Convolutional Neural Networks (CNN) have become a research hotspot in motor imagery brain-computer interfaces, but their limited receptive field may be deficiencies in handling long-distance dependent ...

However, the application of this information in 2D-to-3D pose estimation remains underdeveloped. To address this issue, we propose the TMGCTE (Two-step Mixed Graph Convolution Transformer Encoder) ...

All the latest science news on convolutional neural networks from Phys.org. Find the latest news, advancements, and breakthroughs.

This repository contains the implementation of our paper: "DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding", serving as a benchmark codebase for EEG decoding models. We implemented ...

We implement the neuromorphic radar system through a printed-circuit board (PCB) prototype and carry out simulations for the IC version. Our experiments verify NeuroRadar’s ability to empower resource ...

Convolutional encoders As previously discussed, an encoder at the transmitter end adds redundancy to the information being sent. Figure 2 shows an implementation with a code rate of ½ and K=3. The ...

A convolutional encoder embedded the input video clip into the hidden states. It was followed by the latent ODE, ODESolver and convolutional decoders to generated the target prediction.

Artificial intelligence is accelerating material discovery and design by automating analysis, guiding experiments, and enabling predictive modeling across spectroscopy, microscopy, and synthesis.

The field of voice separation has made revolutionary progress in addressing the challenging 'cocktail party problem' with the ...

A research team has developed a deep learning–driven computed tomography (CT) imaging pipeline that enables precise, ...