This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get sp...This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.展开更多
Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with on...Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with only intensity information, highlight detection and removal becomes a difficult issue. To solve this problem, the single grayscale image highlight detection and removal method based on Markov random field is presented. Each reflection component modeling is estimated by geometric relation of surface normal in diffuse and specular reflection component in the framework of Markov random field. Their maximum a posteriori estimation is calculated under Bayesian formula and highlight area is detected. Finally, image inpainting method based on the BSCB model removes highlights. Experiment reveals that this method can effectively detect grayscale image specular reflection area, improve highlight areas the repair rate.展开更多
In order to investigate the benefit of multiple-input multiple-output(MIMO) technique applying to the high altitude platform(HAP), a 2×2 MIMO statistical model, which can accurately describe the channel between H...In order to investigate the benefit of multiple-input multiple-output(MIMO) technique applying to the high altitude platform(HAP), a 2×2 MIMO statistical model, which can accurately describe the channel between HAP and high-speed train, is presented. And dual polarization diversity is particularly considered. Based on first-order three-state Markov chain, the single-input single-output(SISO) channel, a subset of the MIMO channel is first established. The ray tracing approach applied to the digital relief model(DRM) which covers the railway between Xi'an and Zhengzhou is used to obtain the state probability vector and matrix of the state transition probability. The proposed model considers both Doppler shift and temporal correlation, and the polarization correlation and spatial correlation statistical properties of large-scale fading and smallscale fading are analyzed. Moreover, useful numerical results on the MIMO HAP channel outage capacity are provided based on which, significant capacity gains with respect to the conventional SISO case are illustrated. Such statistical channel model can be applied to the future wireless communication system between HAP and high-speed train.展开更多
基金sponsored by National Basic Research Program of China (973 Program, No. 2013CB329003)National Natural Science Foundation of China (No. 91438205)+1 种基金China Postdoctoral Science Foundation (No. 2011M500664)Open Research fund Program of Key Lab. for Spacecraft TT&C and Communication, Ministry of Education, China (No.CTTC-FX201305)
文摘This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.
基金This work was financially supported by National Natural Science Foundation of China (61440025), the research project of science and technology of Heilongjiang provincial education department (12541119).
文摘Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with only intensity information, highlight detection and removal becomes a difficult issue. To solve this problem, the single grayscale image highlight detection and removal method based on Markov random field is presented. Each reflection component modeling is estimated by geometric relation of surface normal in diffuse and specular reflection component in the framework of Markov random field. Their maximum a posteriori estimation is calculated under Bayesian formula and highlight area is detected. Finally, image inpainting method based on the BSCB model removes highlights. Experiment reveals that this method can effectively detect grayscale image specular reflection area, improve highlight areas the repair rate.
基金sponsored by National Natural Science Foundation of China (No.91538104,No.91438205)China Postdoctoral Science Foundation (No.2011M500664)
文摘In order to investigate the benefit of multiple-input multiple-output(MIMO) technique applying to the high altitude platform(HAP), a 2×2 MIMO statistical model, which can accurately describe the channel between HAP and high-speed train, is presented. And dual polarization diversity is particularly considered. Based on first-order three-state Markov chain, the single-input single-output(SISO) channel, a subset of the MIMO channel is first established. The ray tracing approach applied to the digital relief model(DRM) which covers the railway between Xi'an and Zhengzhou is used to obtain the state probability vector and matrix of the state transition probability. The proposed model considers both Doppler shift and temporal correlation, and the polarization correlation and spatial correlation statistical properties of large-scale fading and smallscale fading are analyzed. Moreover, useful numerical results on the MIMO HAP channel outage capacity are provided based on which, significant capacity gains with respect to the conventional SISO case are illustrated. Such statistical channel model can be applied to the future wireless communication system between HAP and high-speed train.
