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Signal Analysis, Classification and Processing

Code: PDEEC0008     Acronym: SACP

Keywords
Classification Keyword
OFICIAL Electrical and Computer Engineering

Instance: 2019/2020 - 1S Ícone do Moodle

Active? Yes
Web Page: https://moodle.up.pt/course/view.php?id=2306&lang=en
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Doctoral Program in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
PDEEC 3 Syllabus since 2015/16 1 - 7,5 70 202,5

Teaching Staff - Responsibilities

Teacher Responsibility
Diamantino Rui da Silva Freitas
Sérgio Reis Cunha
Aníbal João de Sousa Ferreira

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Aníbal João de Sousa Ferreira 0,75
Diamantino Rui da Silva Freitas 1,50
Sérgio Reis Cunha 0,75

Teaching language

Suitable for English-speaking students

Objectives

To review the knowledge and mathematical bases of signal processing in a uniformization perspective.

To learn the third cycle of studies advanced topics in signal processing.

To learn how to combine and apply knowledge into projects.

To learn how to evaluate solutions.

Learning outcomes and competences

To know and be able to apply: analysis and synthesis of signals and systems with transforms in the continuous and the discrete-time domains; multiresolution/multirate processing, analysis and synthesis and filter banks; spectral estimation techniques, parametric analysis and synthesis techniques;  feature extracion and signal detection; cepstral analysis, synthesis and deconvolution; multi-channel analysis and instrumentation.
Analysis and spectral estimation using signal and noise subspace methods.
Create solutions to signal processing problems in acoustic measurements, speech anaçysis and synthesis, sonar/radar and synthetic aperture applications.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Basic knowledge of 2nd study cycle signal processing: signals and systems transforms in the continuous and discrete domains; signals theory, convolution and signal processing in linear and time-invariant systems, impulse response; continuous and discrete-time conversion; spectral analysis, filtering.

Program

1.0 Review: Frequency analysis - Fourier analysis in practical application environments. Zoom FFT techniques. Hilbert transform techniques, analytic signal processing, demodulation, envelope detection. Dual channel processing techniques, cross spectrum, coherence function, frequency response estimation, effects of noise.

2.0 Correlation functions, time averages. Cepstral analysis and homomorphic deconvolution - eco detection, deconvolution, minimum phase equalization.

3.0 Optimal digital signal processing and Spectral estimation; Linear prediction and optimum linear filters; Lattice filters; The Levinson Recursion; AR and ARMA filters; System modeling, identification and processing by least squares methods; Parametric modeling (AR, MA and ARMA). Application to speech processing, analysis and syntesis. Interpolation by L and decimation by M; Rate conversion by L/M; 

4.0 The matched filter in continuous time; Signal processing in sonar/radar. Signal processing in synthetic aperture radiation.

5.0 A review of the autocorrelation and cross-correlation functions. Biased and unbiased practical estimation. Experiments in spectrum estimation using periodogram based methods and their variations. Importance of the analysis window regarding
spectral resolution and leakage. Periodogram-based accurate frequency estimation of sinusoidal components. Autocorrelation and autocovariance matrices. Eigenvalues and Eigenvectors of
autocorrelation matrices. Eigendecomposition of the autocorrelation matrix. Concept of pseudo-spectrum. Use of the pseudo-spectrum in the estimation of the frequency of complex exponentials.
Frequency estimation using eigendecomposition of the autocorrelation matrix, the orthogonality between noise subspace and signal subspace, and the pseudo-spectrum.
Classic particular cases: the Pisarenko Harmonic Decomposition and the MUSIC algorithm. Principal components spectrum estimation.

Mandatory literature

Alan V. Oppenheim, Ronald W. Schafer, John R. Buck; Discrete-time signal processing. ISBN: 0-13-083443-2
Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon.; Statistical and adaptive signal processing. ISBN: 1580533663

Teaching methods and learning activities

Presentation of topics, examples of applications followed by problems to be solved. Proposal of exercises for autonomous work and their resolution. Proposal of home assignments and small projects.

Software

Matlab

keywords

Technological sciences > Technology > Computer technology > Signal processing
Technological sciences > Technology > Computer technology > Speech processing

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Apresentação/discussão de um trabalho científico 30,00
Trabalho prático ou de projeto 70,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Apresentação/discussão de um trabalho científico 30,00
Estudo autónomo 80,00
Frequência das aulas 36,00
Trabalho escrito 56,50
Total: 202,50

Eligibility for exams

Sufficient assiduity and realization with approval of the activities during classes.

Calculation formula of final grade

Final grade will be calculated as the average between the classifications obtained in the home assignments or small projects.

Each classification shall not be less than 10 in 20.

 

Examinations or Special Assignments

Assignments or mini-projects: there will be 6.

Internship work/project

not applicable

Special assessment (TE, DA, ...)

Except for assiduity, all other activities must be done.

Classification improvement

The final classification cannot be improved.

Observations

no remarks

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