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Lab "Real-Time Signal Processing"


Basic Information

Lab overview  
  Lecturers:   Gerhard Schmidt and Bastian Kaulen  
  Room:   -  
  Language:   English  
  Target group:   Students in electrical engineering and computer engineering  
  Prerequisites:   Skills in C programming language (for the DSS part), basic MATLAB knowledge (for the LNT part), diverse coding skills (for the ICT part)  
  Registration procedure:  

If you want to sign up for this laboratory, you need to register with the following information in the registration form

  • surname, first name
  • e-mail address
  • matriculation number
  • desired topic

Please note, that the registration period starts the 11.03.2020 at 11:00 am and ends the 08.04.2020 at 23:59 am. All applications before and after this registration period, will not be taken into account.

Registration will be possible within the before mentioned time by sending a mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it..

The registration is binding. A deregistration is possible by sending a mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Monday, 01.04.2020 at 12:00 am. All later cancellations of registration will be considered as having failed the lab.

Requirements, rules and commitments depend on the chosen topic and will be announced at the preliminary meeting (attendance is mandatory).

Attendance at all of the final presentations is mandatory as well to pass the lab.

The lab Real-Time Signal Processing and the preliminary meeting will take place with an online support.

  • Preliminary meeting, online meeting, 14.04.2020 at 17:00
  • Final presentations, ICT/DSS Bib D-037a, tbd
  Contents:   See the detailed introduction of the topics below.  



ICT.1: Optical CDMA in a VLC Transmission System (1 group of 3 students, Prof. Dr.-Ing. P.A. Hoeher)

CDMA is a well-known orthogonal transmission scheme in time domain for multi-user scenarios in RF systems. In this lab, the optical variant of CDMA (OCDMA) is to be evaluated in terms of applicability and performance in optical free-space transmissions. With respect to RF systems, the main difference in incoherent optical system is the lack of constructive and destructive signal interference. Signals cannot be canceled out, as only the intensities are added up.

Two different variants should be taken into account: i) two transmitter LED are used independently with two different CDMA sequences and ii) one single LED is used with electrically combined CDMA sequences as single transmitter.

Basic Python programming skills are required as well as basic digital signal processing knowledge.

Further contact: Adrian Krohn, ICT/NT, This email address is being protected from spambots. You need JavaScript enabled to view it.

ICT.2: Text Transmission Using Fluorescent Dyes (1 group of 3 students, Prof. Dr.-Ing. P.A. Hoeher)

Molecular communication is a biologically inspired communication paradigm, which uses molecules as information carrier. Apart from being used in microscopic applications such as targeted drug delivery, its research focuses on potential applications in industrial facilities. For this purpose, the realization and evaluation of macroscopic testbeds are essential.

In this project, a communication link in our macroscopic testbed based on fluorescent dyes will be implemented to send short text messages through a pipe. A special focus will be on different detection algorithms.

The participating students should be familiar with hardware and require basic Python programming skills.

Further contact: Sunasheer Bhattacharjee & Martin Damrath, ICT, {sub,md}

NT.1: Spectral estimation and pre-compensation for optical IM/DD systems using the Burg algorithm (1 group of 2 students, Prof. Dr.-Ing. S. Pachnicke)

The Burg algorithm is an iterative procedure known from parametric power spectrum estimation. This algorithm estimates the parameters of an autoregressive model, that represents the spectral properties of a signal. This representation can be used for different applications. One potential application is the pre-compensation of bandwidth limitations in a fiber optic transmission system. For this, the Burg method is applied to a signal that was received after transmission over the system of interest. The inverse of the extracted spectral model can be applied at the transmitter to pre-compensate for bandwidth limitations.

The group's task is to implement the Burg algorithm in Matlab. Afterward, the applicability of the Burg algorithm for pre-compensation in IM/DD systems should be investigated numerically using the simulation tool MOVE-IT.

NT.2: Neural Network (1 group of 2 students, Prof. Dr.-Ing. S. Pachnicke)

Machine learning algorithms have been successfully applied to a variety of problems. Especially the idea of deep learning based on artificial neural networks (ANN) achieved a breakthrough for statistical learning in language and image processing. The application of ML to optical commutation technology can also be of great use for many applications, such as nonlinear equalization, polarization recovery, carrier recovery, etc.

The main purpose of this lab is to develop a neural network for data classification for compensation of nonlinear distortions, such as nonlinear phase noise.

Without any prior knowledge, the NN should be able to learn and capture the link properties from only a few training data. The NN should be able to calculate non-linear decision thresholds and demodulate the received data.

NT.3: Photonic Reservoir Computing (1 group of 2 students, Prof. Dr.-Ing. S. Pachnicke)

As part of the machine learning family, artificial neural networks (ANN) are experiencing enormous growth in a variety of applications. Among different ANNs, photonic reservoir computing (RC) enables the physical implementation of an NN in hardware.

Especially for optical communications applications such as equalization tasks the conventional digital signal processing (DSP) cannot keep up with the future bandwidth requirements without losing energy efficiency. Here, RC can provide a more future-proof alternative.

The group task is to implement for an existing RC model a deep learning concept in Python. Afterward, the performance of the deep learning RC model should be evaluated using standard NN tasks.

DSS.1-3: Real-Time Audio Processing (3 groups of 2 students, Prof. Dr.-Ing. G. Schmidt)

In this project, students are going to implement a speech enhancement system in the Kiel Real-Time Audio Toolkit (KiRAT). Algorithms within this framework are to be programmed in C language, the graphical user interface is written in C++ using the QT framework. Thus, it is expected that the participants have programming skills in C/C++. There will be up to three groups of three students that will create their own speech enhancement systems. Each group will specialize on one of the following algorithmic components:

  • Analysis and synthesis filterbanks,
  • Noise estimation, and
  • Noise reduction.


Schedule of talks

Attendance during all presentations as well as active paticipation in the discussions is mandatory to pass the lab.

The schedule can be found below:

XX.07.2020   Group   Topic   Lecturer(s)   Talk duration  
  TBD       Opening   Bastian Kaulen   -  



Link   Content   Link   Content  
    Current evaluation     Completed evaluations