Results 41 to 50 of about 204,781 (309)
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction [PDF]
, 2023 Spiking neural networks (SNNs) can be used in low-power and embedded systems e.g. neuromorphic chips due to their event-based nature. They preserve conventional artificial neural networks (ANNs) properties with lower computation and memory costs.
Fatemeh Sadat Tabatabaei Far +14 more
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Color Filter Array Demosaicking Using Densely Connected Residual Network
IEEE Access, 2019 Deep convolutional neural networks have been used extensively in recent image processing research, exhibiting drastically improved performance. In this study, we apply convolutional neural networks to color filter array demosaicking, which plays an ...
Bumjun Park, Jechang Jeong
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Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
IEEE Access, 2021 A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao +4 more
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Quantum convolutional neural networks [PDF]
Nature Physics, 2019 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor ...
Iris Cong +2 more
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Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Enhanced image classification with a fast-learning shallow convolutional neural network [PDF]
, 2015 We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and the absence of iteratively-tuned parameters, the method has strong potential for ...
Vladusich, T. +3 more
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Data-Driven Bearing Fault Diagnosis for Induction Motor
Journal of Electrical and Computer Engineering, 2023 Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs.
Aqib Raqeeb +5 more
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Journal of Education, Health and Sport, 2021 Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka +4 more
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Factorial Convolution Neural Networks
CoRR, 2021 In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection. However, it experiences challenges of contaminated deep features when concatenating elements with different properties.
Jaemo Sung, Eun-Sung Jung
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Noise-Enhanced Associative Memories [PDF]
, 2013 Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms allow reliable learning and recall of exponential numbers of patterns.
Amir Hesam Salavati +7 more
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