Statistical Analysis and Signal Processing

Code:

M4062

Acronym:

M4062

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2016/2017 - 1S

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=292
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Mathematical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:AST	2	Plano de Estudos oficial desde_2013/14	1	-	6	56	162
			2
M:CC	1	Study plan since 2014/2015	1	-	6	56	162
M:EG	5	Plano de Estudos do M: ENG.GEO_2013-2014	1	-	6	56	162
M:ENM	2	Official Study Plan since 2013-2014	1	-	6	56	162
M:M	3	Plano de Estudos do M:Matemática	1	-	6	56	162
			2

Teaching language

Suitable for English-speaking students

Objectives

To acquire knowledge on stochastic signals digital analysis

Learning outcomes and competences

The student should be able to:

 

-review essential discrete-time signal processing topics, including sampling effect and transforms

-characterize random signals in time, frequency and time-frequency/scale domain, formulate suitable models, estimate the parameters, and evaluate the quality of the estimates.

-use linear estimation theory, optimal linear estimation, Kalman and Wiener filtering, to solve estimation problems with applications in several technical disciplines, for example in telecommunications and signal processing but also in other disciplines, such as econometrics and statistics.

- use adaptive signal processing algorithms for extracting relevant information from noisy signals: emphasis is on recursive, model based estimation methods for time-variant signals and systems. Applications in, for example, communications, finance and medicine are discussed.

- critically select the methods for each concrete case study with interpretation of the obtained results.

Working method

Presencial

Program

Random Processes. Characterization in time and frequency domains. Stationarity and ergodicity. Linear models. Spectral estimation. Parametric and non parametric methods. Introduction to time-frequency/scale analysis and wavelets. Optimal and adaptive signal processing fundamentals. Least mean squares and recursive algorithms. Introduction to novel paradigms in statistical signal processing.

Applications/illustrations of the methods to case studies.

Mandatory literature

000039792. ISBN: 0-471-59431-8
000089741. ISBN: 0-12-088581-6
000080761. ISBN: 1-58053-610-7

Complementary Bibliography

Semmlow John L.; Biosignal and medical image processing. ISBN: 9781466567368 (Biosignal and medical image processing / John L. Semmlow, Benjamin Griffel)
Vaseghi Saeed V.; Advanced digital signal processing and noise reduction. ISBN: 9780470754061 (Advanced digital signal processing and noise reduction / Saeed V. Vaseghi)

Teaching methods and learning activities

Lectures TP to present and illustrate the topics. Problems / Projects with strong laboratorial computation component using Matlab (R).

A special attention is given to the understanding of the concepts and methods with an effective use of simulated and experimental data. One of the aims of the discipline is also the improvement of the oral and written competences. The discipline presents an important computational component with MATLAB or other adequate computational environment

Software

Matlab
R

keywords

Physical sciences > Mathematics > Statistics
Physical sciences > Mathematics > Applied mathematics
Physical sciences > Mathematics > Applied mathematics > Engineering mathematics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Prova oral	50,00
Trabalho escrito	50,00
Total:	100,00

Eligibility for exams

Minimum of 8 on continous evaluation.
Minimum of 8 in individual project

Calculation formula of final grade

Work / Labs (T-50%) and final Project (P-50%). The final Project evaluation, includes discussion (30%), final presentation (20%) and written report(50%).

Examinations or Special Assignments

Not applicable. Identical for all of the students.

Special assessment (TE, DA, ...)

Not applicable. Identical for all of the students.

Classification improvement

Not applicable for the continous evaluation component T.