Data Acquisition Systems
Objectives
The discipline provides that students develop competencies in the domain of data acquisition systems, namely they learn and understand the basic concepts and know how to apply them in an engineering context in diversified application domains.
Objetives:
Knowledge:
- Basic concepts of data acquisition systems
- Architectures for Data Acquisition Systems (SAD)
- Signal conditioning and D/A – A/D converters
- Interfaces for data acquisition
Know-how:
- Design of data acquisition systems
- Development of solutions in different operational contexts
Soft-Skills:
- Analysis and formalization of problems’ specifications
- Learn how to behave in teamwork
- Learn how to present publically a proposal of work
- Learn how to manage time
General characterization
Code
7317
Credits
6.0
Responsible teacher
Ricardo Luís Rosa Jardim Gonçalves
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Available soon
Bibliography
Practical Data Acquisition for Instrumentation and Control Systems; John Park, Steve Mackay; 2003; Newnes
Data Acquisition Techniques Using PCs; Howard Austerlitz, 2003; Academic Press
Data Acquisition and Control Handbook, A Guide to Hardware and Software for Computer-Based Measurement and Control, Keithley
Sensors and Signal Conditioning, Ramón Pallás-Areny, John G. Webster, Wiley
Data acquisition for sensor systems, H. Rosemary Taylor, Springer
Teaching method
Available soon
Evaluation method
Available soon
Subject matter
- Architectures for Data Acquisition Systems (SAD), general characterization of SAD, identification of principal blocks, characterization of functions, methodologies for the development of dedicated systems.
- Signal conditioning: use of operational amplifiers, compensations and linear/non-linear corrections, conversions V/A/F, galvanic isolation, use cases and applications using different transducers.
- Digital-Analog Conversion: characterization, converters specification, direct and indirect methods, architectures.
- Analog-Digital Conversion: characterization, converters specification, asynchronous methods with direct and indirect controller, architectures.
- Solutions for the acquisition of data (centralized versus distributed); traditional instrumentation versus virtual instrumentation; LabView; remote systems; interfaces.
- Analysis of use cases.
Programs
Programs where the course is taught: