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.