Communications Engineering II

• Description, implementation and analysis of bandpass systems in the equivalent low-pass range

• Description and analysis of modulation and demodulation methods in the complex signal domain

• Interpretation and representation of multidimensional signals in the space / time domain

• Description and analysis of time and place variants Systems

Communications Engineering 2 - SoSe 2020

Enrollment key / Einschreibeschlüssel: SoSe2020

You will find the lecture recordings and lecture slides from last year in this course, please use them to complete one lecture evey week in self-study. We will switch to classroom teaching later, depending on the development of the current covid19 pandemic situation.

Contents:

Systemtheory of Lowpass and Bandpass Systems and Signals:

  • Lowpasses, ideal lowpass,
  • Lowpass with non ideal transfer functions,
  • Discrete time lowpass systems,
  • Bandpass signals and bandpass systems, ideal bandpass
  • Description of bandpass systems and signals in equivalent lowpass domain,
  • complex signals, analytical signals, Hilbert transformation,
  • Instantaneous Phase and instantaneous frequency,
  • Equivalent operations of bandpass and lowpass signals, transmission of bandpass signals over bandpass systems,
  • Realisation of bandpass systems in equivalent lowpass domain, bandpass sampling, discrete time processing of bandpass signals,

Modulation:

  • Amplitude modulation, incoherent demodulation of amplitude modulated signals, single side band modulattion,
  • Angular Modulation, phase and frequency modulation
  • Spectra of angularly modulated signals,
  • Special cases of phase and frequency modulated signals, signals with quadratic phase, chirps,
  • Demodulation of frequency modulated signals,

Multidmensional Systemtheory:

  • Multidimensional signals, multidimensional Dirac impulses, multidimensional convolution,
  • Multidimensional Fouriertransformation,
  • Coordinate Transformtion, important theorems
  • Central slices and projection theorem
  • Radon Transformation
  • Multidimensional sampling
  • Lehrinhalte:

German:

Systemtheorie der Tiefpass und Bandpasssignale und -systeme:
Tiefpaßsysteme, der ideale Tiefpaß,
Tiefpaßsysteme mit nicht idealer Übertragungsfunktion,
Zeitdiskrete Tiefpaßsysteme, Bandpaßsysteme und Bandpaßsysteme,Der ideale Bandpaß,
Bandpaßsignale und ihre Beschreibung im äquivalenten Tiefpaßbereich, Komplexe Signaldarstellung, Analytische Signale - Hilberttransformation,
Momentanphase und Momentanfrequenz eines Signals,Äquivalenzen im Bandpaßbereich, Übertragung von Bandpaßsignalen über Bandpaßsysteme,
Übertragung des eingeschalteten Cosinus-Signals über den idealen Bandpaß, Realisierung von Bandpaßsystemen durch Tiefpaßsysteme,Abtastung von Bandpaßsignalen im äquivalenten Tiefpaßbereich, Zeitdiskrete Verarbeitung von Bandpaßsignalen, Phasen- und Gruppenlaufzeit eines Systems, Modulationsverfahren - Übertragung analoger Signale, Lineare Modulationsverfahren, Amplitudenmodulation, Inkohärenter Empfang in AM-Systemen,
Einseitenband-Amplitudenmodulation, Winkelmodulationsverfahren,Phasenmodulation und Frequenzmodulation, Das Spektum eines winkelmodulierten Signals, Spezielle frequenz- oder phasenmodulierte Signale - Signale mit quadratischer Phase (Chirps), Empfang von FM-Signalen,
Mehrdimensionale Systemtheorie:
Erweiterung der aus der eindimensionalen Systemtheorie bekannten Zusammenhänge, Faltungsintegra, Diracstoß, Mehrdimensionale Fouriertransformation, Rechengesetze der mehrdimensionalen Fouriertransformation, Koordinatentransformation, Betrachtung des Drehsinns einer Drehmatrix, Wichtige Theoreme, Verschiebungstheorem, Zirkular symmetrische Systeme, Projektionstheorem, Radialschnitte in beliebiger Richtung,
Mehrdimensionale Abtastung

Stochastische Modelle und Estimationstheorie 2 -- SS 2008

Inhalte:
Stochastic Systems: Stochastic processes, noise, processes with independent increments, Brownian motion, Wiener Processes,continuity and differentialability of stochastic processes, white noise, modeling with additive noise processes, integration of stochastic processes, Wiener's stochastic integral, Markovian processes, Gaussian-Markovian processes, linear systems with white noise Gaussian input processes, Wiener-Lee theory
Estimation Approaches: Bayesian estimation, conditional mean, maximum a-posteriori, maximum likelihood estimation, minimum variance and weighted least squares estimation, maximum entropy approaches


Optimal filtering: Kalman filter as a recursive algorithm, covariance cycle and inverse covariance cycle, Fisher's information, orthogonal projections, innovations approach, information filter

LERNZIELE:
Provide a profound basement of stochastic modeling and optimal filtering
FAKTENWISSEN:
Optimal processing of noisy measurements based on stochastic modeling of sensors and plants, Kalman filter and Kalman smoother design

METHODENKOMPETENZ:
Probability theory, theory of stochastic processes and optimal filtering

LERNZIELELITERATUR:
W.B. Davenport, Probability and Random Variables, Mc Graw-Hill,
P.S. Maybeck, Stochastic Models Estimation and Control, Academic Press
B.D.O. Anderson, J.B. More, Optimal Filtering, Prentice Hall
O. Loffeld; Estimationstheorie I, II, Oldenbourg Verlag, in Bibliothek vorhanden
PRUEFUNGSFORM: oral examinations offered on request

Communications Engineering I WS 2020/2021

English Course - Contents Identical with German Version of Allgemeine Nachrichtentechnik I



Einschreibeschlüssel/Enrolment key: ws2021



Compressed Sensing SoSe 2023 / Estimation Theory

Enrollment key / Einschreibeschlüssel: SoSe2020

you will find the lecture recordings and lecture slides from year 2018 in this course, please use them to complete one lecture evey week in self-study. We will switch to classroom teaching later, depending on the development of the current covid19 pandemic situation.

Stochastic Models - Introduction to Compressive Sensing I WS 2023/2024

Einschreibeschlüssel / enrollment key: ws2122