The course gives a broad overview of modeling using stochastic processes in electrical engineering applications. Formulating problems using mathematical modeling is an important part of the course.

Basics about continuous time an discrete time stochastic processes, especially weakly stationary processes. Definitions of probability distribution and density functions, statistical mean, mean power, variance, autocorrelation function, power spectral density,

Gaussian processes and white noise. Linear filtering of stochastic processes, Ergodicity: Estimation of statistical properties from measurements. Sampling and reconstruction: Transformations between continuous and discrete time signals. Influence of sampling, sampling theorem. Pulse amplitude modulation. Errrors in the reconstruction of stochastic signals. Estimation theory: Linear estimation, orthogonality conditions. Prediction and Wiener filtering. Model based signal processing: Linear signal models, AR-models. Spectral estimation. Application of the above to simpler electrical engineering applications.

After passing the course you should be able to

• Analyze given problems regarding properties of weakly stationary stochastic processes.
• Analyze given problems in at least one of the areas filtering, sampling and reconstruction of weakly stationary processes.
• Analyze given problems in estimation and/or optimal filtering.
• Apply mathematical modeling tools to problems in electrical engineering. Develop simple software codes using, e.g., Matlab, and use this to simulate and analyze problems in the area. Report the methodology and results.
• Use a given mathematical model, or formulate one on your own, to solve a given technical problem in the area, analyze the result and justify if it is reasonable.

If you are passing the course with higher grades, you should, in addition to the above, be able to

• Analyze given problems in filtering, sampling and reconstruction of weakly stationary processes.
• Analyze given problems in estimation and optimal filtering.
• Formulate mathematical models which are applicable and relevant to a given problem formulation within the area. When vital information is missing, you should be able to judge and compare different possibilities as well as make reasonable assumptions to achieve a satisfactorily modeling performance.
• Use a given mathematical model, or one formulated by yourself, to solve a problem in the area; e.g., a problem composed of several interacting sub-problems or other problems requiring a more complex modeling, analyze the result and its validity.

EQ1100 Signals and systems II, or equivalent
SF1901 Probability Theory and Statistics, or equivalent 
EL1150 Introductory Matlab Course, or equivalent.

• Script: ”Signal Theory” by Händel, Ottoson, Hjalmarsson,   translated to English by M. Jansson.
• Collection of problems: ”Exercises in Signal Theory”   
• ”Collection of Formulas in Signal Processing”   

Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

A, B, C, D, E, FX, F

• PRO1 - Project, 1.0 credits, Grading scale: P, F
• PRO2 - Project, 1.0 credits, Grading scale: P, F
• TEN1 - Examination, 5.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Written exam, (TEN1; 5,5 ECTS credits; Grading: A-F).
Project assignment 1 and 2 (PRO1; 1 ECTS credits PRO2; 1 ECTS credits; Grading: Pass/Fail).

To pass the course, you need

• to pass each of the two projects, i.e., the project report needs to be approved
• to have achieved a grade A-E in the exam. 

To pass the exam, you need

• to pass part A of the exam which is a correct/false questionaire where bonus points from this cours round count (bonus points from previous course round do not count)
• to pass part B of the exam which is problem solving. 

Bonus points for Part A of the exam can be voluntarily obtained during the course round by

• doing the online diagnostic test
• active participation during the tutorial
• answering the reflective questions or problems before the reflective lectures

Note, bonus point are an incentive to be active from the very beginning, but they are not mandatory and not needed to pass the course.

The teacher welcomes students to improve the grade by repeating an exam. For students who passed the course previously, this is however only possible if the examination room has sufficient space.  Please dirctly contact the adminstration if you are interested in this - the teacher always supports this.

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.