Multimedia Broadcast/Multicast Service (MBMS) based on point-to-multipoint (p-t-m) radio transmission was introduced by 3GPP to deliver broadcast service to a group of user equipments (UEs) in a cellular system. As a broadcast system with uplink feedback, its performance is limited by UEs in poor connections. To utilize the limited radio resource more efficiently, a simple but effective mechanism is proposed to improve the system throughput by sacrificing those UEs that are expensive to cover, which means the cost of this improvement is coverage degradation. We set a threshold for the SINR of UEs, and uplink feedback from UEs with G-factor lower than the threshold will be discarded in packet scheduling and link adaptation. Compared with other methods to increase the throughput, this mechanism has two advantages. First, the coverage can be exactly controlled by adjusting the threshold. Second, much higher throughput gain can be achieved for the same cost. Simulation results show that throughput will be increased by 22% with reasonable coverage degradation when there are 10 UEs per sector. Larger gains can be achieved with more UEs per sector.
Delineation of major torso organs is a key step of mouse micro-CT image analysis. This task is challenging due to low soft tissue contrast and high image noise, therefore anatomical prior knowledge is needed for accurate prediction of organ regions. In this work, we develop a deeply supervised fully convolutional network which uses the organ anatomy prior learned from independently acquired contrast-enhanced micro-CT images to assist the segmentation of non-enhanced images. The network is designed with a two-stage workflow which firstly predicts the rough regions of multiple organs and then refines the accuracy of each organ in local regions. The network is trained and evaluated with 40 mouse micro-CT images. The volumetric prediction accuracy (Dice score) varies from 0.57 for the spleen to 0.95 for the heart. Compared to a conventional atlas registration method, our method dramatically improves the Dice of the abdominal organs by 18%–26%. Moreover, the incorporation of anatomical prior leads to more accurate results for small-sized low-contrast organs (e.g. the spleen and kidneys). We also find that the localized stage of the network has better accuracy than the global stage, indicating that localized single organ prediction is more accurate than global multiple organ prediction. With this work, the accuracy and efficiency of mouse micro-CT image analysis are greatly improved and the need for using contrast agent and high x-ray dose is potentially reduced.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.