scholarly journals Palm Personal Identification for Vehicular Security with a Mobile Device

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Chih-Yu Hsu ◽  
Pei-Shan Lee ◽  
Kuo-Kun Tseng ◽  
Yifan Li
Keyword(s):  
Mobile Phone ◽  
Image Recognition ◽  
Mobile Device ◽  
Personal Identification ◽  
Recognition Method ◽  
Biometric Technology ◽  
Identification Approach

Security certification is drawing more and more attention in recent years; the biometric technology is used in a variety of different areas of security certification. In this paper, we propose a palm image recognition method to identify an individual for vehicular application; it uses palm image as a key for detecting the car owner. We used mobile phone cameras to take palm images and performed a new identification approach by using feature regularization of palm contour. After identification is confirmed, the phone uses Bluetooth/WiFi to connect the car to unlock it. In our evaluation, the experiments show that our approach is effective and feasible.

scholarly journals 0013 Mobile Device Use in Bed and Relationships to Work Productivity: Impact of Anxiety

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A5-A5
Author(s):  
A Gozar ◽  
A Seixas ◽  
L Hale ◽  
C Branas ◽  
M Barrett ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Device ◽  
Productivity Loss ◽  
Work Productivity ◽  
Well Being ◽  
Mobile Phone Use ◽  
Mediating Role ◽  
Mobile Phone Usage ◽  
Device Use ◽  
Healthy Activity

Abstract Introduction Mobile phone use at night is associated with worse sleep quality. It may also be associated with daytime productivity, possibly via anxiety. Methods Data were obtained from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study, including N=1007 adults age 22–60. Mobile device use in bed was assessed as the frequency that participants reported: a device in the bedroom, use of the device in bed, texting, emails, internet browsing, calls, and/or social networking in bed, being woken up by the device in a planned (alarm) or unplanned (alert/call/message) way, and checking the phone at night. Each of these were coded as “never,” “rarely,” or “often.” Work productivity was assessed with the Well-Being Assessment of Productivity (WBA-P; scores 0–22 measure productivity loss). Regressions with WBA-P score as outcome and mobile phone variables as predictors were adjusted for age, sex, race/ethnicity, education, and income level. Post-hoc analyses included GAD7 score to examine the mediating role of anxiety. Results The presence of a device was not associated with productivity loss, but frequent use (“often”) was (B=1.26,p=0.01). Increased productivity loss was also seen in those who frequently (“often”) sent texts (B=1.20,p=0.008), browsed internet (B=1.14,p=0.01), emailed (B=2.09,p<0.0005), called (B=1.42,p=0.004), and used social media (B=1.26,p=0.004). Productivity loss was associated with being woken by a call/alert “rarely” (B=1.20,p=0.001) or “often” (B=1.72,p=0.005), but not by alarm. Checking the phone at night “rarely” (B=0.89,p=0.01) and “often” (B=1.73,p<0.0005) were also associated with productivity loss. When anxiety was entered into the model, all relationships except those with frequent emails and calls in bed became nonsignificant. Conclusion Anxiety may be the underlying cause for both increased mobile phone usage and reduced productivity. Reducing anxiety levels may indirectly aid in decreasing nighttime mobile phone use and increasing daytime productivity. Support The SHADES study was funded by R21ES022931 Dr. Grandner is supported by R01MD011600

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

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