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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 2013-02-14 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

The course is closed for further applications
Teaching language: English
Open for exchange students
Block schedule: D

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5c Grading: TH   7,5c   14 Jan 2015 pm H,  16 Apr 2015 pm M,  25 Aug 2015 pm V

In programs

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPCOM COMMUNICATION ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
MPEES EMBEDDED ELECTRONIC SYSTEM DESIGN, MSC PROGR, Year 2 (elective)
MPWPS WIRELESS, PHOTONICS AND SPACE ENGINEERING, MSC PROGR, Year 2 (elective)

Examiner:

Professor  Tomas McKelvey


Replaces

ESS145   Applied signal processing

Course evaluation:

http://document.chalmers.se/doc/788d497d-5ddd-4074-9a03-bea298ff4861


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

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 Random signals analysis 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

  • apply LMS, RLS and Kalman filters to linear adaptive filtering problems and do simplified analysis regarding stability and rate of
    convergence

  • apply multirate techniques to signal processing problems to increase efficiency

  • explain how quantization and finite word lengths affect the signal and algorithm quality and calculate the effect on the SNR

  • discuss the effect of using a linear finite dimensional model as an approximation for an infinite dimensional linear systems.

  • implement signal processing algorithms on a DSP-system


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

  • Finite word length effects: quantization of signal and filter coefficients

  • Implementation on DSP systems

Organisation

The course is comprised of approximately 18 lectures, 6 exercise sessions, 3 hand-in problems and 2 projects.

Literature

See course homepage.

Examination

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


Page manager Published: Thu 04 Feb 2021.