.png){kind=icon}
返回
Efficient GPU implementation of the multivariate empirical mode decomposition algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.jocs.2023.102180.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An efficient GPU implementation of the Multivariate Empirical Mode Decomposition (MEMD) method is presented for speeding up the process of decomposing non-stationary multi-channel bioelectric signals into different oscillation modes. Each step of the MEMD algorithm is designed with performance in mind and implemented to remove all unnecessary overheads caused by CPU-GPU communication, data transfer operations and synchronisation. The implementation is validated with synthetic and real EEG signals of different lengths and channels (up to 128 channels) on different GPU cards, and compared to existing serial MEMD implementations. The final implementation achieved between 180x-430x speedup compared to MATLAB and a 10x improvement over the only known existing GPU implementation. The average decomposition error of our implementation is below 1.2 %. Our GPU program is the fastest known GPU implementation of the MEMD algorithm that reduces execution time from hours to seconds and as such makes it possible to perform MEMD time-frequency analysis of highdensity EEG (MEG) or similar multi-channel signals in a fraction of time and opens the road towards its practical applicability.
Keyword:
Multivariate empirical mode decomposition
GPU
CUDA
EEG
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
Immobilizing Molecular Metal Dithiolene–Diamine Complexes on 2D Metal–Organic Frameworks for Electrocatalytic H2 Production将分子金属二硫烯-二胺配合物固定在2D金属有机框架上，用于电催化H2 生产
.png){kind=icon}
PREAI
分享收藏Purchasing UK public sector property and construction professional services: competition v qualityOAAI
OAAI分享收藏
Fast diffusion in a room temperature ionic liquid confined in mesoporous carbon限制在介孔碳中的室温离子液体中的快速扩散
PREAI
分享收藏
查看更多内容