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Li Y, Xie L, Xiao P, Zheng C, Hong Q. Drift speed adaptive memristor model. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08401-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Kang Q, Fan Q, Zurada JM, Huang T. A pruning algorithm with relaxed conditions for high-order neural networks based on smoothing group L1/2 regularization and adaptive momentum. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yang J. Sports Video Athlete Detection Based on Associative Memory Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6986831. [PMID: 35211167 PMCID: PMC8863475 DOI: 10.1155/2022/6986831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/31/2022]
Abstract
Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes' detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium information. Combined with the images of nonathletes, a training set is constructed to train the NN classifier. For the input image frames, image pyramids of different scales are constructed by subsampling and the positions of several candidate athletes are detected by NN. The center of gravity of candidate athletes is calculated, a representative candidate athlete is obtained, and then, the final athlete position through a local search process is determined. Experiments show that the system can accurately detect the motion shape of moving targets, can process images in real time, and has good real-time performance.
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Affiliation(s)
- Jingwei Yang
- School of Physical Education, Xinyang Normal University, Xinyang 464000, China
- School of Physical Education, Central China Normal University, Wuhan 430079, China
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Xu C, Wang C, Sun Y, Hong Q, Deng Q, Chen H. Memristor-based neural network circuit with weighted sum simultaneous perturbation training and its applications. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.072] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Gerasimova SA, Belov AI, Korolev DS, Guseinov DV, Lebedeva AV, Koryazhkina MN, Mikhaylov AN, Kazantsev VB, Pisarchik AN. Stochastic Memristive Interface for Neural Signal Processing. SENSORS 2021; 21:s21165587. [PMID: 34451027 PMCID: PMC8402302 DOI: 10.3390/s21165587] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/09/2021] [Accepted: 08/16/2021] [Indexed: 11/16/2022]
Abstract
We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.
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Affiliation(s)
- Svetlana A. Gerasimova
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Alexey I. Belov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Dmitry S. Korolev
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Davud V. Guseinov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Albina V. Lebedeva
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
| | - Maria N. Koryazhkina
- Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
| | - Alexey N. Mikhaylov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Victor B. Kazantsev
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
- Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 420500 Innopolis, Russia
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Alexander N. Pisarchik
- Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
- Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 420500 Innopolis, Russia
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
- Correspondence:
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Dong Z, Zhang X, Wang X. State estimation for discrete-time high-order neural networks with time-varying delays. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Yao W, Wang C, Cao J, Sun Y, Zhou C. Hybrid multisynchronization of coupled multistable memristive neural networks with time delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.014] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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