Semester Projects (Semesterarbeiten)

If you are interested in one of the following topics, please contact the person listed with the respective topic.

If you don't find a compelling topic in our list, we are always keen on hearing about your ideas in the area of our research interests (see the Research section of our website). You are most welcome to discuss your interests with us.

Also, we have a list of finished student projects on our website.

List of Projects



Implementation of an RF channel emulator (SA)

mlerjen_ChannelEmulator.jpg
In the development process of radio transceivers, it is important to test and optimize the performance of the implemented hardware, coding schemes, and signal processing algorithms under different channel conditions. This can be done with real-world channels in over-the-air tests. However, such an approach has several disadvantages, namely interference, logistical problems, and legal restrictions on certain frequency bands. Furthermore, the results will depend on environmental conditions and are therefore often not reproducible.

A channel emulator, i.e., hardware that emulates a channel and applies it to a communication signal, is a tool built exactly to solve this problem. The parameters of the channel (e.g. Doppler spread, delay spread, noise, ...) can be controlled through the configuration of the emulator [1]. Channel emulators often also have multiple inputs/outputs in order to emulate MIMO channels. MIMO channel emulation devices are available on the market [2], but expensive and sometimes inflexible.

The aim of this project is to implement a MIMO channel emulator that works in the digital baseband domain and can be controlled from an external PC. An FPGA platform including an RF transceiver and analog/digital (ADC) and digital/analog converters (DAC) was already selected in a previous project and can be used as a starting point.

Type of project: 70% VHDL programming and simulation, 20% measurements, 10% theory
Prerequisites: Interest in RF hardware and wireless systems, Matlab, VHDL
Supervisor: Michael Lerjen
Professor: Helmut Bölcskei

References:
[1] S. Yang and Z. Can, "Design and implementation of multiple antenna channel emulator for LTE system," Proc. 9th International Conference on Communications and Networking in China (CHINACOM), pp. 208–213, 2014 [Link to Document]

[2] Keysight Technologies, Inc., "Wireless device test sets & wireless solutions" [Link to Document]



Convolutional recurrent neural networks for electrocardiogram classification (SA)

pdmytro_example_waveforms.png
Deep convolutional neural networks [1,2] have the ability to extract features invariant to local spectral and spatial/temporal variations, and have led to several breakthrough results, most prominently in computer vision [2, Chap. 9]. Recurrent neural networks, on the other hand, were shown to effectively capture long term temporal dependencies in time series [2, Chap. 10].

In this project, we propose to combine these two network structures into a Convolutional Reccurent Neural Network [3-5], apply it to the classification of electrocardiogram recordings, and evaluate it on the atrial fibrilation classification dataset provided by the PhysioNet/CinC Challenge 2017 [6].

Type of project: 20%-40% theory, 60%-80% programming
Prerequisites: Programming (preferably Python), linear algebra, machine learning, experience with neural networks is a plus
Supervisor: Dmytro Perekrestenko, Michael Tschannen
Professor: Helmut Bölcskei

References:
[1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015. [Link to Document]

[2] I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning," MIT Press, 2016. [Link to Document]

[3] Z. C. Lipton, D. C. Kale, C. Elkan and R. Wetzell, "Learning to diagnose with {LSTM} recurrent neural networks," International Conference on Learning Representations, 2016. [Link to Document]

[4] P. Bashivan, I. Rish, M. Yeasin, and N. Codella, "Learning representations from {EEG} with deep recurrent-convolutional neural networks," International Conference on Learning Representations, 2016. [Link to Document]

[5] E. Çakır, G. Parascandolo, T. Heittola, H. Huttunen, and T. Virtanen, "Convolutional recurrent neural networks for polyphonic sound event detection," {IEEE/ACM} Trans. on Audio, Speech, and Language Processing, pp. 1291-1303, 2017. [Link to Document]

[6] AF Classification from a short single lead ECG recording: The PhysioNet/Computing in Cardiology Challenge 2017 [Link to Document]



Deformation sensitivity bounds for deep convolutional neural networks (SA/DA)

withomas_hwd4.jpg
Feature extractors based on so-called deep convolutional neural networks (DCNNs) have been applied with tremendous success in a wide range of practical signal classification tasks [1] such as, e.g., in handwritten digit classification. The features to be extracted in this case correspond to the edges of the digits, and we would want these features to be robust with respect to handwriting styles. This can be accomplished by demanding that the feature extractor have limited sensitivity to certain non-linear deformations.

Recently, [2], [3] established deformation sensitivity bounds for a wide class of DCNN-based feature extractors. These bounds apply to a variety of input signal classes such as band-limited functions, cartoon functions, and Lipschitz-functions. Many signals of practical interest (such as textures) exhibit, however, sharp oscillations and are therefore not captured by these results.

The goal of this project is to use the theory of approximately time- and band-limited functions [4] to develop general deformation sensitivity bounds.

Type of project: 80%-100% theory, 0-20% simulations, depending on the student's preference
Prerequisites: Analysis, Linear Algebra, Signals and Systems I
Supervisor: Thomas Wiatowski
Professor: Helmut Bölcskei

References:
[1] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” MIT Press , 2016. [Link to Document]

[2] T. Wiatowski and H. Bölcskei, “A mathematical theory of deep convolutional neural networks for feature extraction,” arXiv:1512.06293, 2015. [Link to Document]

[3] P. Grohs, T. Wiatowski, and H. Bölcskei “Deep convolutional neural networks on cartoon functions,” Proc. IEEE Int. Symp. on Inf. Theory (ISIT), pp. 1163–1167, 2016. [Link to Document]

[4] D. Slepian, "On bandwidth," Proc. of the IEEE, pp. 292–300, 1976. [Link to Document]



Rate of energy decay in deep convolutional neural networks (SA)

withomas_energydecay.png
Many practical machine learning tasks employ very deep convolutional neural networks (CNNs) [1]. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how fast the energy contained in the propagated signals (a.k.a. feature maps) decays across layers. Recently, [2] characterized analytically feature map energy decay rates for CNNs that employ general filters, wavelets, or Weyl-Heisenberg filters.

The goal of this project is to implement the CNNs analyzed in [2] and to experimentally verify the corresponding energy decay rates.

Type of project: 10%-20% theory, 80-90% simulations
Prerequisites: Signals and Systems I, Python
Supervisor: Thomas Wiatowski
Professor: Helmut Bölcskei

References:
[1] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2015. [Link to Document]

[2] T. Wiatowski, P. Grohs, and H. Bölcskei, “Energy propagation in deep convolutional neural networks,” IEEE Transactions on Information Theory, 2017, to appear. [Link to Document]



Control policy learning for a flight simulator (SA)

mlerjen_simulator.jpg
Reinforcement learning (RL) [1] refers to the problem of finding suitable actions to take in a given situation in order to maximize a reward. RL algorithms that recently led to breakthrough results in practical machine learning tasks (such as learning to play Atari games [2] or the strategy board game Go [3]) typically employ a deep convolutional neural network [4] for feature extraction.

This project aims at developing an RL-based approach for learning control policies for a flight simulator. The scope of the project may encompass value function design, training data acquisition, as well as a first implementation in Python.

Type of project: 90% Theory/Literature research, 10% Programming
Prerequisites: Python, machine learning basics is a plus
Supervisor: Michael Tschannen, Michael Lerjen
Professor: Helmut Bölcskei

References:
[1] R. S. Sutton and B. A. Barto, "Reinforcement learning: An introduction," MIT press, 1998.

[2] V. Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518 (7540), pp. 529–533, 2015. [Link to Document]

[3] D. Silver et al., "Mastering the game of Go with deep neural networks and tree search," Nature, vol. 529 (7587), pp. 484–489, 2016. [Link to Document]

[4] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521 (7553), pp. 436–444, 2015. [Link to Document]