Dr.-Ing. Römer, FlorianDr.-Ing. Römer, Florian


Phone: +49 3677 69-4286
E-Mail: florian.roemer@izfp.fraunhofer.de
Address: Technische Universität Ilmenau
  Fakultät für Elektrotechnik und Informationstechnik
  Fachgebiet Elektronische Messtechnik und Signalverarbeitung
  Postfach 100 565
  98684 Ilmenau


Year Degree Subject Topic of Thesis
2012 Dr.-Ing. (Doctorate) Ph.D. studies in electrical engineering with major in communications research at TU Ilmenau Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing
2006 Dipl.-Ing. Study of Computer Engineering at TU Ilmenau Advances in subspace-based parameter estimation: Tensor-ESPRIT-type methods and non-circular sources
2004 Study Abroad at McMaster University, Hamilton/ON (Canada)  

Professional Path

since 2018 Group leader at Fraunhofer Institute for Nondestructive Testing IZFP: Group leader of SigMaSense (Signal processing for material data acquisition using smart sensors)
2012 - 2017 Postdoctoral research fellow at Digital Broadcasting Research Laboratory at TU Ilmenau
2006 - 2012 Research fellow at Communications Research Lab at TU Ilmenau
2003 - 2006 Student research assistant at the Communications Research Lab at TU Ilmenau: research on high-resolution parameter estimation, non-circular complex random signals, electronically steerable (ESPAR) antennas
2005 - 2006 Internship at the Fraunhofer Institute for Digital Media Technology (IDMT): research on diagonalization of pseudo-circulant polyphase matrices


2016 Best paper award of TU Ilmenau for the publication "R-Dimensional ESPRIT-Type Algorithms for Strictly Second-Order Non-Circular Sources and Their Performance Analysis" (J. Steinwandt et. al.)
2016 EURASIP Best PhD Award 2013
2016 Elevation to IEEE Senior Member
2015 Postdoctoral scholarship of Carl-Zeiss-Stiftung
2013 Best dissertation award of the Förder- und Freundeskreises der TU Ilmenau
2007 Siemens Communications Academic Award 2006

Projects Related to the Research Unit

  • Nondestructive Testing
  • Efficient acquisition of analog signals, e. g. via Compressive Sensing
  • Machine Learning and Artificial Intelligence
  • High-resolution parameter estimation: Parametric spectral estimation (ESPRIT, MUSIC), Maximum Likelihood methods
  • Array signal processing


Anzahl der Treffer: 165
Erstellt: Tue, 28 May 2024 23:25:56 +0200 in 0.0833 sec

Rashidifar, Ali; Römer, Florian; Semper, Sebastian; Gutzeit, Nam; Del Galdo, Giovanni
Broadband DRA with uniform angular dependent delay for indoor localization. - In: IEEE access, ISSN 2169-3536, Bd. 12 (2024), S. 63644-63654

Estimating the Time Difference of Arrival (TDoA), is a simple yet reliable technique to accurately perform an indoor monostatic localization. To implement TDoA estimation, one approach is to utilize a broadband radar system equipped with multiple receiving antenna elements. To obtain the Time of Arrival (ToA) at each antenna element, the round-trip time is required. However, the round-trip time does not only consist of the propagation delay in free space but the propagation delay within the antenna as well. To perform the localization precisely, it is desired that an antenna element introduces a uniform delay in all directions. To this end, a compact rectangular dielectric resonator antenna is designed for the operating frequency of 6.5 GHz with a fractional bandwidth of 20%. Al2O3 with a dielectric constant of 9.8 is used for the substrate as well as the dielectric resonator. The antenna is designed to provide a high correlation between the input and the output pulses. To investigate the correlation, the antenna is excited with a modulated Gaussian pulse and the radiated pulses are studied. The antenna possesses an excellent behavior in terms of pulse preservation for the upper hemisphere. Therefore, when incoming pulses from the same distance but different directions impinge on the antenna, they reach the port of the antenna at a similar time. It is shown that this feature of the proposed antenna allows the utilization of TDoA estimation without the need for a calibration step. The characteristics of the antenna are verified by simulation and measurement.

¸Cakiro&bovko;glu, Ozan; Pérez, Eduardo; Römer, Florian; Schiffner, Martin Friedrich
Autoencoder-based learning of transmission parameters in fast pulse-echo ultrasound imaging employing sparse recovery. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 516-520

There is recently a notable rise in the exploration of pulse-echo ultrasound image reconstruction techniques that address the inverse problem employing sparse signal and rely on a single measurement cycle. Nevertheless, these techniques continue to pose significant challenges with regard to accuracy of estimations. Previous studies have endeavored to decrease the correlation between received samples in each transducer array in order to enhance accuracy of sparsely approximated solutions to inverse problems. In this paper, our objective is to learn the transmission parameters within a parametric measurement matrix by employing an autoencoder, which encodes sparse spatial data with a parametric measurement matrix and subsequently decodes it using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Outcomes exhibit superior performance in comparison to both state-of-art random selection of the parameters and conventional plane wave imaging (PWI) scenarios in terms of reconstruction accuracy.

Wang, Han; Pérez, Eduardo; Römer, Florian
Data-driven subsampling matrices design for phased array ultrasound nondestructive testing. - In: IEEE IUS 2023, International Ultrasonics Symposium, Palais des congrès de Montréal, September 3-8, 2023, (2023), insges. 4 S.

By subsampling optimally in the spatial and temporal domains, ultrasound imaging can achieve high performance, while also accelerating data acquisition and reducing storage requirements. We study the design of experiment problem that attempts to find an optimal choice of the subsampling patterns, leading to a non-convex combinatorial optimization problem. Recently, deep learning was shown to provide a feasible approach for solving such problems efficiently by virtue of the softmax function as a differentiable approximation of the one-hot encoded subsampling vectors. We incorporate softmax neural networks into information theory-based and task-based algorithms, respectively, to design optimal subsampling matrices in Full Matrix Capture (FMC) measurements predicated on compressed sensing theory.

Wang, Han; Pérez, Eduardo; Römer, Florian
Deep learning-based optimal spatial subsampling in ultrasound nondestructive testing. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1863-1867

Traditional ultrasound synthetic aperture imaging relies on closely spaced measurement positions, where the pitch size is smaller than half the ultrasound wavelength. While this approach achieves high-quality images, it necessitates the storage of large data sets and an extended measurement time. To address these issues, there is a burgeoning interest in exploring effective subsampling techniques. Recently, Deep Probabilistic Subsampling (DPS) has emerged as a feasible approach for designing selection matrices for multi-channel systems. In this paper, we address spatial subsampling in single-channel ultrasound imaging for Nondestructive Testing (NDT) applications. To accomplish a model-based data-driven spatial subsampling approach within the DPS framework that allows for the optimal selection of sensing positions on a discretized grid, it is crucial to build an adequate signal model and design an adapted network architecture with a reasonable cost function. The reconstructed image quality is then evaluated through simulations, showing that the presented subsampling pattern approaches the performance of fully sampling and substantially outperforms uniformly spatial subsampling in terms of signal recovery quality.

¸Cakiro&bovko;glu, Ozan; Pérez, Eduardo; Römer, Florian; Schiffner, Martin
Optimization of transmission parameters in fast pulse-echo ultrasound imaging using sparse recovery. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 441-445

In pulse-echo ultrasound imaging, the goal is to achieve a certain image quality while minimizing the duration of the signal acquisition. In the past, fast ultrasound imaging methods applying sparse signal recovery have been implemented by accepting a single pulse-echo measurement. However, they have experienced a certain amount of reconstruction error. In sparse signal recovery, reducing the correlation between the samples of the measurements observed by the different receivers is beneficial for lowering the reconstruction error. Exploiting the Born approximation and Green's function for the wave equation, the analytical inverse scattering problem can be defined in matrix-vector form. Adopting this setting, it has been suggested in the past to reduce the correlation between the samples of the measurement using Cylindrical Waves (CWs) with randomly selected delays and weights. In a similar setting, we created an optimization problem accepting transmission delays and weights as variables to minimize the correlation between the samples of the measurement in each receiver. We demonstrate via simulations that CWs employing the optimized transmission parameters outperformed the cases with Plane Wave Imaging (PWI) and CWs with random transmission parameters in terms of reconstruction accuracy.

Gourishetti, Saichand; Schmidt, Leander; Römer, Florian; Schricker, Klaus; Kodera, Sayako; Böttger, David; Krüger, Tanja; Kátai, András; Bös, Joachim; Straß, Benjamin; Wolter, Bernd; Bergmann, Jean Pierre
Monitoring of joint gap formation in laser beam butt welding using neural network-based acoustic emission analysis. - In: Crystals, ISSN 2073-4352, Bd. 13 (2023), 10, 1451, S. 1-14

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.

Römer, Florian; Kirchhof, Jan; Krieg, Fabian; Pérez, Eduardo
Compressed Sensing: from big data to relevant data. - In: Handbook of Nondestructive Evaluation 4.0, (2022), S. 329-352

Though the ever-increasing availability of digital data in the context of NDE 4.0 is mostly considered a blessing, it can turn to a curse quite rapidly: managing large amounts of data puts a burden on the sensor devices in terms of sampling and transmission, the networks, as well as the server infrastructure in terms of storing, maintaining, and accessing the data. Yet, NDE data can be highly redundant so the storage of massive amounts of data may indeed be wasteful. This is the main reason why focusing on relevant data as early as possible in the NDE process is highly advocated in the context of NDE 4.0. This chapter introduces Compressed Sensing as a potential approach to put this vision to practice. Compressed Sensing theory has shown that sampling signals with sampling rates that are significantly below the Shannon-Nyquist rate is possible without loss of information, provided that prior knowledge about the signals to be acquired is available. In fact, we may sample as low as the actual information rate if our prior knowledge is sufficiently accurate. In the NDE 4.0 context, prior knowledge can stem from the known inspection task and geometry but it can also include previous recordings of the same piece (such as in Structural Health Monitoring), information stored in the digital product memory along the products’ life cycle, or predictions generated through the products’ digital twins. In addition to data reduction, reconstruction algorithms developed in the Compressed Sensing community can be applied for enhanced processing of NDE data, providing added value in terms of accuracy or reliability. The chapter introduces Compressed Sensing basics and gives some concrete examples of its application in the NDE 4.0 context, in particular for ultrasound.

Kodera, Sayako; Römer, Florian; Pérez, Eduardo; Kirchhof, Jan; Krieg, Fabian
Deep learning aided interpolation of spatio-temporal nonstationary data. - In: 30th European Signal Processing Conference (EUSIPCO 2022), (2022), S. 2221-2225

Despite the growing interest in many fields, spatio-temporal (ST) interpolation remains challenging. Given ST nonstationary data distributed sparsely and irregularly over space, our objective is to obtain an equidistant representation of the region of interest (ROI). For this reason, an equidistant grid is defined within the ROI, where the available time series data are arranged, and the time series of the unobserved points are interpolated. Aiming to maintain the interpretability of the whole process while offering flexibility and fast execution, this work presents a ST interpolation frame-work which combines a statistical technique with deep learning. Our framework is generic and not confined to a specific application, which also provides the prediction confidence. To evaluate its validity, this framework is applied to ultrasound nondestructive testing (UT) data as an example. After the training with synthetic UT data sets, our framework is shown to yield accurate predictions when applied to measured UT data.

Pandey, Rick; Kirchhof, Jan; Krieg, Fabian; Pérez, Eduardo; Römer, Florian
Preprocessing of freehand ultrasound synthetic aperture measurements using DNN. - In: 29th European Signal Processing Conference (EUSIPCO 2021), (2021), S. 1401-1405

Manual ultrasonic inspection is a widely used Nondestructive Testing (NDT) technique due to its simplicity and compatibility with complex structures. However, in contrast to the data acquired using a robotic positioner, manual measurements suffer from perturbations caused by a variable coupling and a varying scanning density. Imaging techniques like the synthetic aperture focusing technique rely on an unperturbed dense measurement from an equidistant measurement grid. Consequently, imaging based on freehand measurements leads to artifacts. This work aims at reducing such artifacts by preprocessing the manual measurements using Deep Neural Networks (DNN). The training of a DNN requires a large set of labeled measurements which is difficult to obtain in NDT. In this work, we present a technique to train the DNN using only synthetic data. We show that the resulting DNN generalizes well on real measurements. We present an improvement in Generalized Contrast to Noise Ratio by a factor of 20 and 3 compared to omitting the preprocessing for synthetic and measurement data, respectively.

Pérez, Eduardo; Semper, Sebastian; Kirchhof, Jan; Krieg, Fabian; Römer, Florian
Compressed ultrasound computed tomography in NDT. - In: IEEE IUS 2021, (2021), insges. 4 S.

Ultrasound Computed Tomography (UCT) is challenging due to phenomena such as strong refraction, multiple scattering, and mode conversion. In NDT, large speed of sound contrasts lead to strong artifacts if such phenomena are not modeled correctly; however, enhanced models are computationally expensive. In this work, a two-step framework for Compressed UCT based on the integral approach to the solution of the Helmholtz equation is presented. It comprises a physically motivated forward step and an imaging step that solves a suitable inverse problem. Multiple scattering is accounted for through the use of Neumann series. Convergence problems of Neumann series in high contrast settings are addressed via Padé approximants. Compressed sensing is employed to reduce the computational complexity of the reconstruction procedure by reducing data volumes directly at the measurement step, avoiding redundancy in the data and allowing the ability to steer the admissible computational effort at the expense of reconstruction quality. The proposed method is shown to yield high quality reconstructions under heavy subsampling in the frequency and spatial domains.