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 
7,5 c 
Grading: TH 


7,5 c






20 Dec 2012 pm V, 
05 Apr 2013 pm V, 
27 Aug 2013 pm V 
In programs
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
MPTSE INDUSTRIAL ECOLOGY, MSC PROGR, Year 2 (elective)
MPWPS WIRELESS, PHOTONICS AND SPACE ENGINEERING, MSC PROGR, Year 2 (elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPCOM COMMUNICATION ENGINEERING, MSC PROGR, Year 1 (compulsory)
MPEES EMBEDDED ELECTRONIC SYSTEM DESIGN, MSC PROGR, Year 2 (elective)
Examiner:
Professor Tomas McKelvey
Replaces
ESS145
Applied signal processing
Course evaluation: http://document.chalmers.se/doc/1aa1092c1ff8475f970127bb886ba414
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 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 semireal 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 timedomain and frequencydomain 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 DSPsystem
Content
 Review of signal theory concepts: continuoustime and sampled signal representation in both time and Fourier domain, sampling, linear processing (filtering) and continuoustime 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 blockbased filtering
 Adaptive filters: Least mean square (LMS), recursive least squares (RLS) and Kalman filtering
 Multirate signal processing: Rate conversion, polyphase 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 handin 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.

