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].

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. 

scholarly journals Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

2019 ◽  
Vol 20 (4) ◽  
pp. e170-e200 ◽  
Author(s):  
Katja Heinisch ◽  
Rolf Scheufele
Keyword(s):  
Real Time ◽  
Optimal Combination ◽  
Financial Data ◽  
Time Data ◽  
Hard Data ◽  
Forecasting Performance ◽  
Real Time Data ◽  
Final Data ◽  
Release Data

Abstract In this paper, we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

scholarly journals Sensors and tribological systems: applications for industry 4.0

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saquib Rouf ◽  
Ankush Raina ◽  
Mir Irfan Ul Haq ◽  
Nida Naveed
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Developing Economies ◽  
Industrial Applications ◽  
Vital Role ◽  
Critical Parameters ◽  
Time Data ◽  
Content Type ◽  
Real Time Data

Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

The role of geosteering in developing the Pyrenees Field

2014 ◽  
Vol 54 (2) ◽  
pp. 494
Author(s):  
Mark Woodall ◽  
Grant Skinner ◽  
Mauro Viandante ◽  
Laura Pontarelli ◽  
Konstantinos Kostas ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
3D Seismic ◽  
Time Data ◽  
Associated Gas ◽  
Excellent Quality ◽  
Viscous Oil ◽  
Gas Bearing ◽  
3D Seismic Data

The Pyrenees Field comprises a series of biodegraded 19° API oil accumulations reservoired in Early Cretaceous sandstones of the Pyrenees Member in the Exmouth Sub-Basin, offshore WA The reservoir comprises excellent quality, poorly consolidated shallow marine to deltaic sands. Variable thickness oil columns (some with associated gas caps), strong bottom water drive, and relatively viscous oil has necessitated the drilling of long (up to 2,000 m) horizontal wells to maximise reservoir exposure while geosteering well to within a few meters of the roof of the reservoir to maximise standoff from the OWCs. The field is covered by excellent quality 3D seismic data; however, pre-drill mapping for well path planning is complicated by the unconformable nature of the top reservoir boundary formed by the sub-cropping Pyrenees Member. Faulting within and localised velocity variations above the reservoir are also a challenge to pre-drill well planning. Cutting-edge geosteering tools have been used to achieve the desired well paths. The tools use azimuthal deep induction resistivity measurements to model and predict reservoir and fluid boundaries, taking advantage of the large resistivity contrasts between the overlying sealing mudstones of the Muderong Formation and the oil (and occasionally gas) bearing Pyrenees reservoir sands. This extended abstract discusses the application of the tools both in pre-drill well path planning and the real-time geosteering operation. Operations are managed between the rig and a sub-surface team located in a dedicated geosteering room onshore. Here real-time data is compared with planned well paths in 3D seismic and geocellular reservoir models and well path adjustments made to optimise final well placement.

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