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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
SSY130 - Applied signal processing
 
Syllabus adopted 2008-02-23 by Head of Programme (or corresponding)
Owner: MPCOM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Electrical Engineering
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0107 Examination 7,5c Grading: TH   7,5c   16 Dec 2008 pm H,  16 Apr 2009 pm V,  20 Aug 2009 pm V

In programs

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPCOM COMMUNICATION ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 2 (elective)
MPIES INTEGRATED ELECTRONIC SYSTEM DESIGN, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR - Control specialization, Year 1 

Examiner:

Professor  Tomas McKelvey


Replaces

ESS145   Applied signal processing

Course evaluation:

http://document.chalmers.se/doc/922255616


Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Working knowledge of linear algebra, probability theory and signals and systems (especially transforms, filtering, convolution, sampling theorem) is required. Knowledge of random processes is very useful, but not essential. Hence, the course MVE135 Random processes with applications is recommended. Experience of MATLAB is required.

Aim

Signal processing involves techniques to recover important information from signals and to suppress irrelevant parts of those signals. The aim of this course is to provide the students with knowledge of standard techniques and applications in digital signal processing. These are relevant for the design and implementation of communication systems, control systems and other measurement systems such as biomedical instrumentation systems. The students are also given the opportunity to practically apply some of the techniques to semi-real signal processing problems and will be given insight into current practice in industry.

Learning outcomes (after completion of the course the student should be able to)

· in both time-domain and frequency-domain analyse the effect of sampling, linear filtering and signal reconstruction
· explain the relation between the Fourier transform, discrete Fourier transform and fast Fourier transform and apply the discrete Fourier transform to perform block based linear filtering
· apply linear filter design techniques to construct FIR and IIR filters satisfying given specifications
· solve FIR and ARX system identification problems and AR and ARMA spectral estimation problems as well as assess the quality of the estimated models
· apply LMS, RLS and Kalman filter to linear adaptive filtering problems and do simplified analysis regarding stability and rate of convergence
· explain how quantization and finite word lengths effects the signal quality and calculate the effect on the SNR
· list implementation platforms and discuss their advantages and disadvantages
· discuss the effect. of using a linear finite dimensional model as an approximation for an infinite dimensional linear systems.

Content

  • Review of signal theory concepts: continuous-time and sampled signal representation in both time and Fourier domain, sampling, linear processing (filtering) and continuous-time signal reconstruction (D/A conversion)
  • Review of random processes: mean values, autocorrelation function, spectrum, linear filtering of a white noise process.
  • Filter design and realization: FIR and IIR filter structures, design methodologies, implementation details, matched filters
  • Discrete Fourier transform: Finite data length, Fast Fourier transform (FFT), use of DFT for linear block-based filtering
  • Adaptive filters: Least mean square (LMS), recursive least squares (RLS) and Kalman filtering
  • Multi-rate signal processing: Rate conversion, poly-phase representation, filter banks
  • System Identification and Spectral Analysis: Estimation of parametric FIR and ARX models, parametric spectral analysis: AR, MA and ARMA models
  • Fix-point and floating-point implementation: Number representation, arithmetic operations, overflow, arithmetic round-off effects, dynamic range scaling
  • Finite word length effects: quantization of signal and filter coefficients
  • Implementation platforms: Software based (PC/Microprocessor / DSP), hardware based (FPGA / ASIC)

Organisation

The course is comprised of approximately 18 lectures, 12 exercise sessions, and 2 projects/labs.

Literature

To be determined

Examination

The final grade is based on scores from projects and a written exam. The labs and projects are mandatory in the sense that they must be passed to pass the course.


Page manager Published: Thu 04 Feb 2021.