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- 저자 : ( Zesong Fei ) (검색결과 4 건)
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Terminal-based Dynamic Clustering Algorithm in Multi-Cell Cellular System
( Jiqing Ni ),( Zesong Fei ),( Chengwen Xing ),( Di Zhao ),( Jingming Kuang ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.9
A terminal-based dynamic clustering algorithm is proposed in a multi-cell scenario, where the user could select the cooperative BSs from the predetermined static base stations (BSs) set based on dynamic channel condition. First, the user transmission rate is derived based on linear precoding and per-cell feedback scheme. Then, the dynamic clustering algorithm can be implemented based on two criteria: (a) the transmission rate should meet the user requirement for quality of service (QoS); (b) the rate increment exceeds the predetermined constant threshold. By adopting random vector quantization (RVQ), the optimized number of cooperative BSs and the corresponding channel conditions are presented respectively. Numerical results are given and show that the performance of the proposed method can improve the system resources utilization effectively.
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( Yuan Zhou ),( Zesong Fei ),( Gaishi Huang ),( Ang Yang ),( Jingming Kuang ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.4
In this paper, we consider an infrastructure-vehicle-vehicle (I2V2V) based Vehicle Ad-hoc Networks (VANETs), where one base station multicasts data to d vehicular users with the assistance of r vehicular users. A Distributed Luby Transform (DLT) codes based transmission scheme is proposed over lossy VANETs to reduce transmission latency. Furthermore, focusing on the degree distribution of DLT codes, a Modified Deconvolved Soliton Distribution (MDSD) is designed to further reduce the transmission latency and improve the transmission reliability. We investigate the network behavior of the transmission scheme with MDSD, called MDLT based scheme. Closed-form expressions of the transmission latency of the proposed schemes are derived. Performance simulation results show that DLT based scheme can reduce transmission latency significantly compared with traditional Automatic Repeat Request (ARQ) and Luby Transform (LT) codes based schemes. In contrast to DLT based scheme, the MDLT based scheme can further reduce transmission latency and improve FER performance substantially, when both the source-to-relay and relay-to-sink channels are erasure channels.
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You Fu,Zesong Fei,Niwei Wang,Chengwen Xing,Lei Wan 한국산학기술학회 2013 SmartCR Vol.3 No.1
Deploying PicoStations in traditional Macro-cellular networks, especially in hot-spot areas, is a promising strategy to boost system throughput and enhance indoor coverage. However, PicoStations’ introduction brings some new problems, such as severe interference and energy consumption, which may result in performance degradation. In order to improve energy efficiency, throughput fairness and PicoStation coverage, a practical energy-efficient dense PicoStation deployment and power control algorithm is proposed. The proposed scheme maximizes both the number of active PicoStations that can achieve the required capacity and the sum cell capacity under individual capacity constraints in two steps. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional cell capacity maximum scheme in terms of user fairness and energy saving.
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Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition
( Sheeraz Arif ),( Jing Wang ),( Zesong Fei ),( Fida Hussain ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.
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