Gui-Bin Bian

TL;DR: This paper tends to review the current artifact removal of various contaminations in encephalogram recordings and discusses the characteristics of EEG data and the types of different artifacts.

TL;DR: This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations and shows that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources.

TL;DR: The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal and a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch block (LBBB) and Atrial Premature Contraction (APC).

TL;DR: An energy-aware green adversary model that runs on real-time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query, thus, facilitating energy consumption minimization.

TL;DR: A novel complementary attention network is designed by connecting the novel channel-wise attention subnetwork with spatial attention sub network sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features.