Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameTillämpad signalbehandling
- CodeSSY130
- Credits7.5 Credits
- OwnerMPICT
- Education cycleSecond-cycle
- Main field of studyComputer Science and Engineering, Electrical Engineering
- DepartmentELECTRICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 13122
- Maximum participants200
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0107 Examination 7.5 c Grading: TH | 7.5 c |
|
In programmes
- MPBME - BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
- MPEES - EMBEDDED ELECTRONIC SYSTEM DESIGN, MSC PROGR, Year 2 (elective)
- MPEPO - SUSTAINABLE ELECTRIC POWER ENGINEERING AND ELECTROMOBILITY, MSC PROGR, Year 2 (elective)
- MPICT - INFORMATION AND COMMUNICATION TECHNOLOGY, MSC PROGR, Year 1 (compulsory)
- MPMOB - MOBILITY ENGINEERING, MSC PROGR, Year 1 (elective)
- MPMOB - MOBILITY ENGINEERING, MSC PROGR, Year 2 (elective)
- MPSYS - SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
- MPWPS - WIRELESS, PHOTONICS AND SPACE ENGINEERING, MSC PROGR, Year 2 (elective)
Examiner
Tomas McKelvey- Deputy Head Of Department, Electrical Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
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 and circular 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 the computational 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.
- implementsignal 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: Uppsampling, downsampling, 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 15 lectures, 6 exercise sessions, 3 hand-in problems, 2 projects and a final written exam.Literature
See course homepage.
Examination including compulsory elements
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.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.