scholarly journals Investigations into near-real-time surveying for geophysical data collection using an autonomous ground vehicle

2014 ◽  
Author(s):  
Geoffrey A. Phelps ◽  
C. Ippolito ◽  
R. Lee ◽  
R. Spritzer ◽  
Y. Yeh
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Geophysical Data ◽  
Ground Vehicle

scholarly journals Real-time monitoring of seismic data using satellite telemetry

1997 ◽  
Vol 40 (4) ◽  
Author(s):  
G. Calderoni ◽  
B. De Simoni ◽  
F. M. De Simoni ◽  
L. Merucci
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Seismic Data ◽  
Data Exchange ◽  
Low Frequency ◽  
Satellite Communications ◽  
Geophysical Data ◽  
Processing Centre ◽  
Duration Magnitude ◽  
Master Station

This article describes the ARGO Satellite Seismic Network (ARGO SSN) as a reliable system for monitoring, collection, visualisation and analysis of seismic and geophysical low-frequency data, The satellite digital telemetry system is composed of peripheral geophysical stations, a centraI communications node (master sta- tion) located in CentraI Italy, and a data collection and processing centre located at ING (Istituto Nazionale di Geofisica), Rome. The task of the peripheral stations is to digitalise and send via satellite the geophysical data collected by the various sensors to the master station. The master station receives the data and forwards them via satellite to the ING in Rome; it also performs alI the monitoring functions of satellite communications. At the data collection and processing centre of ING, the data are received and analysed in real time, the seismic events are identified and recorded, the low-frequency geophysical data are stored. In addition, the generaI sta- tus of the satellite network and of each peripheral station connected, is monitored. The procedure for analysjs of acquired seismic signals allows the automatic calculation of local magnitude and duration magnitude The communication and data exchange between the seismic networks of Greece, Spain and Italy is the fruit of a recent development in the field of technology of satellite transmission of ARGO SSN (project of European Community "Southern Europe Network for Analysis of Seismic Data" )

Virtual private network technologies in real-time geophysical data collection systems

2015 ◽  
Vol 51 (1) ◽  
pp. 44-47 ◽  
Author(s):  
I. M. Aleshin ◽  
A. E. Vasiliev ◽  
K. I. Kholodkov ◽  
F. V. Perederin
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Virtual Private Network ◽  
Geophysical Data ◽  
Private Network ◽  
Network Technologies

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Real Time Monitoring of the CNC Process in a Production Environment- the Data Collection & Analysis Phase

Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 920-926 ◽  
Author(s):  
Jonathan Downey ◽  
Denis O'Sullivan ◽  
Miroslaw Nejmen ◽  
Sebastian Bombinski ◽  
Paul O’Leary ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Production Environment ◽  
Real Time Monitoring ◽  
Analysis Phase

A Proposal of Data Collection by Mobility Users on Real-time for e-Bike Navigation System

2021 ◽  
Author(s):  
Ryuta Yamaguchi ◽  
Panote Siriaraya ◽  
Da Li ◽  
Tomoki Yoshihisa ◽  
Shinji Shimojo ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Navigation System
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