This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to va...This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.展开更多
To provide a certain level of Quality of Service (QoS) guarantees for multiuser wireless downlink video streaming transmissions, we propose a multiuser scheduling scheme for QoS guarantees. It is based on the classic ...To provide a certain level of Quality of Service (QoS) guarantees for multiuser wireless downlink video streaming transmissions, we propose a multiuser scheduling scheme for QoS guarantees. It is based on the classic Queue-Length-Based (QLB)-rate maximum scheduling algorithm and integrated with the delay constraint and the packet priority drop. We use the large deviation principle and the effective capacity theory to construct a new analysis model to find each user's queue length threshold (delay constraint) violation probability. This probability corresponds to the upper bound of the packet drop probability, which indicates a certain level of statistical QoS guarantees. Then, we utilize the priority information of video packets and introduce the packet priority drop to further improve the quality perceived by each user. The simulation results show that the average Peak Signal to Noise Ratio (PSNR) value of the priority drop is 0.8 higher than that of the non-priority drop and the PSNR value of the most badly damaged video frame in the priority drop is on an average 4 higher than that of the non-priority drop.展开更多
文摘This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.
基金supported by a Gift Funding from Huawei Technologies and Science Foundation of Education Bureau of Sichuan Province, China, under Grant No.10ZB019
文摘To provide a certain level of Quality of Service (QoS) guarantees for multiuser wireless downlink video streaming transmissions, we propose a multiuser scheduling scheme for QoS guarantees. It is based on the classic Queue-Length-Based (QLB)-rate maximum scheduling algorithm and integrated with the delay constraint and the packet priority drop. We use the large deviation principle and the effective capacity theory to construct a new analysis model to find each user's queue length threshold (delay constraint) violation probability. This probability corresponds to the upper bound of the packet drop probability, which indicates a certain level of statistical QoS guarantees. Then, we utilize the priority information of video packets and introduce the packet priority drop to further improve the quality perceived by each user. The simulation results show that the average Peak Signal to Noise Ratio (PSNR) value of the priority drop is 0.8 higher than that of the non-priority drop and the PSNR value of the most badly damaged video frame in the priority drop is on an average 4 higher than that of the non-priority drop.
