Model selection of the generalized von Mises distribution based on empirical mode decomposition with data analyses

Von Mises Distribution ◽

Data Analyses ◽

Mode Decomposition ◽

Von Mises ◽

Selection of Empirical Mode Decomposition Techniques for Extracting Breathing Rate From PPG

IEEE Signal Processing Letters ◽

10.1109/lsp.2019.2900923 ◽

2019 ◽

Vol 26 (4) ◽

pp. 592-596 ◽

Cited By ~ 11

Author(s):

Mohammod Abdul Motin ◽

Chandan Kumar Karmakar ◽

Marimuthu Palaniswami

Keyword(s):

Breathing Rate ◽

Decomposition Techniques ◽

Mode Decomposition ◽

Selection of singular spectrum analysis components via empirical mode decomposition for extracting information for noninvasive blood glucose estimation system

2016 IEEE 14th International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin.2016.7819319 ◽

2016 ◽

Author(s):

Pei-Ru Lin ◽

Weixi Li ◽

Tuhong Zheng ◽

Wing-Kuen Ling ◽

Chi-Kong Li

Keyword(s):

Blood Glucose ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Singular Spectrum ◽

Mode Decomposition ◽

Estimation System ◽

Glucose Estimation ◽

Selection of Intrinsic Mode Functions for Epileptic EEG Classification Using Ensemble Empirical Mode Decomposition

2019 Medical Technologies Congress (TIPTEKNO) ◽

10.1109/tiptekno.2019.8895000 ◽

2019 ◽

Author(s):

Ozlem Karabiber Cura ◽

Aydin Akan ◽

Sibel Kocaaslan Atli

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Eeg Classification ◽

Mode Decomposition ◽

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

Speech Analysis Method Based on Source-Filter Model Using Multivariate Empirical Mode Decomposition

10.1587/transfun.e99.a.1762 ◽

2016 ◽

Vol E99.A (10) ◽

pp. 1762-1773 ◽

Cited By ~ 1

Author(s):

Surasak BOONKLA ◽

Masashi UNOKI ◽

Stanislav S. MAKHANOV ◽

Chai WUTIWIWATCHAI

Keyword(s):

Speech Analysis ◽

Analysis Method ◽

Filter Model ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition

Identification of drilling stick-slip vibration conditions based on Empirical Mode Decomposition threshold denoising and Support Vector Machine

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9189647 ◽

2020 ◽

Author(s):

Ke Zhang ◽

Weihua Cao ◽

Chao Gan ◽

Wenkai Hu

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Stick Slip ◽

Mode Decomposition

ATTENTION COMPARISON OF DIFFERENT OFFICE LIGHTING SCENARIOS BY BRAINWAVE MEASUREMENTS AND EMPIRICAL MODE DECOMPOSITION

PROCEEDINGS OF CIE 2018 TOPICAL CONFERENCE ON SMART LIGHTING ◽

10.25039/x45.2018.po02 ◽

2018 ◽

Author(s):

I-Chieh Chen ◽

Chin-Hsien Wu ◽

Yi-Chun Chen

Keyword(s):

Mode Decomposition ◽

Office Lighting

Deep Learning Model Selection of Suboptimal Complexity

Автоматика и телемеханика ◽

10.31857/s000523100001252-1 ◽

2018 ◽

pp. 129-147

Author(s):

Oleg Bakhteev ◽

◽

Vadim Strijov ◽

Keyword(s):

Deep Learning ◽

Model Selection ◽

Learning Model ◽

Deep Learning Model ◽

Electromagnetic ultrasonic diagnosis based on lifting wavelet packet and set empirical mode decomposition

Advance in Modern Physics ◽

10.35534/amp.0202007c ◽

2020 ◽

Vol 2 (2) ◽

pp. 43-51

Author(s):

Cao Jiangying

Keyword(s):

Wavelet Packet ◽

Ultrasonic Diagnosis ◽

Mode Decomposition ◽

Lifting Wavelet

Energy-Efficient ECG Signal Compression Method in Wearable Body Sensor Networks by Leveraging Empirical Mode Decomposition

10.37099/mtu.dc.etdr/731 ◽

2018 ◽

Author(s):

Hui Huang

Keyword(s):

Sensor Networks ◽

Energy Efficient ◽

Body Sensor Networks ◽

Ecg Signal ◽

Signal Compression ◽

Compression Method ◽

Mode Decomposition ◽

Signal Compression Method

Model selection of extreme learning machine based on latent feature space

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.01600 ◽

2013 ◽

Vol 33 (6) ◽

pp. 1600-1603

Author(s):

Wentao MAO ◽

Zhongtang ZHAO ◽

Huanhuan HE

Keyword(s):

Model Selection ◽

Extreme Learning Machine ◽

Feature Space ◽

Learning Machine ◽