To address this gap, we designed a novel MI paradigm inspired by daily life, where subjects imagined variations in force intensity during dynamic unilateral upper-limb movements. In a single trial, ...
ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...
ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...
Abstract: This study introduces a CNN-based Autoencoder implementation on FPGA using High-Level Synthesis (HLS), focusing on optimizing convolutional computing for reduced latency and enhanced ...
Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements in scaling generator models have been ...
With modern cars, trucks, and SUVs coming equipped with countless new life-saving technological safety features and even a wave of new data-driven safety functions, it can be difficult to figure out ...
Abstract: With the ongoing development of Indoor Location-Based Services, the location information of users in indoor environments has been a challenging issue in recent years. Due to the widespread ...
This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.
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