Advanced Applications of Biomedical Instrumentation

Objectives

-        Provide the tools to implement complex instrumentation systems for real time and offline data analysis. The student will be able to develop visualization and reporting application based on the instrumentation data sources.

-        The course will combine the acquired knowledge form the analogic, digital and microcontroled instrumentation previous courses, combining both to create final applications.

-        The student will be introduced to programming languages for the rapid creation of scientific application for instrumentation.

-        Decision-making and control examples will be developed to close the application cycle.

General characterization

Code

11825

Credits

6.0

Responsible teacher

Hugo Filipe Silveira Gamboa

Hours

Weekly - 4

Total - 84

Teaching language

Português

Prerequisites

Analog Instrumentation ; Digital Instrumentation

Bibliography

Measurement and Instrumentation: Theory and Application

by Alan S. Morris Reza; Langari Butterworth-Heinemann Ltd (2011)

 

Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython

By Wes McKinney; O''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''Reilly Media (2012)

 

Practical Arduino Engineering, by Harold Timmis, APRESS (2011)

 

Teaching method

The course will be composed of theoretical classes (2 hour) where the program will be presented, and lab time (2 hours) where the student will incrementally implement a complete instrumentation application. Two moments of evaluation in the lab will exist with the final evaluation of the complete project.

Evaluation method

Two Tests or final examination (40%)

Final Project (60%) 

The project submission date will be defined during the semester. An extension date for the project will be the same as the exam date with a grade limited to 15 values.

Mínimum grade for each evaluation element: 9.5

Subject matter

  1. Physical measurements integration, based on standard communication protocols
  2. Integrated signal acquisition systems. Usage of instrumentation prototyping systems .
  3. Languages for scientific computation to apply on features extraction and visualization, both on time and frequency domains.
  4. Machine learning applied to decision and control on instrumentation systems.
  5. Rapid application development for aggregated information presentation. Data discovery based on the measurements databases.

Programs

Programs where the course is taught: