Abschlussarbeiten

Motivation

Object detection is a crucial task in the field of autonomous vehicles. To train the object detection models, data such as RGB data is collected either offline or online during the operation of the cars, and this data is used to train the models. However, in supervised learning approaches, which have superior performance, the data needs to be annotated.

Active learning is an approach that aims to reduce the amount of annotated data required. It achieves this by selecting the most informative information from the unannotated data pool. Unfortunately, most active learning approaches for object detection assume that the data is already gathered and located in a central entity (offline). These approaches overlook the consumption of networking resources that occur when transmitting the data in the online data gethering.

In this thesis, the student will have the chance to enable online data collection in a multi-agent system (e.g., vehicular system), while considering the available network resources. The objective is to develop an active learning approach that optimizes the utilization of the network resources while maintaining high object detection performance. This would involve selecting the most informative data for annotation and training, taking into account the limitations of the available network resources.

The goal is to enhance the efficiency and effectiveness of object detection in autonomous vehicles by developing an active learning approach that leverages the available network resources without compromising the performance of the models.

Task

This thesis is open for master/bachelor students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python
•    Very good machine learning / Deep learning knowledge

Related Literature

[1] Yu, Weiping, et al. "Consistency-based active learning for object detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

[2] Choi, Jiwoong, et al. "Active learning for deep object detection via probabilistic modeling." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

[3] Feng, Di, et al. "A review and comparative study on probabilistic object detection in autonomous driving." IEEE Transactions on Intelligent Transportation Systems 23.8 (2021): 9961-9980.

Distributed Stream Processing Systems (DSPS) allow real-time processing of data to fetch meaningful information. Big giants like Twitter, Facebook, and Alibaba rely on DSP for real-time data analytics. For example, click analytics, credit card fraud detection, etc. In DSPS, queries are usually long-running. They typically deal with a very high workload of millions or even billions of events per second. Under such scenarios, parallelism plays an important role in providing scalability for DSPS where either the input stream is partitioned into multiple processes or sub-streams or duplicated to be processed by operators and their instances. However, existing DSPS lacks the benchmarking of the parallel processing capability of operators under various workloads, event rates, and different queries.

Research Goals:

• Literature study of benchmarking parallel stream processing.
• Implementing Window and Pane-based parallelization strategy.
• Evaluating the performance for various benchmarks
• Displaying the performance evaluation result on the front end.
• Possible DSPS to be used: Apache Flink

Motivation 

As the development of autonomous vehicles continues to progress, the need for improving detection models becomes increasingly important. However, sharing the data gathered from vehicles with a central server can raise privacy concerns and consume a significant amount of network capacity. Federated learning provides a solution to this challenge by allowing for the training of machine learning models on decentralized data, without the need to transfer raw data to a central server.

However, for real-time compatibility, the process of training models locally in the vehicle needs to be fast and consume less computational power. This means developing efficient federated learning algorithms that converge optimally and consume less power is indispensable in the vehicular perception world. By analyzing state-of-the-art approaches and developing novel methods that take into account the real-time constraints of vehicular applications, the student will help improving the accuracy and reliability of perception models. Moreover, training models locally in the vehicle poses significant challenges that require careful consideration, such as dealing with the labeling issue of data collected in real-time. The student will have the opportunity to address these challenges and contribute to the future of autonomous vehicles.

Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Ein resilientes Kommunikationssystem soll immer verfügbar und zuverlässig sein, wie erwaretet funktionieren und sich schnell und selbstständig erholen, wenn es zu Störungen oder Angriffen kommt.

In meiner Forschung geht es darum, die Resilienz von aktuellen und zukünftigen mobilen Zugangsnetzen (5G und Beyond-5G) zu verbessern.

Der Kern eines 5G-Netzes besteht aus verschiedenen virtualisierten Netzfunktionen. Dadurch ergeben sich verschiedene Möglichkeiten diese Netzwerkfunktionen zu nutzen. Beispielsweise können sie verteilt auf mehreren VMs platziert oder in K8s-Pods bereitgestellt werden.

Eine ausfallsichere Lösung sollte dann in der Lage sein, den Absturz und den anschließenden Neustart einzelner Netzwerkfunktion zu bewältigen, ohne dass das Gesamsystem übermäßig beeinflusst wird. Desweiteren sollte das Kernnetz in der Lage sein, einen effizienten Lastausgleich zu ermöglichen und sich an Änderungen anzupassen, indem die Anzahl der Netzwerkfunktionen erhöht oder verringert wird.

Ich biete Bachelor- und Masterarbeiten an, die sich mit dem 5G-Kernsystem befassen und Aspekte im Zusammenhang mit dessen Resilienz untersuchen.

Motivation

 In Distributed Stream Processing Systems (DSPS), queries are usually long-running. They typically deal with a very high workload of million or even billion of events per second. Under such scenarios, parallelism plays an important role in providing scalability for DSPS. One of the core
decisions here is to determine the right parallelization degree to process a high load of events and queries while ensuring high throughput and low latency requirements. In this context, parallelization can be controlled by defining the degree of parallelism for each operator in the operator graph.
The main questions we want to investigate here are - 

•  What is the right parallelism degree for query processing? (what is the performance in terms of end-to-end latency for parallel stream processing? At what extend the model can be used - seen and unseen behaviour? What are the limitations of models? At which can be extended?)
• How to parallelize the processing of DSPs operators? 

 Prerequisites

 There are no hard guidelines for this topic. However, it is preferable if you have:

• Good programming skills in Java and Python
• Good understanding of the concepts of machine learning
• Understanding of the concept of communication networks, big data processing engines, e.g., Apache Flink, Kafka. 

 Reference Literature

• Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows.
• A Comprehensive Survey on Parallelization and Elasticity in Stream Processing
• Resource Management and Scheduling in Distributed Stream Processing Systems: A Taxonomy, Review, and Future Directions
• One Model to Rule them All: Towards Zero-Shot Learning for Databases Benjamin
• Apache Flink documentation: nightlies.apache.org/flink/flink-docs-master/docs/learn-flink/overview/ [https://nightlies.apache.org/flink/flink-docsmaster/docs/learn-flink/overview/] 

Contact:

I am looking for motivated students interested in working on cutting-edge technologies and IoT applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me. You can email me the following information:

• Your CV or small text about your courses and prior experiences in this area.
• The short note on your motivation about this topic and,
• Transcript or TUCaN grade list.

Overview

P2P, and in particular blockchain systems, promise censorship resistance through immutable histories and no central point of failure. One of these systems is LBRY, and its Odysee frontend, which functions as a video platform. The promise of an uncensorable public content library is appealing to, e.g., journalists under oppressive regimes, but is potentially open to abuse as well.

To date, however, nobody has investigated a) how resistant to censorship LBRY is conceptually, as well as b) how decentralized (and thus without single point(s) of failure) it is in practice.

Tasks

1. Familiarize yourself with, and understand, how LBRY works.
2. Familiarize yourself with scientific literature relating to
   1. LBRY
   2. Measuring P2P Systems w.r.t. client population, geolocation, decentralization, and size.
3. Investigate how to measure the above in LBRY, in particular
   1. Exploiting the structure of the network
   2. Exploiting unstructured message-passing in the network, if any.
4. Measure the network.
5. Analyze the collected data.
6. Write a thesis about it.

Prerequisites

• Understanding of P2P Systems in general, and in particular
  
  • DHTs, in particular Kademlia
  • Some understanding of, and interest in, blockchain systems
• Knowledge of Linux
• Proficiency in any programming language (the LBRY SDK is written in Python)

Motivation:

Internet of Things describes a new world of heterogeneous objects such as sensors, smartphones, actuators etc, which intelligently interacts and communicate with each other and offer a better quality of life. With the increase of these objects, the data volume is growing as well. Processing this volume of data can be helpful to various application domains to reveal complex patterns, gain fast insights or react to observed situations, e.g., fraud detection, smart factories, telecommunication, etc. However, it becomes extremely challenging to continuously process this high volume of data in a timely manner and meet these applications' demands. One of the possible ways can be to use heterogeneous resources' capability such as GPUs, FPGAs, etc. to accelerate the big data analytics instead of relying on homogeneous resources, e.g., CPU.

Task:

• Analyze existing related literature.
• Formalizing the problem and developing the solution and
• Setting up development environment and implementing the proof of concept or prototype.
• Evaluation of the proposed solution in comparison to the existing approaches. .

Prerequisites:

There are no hard guidelines for this topic. However, it is preferable if you have:

• Good programming skills in Java and/or Scala.
• Understanding of the concept of .
• communication networks and .
• big data processing engines, e.g., Apache Flink, Kafka.

Contact:

I am looking for motivated students interested in working on cutting-edge technologies and IoT applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me. You can email me the following information:

• Your CV or small text about your courses and prior experiences in this area.
• The short note on your motivation about this topic and,
• Transcript or TUCaN grade list.

• Design und Implementierung geeigneter Mechanismen für den Betrieb redundanter UPFs im 5G Core
• Evaluation der Auswirkungen der Migration von User Sessions zu replizierten UPFs
• Evaluation verschiedener Kommunikationsstrategien zwischen duplizierten UPFs und
  anderen Netzwerkfunktionen

Motivation 

As the development of autonomous vehicles continues to progress, the need for improving detection models becomes increasingly important. However, sharing the data gathered from vehicles with a central server can raise privacy concerns and consume a significant amount of network capacity. Federated learning provides a solution to this challenge by allowing for the training of machine learning models on decentralized data, without the need to transfer raw data to a central server. However, federated learning inherits convergence issues, especially when the data is non-iid (non-independent and identically distributed). This makes studying how the data distribution influences the performance of perception models trained in a federated learning setup in the vehicular collective perception field crucial. By analyzing state-of-the-art approaches and developing novel methods that take into account the data heterogeneity, the student will help improving the accuracy and reliability of perception models. Moreover, training models locally in the vehicle poses significant challenges that require careful consideration, such as dealing with the labeling issue of data collected in real-time. The student will have the opportunity to address these challenges and contribute to the future of autonomous vehicles.

Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Motivation

Time-Sensitive Networking (TSN) is an evolving set of standards for reliable real-time ethernet communication.

It's possible to deploy current or future TSN mechanisms on hardware switching devices for reliable Ethernet communication. The Co-existence of multiple TSN mechanisms is however still challenging problem, as complex interplay between different approaches can occur.

Tasks

1. Review related work of time-sensitive networking mechanisms and simultaneous deployments
2. Design approaches to enable simultaneous deployments
3. Implement the approach on test-devices
4. Evaluate your deployment
5. Write a thesis

Requirements

1. Basic computer networking knowledge
2. Interest in real-time networking