scholarly journals Classification of right and left hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy

2020 ◽  
Vol 22 (3) ◽  
pp. 95-104
Author(s):  
Hamid Ebrahimi ◽  
Ahmad Shalbaf ◽  
Nader Jafarnia Dabanloo ◽  
◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Motor Imagery ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Left Hand ◽  
Right And Left Hand

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

2019 ◽  
Vol 90 (9-10) ◽  
pp. 1057-1066 ◽  
Author(s):  
Zhengdong Liu ◽  
Wenxia Li ◽  
Zihan Wei
Keyword(s):  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Convolutional Network ◽  
The Sustainable Development ◽  
Softmax Classifier ◽  
Qualitative Classification ◽  
Reliable Classification

The recycling of waste textiles has become a growth point for the sustainable development of the textile and clothing industry. In addition, sorting is a key link in the follow-up recycling process. Since different fabrics are required to be processed by different technologies, manual sorting not only takes time and effort but also cannot achieve accurate and reliable classification. Based on the analysis of near infrared spectroscopy, the theory and methods of deep learning are used for the qualitative classification of waste textiles in order to complete the automatic fabric composition recognition in the sorting process. Firstly, a standard sample set is established by waveform clipping and normalization, and a Textile Recycling Net deep web suitable for near infrared spectroscopy is established. Then, a pixilated layer is used to facilitate the deep learning of features, and the multidimensional features of the spectrum are extracted by using the multi-layer convolutional and pooling layers. Finally, the softmax classifier is adopted to complete the qualitative classification. Experimental results show that the convolutional network classification method using normalized and pixelated near infrared spectroscopy can realize the automatic classification of several common textiles, such as cotton and polyester, and effectively improve the detection level and speed of fabric components.

Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface

NeuroImage ◽  
2007 ◽  
Vol 34 (4) ◽  
pp. 1416-1427 ◽  
Author(s):  
Ranganatha Sitaram ◽  
Haihong Zhang ◽  
Cuntai Guan ◽  
Manoj Thulasidas ◽  
Yoko Hoshi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Motor Imagery ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brain Computer Interface ◽  
Computer Interface

Application of Near-Infrared Spectroscopy for the Classification of Fresh Pork Quality in Cooked Ham Production

2015 ◽  
Vol 8 (12) ◽  
pp. 2383-2391 ◽  
Author(s):  
Ellen Neyrinck ◽  
Stefaan De Smet ◽  
Liesbeth Vermeulen ◽  
Danny Telleir ◽  
Stefaan Lescouhier ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Pork Quality ◽  
Cooked Ham
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