A drowsiness detection method using distance and gradient vectors

2015 ◽  
Author(s):  
Sorn Sooksatra ◽  
Toshiaki Kondo ◽  
Pished Bunnun
Keyword(s):  
Detection Method ◽  
Drowsiness Detection

scholarly journals Real Time Driver Drowsiness Detection System using OpenCV

2021 ◽  
Vol 9 (VI) ◽  
pp. 3254-3260
Author(s):  
Ritish H
Keyword(s):  
Detection Method ◽  
Detection System ◽  
The Body ◽  
Facial Region ◽  
Drowsiness Detection ◽  
Region Detection ◽  
Video Clips ◽  
Driver Drowsiness ◽  
Key Points ◽  
State Of Exhaustion

Most knowledge is transmitted by the eyes, an essential part of the body. When an operator is in a state of exhaustion, facial expressions, e.g., blinking and yawning rate, vary from those in normal condition. In this venture, we are proposing a system named Driver-Drowsiness Detection System, which monitors the exhaustion state of the drivers, such as yawning, and eye closing length, using video clips, without equipping their bodies with sensors. We are using face-tracking algorithm to improve tracking reliability due to the limitations of previous algorithms. We have used facial region detection method based on 68 key points. Then we use these areas of the head to determine the condition of the passengers. Through integrating the eyes and mouth, Driver-Drowsiness Detection System can use an exhaustion alarm to alert the driver.

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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