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# Details for the Course Syllabus for Course BMEN15F valid from Autumn 2018

General
Aim
• The course provides basic knowledge in statistical signal processing and the theory of optimal methods and how they can be applied. The course presents signal processing methodology and solutions to problems where digital systems tune in automatically and adapt to the environment. The student is given enough theoretical and practical knowledge to independently be able to formulate the mathematical problem, solve it and implement the solution for use with real-life signals.
Contents
• Optimum filtering

-Wiener filters
-Linear prediciton
-The Levinson-Durbin algorithm

-Cost functions, minimization problems and iterative procedures
-Convergence and tracking capability, implementation aspects
-Strategies for how to connect adaptive filters

The LMS family

-Principle and derivation
-Convergence analysis and parameter settings
-Variants including Normalized LMS, Leaky LMS, Fast LMS and Sign LMS
-Matlab implementation
-LMS in fixed-point arithmetic
-Principle and derivation
-Parameter settings

The RLS family

-Aspects when used
-Matlab implementation
-Numerical properties
Knowledge and Understanding
• For a passing grade the doctoral student must
• have knowledge about and understand the main concepts in optimum and adaptive filter theory
be able to apply the most commonly used methods to real problems and real-life signals (Matlab-level)
be able to formulate mathematical problems based on described situations
Competences and Skills
• For a passing grade the doctoral student must
• be able to explain the main principles behind the most common adaptive methods (LMS and RLS)
be able to explain/calculate the convergence and stability properties for these methods
be able to sketch the most common block diagrams/structures used for adaptive filters and their properties
be able to set parameters needed to make the algorithms work
be able to foresee the consequences for the algorithms when implemented in fixed-point arithmetic
be able to implement adaptive filters
Judgement and Approach
• For a passing grade the doctoral student must
• have the ability to analyze, evaluate and implement adaptive algorithms, and be able to interpret and describe the principles which they are based on.
have the insight that many different technical problems can be solved using the same methods.
Types of Instruction
• Lectures
• Laboratory exercises
• Exercises
• Project
• Exercises 14 h, computer exercises 14 h and laboratory work 2 x 4 h
Examination Formats
• Written exam
• Written report
• Failed, pass
Assumed Prior Knowledge
Selection Criteria
Literature
• Haykin, S.: Adaptive Filter Theory. Pearson Education, 2014. ISBN 9780273764083.
Further Information
Course code
• BMEN15F