Intelligent Instrumentation Systems

Recommended Prerequisites

Electronics (analogue and digital); Embedded Systems Technology; Industrial Instrumentation(large-scale systems architectures and technologies).

Teaching Methods

This curricular unit being offered to three courses which are significantly different in nature and objectives, classes are organised as (i) seminars, which are common to all students, and (ii) a choice of project works adequate to the background and foreseeable future interests of the students.

Learning Outcomes

1. State-of-the-art scientific knowledge in what drives innovation in the broad scope of instrumentation systems, especially in the fields of both smart sensors and wireless sensor metworks;
2. Understanding the objectives, methods and technologies involved in the 'Internet of Things' approach ;
3. Development of professional soft skills in engineering: leadership and professional ethics, evaluation and decision abilities, implementation drive, and thorough project management ability.

Work Placement(s)

No

Syllabus

Frequency of two out of the four following modules:

1. M2M integration of embedded systems in industrial instrumentation
Distributed systems and field networks
Meshed wireless sensor networks
Integration of heterogeneous systems over the Internet
Remote management of dispersed infra-structures and processes: asset management and tracking
Big-data analytics: systems structuring and methods

2. Virtual Instrumentation techniques
Semantics, structuring and instrument programming
Remote programming of applications
Mapping computing processes: middleware, and backhaul networks

3. Building technologies of embedded systems
Microcontrollers, DSP, FPGA: functionalities, opportunities and tools
Energy harvesting technologies
Integration with SoC; making RFID and WSN to converge

4. Real-time systems
State machines
Real-time operating systems
Real-time communication interfacing
Task synchronisation and time management.

Head Lecturer(s)

João Manuel de Sá Campos Gil

Assessment Methods

Assessment
Report of a seminar or field trip: 30.0%
Project: 70.0%

Bibliography

• Ajay D. Kshemkalyani, e Mukesh Singhal, Distributed Computing: Principles, Algorithms, and Systems, Cambridge University Press, 2008.
• Andrew S. Tanenbaum, Maarten van Steen, Distributed Systems: Principles and Paradigms, Pearson Education (2nd edition), 2008.
• Brian Otis, Ultra-low Power Wireless Technologies for Sensor Networks, Wiley Series on Parallel and Distributed Computing, 2009.
• Wolfgang Mahnke, Stefan-Helmut Leitner, e Matthias Damm, OPC Unified Achitecture, Springer, 2009.