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Convolutional Neural Network for Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

Thesis
Convolutional Neural Network for Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface
  • Thanawin, Trakoolwilaiwan
  • 2017
  • Thanawin, Trakoolwilaiwan. (2017). Convolutional Neural Network for Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface. doi: 10.22677/thesis.2377910
  • DGIST
  • View : 950
  • Download : 293
Article
Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: Three-class classification of rest, right-, and left-hand motor execution
  • Trakoolwilaiwan, Thanawin ; 
  • Behboodi, Bahareh ; 
  • Lee, Jae Seok ; 
  • Kim, Kyung Soo ; 
  • Choi, Ji Woong
  • 2018-01
  • Trakoolwilaiwan, Thanawin. (2018-01). Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: Three-class classification of rest, right-, and left-hand motor execution. Neurophotonics, 5(1). doi: 10.1117/1.NPh.5.1.011008
  • SPIE
  • View : 693
  • Download : 0
Article
Human-vehicle classification scheme using doppler spectrum distribution based on 2D range-doppler FMCW radar
  • 2018-12
  • Hyun, Eugin. (2018-12). Human-vehicle classification scheme using doppler spectrum distribution based on 2D range-doppler FMCW radar. Journal of Intelligent and Fuzzy Systems, 35(6), 6035–6045. doi: 10.3233/JIFS-169844
  • IOS Press
  • View : 889
  • Download : 0
Article
Locally Important Pattern Clustering Code for Pedestrian Classification
  • 2015-08
  • Lim, Young Chul. (2015-08). Locally Important Pattern Clustering Code for Pedestrian Classification. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E98A(8), 1875–1878. doi: 10.1587/transfun.E98.A.1875
  • Oxford University Press
  • View : 643
  • Download : 0

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

Article
Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards
  • 2020-12
  • Lyu, Hong-Kun. (2020-12). Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards. doi: 10.3390/agronomy10121926
  • MDPI AG
  • View : 760
  • Download : 445
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