Papers by Author: Wei Feng Sun

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Authors: Zhen Quan Qin, De Long Liu, Wei Feng Sun, Yan Hu, Huang Hui
Abstract: Energy detection is the most commonly used algorithm in spectrum sensing. For Unknown signal, the current energy detection has shortcomings on the performance of perceiving the primary users signals in AWGN channel or fading channel. The diversity technology can receive the correlation signals which contain the same information in different branches, then merge and output the signals to reduce the probability of deep fading at the receiving terminal greatly. Therefore, we introduce diversity into the energy detection to improve the ability of detection. Simulation result shows that, it can improve the cognitive ability and obtain diversity gain through reducing the impact of fading. With the increase of diversity numbers, our method obtains lower probability of false alarm detection and performs much better than the traditional energy detection.
Authors: Ming Yang Sun, Wei Feng Sun, Xi Dong Liu, Lei Xue
Abstract: Recommendation algorithms suffer the quality from the huge and sparse dataset. Memory-based collaborative filtering method has addressed the problem of sparsity by predicting unrated values. However, this method increases the computational complexity, sparsity and expensive complexity of computation are trade-off. In this paper, we propose a novel personalized filtering (PF) recommendation algorithm based on collaborative tagging, which weights the feature of tags that show latent personal interests and constructs a top-N tags set to filter out the undersized and dense dataset. The PF recommendation algorithm can track the changes of personal interests, which is an untilled field for previous studies. The results of empirical experiments show that the sparsity level of PF recommendation algorithm is much lower, and it is more computationally economic than previous algorithms.
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