Results 211 to 220 of about 54,400 (268)
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2023
The field of artificial intelligence (AI) is very promising with the emergence of machine learning and deep learning algorithms. The rise of convolutional neural networks (CNN) is very propitious in deep learning as it is more accurate and powerful than previously known soft computational models like artificial neural networks (ANN) and recurrent ...
Mohan Kumar Dehury +2 more
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The field of artificial intelligence (AI) is very promising with the emergence of machine learning and deep learning algorithms. The rise of convolutional neural networks (CNN) is very propitious in deep learning as it is more accurate and powerful than previously known soft computational models like artificial neural networks (ANN) and recurrent ...
Mohan Kumar Dehury +2 more
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Teaching CNN and learning by using CNN
Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, 2003In this communication we remark our experience in teaching CNN technologies at the Universita degli Studi di Catania in the course of Adaptive Systems. The main result regards the possibility of using the CNN subject to introduce further topics in circuits and dynamical systems. The students reached high level skills in the related field. Moreover they
BUCOLO, MAIDE ANGELA RITA +3 more
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IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1993
A concise tutorial description of the cellular neural network (CNN) paradigm is given, along with a precise taxonomy. The CNN is defined, and the canonical equations are described. The importance of many independent input signal arrays, adaptive templates, and the multilayer capability is emphasized and motivated by examples.
Chua, Leon O., Roska, Tamás
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A concise tutorial description of the cellular neural network (CNN) paradigm is given, along with a precise taxonomy. The CNN is defined, and the canonical equations are described. The importance of many independent input signal arrays, adaptive templates, and the multilayer capability is emphasized and motivated by examples.
Chua, Leon O., Roska, Tamás
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International Journal of Bifurcation and Chaos, 2003
A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology.
Itoh, Makoto, Chua, Leon O.
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A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology.
Itoh, Makoto, Chua, Leon O.
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International Journal of Bifurcation and Chaos, 2004
In this paper, we propose a multipurpose hysteresis CNN (cellular neural network) made of first-order cells with hysteresis switches. The hysteresis CNN has applications not only in image processing, but also in pattern formation, nonlinear wave propagation and associative and dynamic memories, because each hysteresis CNN cell has two operating modes,
Itoh, Makoto, Chua, Leon O.
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In this paper, we propose a multipurpose hysteresis CNN (cellular neural network) made of first-order cells with hysteresis switches. The hysteresis CNN has applications not only in image processing, but also in pattern formation, nonlinear wave propagation and associative and dynamic memories, because each hysteresis CNN cell has two operating modes,
Itoh, Makoto, Chua, Leon O.
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International Journal of Embedded and Real-Time Communication Systems, 2021
The paper is dedicated to distributed convolutional neural networks on a resource constrained devices cluster. The authors focus on requirements that meet the users' needs. Based on this, architecture of the system is proposed. Two use cases of CNN computations on a ROCK-CNN cluster are mentioned, and algorithms for organizing distributed convolutional
Rezeda Khaydarova +5 more
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The paper is dedicated to distributed convolutional neural networks on a resource constrained devices cluster. The authors focus on requirements that meet the users' needs. Based on this, architecture of the system is proposed. Two use cases of CNN computations on a ROCK-CNN cluster are mentioned, and algorithms for organizing distributed convolutional
Rezeda Khaydarova +5 more
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International Journal of Bifurcation and Chaos, 1999
A cellular neural/nonlinear network (CNN) [Chua, 1998] is a biologically inspired system where computation emerges from a collection of simple nonlinear locally coupled cells. This paper reviews our recent research results beginning from the standard uncoupled CNN cell which can realize only linearly separable local Boolean functions, to a generalized
Dogaru, Radu, Chua, Leon O.
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A cellular neural/nonlinear network (CNN) [Chua, 1998] is a biologically inspired system where computation emerges from a collection of simple nonlinear locally coupled cells. This paper reviews our recent research results beginning from the standard uncoupled CNN cell which can realize only linearly separable local Boolean functions, to a generalized
Dogaru, Radu, Chua, Leon O.
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International Journal of Software Science and Computational Intelligence, 2022
Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance.
Zhiwei Zhan +6 more
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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance.
Zhiwei Zhan +6 more
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G-CNN and F-CNN: Two CNN based architectures for face recognition
2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), 2017In the recent past, deployment of Convolutional Neural Networks (CNN) has led to prodigious success in many pattern recognition tasks. This is mainly due to the very nature of CNN, that is its ability to work in a similar manner to that of the visual system of the human brain.
A Vinay +7 more
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Nonlinear CNN: improving CNNs with quadratic convolutions
Neural Computing and Applications, 2019In this work, instead of designing deeper convolutional neural networks, we investigate the relationship between the nonlinearity of convolution layer and the performance of the network. We modify the normal convolution layer by inserting quadratic convolution units which can map linear features to a higher-dimensional space in a single layer so as to ...
Yiyang Jiang +4 more
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