Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric

2007 ◽  
Vol 23 (4) ◽  
pp. 676-692 ◽  
Author(s):  
Nabhendra Bisnik ◽  
Alhussein A. Abouzeid ◽  
Volkan Isler
Keyword(s):  
Mobile Sensors ◽  
Quality Metric ◽  
Event Capture ◽  
Stochastic Event

CHASE: Charging and Scheduling Scheme for Stochastic Event Capture in Wireless Rechargeable Sensor Networks

2020 ◽  
Vol 19 (1) ◽  
pp. 44-59 ◽  
Author(s):  
Haipeng Dai ◽  
Qiufang Ma ◽  
Xiaobing Wu ◽  
Guihai Chen ◽  
David K. Y. Yau ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Scheduling Scheme ◽  
Wireless Rechargeable Sensor Networks ◽  
Event Capture ◽  
Stochastic Event

Practical scheduling for stochastic event capture in energy harvesting sensor networks

2015 ◽  
Vol 18 (1/2) ◽  
pp. 85 ◽  
Author(s):  
Haipeng Dai ◽  
Xiaobing Wu ◽  
Lijie Xu ◽  
Fan Wu ◽  
Shibo He ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Event Capture ◽  
Stochastic Event

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric

Objective Video Quality Method Based on Mutual Information and Human Visual System

2012 ◽  
Vol 58 (2) ◽  
pp. 147-152
Author(s):  
Michal Mardiak ◽  
Jaroslav Polec
Keyword(s):  
Mutual Information ◽  
Visual System ◽  
Human Visual System ◽  
Quality Evaluation ◽  
Video Quality ◽  
Evaluation Criteria ◽  
Test Sequence ◽  
Quality Metric ◽  
Two Stages ◽  
Video Quality Metric

Objective Video Quality Method Based on Mutual Information and Human Visual SystemIn this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.

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