Bloom

2015 ◽  
Vol 5 (2) ◽  
pp. 78-85
Author(s):  
Ken Goldberg ◽  
Sanjay Krishnan ◽  
Fernanda Viégas ◽  
Martin Wattenberg

In Bloom, an Internet-based earth-art-work, minute movements of the Hayward Fault in California are detected by a seismograph, transmitted continuously via the Internet, and processed to generate an evolving field of circular blooms. The size and position of each bloom is based on real-time changes in Earth’s motion, measured as a vertical velocity continuously updated from the seismometer. The horizontal position of blooms is based on time, and their vertical position is based on magnitude of the second derivative or rate of change. Large movements create large blooms; small jitters create tiny buds. This essay presents several stills from the Bloom project, and an essay on the work and its creator, Ken Goldberg.

2020 ◽  
Vol 9 (07) ◽  
pp. 25113-25115
Author(s):  
Minakshi Roy ◽  
Prakar Pradhan ◽  
Jesson George ◽  
Nikhil Pradhan

Since we are now currently present in an era of Computing Technology, it is essential for everyone and everything to be connected to the internet. IOT is a technology that brings us more and more close to this goal. Our project comprises of a smart water monitoring system which is a small prototype for flood detection and avoidance system. This paper explains the working and the workflow of all the components present inside our project. The sensors sense the environment and sends real-time data to the cloud (firebase cloud) and users can view and access this data via their mobile platform. The model gives a warning after the water level rises to a particular height. Since it is a small scaled prototype for flood detection and avoidance system, the working of this model is good. The data are uploaded and changed in the cloud in precision to the sensor and real-time changes in the mobile application is achieved. This model can be used to greatly reduce the casualties in a devastating event of flood.


Author(s):  
M. Jonas

The increasing progress in the field of satellite navigation systems (GNSS, SBAS) in the recent decades supports effort to use it for determination of train position for railway safety-related systems. Satellite-based augmentation systems (SBAS) such as WAAS in the USA, and EGNOS in Europe, are available and a new global satellite navigation system Galileo is being built by the European GNSS agency (GSA). The currently available SBAS systems were developed in order to satisfy aviation requirements. But the safety concept on railways is very different from the aviation safety concept. The railway safety concept in Europe is determined by means of the CENELEC standards (EN 50126, EN 50129, EN IEC 61508). So it is necessary to find a way how to use GNSS systems in accordance with strict railway standards. The main problem is attainment of sufficient integrity of position solution [5, 12]. Satisfaction of safety integrity level 4 (SIL4) is necessary for railways [6, 7, 8, 9]. At the beginning, it can provide low-cost controlling system for the local, regional and freight railway lines. GNSS provides a 3D position (position in horizontal and vertical plane). The value of altitude is cruical for application in aviation, in ground transportation this value is not so important. On the contrary, the value of horizontal position is cruical. For the purpose of increasing the integrity of GNSS-based position determination we propose a new method of the detection of a GNSS horizontal position error based on the relation between vertical and horizontal position error. As was mentioned for example in [4], as GPS is a three dimensional positioning system, errors between any two coordinates may be correlated, and so there can be relations between errors in individual dimensions. The general 3D GPS-based position solution can be divided into two parts: - 2D horizontal position - 1D vertical position We investigated the relation between errors in the horizontal and vertical plane in real data measured by a GNSS receiver. It was static measurement and the antenna location was exactly known. The vertical position provided by GNSS is not constant. In ground transportation we can mostly make an assumption of nearly a constant value of altitude during the ride. Especially in railway transportation the changing of altitude during the ride is limited by many factors (railway standards, properties of track) So we investigate the possibility of using values of altitude to estimate a position error in the horizontal plane. As the receiver determines the values of the vertical position in real time, the detection of the horizontal position error based on the values of altitude can help detect the actual position error in horizontal plane during the train ride also in real time. The sensitivity of this method to errors in pseudoranges (error caused by multipath) was also investigated. This was done by simulation with software receiver Pegasus (Eurocontrol). The analysis was based on real data from GNSS.


BioTechniques ◽  
2005 ◽  
Vol 38 (2) ◽  
pp. 287-293 ◽  
Author(s):  
Van Luu-The ◽  
Nathalie Paquet ◽  
Ezequiel Calvo ◽  
Jean Cumps

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran

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.


Sign in / Sign up

Export Citation Format

Share Document