Short bio:
Pratheesh, B.Sc
Master Thesis Student at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria
Email:
Hi! My Name is Melanie Neubauer and I started at the CPS-Chair in April 2023.
I studied Industrial Logistics at the Montanuniversität Leoben, where I passed my Master’s defense in March 2023.
In my doctoral work, I investigate deep neural networks for image processing in cyber-physical-systems combined with inverse reinforcement learning techniques.
The title of my doctoral work is: Vision-based Deep Inverse Reinforcement Learning
M.Sc. Melanie Neubauer
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since April 2023.
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria
Phone: +43 3842 402 – 1901 (Sekretariat CPS)
Email: melanie.neubauer@unileoben.ac.at
Web Work: CPS-Page
Chat: WEBEX
Time : 11:00 – 11:45
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Topic 1: Iterative Empowerment
To be added
TBD
Start date: Open
Location: Leoben
Job Type: Internship
Duration: 3-6 months, depending on the level of applicant’s proficiency on the asked qualifications.
Keywords: Self-supervised learning, Few-shot learning, Deep learning, PyTorch, Research
Supervisors:
We are looking for a highly motivated research intern to work on the development of novel self-supervised learning algorithms to improve few-shot learning. The intern will be responsible for conducting research on self-supervised learning techniques such as contrastive learning and generative models, and their applications to few-shot learning. The intern will also be responsible for implementing and evaluating these algorithms on benchmark datasets.
This internship provides an excellent opportunity to gain hands-on experience in cutting-edge research on self-supervised learning for few-shot learning, working with a highly collaborative and supportive team. The intern will also have the opportunity to co-author research papers and technical reports, and participate in conferences and workshops.
Send us your CV accompanied by a letter of motivation at fotios.lygerakis@unileoben.ac.at with the subject: “Internship Application | Self-supervised Learning”
We will support you during your application for an internship grant. Below we list some relevant grant application details.
Find details on the Central European Exchange Program for University Studies program at https://grants.at/en/ or at https://www.ceepus.info.
In principle, you can apply at any time for a scholarship. However, also your country of origin matters and there exist networks of several countries that have their own contingent.
Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.
Apply online at http://www.scholarships.at/
M.Sc. Students/Interns
Science Breakfast @MUL: 14/02 11:00-12:00
Anymal Robot at Mining chair on 15/02?
Time : 13:30 – 14:00
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Topic 1: Iterative Empowerment
To be added
TBD
Supervisor: DI Nikolaus Feith;
Konrad Bartsch;
Univ.-Prof. Dr Elmar Rückert
Start date: 8th Februar 2023
Theoretical difficulty: low
Practical difficulty: high
Modern IoT devices are powerful elements in complex Cyber-Physical-Systems (CPS).
However, communicating with such microcontrollers can be challenging and often requires custom software and hardware interfaces. When working with many different devices, this can quickly become overwhelming.
The goal of this thesis is to develop a generic hardware interface for the ESP32 microcontroller.
Individual hardware devices, sensors, and actuators can be integrated into a CPS through configuration files. Adjusting these files does not require in-depth hardware or software knowledge and allows rapid IoT development and integration via ROS 2.
The power of the generic ROS2 device interface is demonstrated in multiple use cases, e.g., the sensor glove with flex sensors, vibration motors and an IMU, or an ODrive motor controller board for mobile robots.
To achieve our objective, the following concrete tasks will be focused on:
[R1] Dauphin, L., Baccelli, E., & Adjih, C. (2018, September). RIOT-ROS2: low-cost robots in IoT controlled via information-centric networking. In 2018 IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN) (pp. 1-6). IEEE.
[R2] Barciś, M., Barciś, A., Tsiogkas, N., & Hellwagner, H. (2021). Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware. Frontiers in Robotics and AI, 8, 685105.
[R3] Jo, W., Kim, J., Wang, R., Pan, J., Senthilkumaran, R. K., & Min, B. C. (2022). Smartmbot: A ros2-based low-cost and open-source mobile robot platform. arXiv preprint arXiv:2203.08903.
[1] ESP32 Tutorials, last visited 09.02.2023, https://randomnerdtutorials.com/getting-started-with-esp32/
[2] ESP32 Tutorials, last visited 09.02.2023, https://www.az-delivery.de/en/blogs/azdelivery-blog-fur-arduino-und-raspberry-pi/esp32-das-multitalent
[3] MAC OS Serial Driver, last visited 09.02.2023, https://github.com/adrianmihalko/ch340g-ch34g-ch34x-mac-os-x-driver
[4] ESP32 Datasheet, last visited 09.02.2023, https://www.espressif.com/sites/default/files/documentation/esp32_datasheet_en.pdf
[5] ROS2 Documentation, last visited 09.02.2023, https://docs.ros.org/en/humble
Development of a generic ROS2 Device Interface based on Micro-ROS on a ESP32
The need for efficient and compact representations of sensory data such as visual and textual has grown significantly due to the exponential growth in the size and complexity of the data. Self-supervised learning techniques, such as autoencoders, contrastive learning, and transformer, have shown significant promise in learning such representations from large unlabeled datasets. This research aims to develop novel self-supervised learning techniques inspired by these approaches to improve the quality and efficiency of unsupervised representation learning.
The study will begin by reviewing the state-of-the-art self-supervised learning techniques and their applications in various domains, including computer vision and natural language processing. Next, a set of experiments will be conducted to develop and evaluate the proposed techniques on standard datasets in these domains.
The experiments will focus on learning compact and efficient representations of sensory data using autoencoder-based techniques, contrastive learning, and transformer-based approaches. The performance of the proposed techniques will be evaluated based on their ability to improve the accuracy and efficiency of unsupervised representation learning tasks.
The research will also investigate the impact of different factors such as the choice of loss functions, model architecture, and hyperparameters on the performance of the proposed techniques. The insights gained from this study will help in developing guidelines for selecting appropriate self-supervised learning techniques for efficient and compact representation learning.
Overall, this research will contribute to the development of novel self-supervised learning techniques for efficient and compact representation learning of sensory data. The proposed techniques will have potential applications in various domains, including computer vision, natural language processing, and other sensory data analysis tasks.
If this topic excites you you, please contact Fotios Lygerakis by email at fotios.lygerakis@unileoben.ac.at or simple visit us at our chair in the Metallurgie building, 1st floor.
Short bio: Björn Ellensohn, B.Sc. started at CPS in April 2023.
Björn Ellensohn is a master student at Montanuniversität Leoben in the program Industrielle Energietechnik with a focus on digitalization. Björn’s research interests include automating drilling operations, his bachelor’s thesis focused on the challenges involved in this drilling process, and at CPS he develops complex ROS2 software and hardware systems.
Björn Ellensohn, B.Sc
Student Assistent at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria
Email:
bjoern.ellensohn@unileoben.ac.at
Vergleich der Publikationen auf unserer Webpage https://cps.unileoben.ac.at/publications/mit den Publikationen in PURE. Fehlende Einträge müssen erstellt werden.
Etwas verwirrend mag die Kategorisierung sein. Alle unsere Arbeiten sind unter dem Hauptpunkt “Contribution to Journal” angesiedelt.
