scholarly journals Ambient backscatter communication-based smart 5G IoT network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qiang Liu ◽  
Songlin Sun ◽  
Xueguang Yuan ◽  
Yang’an Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Transmission ◽  
Big Data Analysis ◽  
Time Data ◽  
Backscatter Communication ◽  
Data Transmission System ◽  
Analysis System ◽  
Data Analysis System ◽  
Ambient Backscatter

AbstractIn this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system based on the combination of shallow neural networks and deep neural networks. The real-time data transmission system based on ambient backscatter communication can extend the standby time of data collection equipment, reduce the size of the equipment, and increase the comfort of wearing. The real-time big data analysis system combining the shallow neural network and the deep neural network can greatly reduce the pressure caused by the frequent deep neural network calculations of the MEC and greatly reduce the energy consumed by the MEC for remote real-time monitoring.

Development of real-time big data analysis system for RHIPE-based marketing in the automobile industry

2018 ◽  
Vol 7 (3.33) ◽  
pp. 248
Author(s):  
Young-Woon Kim ◽  
Hyeopgeon Lee
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Real Time ◽  
Automobile Industry ◽  
Relational Databases ◽  
Distributed Processing ◽  
Big Data Analysis ◽  
Distributed Structure ◽  
Analysis System ◽  
Data Analysis System

In the automobile industry, the contract information of vehicles contracted through sales activities, as well as the order data of customers who purchased cars, and vehicle maintenance history information all accumulate in relational databases over time. Although accumulated customer and vehicle information is used for marketing purposes, processing and analyzing this massive data is difficult, as its volume con-stantly increases. This problem of managing big data is commonly solved by utilizing the MapReduce distributed structure of Hadoop, which uses big data distributed processing technology, and R, which is a widely used big data analysis technology. Among the methods that interconnect Hadoop and R, the R and Hadoop integrated programming environment (RHIPE) was developed in this study as a real-time big data analysis system for marketing in the automobile industry. RHIPE allows us to maintain an interactive environment and use the powerful analytical features of R, which is an interpreter language, while achieving a high processing speed using Map and Reduce func-tions. In this study, we developed a real-time big data analysis system that can analyze the orders, reservations, and maintenance history contained in big data using the RHIPE method. 

Design and Implement of the Wireless Real-Time Data Transmission System Based on USB

2013 ◽  
Vol 310 ◽  
pp. 596-600 ◽  
Author(s):  
Xiao Guang Li
Keyword(s):  
Real Time ◽  
Error Rate ◽  
Data Transmission ◽  
Transmission Rate ◽  
Transmission System ◽  
Time Data ◽  
Real Time Data ◽  
Data Transmission System ◽  
Data Error ◽  
Wireless Module

In order to overcome the disadvantages of small broadband, low transmission rate, network delay and high error rate in the locomotive real-time data transmission. A new wireless real-time data transmission system is designed by using nRF24L01 based on USB2.0.Which includes CY7C68013A chip of CYPRESS company and nRF24L01 wireless module, gives communication principle interface electrics circuit between CY7C68013A chip and nRF24L01 wireless module, focus on the hardware and software designs according to practical application and analyze real-time of the data during transporting. An algorithm for reducing real-time data error rate is developed to achieve efficient real-time data transmission. Experiments proved that the system is easy to control and works stably to perform reliable wireless real-time data transmission.

scholarly journals Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

2019 ◽  
Author(s):  
Golan Karvat ◽  
Artur Schneider ◽  
Mansour Alyahyaey ◽  
Florian Steenbergen ◽  
Ilka Diester
Keyword(s):  
Real Time ◽  
Neurofeedback Training ◽  
Time Data ◽  
Beta Band ◽  
Neural Oscillation ◽  
Oscillatory Power ◽  
Analysis System ◽  
Data Analysis System ◽  
Induced Changes

AbstractNeural oscillations are increasingly interpreted as transient bursts, yet a method to measure these short-lived events in real-time is missing. Here we present a real-time data analysis system, capable to detect short and narrowband bursts, and demonstrate its usefulness for volitional increase of beta-band burst-rate in rats. This neurofeedback-training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.

Real-time EELS spectra classification using Principal Components Analysis (PCA) and Artificial Neural Networks (ANN)

2001 ◽  
Vol 7 (S2) ◽  
pp. 1164-1165
Author(s):  
P-G Åstrand ◽  
S. Csillag
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Short Exposure ◽  
Data Recording ◽  
Time Data ◽  
Real Time Data ◽  
Recent Developments ◽  
Analysis System ◽  
Data Analysis System ◽  
Exciting Possibility

Recent developments in detector technology [1] for EELS and Energy Filtered TEM has made possible to obtain large number of spectra and energy filtered images during very short exposure times. This in turn opens the exciting possibility of studying time dependent processes in the electron microscope, during exposure to the electron beam as well as the study of different radiation sensitive samples which are being degraded during lengthily data recording. This kind of data recording generates a large amount of data and manual data analysis should be avoided in order to be able to fully benefit from the improved sensitivity and increased speed of these new detectors. Thus a fast, real-time data analysis system is highly desirable.A system for real-time data analysis (spectra classification) of data generated from such a detector has been simulated in a program based on the object oriented C++ framework ROOT [2][3].

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