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



Telemetry for a sounding rocket (SA)

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The Akademische Raumfahrt Initiative Schweiz (ARIS) aims at engaging students from Swiss educational institutions in hands-on space engineering challenges, specifically in the design, construction, and testing of a sounding rocket to compete in the 2018 Spaceport America Cup in New Mexico, USA.

The Spaceport America Cup is the world’s largest university rocket engineering competition. Student teams are challenged to design and build a rocket capable of launching a 4 kg payload to target altitudes of up to 30,000 ft and to recover the rocket completely post-flight.

In this multi-disciplinary project, you will contribute to the development of the telemetry system of the ARIS rocket. The goal is to implement, test, and verify a proof-of-concept solution for intra-rocket and rocket-to-ground communication. In a first step different options for antennas, communication modules, and protocols shall be analyzed and compared with telemetry systems of commercial rocket manufacturers. In particular, performance, implementation complexity, and cost shall be analyzed in the context of the stucture of the rocket and the specific environment (e.g. high velocity and acceleration). In a second step the favored solution shall then be implemented, tested, and verified on an ARIS model rocket.

Type of project: 30% concept, 70% avionics development, test, and measurements
Prerequisites: Interest in embedded and wireless systems
Supervisor: Michael Lerjen
Professor: Helmut Bölcskei

References:
[1] Homepage of the ARIS project [Link to Document]



Implementation of an RF channel emulator (SA)

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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)

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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]



Control policy learning for a flight simulator (SA)

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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]



Analyzing cost functions for generative adversarial networks (SA/DA)

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Generative adversarial networs (GANs) [1] are a family of machine learning models that have led to remarkable results in image generation tasks [2]. The GAN-learning-problem is a two-player game between the so-called generator, which learns how to generate samples resembling the training data, and a so-called discriminator, which learns how to discriminate between real and fake data points. Both players aim to minimizie their own cost function until the Nash-equilibrium is established.

The goal of this project is to analyze---mathematically and possibly experimentally---different cost functions for image generation tasks.

Type of project: 80%-90% theory, 10-20% simulation
Prerequisites: Analysis, linear algebra, probability theory, Python
Supervisor: Thomas Wiatowski, Michael Tschannen
Professor: Helmut Bölcskei

References:
[1] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Proc. of Neural Information Processing Systems (NIPS), pp. 2672–2680, 2014. [Link to Document]

[2] A. Radford, L. Metz, and S. Chintala “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv:1511.06434, 2015. [Link to Document]