The application of deep learning in 3D mesh segmentation has attracted increasing attention. In supervisedlearning based mesh segmentation, back propagation is often based on cross entropy loss. Some prior knowledge, ...
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The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a ...
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With the development of remote sensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction...
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In orthogonal frequency division multiple access (OFDMA) uplink system, estimations of time delay and carrier frequency offset (CFO) are both particularly challenging. In this paper, a coarse CFO and time delay estima...
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ISBN:
(纸本)9781424444625
In orthogonal frequency division multiple access (OFDMA) uplink system, estimations of time delay and carrier frequency offset (CFO) are both particularly challenging. In this paper, a coarse CFO and time delay estimation method is proposed for interleaved OFDMA uplink system. By exploiting the special scheme of interleaved OFDMA, the repetitive time-domain training symbol is designed, and the training sequences for different users have good autocorrelation and cross-correlation properties. Furthermore, the time delay and CFO estimation metrics are presented by making certain correlation between the received training symbol and the local sequence. Through searching peak of the proposed metrics, integral CFO of many subcarriers spaces for each user can be estimated, and then timing delay for each user can be found. Simulations illustrate the proposed estimation scheme can get good performance under multipath channel.
Though weighted voting matching is one of most successful image matching methods, each candidate correspondence receives voting score from all other candidates, which can not apparently distinguish correct matches and...
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Feature selection is an important component of many machine learning applications. In this paper, we propose a new robust feature selection method for multi-class multi-label learning. In particular, feature correlati...
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Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle...
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ISBN:
(纸本)9781509028610
Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.
In this paper, we propose an error recovery scheme to cope with the frame loss problem in large end-to-end delay scenario. Because Wyner-Ziv coding can produce deterministic output with nondeterministic inputs, we ado...
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In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access int...
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Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on comp...
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Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on compressive sensing principles, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. So the proposed algorithm can be useful in those video applications that require very low complex encoders. At the decoder, the compressed video can be efficiently reconstructed with adaptive dictionary learning. The simulation results show that the proposed algorithm outperforms the distributed compressive video sensing with non-adaptive learning local dictionary and global dictionary.
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