3 papers
eess.SP2025
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network
Sainan xiao, Wangdong Yang, Buwen Cao +1
Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), which significantly degrade their…
cs.DC2024
cuFastTuckerPlus: A Stochastic Parallel Sparse FastTucker Decomposition Using GPU Tensor Cores
Zixuan Li, Mingxing Duan, Huizhang Luo +3
Sparse tensors are prevalent in real-world applications, often characterized by their large-scale, high-order, and high-dimensional nature. Directly handling raw tensors is impract…
cs.DC2022
cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform
Zixuan Li
Currently, the size of scientific data is growing at an unprecedented rate. Data in the form of tensors exhibit high-order, high-dimensional, and highly sparse features. Although t…