Teaching plan for the course unit

 

 

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On 3 April 2020 and in agreement with the President of the Government of Catalonia, the Catalan Minister of Business and Knowledge and the rectors of the other Catalan universities, the Rector of the Universitat de Barcelona decided to suspend all second-semester face-to-face teaching activities until the end of the academic year. For this reason, our university's teaching staff may need to make certain changes to the course plans of the subjects they teach, so that they can teach subjects online. When and where such changes are made, they will be explained in a new appendix attached to the end of the original course plan.



General information

 

Course unit name: Data Analysis and Signal Processing for Chemical Sensing

Course unit code: 573780

Academic year: 2019-2020

Coordinator: Santiago Marco Colas

Department: Department of Electronic and Biomedical Engineering

Credits: 5

Single program: S

 

 

Estimated learning time

Total number of hours 125

 

Face-to-face and/or online activities

60

 

-  Lecture

Face-to-face

 

22.5

 

-  Problem-solving class

Face-to-face

 

7.5

 

-  Laboratory session

Face-to-face

 

30

Supervised project

25

Independent learning

40

 

 

Recommendations

 

Students should have taken previous courses on instrumentation, signal processing and statistics.

 

 

Competences to be gained during study

 

— Capacity to apply the proper chemical sensor to a point-of-care chemical sensing system.

 

— Capacity to design a calibration plan for multisensor systems and to select the proper conditions and their sequencing.

 

— Capacity to report orally on the design and test of a chemical multisensor system. 

 

 

 

 

Learning objectives

 

Referring to knowledge

— Be able to describe the principle of operation of the most common chemical sensor technologies.

 

— Understand and interpret the figures of merit related to chemical measurements correctly.

 

— Become familiar with the basic blocks in a multivariate predictive model algorithm.

 

— Understand the concepts of internal and external validation and their roles.

 

Referring to abilities, skills

— Be able to select a chemical sensor to operate in a default scenario after a proper definition of the expected spec.

 

— Be able to estimate the uncertainty of a measurement.

 

— Be able to estimate the measurement errors using propagation of uncertainty calculations or Monte Carlo methods.

 

— Provide a basic signal data processing workflow in a high level programming language to implement the signal pocessing associated to a chemical multisensor system.

 

 

Teaching blocks

 

1. Introduction to chemical sensing

1.1. Signal detection theory

1.2. Linear calibration

1.3. Measurement uncertainty

1.4. Introduction to non-linear calibration models

1.5. Sensor fundamentals

2. Introduction to sampling

2.1. Sampling fundamentals

2.2. Sampling of gases and volatile organic compounds

2.3. Sampling of liquids

2.4. Sampling of solids

3. Flow control components

3.1. Valves

3.2. Pumps

4. Sensor technologies

4.2. Chemomechanical sensors

4.3. Temperature sensors

4.4. Optical sensors

4.5. Electrochemical sensors

4.6. Photoionisation sensors

4.7. Chromatographic detectors

5. Chemical sensor signals

5.2. Dimensionality reduction

5.3. Signal preprocessing

6. Introduction to multivariate predictive models

6.1. Model validation techniques

7. Multivariate calibration

7.1. Principal component regression, partial least squares, and ridge and lasso regression

7.2. Support vector regression

8. Introduction to classifiers

8.1. Quadratic classifiers

8.2. Partial least squares and discriminant analysis

8.3. Multilayer perceptron

8.4. Random forest

 

 

Teaching methods and general organization

 

The learning methodology combines:

— Lectures

— Independent learning and problem solving

— Computer exercises

— Guided instrumentation design studies

All activities are carried out in English.

 

 

Official assessment of learning outcomes

 

— Questionnaires on readings: 20%

— Selected laboratory report: 20%

— Short project: 20%

— Examination: 40% (a mark of at least 5 out of 10 is needed to pass the course)

 

Examination-based assessment

Examination: 100% of the final grade. 

 

 

Reading and study resources

Consulteu la disponibilitat a CERCABIB

Book

Danzer, Klaus. Analytical chemistry: theoretical and metrological fundamentals. Springer Science & Business Media, 2007.

Tagawa, Tatsuo, Toshiyo Tamura, and P. Ake Oberg. Biomedical sensors and instruments. CRC press, 2011.

Comini, Elisabetta, Guido Faglia, and Giorgio Sberveglieri, eds. Solid state gas sensing. Vol. 20. Springer Science & Business Media, 2008.

Kohl, Claus-Dieter, and Thorsten Wagner, eds. Gas sensing fundamentals. Vol. 15. Springer, 2014.

Rouessac, Francis, and Annick Rouessac. Chemical analysis: modern instrumentation methods and techniques. John Wiley & Sons, 2013.

Adams, Mike J. Chemometrics in analytical spectroscopy. Royal Society of Chemistry, 2007.

Wehrens, Ron. Chemometrics with R: multivariate data analysis in the natural sciences and life sciences. Springer Science & Business Media, 2011.

Varmuza, Kurt, and Peter Filzmoser. Introduction to multivariate statistical analysis in chemometrics. CRC press, 2016.

Kuhn, Max, and Kjell Johnson. Applied predictive modeling. Vol. 26. New York: Springer, 2013.

Orfanidis, Sophocles J. Introduction to signal processing. Prentice-Hall, Inc., 1995.

Kirkup, Les, and Robert B. Frenkel. An introduction to uncertainty in measurement: using the GUM (guide to the expression of uncertainty in measurement). Cambridge University Press, 2006.

 

 

ADAPTATION OF THE COURSE PLAN TO ONLINE TEACHING MODE FOR THE REMAINDER OF THE ACADEMIC YEAR 2019-2020, IN RESPONSE TO THE COVID-19 CRISIS

 

Changes to subject content
Changes to the theory content are minimal as the planned classes are being taught online. We are currently only one week behind the original schedule. Three of the practical sessions require use of the laboratory so it has not been possible to reschedule them. The content is unchanged, but unfortunately the competences acquired in the electronic instrumentation laboratory will be affected and this may impact negatively on the students’ assimilation of the related theory content.

Changes to teaching methods and general organization
Lectures have been delivered via the Virtual Campus using Blackboard Collaborate. Sessions have been held in real-time, maintaining the scheduled class hours. Students communicate with the teacher using the chat or directly via microphone. The practical classes linked to laboratory work have been replaced by specially designed MATLAB sessions, which students can access from home using the campus licence. The new practical sessions will enable students to consolidate their understanding of signal and data processing, as envisaged in the original course plan.

Changes to assessment
The main change affects the theory examination, which students will now sit online. The examination will be redesigned so that students are able to use their notes and access the Internet to consult resources. There are no changes to the remaining assessment activities (reports on practical sessions and assignments). There are no changes to the weighting of each activity.