PXI-based architecture for real time data acquisition and distributed dynamical data processing

2005 ◽  
Author(s):  
E. Barrera ◽  
M. Ruiz ◽  
S. Lopez ◽  
D. Machon ◽  
J. Vega
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Real Time Data

Distributed real time data processing architecture for the TJ-II data acquisition system

2004 ◽  
Vol 75 (10) ◽  
pp. 4261-4264 ◽  
Author(s):  
M. Ruiz ◽  
E. Barrera ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Processing Architecture

Real-time data acquisition and parallel data processing solution for TJ-II Bolometer arrays diagnostic

2006 ◽  
Vol 81 (15-17) ◽  
pp. 1863-1867
Author(s):  
E. Barrera ◽  
M. Ruiz ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Parallel Data ◽  
Real Time Data ◽  
Bolometer Arrays ◽  
Processing Solution

Hardware Implementation of a Real-Time Data Processing Algorithm in Marine Engineering Data Acquisition

2011 ◽  
Vol 268-270 ◽  
pp. 110-115
Author(s):  
Ling Ma ◽  
Ke Zhu Song ◽  
Jun Feng Yang ◽  
Ping Cao
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Hardware Implementation ◽  
Sequence Data ◽  
Time Sequence ◽  
Time Data ◽  
Real Time Data ◽  
Marine Seismic ◽  
Real Time Data Processing

According to the architecture characteristics of the mass data acquisition system in marine seismic exploration, this paper designed a real-time data processing algorithm which can convert the collected time-sequence data to channel-sequence data. A hardware implementation of the algorithm based on FPGA+DDR SDRAM is developed to complete the whole conversion process. Here, FPGA is used to achieve time sequence data receiving, analyzing, preliminary processing and the interface to DDR SDRAM. Two DDR SDRAM’s are used in ping-pang mode to store time-sequence data and to cooperate with FPGA in realizing time-to-channel sequence data conversion. Test results showed that, after applying the algorithm to the FCI in high-precision marine seismic data acquisition and recording system, this arithmetic could realize caching collected data without redundancy and converting data from time sequence to channel sequence without dead time, besides, this algorithm also greatly improved the efficiency and reliability of data processing.

Real-Time Data Acquisition and Output System of Data Processing System

1975 ◽  
Vol 8 (1) ◽  
pp. 617-622
Author(s):  
A.E. Rosinkin ◽  
S.B. Mikhalev ◽  
V.S. Esipenko ◽  
A.N. Zazharsky
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Processing System ◽  
Data Processing System ◽  
Time Data ◽  
Real Time Data ◽  
Output System

PXI-based architecture for real-time data acquisition and distributed dynamic data processing

2006 ◽  
Vol 53 (3) ◽  
pp. 923-926 ◽  
Author(s):  
E. Barrera ◽  
M. Ruiz ◽  
S. Lopez ◽  
D. Machon ◽  
J. Vega
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Dynamic Data ◽  
Real Time Data

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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