Web-enabled Software for Real-time Autonomous Wireless Sensors Data Visualization

2019 ◽  
Author(s):  
Felix G. Hamza-Lup ◽  
Hongjun Su ◽  
Coby Wissing
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Wireless Sensors ◽  
Time Autonomous

The design of an intelligent real‐time autonomous vehicle, TAIWANiTS‐1

2007 ◽  
Vol 30 (5) ◽  
pp. 829-842 ◽  
Author(s):  
Bing‐Fei Wu ◽  
Chao‐Jung Chen ◽  
Hsin‐Han Chiang ◽  
Hsin‐Yuan Peng ◽  
Jau‐Woei Perng ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Time Autonomous

scholarly journals Real-Time Primitives for CoAP: Extending the Use of IoT for Time Constraint Applications for Social Good

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1257
Author(s):  
Gabriel Eggly ◽  
Mariano Finochietto ◽  
Emmanouil Dimogerontakis ◽  
Rodrigo Santos ◽  
Javier Orozco ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Real Time ◽  
Processing Speed ◽  
Wireless Sensors ◽  
Communication Protocols ◽  
Social Good ◽  
Vulnerable Communities ◽  
The Social

Internet of Things (IoT) have become a hot topic since the official introduction of IPv6. Research on Wireless Sensors Networks (WSN) move towards IoT as the communication platform and support provided by the TCP/UDP/IP stack provides a wide variety of services. The communication protocols need to be designed in such a way that even simple microcontrollers with small amount of memory and processing speed can be interconnected in a network. For this different protocols have been proposed. The most extended ones, MQTT and CoAP, represent two different paradigms. In this paper, we present a CoAP extension to support soft real-time communications among sensors, actuators and users. The extension facilitates the instrumentation of applications oriented to improve the quality of life of vulnerable communities contributing to the social good.

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.

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