The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
Abstract: Operational reliability assessment (ORA), which evaluates the risk level of power systems, is hindered by accumulated computational burdens and thus cannot meet the demands of real-time ...
A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks
Abstract: Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based ...
Epilepsy detection using artificial intelligence (AI) networks has gained significant attention. However, existing methods face challenges in accuracy, computational cost, and speed. CNN excel in ...
Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in ...
1 College of Information Engineering, Xinchuang Software Industry Base, Yancheng Teachers University, Yancheng, China. 2 Yancheng Agricultural College, Yancheng, China. Convolutional auto-encoders ...
Dr. Cohen and Dr. Riofrancos are strategic co-directors of the Climate and Community Institute. In the lead-up to the November election, Donald Trump threatened to “terminate” President Biden’s ...
Thank you for the nice tutorial. I compared the gradients obtained using your implementation with PyTorch's conv2d. For some values of strides and kernel sizes, the results seem to be incorrect.
@haesleinhuepf Hi mate. I'n playing with the RL deconv on GPU in CLIJ. I noticed that the imageJ pixel data types need to be the same (32 bit works) for the source image and the kernel image (PSF), ...
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