Heuristic Synchronization of Real-Time Response Data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Samuel Weishaupt ◽  
Linus Feiten ◽  
Bernd Becker ◽  
Uwe Wagschal ◽  
Thomas Waldvogel ◽  
...  
Keyword(s):  
Real Time ◽  
Time Response ◽  
The Internet ◽  
Cable Tv ◽  
Response Data ◽  
Playout Delay ◽  
Delay Difference

Abstract When real-time response data from viewers of a televised debate is collected via the internet, the server timestamps of the received responses may not match the correct times of the debate. This paper addresses the question of how the data could be aligned in retrospect, using an algorithm that approximates the playout delay difference between each viewer’s TV signal. The validity is shown by successfully approximating distinctive delays for viewers with satellite or cable TV.

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

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