News
20.12.2024: The Institut für Theoretische Elektrotechnik hosted Dr.-Ing. Thomas Fiedler, Project Leader of the Electromagnetic Simulations and RF Safety Group at the Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ German Cancer Research Center), for an insightful presentation on MRI Safety at 7 Tesla and Beyond. Dr. Fiedler’s presentation covered the electromagnetic fields used in MRI and the associated patient safety risks. He also introduced methods for optimizing the RF transmit field using multi-channel RF transmission systems and real-time SAR monitoring techniques. Following the talk, we had the opportunity to exchange ideas on our recent work, including full-wave numerical SAR prediction using machine learning presented by M. Sc. Hamideh Esmaeili. We also showcased, firstly under the water, our high-speed, high-resolution near-field measurement capabilities. Thank you Dr. Fiedler, we are excited for future collaborations and challenges in advancing MRI technologies! The picture shows from left to right: Dr. Cheng Yang, Dr. Thomas Fiedler and Hamideh Esmaeili
27.11.2024: We are honored and delighted to announce that the IEEE Board of Directors, at its November 2024 meeting, has elevated Prof. Schuster to the status of IEEE Fellow, effective January 1, 2025, with the following citation:
For contributions to physics-based modeling, design, and optimization of interconnects in servers and networking equipment.
Special thanks are extended to his nominator, Prof. James Drewniak, for his invaluable guidance and encouragement, to the exceptional members of the IEEE EMC Society, and to Hamburg University of Technology, which has supported Prof. Schuster’s research for over 18 years.
28.11.2024: PhD Opportunity “Advancing 6G Communication with Smart Field Scanning and Machine Learning”. Supported by the “Next Generation City Networking” (NGCN) project, the Institut für Theoretische Elektrotechnik at the Hamburg University of Technology (TUHH) is seeking a PhD candidate on pioneering intelligent mobile field scanning techniques for 6G radio channels in realistic communication environments.
Your Tasks:
- Modeling and characterizing 6G radio channels using simulation tools.
- Conducting robotic field measurements of high-frequency transmitters in laboratory settings.
- Performing mobile field measurements of 6G transmitters in outdoor environments using patent-pending on-the-fly scanning techniques.
- Developing and demonstrating a standalone 6G vector field scanner mounted on a high-speed mobile platform.
- Applying advanced AI-based algorithms and models for intelligent modeling and characterization of 6G radio channels.
Your Profile:
- Master’s degree in Microwave Engineering, Communication Engineering, Electrical Engineering, Information and Communication Technology, Data Science, or related fields.
- Proficiency in numerical simulation and experimental measurement of high-frequency antennas.
- Strong understanding of electromagnetic fields and digital signal processing.
- Advanced programming skills, particularly in machine learning algorithms and instrument control.
- Experience with robotics, drones, software-defined radios (SDR), or FPGA is advantageous.
The position is full-time, starting from February 1, 2025 to December 31, 2027. Applications are open until December 25, 2024. For more details and to apply, please visit the job portal of TUHH.
18.11.2024: We are thrilled to share that our paper, “Machine Learning Based Data Validation for Signal Integrity and Power Integrity Using Supervised Time Series Classification”, has been accepted for publication in the IEEE Transactions on Electromagnetic Compatibility. This work is the result of a fruitful collaboration between researchers from the Institut für Theoretische Elektrotechnik of the Hamburg University of Technology and the Chair of Integrated Electronic Systems of the Otto-von-Guericke University Magdeburg Fabian Lurz. In this work, we present a novel machine learning-based approach for validating data in signal integrity (SI) and power integrity (PI). By training time series classification networks on data labeled by expert engineers, we replicate human visual assessments to predict the agreement between two curves. In comparison to other error and similarity metrics, this method offers the advantage of capturing domain-specific expert knowledge in the comparison of curves. This approach allows the systematic, fast and objective validation of large datasets.
Details:
📌 Title: Machine Learning Based Data Validation for Signal Integrity and Power Integrity Using Supervised Time Series Classification.
📌 Authors: Youcef Hassab, Til Hillebrecht, Fabian Lurz, Christian Schuster.
📌 Journal: IEEE Transactions on Electromagnetic Compatibility.
📌 Date: October 2024.
📌 DOI: 10.1109/TEMC.2024.3450307.
04.11.2024: We are excited to share our recent paper “The EMC of Orbital Angular Momentum (OAM) Based Wireless Communication”, published in IEEE Electromagnetic Compatibility Magazine, authored by Michael Wulff, Prof. Lei Wang, and Prof. Christian Schuster. This work explores the potential of orbital angular momentum (OAM) modes in wireless communication systems! You can find more information here: https://lnkd.in/eeyG3miF This article summarizes several years of research. It all started with Woocheon Park as exchange student, then Lei Wang as AvH-Fellow joined Hamburg University of Technology, and finally Michael Wulff as Ph.D. student. Not to forget: Dr. Brüns and Dr. Yang as support along the way. Great team :-)!
30.10.2024: Excited to share our research “Efficient Iterative Data Generation Using Evaluation of Prioritized Input Parameters in ANNs for SAR Prediction in Human Head Models at 13.56 MHz”, by Hamideh Esmaeili, Cheng Yang and Prof. Christian Schuster. Our lates work has just been accepted for publication in IEEE Transactions on Electromagnetic Compatibility and is now available in IEEE Early Access!
In this work, using parameter prioritization combined with artificial neural networks (ANNs), we iteratively evaluate high-impact variables, focusing on mass-averaged SAR variations in human head models. We present an iterative dataset generation method that significantly reduces sample requirements by emphasizing key input parameters. By fine-tuning inputs with each iteration, our approach achieves >95% ML prediction accuracy with just 5–10 times the number of samples as the input parameters, resulting in a ~60% reduction in simulation time and memory use. This method effectively avoids redundancy and maintains accuracy, offering a scalable solution for high-dimensional BEM problems. You can find more information here: https://doi.org/10.1109/TEMC.2024.3439468
18.09.2024: Large Signal Integrity PCB Dataset Created
Excited to share our recent work, by Til Hillebrecht, Morten Schierholz, Youcef Hassab, Johannes Alfert and Christian Schuster, where we generated and analyzed a large dataset of PCB-based high-speed interconnect data for signal integrity (SI) using physics-based modeling and machine learning. This dataset, features frequencies up to 60 GHz and is available in the SI/PI-Database (https://lnkd.in/eRZquJmh). Our approach addresses the complexity of SI design, facilitates data-driven analysis in the SI domain. Find the article on IEEEXplore: https://lnkd.in/eDwKQRmM
31.07.2024: As part of the vacation program at Hamburg University of Technology our university recently hosted an exciting workshop using the BOB3 learning module to teach middle school students C++ in a fun, hands-on way! Together with Swantje Plambeck and Til Hillebrecht, the young coders engaged in an interactive sessions, solving problems, and bringing their little robot to life. Their enthusiasm and creativity were truly inspiring!
29.11.2024: Hamburg University of Technology and the Institut für Theoretische Elektrotechnik had the honor of hosting Dr.-Ing. Hubert Harrer, Senior Technical Staff Member at IBM Systems and Technology Group, IBM Deutschland GmbH. Dr.-Ing. Hubert Harrer received his Dipl.-Ing. degree in 1989 and Ph.D. in 1992 from the Technical University of Munich, Germany. Since 1994 he has worked for IBM in the Boeblingen Packaging Department. In 1999 he was on international assignment at IBM Poughkeepsie, NY, USA, leading the z900 MCM designs.
Dr. Harrer gave a presentation about: “30 Years of IBM System z Packaging”.
“System z” or “IBM Z” is a family name IBM uses for its Z architecture main frame computers. The systems are fully backward compatible and the descendants of the famous IBM System/360 first introduced in 1965.
In this presentation, Dr. Harrer reflected on his three decades of work in the IBM Systems and Technology Group, where he was responsible for the design of printed circuit boards and packages for System z servers.
We are deeply grateful for the opportunity to learn from such an experienced and inspiring professional. Thank you, Dr. Harrer, for sharing your knowledge and passion with us!