J
Jitae Shin
Researcher at Sungkyunkwan University
Publications - 163
Citations - 1697
Jitae Shin is an academic researcher from Sungkyunkwan University. The author has contributed to research in topics: Quality of service & Video quality. The author has an hindex of 18, co-authored 145 publications receiving 1394 citations. Previous affiliations of Jitae Shin include University of Southern California.
Papers
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Proceedings Article
Interference management in LTE femtocell systems using Fractional Frequency Reuse
TL;DR: Simulation results show that proposed interference management scheme in the LTE femtocell systems using Fractional Frequency Reuse enhances total/edge throughputs and reduces the outage probability in overall network, especially for the cell edge users.
Journal ArticleDOI
Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge
Li Wang,Dong Nie,Guannan Li,Elodie Puybareau,Jose Dolz,Qian Zhang,Fan Wang,Jing Xia,Zhengwang Wu,Jia-Wei Chen,Kim-Han Thung,Toan Duc Bui,Jitae Shin,Guodong Zeng,Guoyan Zheng,Vladimir S. Fonov,Andrew Doyle,Yongchao Xu,Pim Moeskops,Josien P. W. Pluim,Christian Desrosiers,Ismail Ben Ayed,Gerard Sanroma,Oualid Benkarim,Adrià Casamitjana,Verónica Vilaplana,Weili Lin,Gang Li,Dinggang Shen +28 more
TL;DR: The iSeg-2017 challenge provides a set of six-month infant subjects with manual labels for training and testing the participating methods, and among the 21 automatic segmentation methods participating, the eight top-ranked teams are reviewed, in terms of Dice ratio, modified Hausdorff distance, and average surface distance.
Journal ArticleDOI
Quality-of-service mapping mechanism for packet video in differentiated services network
TL;DR: Results show that the proposed QoS mapping mechanism can exploit the relative DiffServ advantage and result in the persistent service differentiation among DiffServ levels and the enhanced end-to-end video quality with the same pricing constraint.
Journal ArticleDOI
Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks
TL;DR: Performance modeling, analysis and simulation results demonstrate that the proposed QoS-based sensory MAC protocol is capable of providing lower delay and better throughput, at the cost of reasonable energy consumption, in comparison to other existing sensory MAC protocols.
Posted Content
3D Densely Convolutional Networks for Volumetric Segmentation.
TL;DR: A novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation that provides a dense connection between layers that aims to improve the information flow in the network.