Results 1 to 10 of about 217,698 (308)
Blind Deblurring Based on Sigmoid Function [PDF]
Sensors, 2021 Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm
Shuhan Sun +3 more
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Some applications of Sigmoid functions [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2021 In numerical analysis, the process of fitting a function via given data is called interpolation. Interpolation has many applications in engineering and science.
M. A. Jafari, A. Aminataei
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Temporal link prediction via adjusted sigmoid function and 2-simplex structure. [PDF]
Sci Rep, 2022 Zhang R, Wang Q, Yang Q, Wei W.
europepmc +3 more sources
The Proposition of Three Approaching Ways to Implement Tan-sigmoid Activation Function in FPGA [PDF]
Engineering and Technology Journal, 2022 Hyperbolic tangents and Sigmoid are commonly used for Artificial Neural Networks as activation functions. The complex equation of the activation function is one of the most difficult to be implemented in hardware because containing division and ...
Manal Ali, Bassam Abed
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Micromachines, 2021 Computing-In-Memory (CIM), based on non-von Neumann architecture, has lately received significant attention due to its lower overhead in delay and higher energy efficiency in convolutional and fully-connected neural network computing.
Siqiu Xu +5 more
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BP Neural Network Fuse with Morphology Edge Detection Method
Journal of Harbin University of Science and Technology, 2021 In order to obtain better image edge information, an edge detection algorithm combining BP (Back Propagation) neural network and morphology is proposed.
YUE Xin-hua, DENG Cai-xia, ZHANG Zhao-ru
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Handling Vanishing Gradient Problem Using Artificial Derivative
IEEE Access, 2021 Sigmoid function and ReLU are commonly used activation functions in neural networks (NN). However, sigmoid function is vulnerable to the vanishing gradient problem, while ReLU has a special vanishing gradient problem that is called dying ReLU problem ...
Zheng Hu, Jiaojiao Zhang, Yun Ge
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Jurnal Serambi Engineering, 2022 Neural network Backpropagation is a good method to use to determine RGB color (Red, Green, Blue) because it can give high accuracy values. Neural network backpropagation there are several activation functions that can be used.
Ikhwan Pamungkas +2 more
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Designing novel LDO voltage regulator implementation on FPGA using neural network [PDF]
Journal of Applied Research on Industrial Engineering, 2021 This paper describes a new technique for implementing an Artificial Neural Network (ANN) using Field Programmable Gate Array (FPGA). The goal is design the Low Drop Output (LDO) voltage-regulator circuit with the desired features depending on the ...
Mahdieh Jahangiri +2 more
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An effective vehicle road scene recognition based on improved deep learning
Measurement: Sensors, 2023 In order to improve the recognition for autonomous vehicles with respect to road scenes, a new activation function called ReLU sigmoid is proposed based on ReLU and the sigmoid model, which resolves the issue of neuron necrosis in the ReLU model. Through
Qiaojun Li, Wei Yang
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