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Dauwels, Justin
หน่วยงาน Nanyang Technological University, Singapore
# หัวเรื่อง
ปี พ.ศ. 2557
1 Real-time feedback system for monitoring and facilitating discussions
2 Improved compressed sensing radar by fusion with matched filtering
3 Transport of intensity phase imaging by intensity spectrum fitting of exponentially spaced defocus planes
4 A computational study of stimulus driven epileptic seizure abatement
ปี พ.ศ. 2556
5 Sparse ACEKF for phase reconstruction
6 A multimodal approach to analysis of steady state visually evoked potentials
7 Real-time comprehensive sociometrics for two-person dialogs
8 Near-lossless multichannel EEG compression based on matrix and tensor decompositions
ปี พ.ศ. 2555
9 Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands
10 Copula Gaussian graphical models with hidden variables
11 Efficient Gaussian inference algorithms for phase imaging
12 Tensor factorization for missing data imputation in medical questionnaires
13 A low-power, reconfigurable smart sensor system for EEG acquisition and classification
14 Online map-matching based on Hidden Markov model for real-time traffic sensing applications
15 Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network
16 Modeling spatially-dependent extreme events with Markov random field priors
17 Multi-channel EEG compression based on 3D decompositions
18 Multichannel EEG compression : wavelet-based image and volumetric coding approach
19 Clustered subsampling for clinically informed diagnostic brain mapping
20 Copula Gaussian multiscale graphical models with application to geophysical modeling
21 Modeling extreme events in spatial domain by copula graphical models
ปี พ.ศ. 2554
22 Quantifying statistical interdependence, part III : N > 2 point processes
23 A two-dimensional approach for lossless EEG compression
24 Slowing and loss of complexity in Alzheimer's EEG : two sides of the same coin?
25 Multi-channel EEG compression based on matrix and tensor decompositions