This paper addresses the problem of collaborative spectrum sensing using sequential detection (SD) in cognitive radios. The goal of sequential processing is to reduce the delay and amount of data needed in identifying...
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ISBN:
(纸本)9781424423026
This paper addresses the problem of collaborative spectrum sensing using sequential detection (SD) in cognitive radios. The goal of sequential processing is to reduce the delay and amount of data needed in identifying underutilized spectrum. Each secondary user (SU) employs a simple and computationally efficient autocorrelation-based detector for Orthogonal Frequency Division Multiplexing (OFDM) signals of the primary user (PU). The decision statistics from individual detectors are combined in a fusion center that may be a separate node or one of the secondary users. The statistical properties of the decision statistics are established. The performance of the scheme is studied by theory and simulations. A comparison of the SD scheme with the Neyman-Pearson Fixed Sample Size (FSS) test for the same false alarm and missed detection probabilities is also carried out.
The identification of disease modules has attracted increasing attention due to the importance in comprehending pathogenesis of complex diseases. Most of the existing methods were based on the protein-protein interact...
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With the rapid advancement of deep neural networks, there has been a significant improvement in the performance of music source separation methods. However, most of them primarily focus on improving their separation p...
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Community detection is of great importance to find hidden information in complex networks. For this problem, local expansion algorithms are becoming popular due to the low time complexity. However, most of them depend...
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A new in time passive localization system based on multi base-line phase comparison receivers is proposed. The new system uses short base-line to avoid the long base-line phase illegibility then uses the phase differe...
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In this work, we investigate causal learning of independent causal mechanisms from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically accessible manner that unlabeled dat...
Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regul...
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ISBN:
(纸本)9781728132945
Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra-and inter-level features combination. Specifically, a child module that takes low-resolution images as input captures larger context information;the larger context information from each layer is densely connected to the main branch of the network. MCUA makes good usage of multi-level features with richer context and performs the image-to-image prediction holistically. We introduce our MCUA scheme for cost volume calculation and test it on PSM-Net. We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets. Experimental results show that our method outperforms state-of-the-art methods by a notable margin and effectively improves the accuracy of stereo matching.
The present paper deals with a new continuous undelayed state observer based trajectory tracking approach by using only the sampled and delayed measurements where the sampling period and delayed value are variable and...
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ISBN:
(纸本)9789881563835
The present paper deals with a new continuous undelayed state observer based trajectory tracking approach by using only the sampled and delayed measurements where the sampling period and delayed value are variable and unknown. This proposed event-triggered sampling observer based control, which is derived by using the theory of a particular hybrid systems called Piecewise Continuous Systems, has a very simple structure and can be easily implemented to the networked visual servoing control systems. To show the proposed method performance, a numerical application example is introduced.
Multi-antenna blindsignal separation (BSS) provides a useful method for co-channel mixed signalprocessing. But the performance of BSS is limited by the condition number of channel matrix in the presence of noise. To...
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ISBN:
(纸本)9781479979820
Multi-antenna blindsignal separation (BSS) provides a useful method for co-channel mixed signalprocessing. But the performance of BSS is limited by the condition number of channel matrix in the presence of noise. To overcome this problem, a new BSS algorithm with the aid of lattice reduction (LR) is proposed for synchronous communication systems. Simulation results show that the proposed algorithm improves the symbol error rate (SER) performance in noise environment while keeping the same complexity level.
An optimum strategy of reference emitter placement for dual-satellite time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization based on particle swarm optimization is proposed to impro...
ISBN:
(数字)9781538682463
ISBN:
(纸本)9781538682470
An optimum strategy of reference emitter placement for dual-satellite time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization based on particle swarm optimization is proposed to improve localization accuracy. Firstly Cramer-Rao lower bound (CRLR) of dual-satellite TDOA and FDOA localization system with ephemeris error and system error is derived. Then, the optimum strategy of reference emitter placement is transformed to multidimensional constraint optimization problem by using CRLB as the criterion for performance of dual-satellite TDOA and FDOA localization system. Finally, optimum strategy of reference emitter placement is obtained by using particle swarm optimization (PSO) to solve the target function. Simulation results demonstrate that the proposed method can derive optimum strategy of reference emitter placement validly. Besides, when the optimum strategy of reference emitter placement is applied, only two reference emitters are demanded to eliminate the effect of ephemeris error and system error on localization accuracy.
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