### Bachelor's Theses

## Efficient Decoding of Tailbiting Convolutional Codes

#### Description

The aim of this thesis is to study decoding of tailbiting convolutional codes, a class of codes suited for short blocklengths. We address their efficient decoding as well as applications to communication systems with feedback.

#### Supervisor:

## OFDM under Coarse Quantization

#### Description

#### Supervisor:

## Vector Quantization with Convolutional Codes

#### Description

#### Supervisor:

## A Summary of the Most Recent Code-Based Digital Signature Schemes based on Variations of the Syndrome Decoding Problem

#### Description

In this thesis, the student will study and summarize the most recent code-based digital signature schemes based on the syndrome decoding problem

#### Supervisor:

## [identification] Implementation of identification with non-cryptographic hash functions

**Keywords:**

universal hash identification

#### Description

Identification is a communication scheme that allows rate doubly exponential in the blocklemght, with the tradeoff that identities cannot be decoded (as messages do) but can only be verified.

The double exponential growth presents various challenges in the finite regime: there are heavy computational costs introduced at the encoder and decoder and heavy trade-offs between the error and the codes sizes.

The ultimate goal is to find a fast, reliable implementation while still achieving large code sizes.

Identification codes can be achieved by first removing the errors from the channel with regular transmission channel coding, and then sending a challenge though the corrected channel. For every identity i, The channenge is generated by picking a random input m and computing the corresponding output T_i(m) using a function T_i that depends on the identity. The challenge is then the pair m,T_i(m) and the receiver wanting to verify an identity j will verify whether j=i by testing the challenge. This is done by recomputing the output with T_j and verifying whether T_j(m)= T_i(m). The errors are reduced by ensuring that the various functions collide on a small fraction of the possible inputs.

It turns out that choosing good sets of funtions {T_i} is the same as choosing error-correction codes {c_i} with large distance, where now each codeword c_i defines a function by mapping positions m (sometimes called code locators) to symbols c_im of the codeword.

We can thus construct identification codes by choosing error-correction codes where we are only interested in the performance of the error correction encoders (we are not interested in the error-correction decoder or error-correction codes).

Your task will be implementing the identification codes described in

aiming at the fastest implementation, and testing their performance in comparison to other current implementations.

For reference, our previous work on identification based on Reed-Solomon and Reed-Muller code can be found at

The coding will be in Python/Sagemath.

The working language will be in English.

Environment: we collaborate with LTI. At LNT and LTI there is currently a lot of funding for research in identification. Therefore you will find a large group of people that might be available for discussion and collaboration.

#### Supervisor:

## Implementation of model poisoning attacks in federated learning

#### Description

Federated learning is a machine learning paradigm where decentralized entities (clients) collaboratively learn using their private data. A central server acts as a coordinator of the learning process. Due to the sensitivity of the private data involved, the data cannot be transferred. A salient problem of federated learning is the presence of malicious clients, which are clients that try to destroy the learning process. Malicious clients can do this by corrupting their data and/or by modifying their local model updates. The goal of this project is to understand how model poisoning attacks and defense strategies perform under different scenarios of federated learning using experiments.

References:

[1]- https://www.ndss-symposium.org/wp-content/uploads/ndss2021_6C-3_24498_paper.pdf

[2]- https://arxiv.org/pdf/1903.03936.pdf

[3]- https://arxiv.org/pdf/2304.00160.pdf

#### Prerequisites

- Basic knowledge of machine learning
- Python programming skills, knowledge of PyTorch is an advantage

#### Contact

marvin.xhemrishi@tum.de

#### Supervisor:

## Search for goldilock quantum devices

#### Description

DiVincenzo in 2000, provides a comprehensive framework for identifying the necessary requirements to achieve successful quantum computation. In this project, we search for ideal conditions necessary for buidling distributed quantum devices.

#### Supervisor:

### Master's Theses

## Decoder Design for Precoded Polar Product Codes

#### Description

The aim of this thesis is to enhance decoding algorithms for precoded polar product codes. We make use of successive cancellation list (SCL) decoding to generate reliability information and scale it based on the maximization of generalized mutual information.

#### Supervisor:

## List decoding of random sum-rank metric codes

**Keywords:**

coding theory, list decoding, rank metric

#### Description

In this thesis, we want to investigate the list decoding complexity of random (linear) codes in the sum-rank metric.

List decoding is a technique to decode beyond the unique decoding radius of a code at the cost of obtaining a list of candidate solutions. The sum-rank metric [1] is a relatively novel metric where the weight of a vector is given by the sum of the ranks of its component blocks.

As a starting point, the student should familiarize themselves with the concept of the sum-rank metric. Then, the list decoding behavior of a random SR code should be investigated, perhaps along the lines of these papers [2,3] that have some similar results on random rank metric codes. It would also be nice to investigate if this other technique [4] can be applied to the sum-rank metric.

Resources:

[1] https://arxiv.org/pdf/2102.02244 (this is not the paper where this metric was first studied, but it has a very nice overview of existing results)

[2] https://arxiv.org/abs/1401.2693

#### Prerequisites

Channel coding lecture or similar (i.e., basics of linear codes and their decoding)

strong background in linear algebra

An interest in combinatorics is beneficial, it is at the core of many of the related papers

#### Contact

anna.baumeister@tum.de

#### Supervisor:

## Improved Solvers for Code Equivalence Problems

**Keywords:**

code-based cryptography

#### Description

Due to the recent advances in quantum computers, the search for cryptosystems that survive quantum attacks is of great interest. Code-based cryptography is a promising candidate, since it is build on the NP-hard problem of decoding a random code [1].

The McEliece cryptosystem is a promising candidate for asymmetric encryption.

However, many attempts at constructing a code-based signature scheme have resulted in impractical parameters or security problems.

NIST's announcement of a competetion dedicated to standardizing post-quantum signatures has lead to the publication of several new code-based schemes

In this work we consider LESS [2] a signature scheme based on the hardness of the code equivalence problem [3]. State-of-the-art solvers of the problem [4] are analysed and modifications are made to improve their performance.

References:

[1] Weger, V., Gassner, N., & Rosenthal, J. (2022). A Survey on Code-Based Cryptography. arXiv preprint arXiv:2201.07119.

[2] Barenghi, A., Biasse, J. F., Persichetti, E., & Santini, P. (2021). LESS-FM: fine-tuning signatures from the code equivalence problem. In Post-Quantum Cryptography: 12th International Workshop, PQCrypto 2021, Daejeon, South Korea, July 20–22, 2021, Proceedings 12 (pp. 23-43). Springer International Publishing.

[3] Barenghi, A., Biasse, J. F., Persichetti, E., & Santini, P. (2022). On the computational hardness of the code equivalence problem in cryptography. Cryptology ePrint Archive.

[4] Beullens, W. (2021, July). Not enough LESS: An improved algorithm for solving code equivalence problems over F q. In Selected Areas in Cryptography: 27th International Conference, Halifax, NS, Canada (Virtual Event), October 21-23, 2020, Revised Selected Papers (pp. 387-403). Cham: Springer International Publishing.

#### Supervisor:

## Polar Codes for Stealth Communication

#### Description

#### Supervisor:

## Coding Methods for Composite DNA Synthesis

#### Description

**DNA Data Storage**

Data storage on DNA molecules is a promising approach for archiving massive data.

In classical DNA storage systems, binary information is encoded into sequences consisting of the four DNA bases {A, C, G, T}. The encoded sequences are used to generate DNA molecules called *strands* using the biochemical process of DNA synthesis. The synthesized strands are stored together in a tube. To retrieve the binary information, the strand must be read via *DNA sequencing* and decoded back into the binary representation.

The synthesis and the sequencing procedures are error-prone, and with the natural degradation of DNA they introduce errors to the DNA strands. To ensure data reliability, the errors have to be corrected by algorithms and error-correcting codes (ECCs).

A 5min video with an overview of DNA storage: https://youtu.be/r8qWc9X4f6k?si=Yzm5sOW-a6VDnBu3

**Composite DNA**

Recently, to allow higher potential information capacity, [1,2] introduced the *DNA composite* synthesis method. In this method, the multiple copies created by the standard DNA synthesis method are utilized to create *composite DNA symbols*, defined by a mixture of DNA bases and their ratios in a specific position of the strands. By defining different mixtures and ratios, the alphabet can be extended to have more than 4 symbols. More formally, a composite DNA symbol in a specific position can be abstracted as a quartet of probabilities {p_A, p_C, p_G, p_T}, in which p_X, 0 ≤ p_X ≤ 1, is the fraction of the base X in {A, C, G, T} in the mixture and p_A+p_C+ p_G+ p_T =1. Thus, to identify composite symbols it is required to sequence multiple reads and then to estimate p_A, p_C, p_G, p_T in each position.

**Problem description**

ECCs for DNA data storage differ in many aspects from classical error correction codes. In this model, new error type gain relevance, like deletions and insertions which affect the synchronization of the sequences. Especially for composite DNA data storage, these error types received only little attention.

The most related work to this problem was recently studied by Zhang et al. in [6]. The authors initiated the study of error-correcting codes for DNA composite. They considered an error model for composite symbols, which assumes that errors occur in at most t symbols, and their magnitude is limited by l. They presented several code constructions as well as bounds for this model. In this thesis, we want to analyse a different way to model the composite synthesis method and studies additional error models. We already have some results for substitution and single deletion errors. This thesis should focus on extending these to combinations of error models [5] or two deletions [3,4].

This should only roughly introduce the problem. No need to review all references. If you are interested, please reach out to me, and we can discuss a suitable direction for you.

**References**

[1] L. Anavy, I. Vaknin, O. Atar, R. Amit, and Z. Yakhini, “Data storage in DNA with fewer synthesis cycles using composite DNA letters,” *Nat Biotechnol*, vol. 37, no. 10, pp. 1229–1236, Oct. 2019, doi: 10.1038/s41587-019-0240-x.

[2] Y. Choi *et al.*, “High information capacity DNA-based data storage with augmented encoding characters using degenerate bases,” *Sci Rep*, vol. 9, no. 1, Art. no. 1, Apr. 2019, doi: 10.1038/s41598-019-43105-w.

[3] V. Guruswami and J. Håstad, “Explicit Two-Deletion Codes With Redundancy Matching the Existential Bound,” *IEEE Transactions on Information Theory*, vol. 67, no. 10, pp. 6384–6394, Oct. 2021, doi: 10.1109/TIT.2021.3069446.

[4] J. Sima, N. Raviv, and J. Bruck, “Two Deletion Correcting Codes From Indicator Vectors,” *IEEE Trans. Inform. Theory*, vol. 66, no. 4, pp. 2375–2391, Apr. 2020, doi: 10.1109/TIT.2019.2950290.

[5] I. Smagloy, L. Welter, A. Wachter-Zeh, and E. Yaakobi, “Single-Deletion Single-Substitution Correcting Codes,” *IEEE Transactions on Information Theory*, pp. 1–1, 2023, doi: 10.1109/TIT.2023.3319088.

[6] W. Zhang, Z. Chen, and Z. Wang, “Limited-Magnitude Error Correction for Probability Vectors in DNA Storage,” in *ICC 2022 - IEEE International Conference on Communications*, Seoul, Korea, Republic of: IEEE, May 2022, pp. 3460–3465. doi: 10.1109/ICC45855.2022.9838471.

#### Prerequisites

Good knowledge and high interest in mathematics, especially

- Linear algebra
- Combinatorics

I highly recommend the channel coding lecture for this thesis.

#### Contact

Frederik Walter (frederik.walter@tum.de)

#### Supervisor:

## Construction of Shaped Polar Codes for List Decoding

#### Description

We investigate code construction algorithms for SC list decoding of polar codes [1] based on methods like [2].

#### Supervisor:

## Entropy Estimation and Compression Scheme for Wildfire Detection

#### Description

We apply information-theoretic perspectives from rate-distortion theory to optimize wildfire notifications from earth observation satellites.

#### Supervisor:

## Post-Quantum Secure Signature Schemes based on the Lee Metric

#### Description

This work shall deal with post-quantum secure schemes utilizing the Lee metric. The student's task is to get familiar with this metric and to design a signature scheme. The student shall show that known attacks in the Hamming metric are not applicable for the Lee metric.

#### Prerequisites

Channel Coding

#### Supervisor:

#### Student

## Event Cameras for Industrial Applications

#### Description

Compared with traditional frame-based cameras, an event-based camera has the advantages of low latency, high dynamic range, (almost) no motion blur, etc., and can respond fast to a brightness change at the image plane with thresholds determined by the previous state of brightness. It can generate event data of structural features without signal processing, such as edges and corners, which saves time, energy and computing effort.

However, it still lacks standard processes for analyzing, characterizing and evaluating event-based camera information. Moreover, data acquisition of this camera demands changes in brightness on a respective pixel. This can be introduced artificially by relative movement of the camera to the object. This might miss out part of the data due to linear translation along structural elements like edges or introduce some error due to error motion or vibration.

This thesis aims to ensure safety and quality when implementing event-based cameras in the field of industrial inspection, by dedicated experiments and related discussion of results. Event based camera imageswill be characterized and evaluated. New methods for event generation and signal processing will be proposed which will make use of the special characteristics of event-based cameras and their special characteristics arising from their working principle.

#### Supervisor:

## AI-Aided LDPC Decoders

#### Description

In theory, the check nodes operations of belief propagation rely on tanh() and arctanh() functions which require high computational power. Hence, in most of the practical applications, an approximation called “MinSum” is used. This method aims to exploit the structure of tanh() function where the absolute value of output sufficiently converges to 1 with increasing absolute value of input. Hence, in a series multiplication of absolute outputs, the most dominant effect comes from the minimum element and other contributions are considered negligible. However, neglection of other attenuation factors cause overcalculated outputs in “MinSum” algorithm which can accumulated by multiple iterations. This drawback can be compensated through adding attenuation or/and offset factors. These factors are mostly iteration specific and intuitively determined, which means one factor which is determined by educated guess is applied to all leaving edges. However, every edge in an unfolded Tanner graph has its own unique identity corresponding to the previous nodes and edges that the message is transmitted.

In addition to approach aiming to close the performance gap between main algorithm and “MinSum ” approximation, we can intend to improve the qualities of main algorithm. Even though belief propagation decoding in LDPC codes is considered as highly successful, it is still a “suboptimal” method compared to very expensive but accurate Maximum A posteriori Probability (MAP) estimation. It means there might be some room for improvement in performance. Additionally, belief propagation requires multiple iterations to converge and the required number of iterations can dramatically increase by decreasing signal-to-noise ratio (SNR). Additional correction weights imposed on iterated messages can be a candidate to improve performance in overall.

5G specification for channel coding is using protograph based LDPC codes. Every node duplicated from same base matrix node is keen to show similar properties, it may be possible to use same weights for these nodes by preserving the good decoding results. This detail can help us to using additional correction weights by minimum additional memory burden.

#### Supervisor:

## Zero-error capacity for multi-user channels with feedback

**Keywords:**

zero-error capacity, multi-user

#### Description

In this project the student should calculate the zero-error capacity for

a multi-user model with feedback.

#### Prerequisites

Information theory

#### Supervisor:

#### Student

### Research Internships (Forschungspraxis)

## Testbed development for coherent optical-freespace communication

#### Description

Design of the hardware-software interface of a real-time intradyne FSOC transceiver to evaluate different signal processing architectures to enable logging and visualization of high-speed data streams from the FPGA as well as configuration and calibration of the signal processing algorithms.

#### Supervisor:

## Secure Federated Learning with Differential Privacy

#### Description

Federated learning is a machine learning paradigm that aims to learn collaboratively from decentralized private data owned by entities referred to as clients. However, due to its decentralized nature, federated learning is susceptible to model poisoning attacks, where malicious clients try to corrupt the learning process by modifying local model updates. Moreover, the updates sent by the clients might leak information about the private data involved in the learning. The goal of this work is to investigate and combine existing robust aggregation techniques in FL with differential privacy techniques.

References:

[1] - https://arxiv.org/pdf/2304.09762.pdf

[2] - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9757841

[3] - https://dl.acm.org/doi/abs/10.1145/3465084.3467919

#### Prerequisites

- Basic knowledge about machine learning and gradient descent optimization

- First experience with machine learning in python

- Undergraduate statistics courses

- Prior knowledge about differential privacy is a plus

#### Supervisor:

## Post-Quantum Cyptography based on Codes: the WAVE signature scheme

**Keywords:**

post-quantum cryptography, code-based, concatenated codes, generalized concatenation

#### Description

Due to the recent advances in quantum computers, searching for cryptosystems that survive quantum attacks is of great interest. Code-based cryptography is a promising candidate since it is built on the NP-hard problem of decoding a random code [5].

Random-looking codes replace random codes for most cryptosystems to enable a trapdoor. McEliece originally proposed to use binary Goppa codes. Later, MDPC codes were successfully introduced.

More recently, it was suggested that generalized (U|U+V) codes be used, which belong to the class of generalized concatenated codes.

Concatenated codes play an important role in classical channel coding, but further research is required to evaluate which variants also form a promising basis for code-based cryptography.

This topic aims to analyze constructions of generalized concatenated codes proposed for cryptographic applications [2-4], in particular, the WAVE signature scheme [1].

The research internship addresses the following questions:

- how do proposed constructions work?

- which properties do they have?

- is the secret structure well hidden?

**Main Papers:**

[1] Debris-Alazard, T., Sendrier, N., & Tillich, J. P. (2018). Wave: A new code-based signature scheme.

[2] Márquez-Corbella, Irene, and Jean-Pierre Tillich. "Using Reed-Solomon codes in the (U| U+ V) construction and an application to cryptography." *2016 IEEE International Symposium on Information Theory (ISIT)*. IEEE, 2016.

[3] Puchinger, S., Müelich, S., Ishak, K., & Bossert, M. (2017). Code-based cryptosystems using generalized concatenated codes. In *Applications of Computer Algebra: Kalamata, Greece, July 20–23 2015* (pp. 397-423). Springer International Publishing.

[4] Cho, J., No, J. S., Lee, Y., Koo, Z., & Kim, Y. S. (2022). Enhanced pqsigRM: code-based digital signature scheme with short signature and fast verification for post-quantum cryptography. *Cryptology ePrint Archive*.

**Further References:**

[5] Weger, V., Gassner, N., & Rosenthal, J. (2022). A Survey on Code-Based Cryptography. *arXiv preprint arXiv:2201.07119*.

#### Prerequisites

Security in Communications and Storage

Channel Coding

#### Supervisor:

## Automatic Generation of an Infotainment Data Access Layer for In-Vehicle Data Collector

#### Description

Modern vehicles boast advanced infotainment systems, delivering a range of services encompassing entertainment, navigation, comfort, and connectivity. These applications require extensive real-time data, necessitating efficient and reliable data processing from vehicle sensors and control units. With every vehicle launch and OS release, new sensors and APIs are introduced, which serve as important data points for high-level analytics and applications. Currently, these changes must be monitored and integrated manually, which is a demanding task prone to errors. The objective of this internship is to develop tools that automatically generate a data collector access layer for the platform APIs in every major OS release. This is done by analyzing the available APIs, followed by the creation of a data dependency tree based on the JSON format. An appropriate graph reduction algorithm is then applied to generate a data structure with minimum edge dependencies while preserving analyzability of the data points. An appropriate lookup schema is then created based on the reduced data points, which calls the requested API based on the supplied configuration. Python and C++ are the languages used within the scope of this project.

#### Supervisor:

## Fleet data evaluation based on MDF files

#### Description

Whenever an automobile manufacturer develops a new car model, the aim is to surpass its predecessors in terms of performance, efficiency, safety, and overall user experience. In order to do so, logged data has to be analyzed to find out where improvements can be made. Whenever a vehicle returns from a test drive, the collected data will be stored as an MDF (Measurement Data Format) file. These files are then read and analyzed with a software tool, but as of now, only a few files can be processed simultaneously due to hardware limitations. This research internship explores how data can be efficiently organized to maximize analyzability. Eventually, a program should be created which allows users to analyze all MDF files a vehicle has produced in parallel.

#### Supervisor:

## Neural Network-Based Signal Predistortion for Direct Detection Systems

#### Description

During the internship, the student will be researching the application of Neural Network-based signal predistortion to mitigate the effects of fiber chromatic dispersion in direct detection systems.

#### Prerequisites

- basic Python skills beneficial

#### Supervisor:

## Rate upper Bounds for Bandlimited Channels with a Memoryless Nonlinearity

#### Description

We are interested in computing upper bounds (on capacity) for bandlimited channels with a memoryless nonlineary at the transmitter/receiver.

One application for these bounds are short-reach fiber-optic communication systems with a single photodiode at the receiver. The photodiode is a memoryless nonlinearity, as it produces an output that is proportional to the squared magnitude of the input signal.

A simple upper bound for the above model is given in [Sec. III D, 2].

D. Plabst *et al*., "Achievable Rates for Short-Reach Fiber-Optic Channels With Direct Detection," in *Journal of Lightwave Technology*, vol. 40, no. 12, pp. 3602-3613, 15 June15, 2022, doi: 10.1109/JLT.2022.3149574.

#### Prerequisites

Information Theory

Linear System Theory

#### Supervisor:

## Forschungspraxis

**Keywords:**

Forschungspraxis

#### Description

#### Supervisor:

## Error-Correction for Partially Stuck Memory Cells

#### Description

#### Supervisor:

#### Student

### Internships

## Coding for DNA Microarrays

#### Description

**Gene expression analysis**

(From Wikipedia: https://en.wikipedia.org/wiki/Gene_expression)

Measuring gene expression is an important part of many life sciences, as the ability to quantify the level at which a particular gene is expressed within a cell, tissue or organism can provide a lot of valuable information. For example, measuring gene expression can:

- Identify viral infection of a cell (viral protein expression).
- Determine an individual's susceptibility to cancer (oncogene expression).
- Find if a bacterium is resistant to penicillin (beta-lactamase expression).

Similarly, the analysis of the location of protein expression is a powerful tool, and this can be done on an organismal or cellular scale. Investigation of localization is particularly important for the study of development in multicellular organisms and as an indicator of protein function in single cells. Ideally, measurement of expression is done by detecting the final gene product (for many genes, this is the protein); however, it is often easier to detect one of the precursors, typically mRNA and to infer gene-expression levels from these measurements.

An approach for gene expression analysis is the hybridization microarray. A single array or "chip" may contain probes to determine transcript levels for every known gene in the genome of one or more organisms.

Here is a video explaining the concepts of gene expression analysis and DNA microarrays: https://www.youtube.com/watch?v=Hv5flUOsE0s

**Relation to coding**

The generation of the DNA microarray bears several interesting challenges from a coding perspective. The microarray needs to be generated, which means that artificial DNA strands are synthesized. Many of these DNA strands are synthesised simultaneously. However, only one of the four bases of the DNA (A, C, G, T) can be written at once. The challenge is to select the best possible sequence in which the bases are written to minimize the overall running time. This problem is called finding the Shortest Common Supersequence (SCS) and is unfortunately np-hard (https://en.wikipedia.org/wiki/Shortest_common_supersequence). Furthermore, there are more than one possible probes that can be used to signal a specific gene. Hence, this is another parameter to optimize.

**Problem description**

This thesis aims to analyze the problem of finding the best probes for a DNA microarray and implement an algorithm to run small-scale experiments.

This should only roughly introduce the problem. No need to understand everything or review all references. If you are interested, please reach out to me, and we can discuss a suitable direction for you.

**Related references**

[1] M. Abu-Sini, A. Lenz, and E. Yaakobi, “DNA Synthesis Using Shortmers,” in *2023 IEEE International Symposium on Information Theory (ISIT)*, Jun. 2023, pp. 585–590. doi: 10.1109/ISIT54713.2023.10206609.

[2] A. Lenz, Y. Liu, C. Rashtchian, P. H. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding for Efficient DNA Synthesis,” in *2020 IEEE International Symposium on Information Theory (ISIT)*, Jun. 2020, pp. 2885–2890. doi: 10.1109/ISIT44484.2020.9174272.

[3] D. Maier, “The Complexity of Some Problems on Subsequences and Supersequences,” *J. ACM*, vol. 25, no. 2, pp. 322–336, Apr. 1978, doi: 10.1145/322063.322075.

[4] K. Makarychev, M. Z. Rácz, C. Rashtchian, and S. Yekhanin, “Batch Optimization for DNA Synthesis,” *IEEE Transactions on Information Theory*, vol. 68, no. 11, pp. 7454–7470, Nov. 2022, doi: 10.1109/TIT.2022.3184903.

[5] N. Xie, S. Xu, and Y. Xu, “A new coding-based algorithm for finding closest pair of vectors,” *Theoretical Computer Science*, vol. 782, pp. 129–144, Aug. 2019, doi: 10.1016/j.tcs.2019.03.011.

#### Prerequisites

Good knowledge and high interest in mathematics, especially

- Linear algebra
- Combinatorics

Good programming knowledge is required for this topic.

#### Contact

frederik.walter@tum.de

#### Supervisor:

## Absicherung des elektrischen Antriebsstranges

#### Description

#### Supervisor:

#### Student

## Erweiterung der Agabiz App um eine automatisierte Standort-Ermittlung und Fahrtzeitkontrolle mit Hilfe von iBeacon-/Bluetooth-Technologie

#### Description

#### Supervisor:

#### Student

## Umsetzung einer frequenzselektiven IQ-Imbalanz Korrektur für OFDM Direct Conversion Receiver

#### Description

#### Supervisor:

#### Student

## translation of coded modulation library from Matlab/C into julia

**Keywords:**

Matlab, C, C++, julia

**Short Description:**

It is the students task to translate function from MATLAB and C into julia language.

#### Description

the students should translate functions from Matlab and C to julia. the functions involve calculation of infomation theoretic quantities to basic function of a discrete time transmission chain.

The students task is to learn julia and matlab to a extend that the translation from one to the other language is possible. We furthermore expect the student to learn how to use git and gitlab for managing larger projects.

#### Prerequisites

basic programming knowledge