies such as [[Semiconductor|semiconductors]]. There are plans to migrate CNN processors to emerging technologies in the future.<ref>R. Carmona, F. Jimenez-Garrido, R. Dominguez-Castro, S. Espejo and A. Rodriguez-Vazquez, "CMOS Realization of a 2-layer CNN Universal Machine", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref><ref>C. Dominguez-Matas, R. Carmona-Galan, F. Sainchez-Fernaindez, A. Rodriguez-Vazquez, "3-Layer CNN Chip for Focal-Plane Complex Dynamics with Adaptive Image Capture", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>C. Dominguez-Matas, F. Sainchez-Femaindez, R. Carmona-Galan, and E. Roca-Moreno, "Experiments on Global and Local Adaptation to Illumination Conditions based on Focal Plane Average Computation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>S. Xavier-de-Souza, M. Yalcın, J. Suykens, and J. Vandewalle, "Toward CNN Chip-Specific Robustness", IEEE Trans. On Circuits And Systems - I, 51(5): 892-902, 2004.</ref><ref>D. Hillier, S. Xavier de Souza, J. Suykens, J. Vandewalle, "CNNOPT Learning CNN Dynamics and Chip-specific Robustness", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>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.</ref>
=== Semiconductors ===
Line 107 ⟶ 105:
=== Digital CNN processors, FPGA ===
Although not nearly as fast and energy efficient, digital CNN processors do not share the problems of process variation and feature size of their analog counterparts. This allows digital CNN processors to include nested processor units, non-linearities, etc. In addition, digital CNN are more flexible, cost less and are easier to integrate. The most common implementation of digital CNN processors uses an [[Field-programmable gate array|FPGA]]. Eutecus, founded in 2002 and operating in Berkeley, provides intellectual property that can be synthesized into an Altera FPGA. Their digital 320x280, FPGA-based CNN processors run at 30 frame/s and there are plans to make a fast digital ASIC. Eustecus is a strategic partner of AnaLogic computers, and their FPGA designs can be found in several of AnaLogic’s products. Eutecus is also developing software libraries to perform tasks including but not limited to video analytics for the video security market, feature classification, multi-target tracking, signal and image processing and flow processing. Many of these routines are derived using CNN-like processing. For those wanting to perform CNN simulations for prototyping, low-speed applications, or research, there are several options. First, there are precise CNN emulation software packages like SCNN 2000. If the speed is prohibitive, there are mathematical techniques, such as Jacobi’s Iterative Method or Forward-Backward Recursions that can be used to derive the steady state solution of a CNN processor. Lastly, digital CNN processors can be emulated on highly parallel, application-specific processors, such as graphics processors. Implementing neural networks using graphics processors is an area of further research.<ref>Z. Nagyt, Z. Voroshazi and P. Szolgay, "A Real-time Mammalian Retina Model Implementation on FPGA", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>S. Kocsardit, Z. Nagyt, S. Kostianevt and P. Szolgay, "FPGA Based Implementation of Water Injection in Geothermal Structure", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>P. Arena, L. Fortuna, G. Vagliasindi and A. Basile, "CNN Chip And FPGA To Explore Complexity", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>W. Fang, C. Wang and L. Spaanenburg, "In Search of a Robust Digital CNN System" Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>Z. Kincsest, Z. Nagyl, and P. Szolgay, "Implementation of Nonlinear Template Runner Emulated Digital CNN-UM on FPGA", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>
=== Holography, nanotechnology ===
Line 120 ⟶ 118:
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,<ref>O. Lahdenoja, M. Laiho and A. Paasio, "Local Binary Pattern Feature Vector Extraction with CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> 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]],<ref>L. Orzo, "Optimal CNN Templates for Deconvolution", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006</ref> [[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,<ref>J. Wu, Z. Lin and C. Liou, "Formation and Variability of Orientation Preference Maps in Visual Cortex: an Approach Based on Normalized Gaussian Arrays", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> pattern learning/recognition,<ref name=":2" /><ref>C. Wu and S. Tsai, "Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> multi-target tracking,<ref>G. Timar and C. Rekeczky, "Multitarget Tracking Applications of the Bi-I Platform: Attention-selection, Tracking and Navigation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image stabilization,<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> resolution enhancement,<ref>T. Otake, T. Konishi, H. Aomorit, N. Takahashit and M. Tanakat, "Image Resolution Upscaling Via Two-Layered Discrete Cellular Neural Network", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image deformations and mapping, image inpainting,<ref>A. Gacsadi and P. Szolgay, "Image Inpainting Methods by Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> optical flow,<ref>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.</ref> contouring,<ref>Szalka, G. Soos, D. Hillier, L. Kek, G. Andrassy and C. Rekeczky, "Space-time Signature Analysis of 2D Echocardiograms Based on Topographic Cellular Active Contour Techniques", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>T. Szabot and P. Szolgay, "CNN-UM-Based Methods Using Deformable Contours on Smooth Boundaries", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> [[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,<ref>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.</ref> laser dot detection,<ref>C. Rekeczky and G. Timar "Multiple Laser Dot Detection and Localization within an Attention Driven Sensor Fusion Framework", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> metal inspection for detecting manufacturing defects,<ref>Z. Szlavikt, R. Tetzlaff, A. Blug and H. Hoefler, "Visual Inspection of Metal Objects Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and [[Seismology|seismic]] horizon picking. They have also been used to perform [[Biometrics|biometric]] functions<ref>R. Dogaru and I. Dogaru, "Biometric Authentication Based on Perceptual Resonance Between CNN Emergent Patterns and Humans", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> 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]].
=== Biology and medicine ===
Line 139 ⟶ 137:
The variety of dynamical behavior seen in CNN processors make them intriguing for communication systems. Chaotic communications using CNN processors is being researched due to their potential low power consumption, robustness and spread spectrum features. The premise behind chaotic communication is to use a chaotic signal for the carrier wave and to use chaotic phase synchronization to reconstruct the original message. CNN processors can be used on both the transmitter and receiver end to encode and decode a given message. They can also be used for data encryption and decryption, source authentication through watermarking,<ref>P. Arena, A. Basile, L. Fortuna, M. E. Yalcin, and J. Vandewalle, "Watermarking for the Authentication of Video on CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> detecting of complex patterns in spectrogram images<ref>K. Slot, P. Korbe, M. Gozdzik, and Hyongsuk Kim, "Pattern detection in spectrograms by means of Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> ([[sound processing]]), and transient spectral signals detection.
CNN processors are [[Neuromorphic engineering|neuromorphic]] processors, meaning that they emulate certain aspects of [[biological neural network]]s. The original CNN processors were based on mammalian retinas, which consist of a layer of [[Photodetector|photodetectors]] connected to several layers of locally coupled neurons.<ref name=":4">D. Balya and B. Roska, "A Handy Retina Exploration Device", Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> This makes CNN processors part of an interdisciplinary research area whose goal is to design systems that leverage knowledge and ideas from neuroscience and contribute back via real-world validation of theories. CNN processors have implemented a real-time system that replicates mammalian retinas, validating that the original CNN architecture chosen modeled the correct aspects of the biological neural networks used to perform the task in mammalian life.<ref name=":4" /> However, CNN processors are not limited to verifying biological neural networks associated with vision processing; they have been used to simulate dynamic activity seen in mammalian neural networks found in the olfactory bulb and locust [[antennal lobe]], responsible for pre-processing sensory information to detect differences in repeating patterns.<ref>C. Dominguez-Matas, R. Carmona-Galan, F. Sanchez-Fernaindez, J. Cuadri, and A. Rodriguez-Vaizquez, "A Bio-Inspired Vision Front-End Chip with Spatio-Temporal Processing and Adaptive Image Capture", Int’l Workshop on Computer Architecture for Machine Perception and Sensing, 2006.</ref><ref>A. Zarandy and C. Rekeczky, "Bi-i: a standalone ultra high speed cellular vision system", IEEE Circuits and Systems Magazine, 5(2):36-45, 2005.</ref>
CNN processors are being used to understand systems that can be modeled using simple, coupled units, such as living cells, biological networks, physiological systems, and ecosystems. The CNN architecture captures some of the dynamics often seen in nature and is simple enough to analyze and conduct experiments. They are also being used for [[stochastic]] simulation techniques, which allow scientists to explore spin problems, population dynamics, lattice-based gas models, [[percolation]], and other phenomena. Other simulation applications include heat transfer, mechanical vibrating systems, protein production,<ref>W. Samarrai, J. Yeol, I. Bajis and Y. Ryu, "System Biology Modeling of Protein Process using Deterministic Finite Automata (DFA)", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> [[Josephson junction|Josephson Junction]] problems,<ref>V. Mladenovt, and A. Slavoval, "On the Period Solutions in One Dimensional Cellular Neural Networks based on Josephson Junctions", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> seismic wave propagation,<ref>K. Huang, C. Chang, W. Hsieh, S. Hsieh, L. Wang and F. Tsai, "Cellular Neural Network For Seismic Horizon Picking", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> and geothermal structures.<ref>P. Lopez, D. Vilarino, D. Cabello, H. Sahli and M. Balsi, "CNN Based Thermal Modeling of the Soil for Anitpersonnel Mine Detection", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> Instances of 3d CNN have been used to prove certain emergent phenomena in complex systems, establishing a link between art, dynamical systems and [[Very Large Scale Integration|VLSI]] technology.<ref>H. Ip, E. Drakakis, and A. Bharath, "Towards Analog VLSI Arrays for Nonseparable 3D Spatiotemporal Filtering", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>P.Arena, L. Fortuna, M. Frasca, L. Patane, and M. Pollino, "An Autonomous Mini-Hexapod Robot Controller through a CNN Based VLSI Chip", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>[[Andrew Adamatzky|A. Adamatzky]], P. Arena, A. Basile, R. Carmona-Galán, B. Costello, L. Fortuna, M. Frasca, and A. Rodríguez-Vázquez, "Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors", IEEE Trans. On Circuits And Systems – I, 51(5):926-938, 2004.</ref><ref>L. Chua, L. Yang, and K. R. Krieg, "Signal Processing Using Cellular Neural Networks", Journal of VLSI Signal Processing, 3:25-51, 1991.</ref>
CNN processors have been used to research a variety of mathematical concepts, such as [[Non-equilibrium statistical mechanics|non-equilibrium systems]], constructing non-linear systems of arbitrary complexity, emergent chaotic dynamics, and discovering new dynamic behavior. They are often used in researching [[systemics]], a trans-disciplinary, scientific field that studies natural systems. The goal of systemics researchers is to develop a conceptual and mathematical framework necessary to analyze, model, and understand systems, including, but not limited to, atomic, mechanical, molecular, chemical, biological, ecological, social and economic systems. Topics explored are emergence, collective behavior, local activity and its impact on global behavior, and quantifying the complexity of an approximately spatial and topologically invariant system.
==Notes==
Line 181 ⟶ 179:
*A. Paasiot and J. Poilkonent, "Programmable Diital Nested CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*M. Znggi, R. Dogaru, and L. Chua, "Physical Modeling of RTD-Based CNN Cells", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
*Z. Voroshazit, Z. Nagyt, A. Kiss and P. Szolgay, "An Embedded CNN-UM Global Analogic Programming Unit Implementation on FPGA", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*A. Loncar, R. Kunz and R. Tetzaff, "SCNN 2000 - Part I: Basic Structures and Features of the Simulation System for Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
Line 192 ⟶ 188:
*L. Chua, S. Yoon and R. Dogaru, "A Nonlinear Dynamics Perspective of Wolfram’s New Kind of Science. Part I: Threshold of Complexity," Int’l Journal of Bifurcation and Chaos, 12(12):2655-2766, 2002.
*P. Ecimovic and J. Wu, "Delay Driven Contrast Enhancement using a Cellular Neural Network with State Dependent Delay", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*H. Aomori, T. Otaket, N. Takahashi, and M. Tanaka, "A Spatial Domain Sigma Delta Modulator Using Discrete Time Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*H. Kim, H. Son. J. Lee, I. Kim and I. Kim, "An Analog Viterbi Decoder for PRML using Analog Parallel Processing Circuits of the CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
Line 198 ⟶ 193:
*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.
*P. Korbelt and K. Sloti, "Modeling of Elastic Inter-node Bounds in Cellular Neural Network-based Implementation of the Deformable Grid Paradigm", 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.
▲*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.
*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.
*M. Bucolo, L. Fortuna, M. Frasca, M. La Rosa, D. Shannahoff-Khalsa, "A CNN Based System to Blind Sources Separation of MEG Signals", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
Line 220 ⟶ 211:
*R. Chen and J. Lai, "Data Encryption Using Non-uniform 2-D Von Neumann Cellular Automata", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*A. Chernihovskyi, C. Elger, and K. Lehnertz, "Effect of in Inhibitory Diffusive Coupling on Frequency-Selectivity of Excitable Media Simulated With Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*P. Arena, M. Bediat, L. Fortuna, D. Lombardo, L. Patane, and M. Velardet, "Spatio-temporal Patterns in CNNs for Classification: the Winnerless Competition Principle", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*V. Perez-Munuzuri, A. P. Munuzuri, M. Gomez-Gesteria, V. Perez-Villar, L. Pivka, and L. Chua, "Nonlinear Waves, Patters, and Spatio-Temporal Chaos in Cellular Neural Networks," Phil. Trans. R. Soc. Lond. A, (353): 101-113, 1995.
*M. Ercsey-Ravasz, T. Roska and Z. Neda, "Random Number Generator and Monte Carlo type Simulations on the CMM-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*P. Szolgay, T. Hidvegi, Z. Szolgay and P. Kozma, "A Comparison of the Different CNN Implementations in Solving the Problem of Spatiotemporal Dynamics in Mechanical Systems ", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
*R. Brown and L. Chua, "Chaos or Turbulence", Int’l Journal of Bifurcation and Chaos, 2(4):1005-1009, 1992.
| |||