scholarly journals Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-3
Author(s):  
Xiaoxian Yang ◽  
Zhiyuan Tan ◽  
Zhiling Luo
Keyword(s):  
Deep Learning ◽  
Mobile Computing ◽  
Computing Architecture ◽  
Future Challenges

scholarly journals Automatic image annotation based on deep learning models: a systematic review and future challenges

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Myasar Mundher Adnan ◽  
Mohd Shafry Mohd Rahim ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Image Annotation ◽  
Automatic Image Annotation ◽  
Learning Models ◽  
Future Challenges

Adaptive Mobile Architecture with Utility Computing

2016 ◽  
pp. 386-404
Author(s):  
Robin Singh Bhadoria ◽  
Chandrakant Patil
Keyword(s):  
Mobile Computing ◽  
Utility Computing ◽  
It Services ◽  
Computing Environment ◽  
Computing Architecture ◽  
Mobile Interface ◽  
Enterprise Applications

In this chapter, we try to elaborate on how we could accommodate such a system using a mobile interface for delivering services for business as well as enterprise applications. Strategically, the problem with today's industry is that there is no established framework about how to adopt changes or to effectively utilize its IT services for any enterprise with mobile computing architecture. This chapter focuses on Mobile Interface Architecture, which can easily demonstrate the ubiquitous nature of today's computing environment.

5G Edge Cloud Power Real-time Inspection Technology Based on YOLOV4-Tiny

2021 ◽  
Author(s):  
Jiaqi Song ◽  
Jing Li ◽  
Di Wu ◽  
Guangye Li ◽  
Jiaxin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Efficiency ◽  
Real Data ◽  
Detection Algorithm ◽  
Power Line ◽  
Edge Computing ◽  
Inspection Method ◽  
Computing Architecture ◽  
Low Efficiency

Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.

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