Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems

By Robert S. H. Istepanian, Nada Y. Philip, Maria G. Martini. 2009

82 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shamim Nemati
2. Xin Xu
3. Chunming Liu
4. Chao Yu
5. Chang Liu
6. Jiming Liu
7. Dewen Hu

In Computer Vision and Pattern Recognition

1. Shamim Nemati
2. Guang-Zhong Yang
3. Xin Xu
4. Benny P. L. Lo
5. Wadee Alhalabi Alhalabi
6. Ni Zhao
7. Chunming Liu
8. Chao Yu

In Electrical and Electronic Engineering

1. Shamim Nemati
2. Guang-Zhong Yang
3. Xin Xu
4. Chunming Liu
5. Chao Yu
6. Jiming Liu
7. Dewen Hu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “[284], [285], [286], [287], [288], functional electrical stimulation (FES) [289], [290], and adaptive rate control for medical video streaming [291], [292].”
• Multiobjective Reinforcement Learning: A Comprehensive Overview (2015, methodology): “For example, many MORL algorithms were tested separately on some ad hoc problems, including traffic tasks [64]–[67], medical tasks [68], robot tasks [69]–[71], network routing tasks [72], grids tasks [73], and so on.”
• Unobtrusive Sensing and Wearable Devices for Health Informatics (2014, methodology): “…on rate control have been proposed for these applications, such as a hybrid solution either using retransmission or retransmission combined with FEC techniques depending on the channel conditions [72], adapting the sending rate of source encoder according to the mobile link throughput [73].”
• Status and trends of mobile-health applications for iOS devices: A developer's perspective (2011, background): “…and the related literature were reviewed, including: (1) network or network security for m-health apps (Fei Hu et al., 2010; Garawi et al., 2006; Istepanian et al., 2009; Rasid and Woodward, 2005), (2) m-health application frameworks (Laakko et al., 2008; Norris et al., 2009), and (3) specific…”
• Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, background): “When it comes to mobile health applications, medical video streaming via adaptive rate control algorithm was studied [158].”