PC-based hardware implementation of the maximum-likelihood classifier for the shuttle ice detection system

1991 ◽  
Author(s):  
Sandeep Jaggi
Keyword(s):  
Maximum Likelihood ◽  
Hardware Implementation ◽  
Detection System ◽  
Maximum Likelihood Classifier ◽  
Ice Detection

Certification and integration aspects of a primary ice detection system

2001 ◽  
Author(s):  
Darren Jackson ◽  
David Owens ◽  
Dennis Cronin ◽  
John Severson
Keyword(s):  
Detection System ◽  
Ice Detection

scholarly journals Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier

2021 ◽  
Vol 1964 (4) ◽  
pp. 042075
Author(s):  
Anisha Rebinth ◽  
S Mohan Kumar ◽  
T Kumanan ◽  
G Varaprasad
Keyword(s):  
Maximum Likelihood ◽  
Image Classification ◽  
Maximum Likelihood Classifier

scholarly journals Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation

Biosensors ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 343
Author(s):  
Chin-Teng Lin ◽  
Wei-Ling Jiang ◽  
Sheng-Fu Chen ◽  
Kuan-Chih Huang ◽  
Lun-De Liao
Keyword(s):  
Eye Movement ◽  
Experimental Validation ◽  
Hardware Implementation ◽  
Detection System ◽  
Real Life ◽  
People With Disabilities ◽  
Research Area ◽  
General Purpose ◽  
Classification Algorithm ◽  
Human Computer Interface

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

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