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

Author(s):  
Felix G. Hamza-Lup ◽  
Hongjun Su ◽  
Coby Wissing
2007 ◽  
Vol 30 (5) ◽  
pp. 829-842 ◽  
Author(s):  
Bing‐Fei Wu ◽  
Chao‐Jung Chen ◽  
Hsin‐Han Chiang ◽  
Hsin‐Yuan Peng ◽  
Jau‐Woei Perng ◽  
...  

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1257
Author(s):  
Gabriel Eggly ◽  
Mariano Finochietto ◽  
Emmanouil Dimogerontakis ◽  
Rodrigo Santos ◽  
Javier Orozco ◽  
...  

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.


2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee

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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