20,403 research outputs found
Enabling Quality-Driven Scalable Video Transmission over Multi-User NOMA System
Recently, non-orthogonal multiple access (NOMA) has been proposed to achieve
higher spectral efficiency over conventional orthogonal multiple access.
Although it has the potential to meet increasing demands of video services, it
is still challenging to provide high performance video streaming. In this
research, we investigate, for the first time, a multi-user NOMA system design
for video transmission. Various NOMA systems have been proposed for data
transmission in terms of throughput or reliability. However, the perceived
quality, or the quality-of-experience of users, is more critical for video
transmission. Based on this observation, we design a quality-driven scalable
video transmission framework with cross-layer support for multi-user NOMA. To
enable low complexity multi-user NOMA operations, a novel user grouping
strategy is proposed. The key features in the proposed framework include the
integration of the quality model for encoded video with the physical layer
model for NOMA transmission, and the formulation of multi-user NOMA-based video
transmission as a quality-driven power allocation problem. As the problem is
non-concave, a global optimal algorithm based on the hidden monotonic property
and a suboptimal algorithm with polynomial time complexity are developed.
Simulation results show that the proposed multi-user NOMA system outperforms
existing schemes in various video delivery scenarios.Comment: 9 pages, 6 figures. This paper has already been accepted by IEEE
INFOCOM 201
Dynamic Interrelation of Births and Deaths: Evidence from Plant Level Data
In this paper, the dynamic panel data method is used to investigate the dynamic interrelation of plant births and plant deaths. The dynamic panel data method considers the endogenous problem and individual effects. Empirical findings support the multiplier effect. In addition, exit does not cause entry, whereas entry causes exit.
Flash-point prediction for binary partially miscible mixtures of flammable solvents
Flash point is the most important variable used to characterize fire and explosion hazard of liquids. Herein, partially miscible mixtures are presented within the context of liquid-liquid extraction processes. This paper describes development of a model for predicting the flash point of binary partially miscible mixtures of flammable solvents. To confirm the predictive efficacy of the derived flash points, the model was verified by comparing the predicted values with the experimental data for the studied mixtures: methanol + octane; methanol + decane; acetone + decane; methanol + 2,2,4-trimethylpentane; and, ethanol + tetradecane. Our results reveal that immiscibility in the two liquid phases should not be ignored in the prediction of flash point. Overall, the predictive results of this proposed model describe the experimental data well. Based on this evidence, therefore, it appears reasonable to suggest potential application for our model in assessment of fire and explosion hazards, and development of inherently safer designs for chemical processes containing binary partially miscible mixtures of flammable solvents
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking
Multi-person human pose estimation and tracking in the wild is important and
challenging. For training a powerful model, large-scale training data are
crucial. While there are several datasets for human pose estimation, the best
practice for training on multi-dataset has not been investigated. In this
paper, we present a simple network called Multi-Domain Pose Network (MDPN) to
address this problem. By treating the task as multi-domain learning, our
methods can learn a better representation for pose prediction. Together with
prediction heads fine-tuning and multi-branch combination, it shows significant
improvement over baselines and achieves the best performance on PoseTrack ECCV
2018 Challenge without additional datasets other than MPII and COCO.Comment: Extended abstract for the ECCV 2018 PoseTrack Worksho
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