scholarly journals Visualization of events using various kinds of synchronized data for the Border Guard

2017 ◽  
pp. 5-13 ◽  
Author(s):  
Bartosz Czaplewski ◽  
Sylwester Kaczmarek ◽  
Jacek Litka ◽  
Mariusz Miszewski
Keyword(s):  
Real Time ◽  
Archival Data ◽  
Time Data ◽  
Digital Maps ◽  
Real Time Data ◽  
Different Types ◽  
Visualization Of Data ◽  
Simultaneous Visualization

STRADAR project is dedicated to streaming real-time data in a distributed dispatcher and teleinformation system of the Border Guard. The Events Visualization Post is a software designed for simultaneous visualization of data of different types in BG headquarters. The software allows the operator to visualize files, images, SMS, SDS, video, audio, and current or archival data on naval situation on digital maps. All the visualized data can be synchronized in time.

scholarly journals Real-Time Data Transfer Based on Software Defined Radio Technique using Gnu radio/USRP

2019 ◽  
Vol 9 (1) ◽  
pp. 279-288
Keyword(s):  
Wireless Communication ◽  
Real Time ◽  
Software Defined Radio ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Software Radio ◽  
Spectrum Efficiency ◽  
Time Data ◽  
Real Time Data ◽  
Different Types

Software Defined Radio (SDR) offers a extensive radio communication platform that uses software updates to make use of fresh technology. From SDR, the idea of an Orthogonal frequency division multiplexing (OFDM) has evolved to personalize SDRs. The channel dispersiveness causes Inter Symbol Interference (ISI) but OFDM is more resistant at these condition because of this reason it is widely used in wireless communication systems. OFDM is having a good performance in terms of Bit Error Rate (BER) and high spectrum efficiency, so it is considered as a key role for next generation wireless communication system. In this paper, three different types of data are transferred in a real time SDR of OFDM transceiver using GNURadio/Universal Software Radio Peripheral (USRP). OFDM is extremely sensitive for synchronization errors such as time and frequency offsets and to estimate channel condition. Therefore, a standard algorithm is applied to solve synchronization and channel estimation problems in SDR based OFDM system. This testbed is implemented using two USRPs of model N210 as transmitter and receiver with an open source of GNURadio as a software. The implementation of OFDM is evaluated for different types of information like text, audio and Image. This evaluates the BER v/s SNR for real time data transmission in SDR Environment

Towards smart manufacturing: Implementation and benefits

2021 ◽  
Vol 15 (02) ◽  
pp. 25-31
Author(s):  
Karim Haricha ◽  
Azeddine Khiat ◽  
Yassine Issaoui ◽  
Ayoub Bahnasse ◽  
Hassan Ouajji
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Smart Manufacturing ◽  
Time Data ◽  
Real Time Data ◽  
Different Types ◽  
New Generation ◽  
Text Images

Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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