Cellular neural network: Difference between revisions

Revision as of 07:35, 27 December 2020

Revision as of 07:39, 27 December 2020

Line 117:

CNN researchers have diverse interests, ranging from physical, engineering, theoretical, mathematical, computational, and philosophical applications.

=== Image processing ===

CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors needed 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, contrast enhancement, [[deconvolution]], image compression, motion estimation, image encoding, image decoding, image segmentation, 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]], axis of symmetry detection, 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]], mine-detection using [[infrared]] imagery, [[calorimeter]] cluster peak for high energy physics, 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]], vein feature extraction, face tracking, and generating visual stimuli via emergent patterns to gauge perceptual [[Resonance|resonances]]. CNN processors have been used for medical and biological research in performing automated nucleated cell counting for detecting [[hyperplasia]], segment images into anatomically and [[Pathology|pathologically]] meaningful regions, measure and quantify cardiac function, measure the timing of neurons, and detect brain abnormalities that would lead to seizures. One potential future application of CNN microprocessors is to combine them with DNA microarrays to allow for a near-real time DNA analysis of hundreds of thousands of different DNA sequences. Currently, the major bottleneck of DNA microarray analysis is the amount of time needed to process data in the form of images, and using a CNN microprocessor, researchers have reduced the amount of time needed to perform this calculation to 7ms.

CNN processors have also been used to generate and analyze patterns and textures. One motivation was to use CNN processors to understand pattern generation in natural systems. They were used to generate [[Turing pattern]]s in order to understand the situations in which they form, the different types of patterns which can emerge, and the presence of defects or asymmetries.<ref name=":0" /> Also, CNN processors were used to approximate pattern generation systems that create stationary fronts, [[spatio-temporal pattern]]s [[Oscillation|oscillating]] in time, [[hysteresis]], memory, and heterogeneity. Furthermore, pattern generation was used to aid high-performance image generation and compression via real-time generation of [[stochastic]] and coarse-grained biological patterns, texture boundary detection, and pattern and [[texture recognition]] and classification.

Line 150 ⟶ 151:

*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.

*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.

*C. Wu and Y. Wu, "The Design of CMOS Non-Self-Feedback Ratio Memory Cellular Nonlinear Network without Elapsed Operation for Pattern Learning and Recognition", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.

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

Line 170:

*G. Pazienza, E. Gomez-Ramirezt and X. Vilasis-Cardona, "Genetic Programming for the CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.

*J. Nossek, G. Seiler, T. Roska, and L. Chua, "Cellular Neural Networks: Theory and Circuit Design," Int’l Journal of Circuit Theory and Applications, 20: 533-553, 1998.

*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.

*A. Zarandry, S. Espejo, P. Foldesy, L. Kek, G. Linan, C. Rekeczky, A. Rodriguez-Vazquez, T. Roska, I. Szatmari, T. Sziranyi and P. Szolgay, "CNN Technology in Action ", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.

*L. Chua, L. Yang, and K. R. Krieg, "Signal Processing Using Cellular Neural Networks", Journal of VLSI Signal Processing, 3:25-51, 1991.

Line 180 ⟶ 179:

*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.

*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.

*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.

*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.

*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.