Algorithmic performance studies on graphics processing units
Journal of Parallel and Distributed Computing
HHT-based time-frequency analysis method for biomedical signal applications
CISST '11 Proceedings of the 5th WSEAS international conference on Circuits, systems, signal and telecommunications
Towards enabling Cyberinfrastructure as a Service in Clouds
Computers and Electrical Engineering
Massively parallel Modelling & Simulation of large crowd with GPGPU
The Journal of Supercomputing
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
The Journal of Supercomputing
Towards energy-efficient parallel analysis of neural signals
Cluster Computing
An expansion-aided synchronous conservative time management algorithm on GPU
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
A GPU-based discrete event simulation kernel
Simulation
Parameter optimization of PEMFC model with improved multi-strategy adaptive differential evolution
Engineering Applications of Artificial Intelligence
Repairing the crossover rate in adaptive differential evolution
Applied Soft Computing
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Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance bymore than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation ofEEMDbases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/ pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R2 by EEMD is significantly higher than that by EMD(p t-test) and the prediction probability Pk by EEMD is also slighter higher than that by EMD (p t-test).