scholarly journals LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 30467-30479 ◽  
Author(s):  
Jie Tang ◽  
Shaoshan Liu ◽  
Liangkai Liu ◽  
Bo Yu ◽  
Weisong Shi
Keyword(s):  
Low Power ◽  
Real Time ◽  
Autonomous Driving ◽  
Computing System ◽  
Edge Computing ◽  
Time Autonomous

scholarly journals Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Autonomous Driving ◽  
Learning Approaches ◽  
Practical Applications ◽  
Task Learning ◽  
Lane Line ◽  
High Level ◽  
Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

scholarly journals Monitoring of Agricultural Conditions in Greenhouse Crops Using Edge Computing and IoT.

2021 ◽  
Author(s):  
Jesús Gil Ruiz ◽  
Franklin Guillermo Montenegro ◽  
Daissi Bibiana Gonzalez Roldan ◽  
Gabriel Vargas Monroy ◽  
Carlos Enrique Montenegro-Marin ◽  
...  
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Real Time ◽  
Weather Conditions ◽  
Complete Information ◽  
Computing System ◽  
Edge Computing ◽  
Greenhouse Crops ◽  
The Status ◽  
Initial Results

Abstract This work presents an Internet of Things (IoT) system and edge computing for monitoring crop conditions by developing a system to collect information on parameters related to the crop weather conditions, this data is recollected with edge computing system. The data is sent to the server for processing, then forwarded to the user via the IoT protocols and procedures. The purpose is to collect data in real time for analysis and allow decision-making by the system and the farmer. The user can interact with the system remotely and receive the specified alerts and conditions. The initial results show that the system provides complete information on the status of the parameters, enabling management of greenhouse crops.

scholarly journals A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method

2018 ◽  
Vol 8 (3) ◽  
pp. 627-638 ◽  
Author(s):  
Hooman Farkhani ◽  
Mohammad Tohidi ◽  
Sadaf Farkhani ◽  
Jens Kargaard Madsen ◽  
Farshad Moradi
Keyword(s):  
Low Power ◽  
Real Time ◽  
High Speed ◽  
Computing System ◽  
Neuromorphic Computing ◽  
Tracking Method ◽  
Real Time Tracking
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