An iBeacon based Real-time context-aware e-healthcare system

2017 ◽  
Author(s):  
Mirza Muhammad Bilal Baig ◽  
Muhammad Taha Jilani
Keyword(s):  
Real Time ◽  
Healthcare System ◽  
Context Aware

scholarly journals The effect of real-time context-aware feedback on occupants’ heating behaviour and thermal adaptation

2016 ◽  
Vol 123 ◽  
pp. 179-191 ◽  
Author(s):  
Marika Vellei ◽  
Sukumar Natarajan ◽  
Benjamin Biri ◽  
Julian Padget ◽  
Ian Walker
Keyword(s):  
Real Time ◽  
Thermal Adaptation ◽  
Context Aware

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

Real-time context-aware social media recommendation

2018 ◽  
Vol 28 (2) ◽  
pp. 197-219 ◽  
Author(s):  
Xiangmin Zhou ◽  
Dong Qin ◽  
Lei Chen ◽  
Yanchun Zhang
Keyword(s):  
Social Media ◽  
Real Time ◽  
Context Aware

scholarly journals Real-time Wireless Healthcare system for Angular Transmission of EEG signal using VL-OCC

2019 ◽  
Vol 152 ◽  
pp. 28-35
Author(s):  
Geetika Aggarwal ◽  
Xuewu Dai ◽  
Richard Binns ◽  
Reza Saatchi
Keyword(s):  
Real Time ◽  
Healthcare System ◽  
Eeg Signal
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