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Faculty of Engineering | Lund University

Detaljer för kursplan för kurs BMEN15F giltig från och med Autumn 2018

Utskriftsvänlig visning

Allmänt
Syfte
  • 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.
Innehåll
  • Optimum filtering



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


    Basics about adaptive filters

    -From optimal to adaptive filters
    -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
Kunskap och förståelse
  • För godkänd kurs skall doktoranden
  • 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
Färdighet och förmåga
  • För godkänd kurs skall doktoranden
  • 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
Värderingsförmåga och förhållningssätt
  • För godkänd kurs skall doktoranden
  • 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.
Undervisningsformer
  • Föreläsningar
  • Laborationer
  • övningar
  • Projekt
  • Exercises 14 h, computer exercises 14 h and laboratory work 2 x 4 h
Examinationsformer
  • Skriftlig tentamen
  • Skriftlig rapport
  • Underkänd, godkänd
Förkunskapskrav
Förutsatta förkunskaper
Urvalskriterier
Litteratur
  • Haykin, S.: Adaptive Filter Theory. Pearson Education, 2014. ISBN 9780273764083.
Övrig information
Kurskod
  • BMEN15F
Administrativ information
  •  -10-08
  • Professor Thomas Johansson

Alla publicerade kurstillfällen för kursplanen

4 kurstillfällen.

Kurskod ▽ Kursnamn ▽ Avdelning ▽ Inrättad ▽ Kursplan giltig från ▽ Startdatum ▽ Slutdatum ▽ Publicerad ▽
BMEN15F Optimum and Adaptive Signal Processing Biomedical Engineering Autumn 2018 2018‑11‑05 2019‑01‑20
BMEN15F Optimum and Adaptive Signal Processing Biomedical Engineering 2019‑05‑07 Autumn 2018 2019‑09‑02 2019‑11‑01 2019‑05‑07
BMEN15F Optimum and Adaptive Signal Processing Biomedical Engineering Autumn 2018 2021‑08‑30 2021‑10‑31
BMEN15F Optimum and Adaptive Signal Processing Biomedical Engineering Autumn 2018 2022‑08‑29 2022‑10‑29

Utskriftsvänlig visning