ACIT4810 Advanced Methods in Modelling, Simulation, and Control Emneplan

All aids are permitted.

This phase is the beginning of a longer research project. The content will be relative to the student's project.

On completion of this course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The course will give the students in depth knowledge on the following topics

• Sampling, digitalization and reconstruction.
• How to obtain the signal spectrum of digital and analog signals
• How to obtain the frequency response of digital and analogue systems
• Filtering
• Detection techniques, correlation and deconvolution
• Reconstruction of images from measured data by inverse methods

Skills

The student will know how to:

• Describe digital signals and systems mathematically in the time domain, the frequency domain and the Z-domain
• Describe digital images mathematically in real space and K-space
• Describe linear time invariant systems using difference equations, impulse responses, point spread functions and transfer functions
• Analyze time discrete systems in the frequency domain and discrete images in K- space
• Use analog filters and the digitized versions of analog filters: FIR and IIR
• Apply post processing of images for filtering
• Apply inverse methods for image reconstruction from measured data

General competence

The student will have general competence on:

• Spectrums, impulse responses, point spread functions, frequency responses, K- space, correlation, convolution and modulation
• Fourier-series (FS), Fourier transform (FT).
• Sampling, reconstruction and aliasing
• Implementation of DSP-filters and inverse methods on a computer.

This course is divided into two parts. The first part with focus on covering the principles of Statistical Learning. Different seminars will be given on the different methodological aspects of Statistical learning, mainly, supervised learning and unsupervised learning.

The second part will focus on the students completing a programming project. This is a real data analysis problem, where the student is asked to carry out the analysis using the tools and techniques from the course and hand in a report documenting the steps he has taken in the analysis. The ultimate goal is to build a predictive model.

The project report will consist of at least 25 pages and max 60 pages.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The following required coursework must be approved before the student can take the exam:

• Four compulsory group assignments.

Two internal examiners. External examiner is used periodically.

One internal examiners. External sensor is used periodically. If oral, two internal examiners.

Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.

This course will contain the following topics:

• Sampling, reconstruction and aliasing
• Impulse response, Point spread function
• Fourier series and Fourier transform
• Frequency analysis and k-space analysis
• Frequency response, filters and transfer functions
• Detection methods, correlation, convolution and modulation
• Inverse methods for image reconstruction

Two internal examiners. External examiner is used periodically.

Associate Professor Tiina Komulainen