Depth Map Driven Hole Filling Algorithm Exploiting Temporal Correlation Information

2014 ◽  
Vol 60 (2) ◽  
pp. 394-404 ◽  
Author(s):  
Chao Yao ◽  
Tammam Tillo ◽  
Yao Zhao ◽  
Jimin Xiao ◽  
Huihui Bai ◽  
...  
Keyword(s):  
Depth Map ◽  
Temporal Correlation ◽  
Hole Filling ◽  
Correlation Information

scholarly journals Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jianfeng Liu ◽  
Xin-Lin Huang ◽  
Ping Wang
Keyword(s):  
Compressive Sensing ◽  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Temporal Correlation ◽  
False Alarm Probability ◽  
Spectrum Estimation ◽  
Sensing Data ◽  
Correlation Information

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

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