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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 ([[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. 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. 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, [[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, and geothermal structures. Instances of 3d CNN have been used to prove known complex shapes are 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>
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*T. Roska, "Cellular Wave Computers and CNN Technology – a SoC architecture with xK Processors and Sensor Arrays", Int’l Conference on Computer Aided Design Accepted Paper, 2005.
*K. Karahaliloglu, P. Gans, N. Schemm, and S. Balkir, "Optical sensor integrated CNN for Real-time Computational Applications", IEEE Int’l Symposium on Circuits and Systems, pp. 21–24, 2006.
*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.
*T. Roska, D. Balya, A. Lazar, K. Karacs, R. Wagner and M. Szuhaj, "System Aspects of a Bionic Eyeglass", IEEE Int’l Symposium on Circuits and Systems, 2006.
*K. Karacst and T. Roskatt, "Route Number Recognition of Public Transport Vehicles via the Bionic Eyeglass", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
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