arXiv subjects

Matthias Wagner

Publications and source records attributed to Matthias Wagner.

14 recordsLinked to original sources

arXiv2026

Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation

Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural representation (INR) that models multimodal longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning. The model is trained with stochastic modality dropout to handle incomplete data, and its continuous coordinate-space formulation enables both spatial and temporal interpolation without resampling to a fixed voxel grid. A self-consistency-based confidence estimator is derived from cross-modal reconstruction performance at inference time. We evaluate the framework on longitudinal MRI from paediatric brain tumour patients, demonstrating statistically significant improvements over linear interpolation for T1CE and FLAIR (p < 0.05), with mean MS-SSIM of 0.95 $\pm$ 0.02 for T1CE. Predicted confidence correlates strongly with true reconstruction quality (Pearson r up to 0.996), suggesting reliable deployment potential in heterogeneous clinical settings.

cs.CV↗

arXiv2026

Microscaled Tunable Magnonic RF Phase Shifters

Tunable, microscopic, and energy-efficient solutions for radio-frequency (RF) signal manipulation in the GHz regime are a key technology for efficient communication and sensing applications. Spin waves offer micrometer wavelengths at GHz frequencies, combined with strong magnetic-field tunability, making them inherently well-suited for tunable analog signal processing. Here, we demonstrate a novel concept: a micron-scale tunable RF phase shifter based on the wavelength shift of propagating spin waves. High energy efficiency is achieved by using the stray field of a micromagnet on a piezoelectrically actuated MEMS cantilever to locally induce this shift. The device shows a phase shift of more than 360{\deg} at a center frequency of 6.1 GHz using a phase-shifting area of less than 0.02mm$^2$. By changing the magnetic bias field, its functionality is experimentally confirmed over a range of center frequencies from 3 GHz to 8.2 GHz, and simulations show its applicability up to 14 GHz. A system-level characterization of an embedded device version demonstrates the qualification of magnonic phase shifters for highly integrated RF systems.

physics.app-ph↗

arXiv2026

Nonlinear frequency shift and bistability of magnon-polarons

We investigate the nonlinear dynamics of strongly coupled surface acoustic waves (SAWs) and spin waves (SWs) in a magnetoacoustic resonator based on a YIG/ZnO heterostructure by combining microwave reflection measurements with microfocused Brillouin light scattering spectroscopy. In the linear regime, the electrical response reveals clear hybridization between standing SAW cavity modes and finite-wave-vector SWs, resulting in pronounced avoided crossings. At elevated drive powers, the hybrid system exhibits a strongly field-dependent nonlinear response characterized by a positive frequency shift of the driven SW mode. Using the vector Hamiltonian formalism for nonlinear spin-wave dynamics, we show that this shift is dominated by a cross-shift term. In our resonator geometry, this contribution becomes significant because the standing SAW cavity mode simultaneously excites counterpropagating SWs with wave vectors $+k$ and $-k$. For suitable field detuning, the nonlinear shift drives the SW mode into resonance with the SAW excitation, leading to a strong enhancement of the magnon population, broadband nonlinear scattering, and bistable foldover behavior. Beyond the foldover threshold, both the magnon and phonon responses stabilize. These results establish SAW-driven $k \neq 0$ magnon-phonon hybrids as a promising platform for nonlinear magnetoacoustics and wave-based information processing.

cond-mat.mes-hall↗

arXiv2026

Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis

We propose a geometric framework for longitudinal multi-parametric MRI analysis based on patient-specific energy modelling in sequence space. Rather than operating on images with spatial networks, each voxel is represented by its multi-sequence intensity vector ($T1$, $T1c$, $T2$, FLAIR, ADC), and a compact implicit neural representation is trained via denoising score matching to learn an energy function $E_{\theta}(\mathbf{u})$ over $\mathbb{R}^d$ from a single baseline scan. The learned energy landscape provides a differential-geometric description of tissue regimes without segmentation labels. Local minima define tissue basins, gradient magnitude reflects proximity to regime boundaries, and Laplacian curvature characterises local constraint structure. Importantly, this baseline energy manifold is treated as a fixed geometric reference: it encodes the set of contrast combinations observed at diagnosis and is not retrained at follow-up. Longitudinal assessment is therefore formulated as evaluation of subsequent scans relative to this baseline geometry. Rather than comparing anatomical segmentations, we analyse how the distribution of MRI sequence vectors evolves under the baseline energy function. In a paediatric case with later recurrence, follow-up scans show progressive deviation in energy and directional displacement in sequence space toward the baseline tumour-associated regime before clear radiological reappearance. In a case with stable disease, voxel distributions remain confined to established low-energy basins without systematic drift. The presented cases serve as proof-of-concept that patient-specific energy manifolds can function as geometric reference systems for longitudinal mpMRI analysis without explicit segmentation or supervised classification, providing a foundation for further investigation of manifold-based tissue-at-risk tracking in neuro-oncology.

cs.CV↗

arXiv2026

Characterizing the Linearity of Magnonic Devices for Radio-Frequency Applications

Magnonic devices exhibit strong amplitude-dependent nonlinearities, which are detrimental to signal integrity in radio-frequency (RF) signal processing applications. They also limit the power that such magnonic devices may process. In this paper we use micromagnetic simulations to characterize the nonlinearity of magnonic RF devices by investigating their intermodulation distortion (specifically third-order intermodulation products, IP$_3$ ). The IP$_3$ is a commonly used metric for RF components in communication systems and allows direct comparison with state-of-the-art electrical counterparts.

cond-mat.other↗

arXiv2025

Modelling spin-wave interference with electromagnetic leakage in micron-scaled spin-wave transducers

Utilization of spin-wave transducers for radio-frequency signal processing provides significant potential due to intrinsic tunability, scalability and nonlinearity. However, such components can exhibit passband ripples diminishing their operation and functionality. Here, we experimentally identify the electromagnetic crosstalk (EM) as a major source of passband ripples and provide a simple analytic model to predict the impact on device operation. The results are in good agreement with the experimental observation. In addition, we test multiple transducer geometries to identify operational regimes and minimize the EM impact. Finally, the effect of nonlinear device operation on the passband ripples is addressed, which is of relevance for the exploitation of the spin-waves intrinsic nonlinear traits.

physics.app-ph↗

arXiv2025

Automotive Radar Online Channel Imbalance Estimation via NLMS

Automotive radars are one of the essential enablers of advanced driver assistance systems (ADASs). Continuous monitoring of the functional safety and reliability of automotive radars is a crucial requirement to prevent accidents and increase road safety. One of the most critical aspects to monitor in this context is radar channel imbalances, as they are a key parameter regarding the reliability of the radar. These imbalances may originate from several parameter variations or hardware fatigues, e.g., a solder ball break (SBB), and may affect some radar processing steps, such as the angle of arrival estimation. In this work, a novel method for online estimation of automotive radar channel imbalances is proposed. The proposed method exploits a normalized least mean squares (NLMS) algorithm as a block in the processing chain of the radar to estimate the channel imbalances. The input of this block is the detected targets in the range-Doppler map of the radar on the road without any prior knowledge on the angular parameters of the targets. This property in combination with low computational complexity of the NLMS, makes the proposed method suitable for online channel imbalance estimation, in parallel to the normal operation of the radar. Furthermore, it features reduced dependency on specific targets of interest and faster update rates of the channel imbalance estimation compared to the majority of state-of-the-art methods. This improvement is achieved by allowing for multiple targets in the angular spectrum, whereas most other methods are restricted to only single targets in the angular spectrum. The performance of the proposed method is validated using various simulation scenarios and is supported by measurement results.

eess.SP↗

arXiv2025

Optimum and Adaptive Complex-Valued Bilinear Filters

The identification of nonlinear systems is a frequent task in digital signal processing. Such nonlinear systems may be grouped into many sub-classes, whereby numerous nonlinear real-world systems can be approximated as bilinear (BL) models. Therefore, various optimum and adaptive BL filters have been introduced in recent years. Moreover, in many applications, such as communications and radar, complex-valued (CV) BL systems in combination with CV signals may occur. Hence, in this work, we investigate the extension of real-valued (RV) BL filters to CV BL filters. First, we derive CV BL filters by applying two or four RV BL filters, and compare them with respect to their computational complexity and performance. Second, we introduce novel fully CV BL filters, such as the CV BL Wiener filter (C-BWF), the CV BL least squares (C-BLS) filter, the CV BL least mean squares (C-BLMS) filter, the CV BL normalized least mean squares (C-BNLMS) filter, and the CV BL recursive least squares (C-BRLS) filter. Finally, these filters are applied to identify CV multiple-input-single-output (MISO) systems and CV Hammerstein models.

eess.SP↗

arXiv2025

Tx and Rx IQ Imbalance Compensation for JCAS in 5G NR

Beside traditional communications, joint communications and sensing (JCAS) is gaining increasing relevance as a key enabler for next-generation wireless systems. The ability to accurately transmit and receive data is the basis for high-speed communications and precise sensing, where a fundamental requirement is an accurate in-phase (I) and quadrature-phase (Q) modulation. For sensing, imperfections in IQ modulation lead to two critical issues in the range-Doppler-map (RDM) in form of an increased noise floor and the presence of ghost objects, degrading the accuracy and reliability of the information in the RDM. This paper presents a low-complex estimation and compensation method to mitigate the IQ imbalance effects. This is achieved by utilizing, amongst others, the leakage signal, which is the direct signal from the transmitter to the receiver path, and is typically the strongest signal component in the RDM. The parameters of the IQ imbalance suppression structure are estimated based on a mixed complex-/real-valued bilinear filter approach, that considers IQ imbalance in the transmitter and the receiver of the JCAS-capable user equipment (UE). The UE uses a 5G New Radio (NR)-compliant orthogonal frequency-division multiplexing (OFDM) waveform with the system configuration assumed to be predefined from the communication side. To assess the effectiveness of the proposed approach, simulations are conducted, illustrating the performance in the suppression of IQ imbalance introduced distortions in the RDM.

eess.SP↗

arXiv2024

Building Europe's first space-based Quantum Key Distribution system -- The German Aerospace Center's role in the EAGLE-1 mission

The EAGLE-1 mission aims to develop Europe's first sovereign, end-to-end space-based quantum key distribution (QKD) system. The mission is led by the European Space Agency (ESA) and SES in collaboration with several European National Space Agencies and private partners. The state-of-the-art QKD system will consist of a payload on board the EAGLE-1 low Earth orbit (LEO) satellite, optical ground stations, quantum operational networks, and key management system. The EAGLE-1 mission represents a major step for next-generation quantum communication infrastructures, delivering valuable technical results and mission data. The Institute of Communications and Navigation (IKN) of the German Aerospace Center (DLR) is a key partner in the EAGLE-1 mission and is involved in the research and development of elements in both space and ground segments. Here we report on the development of the QKD transmitter, a vital part of the QKD payload, and the customization of the Optical Ground Station Oberpfaffenhofen (OGS-OP) to conduct the IOT phase of EAGLE-1. For the space segment, DLR-IKN is in charge of the design of the QKD transmitter, including the development of the software and firmware. This transmitter generates quantum states which are used to implement a QKD protocol based on an optical signal, that will be transmitted to ground. For the ground segment, The OGS-OP will serve as the in-orbit testing ground station for EAGLE-1. Building upon the expertise with a range of satellites for quantum communication, as well as new implementations, OGS-OP will validate the performance of the payload, optical link and QKD system for the first time. We present the main developments of OGS-OP for the mission, which includes the implementation of an upgraded adaptive optics system to correct for atmospheric distortions and optimize the coupling of the incoming light into a single mode optical fiber.

quant-ph↗

arXiv2023

Bi-Linear Homogeneity Enforced Calibration for Pipelined ADCs

Pipelined analog-to-digital converters (ADCs) are key enablers in many state-of-the-art signal processing systems with high sampling rates. In addition to high sampling rates, such systems often demand a high linearity. To meet these challenging linearity requirements, ADC calibration techniques were heavily investigated throughout the past decades. One limitation in ADC calibration is the need for a precisely known test signal. In our previous work, we proposed the homogeneity enforced calibration (HEC) approach, which circumvents this need by consecutively feeding a test signal and a scaled version of it into the ADC. The calibration itself is performed using only the corresponding output samples, such that the test signal can remain unknown. On the downside, the HEC approach requires to accurately scale the test signal, impeding an on-chip implementation. In this work, we provide a thorough analysis of the HEC approach, including limitations such as the effects of an inaccurately scaled test signal. Furthermore, the bi-linear homogeneity enforced calibration (BL-HEC) approach is introduced and suggested to account for an inaccurate scaling and, therefore, to facilitate an on-chip implementation. In addition, a comprehensive stability analysis of the BL-HEC approach is carried out. Finally, we verify our concept with behavioral Matlab simulations and measurements conducted on 24 integrated ADCs.

eess.SP↗

arXiv2022

Homogeneity Enforced Calibration of Stage Nonidealities for Pipelined ADCs

Pipelined analog-to-digital converters (ADCs) are fundamental components of various signal processing systems requiring high sampling rates and a high linearity. Over the past years, calibration techniques have been intensively investigated to increase the linearity. In this work, we propose an equalization-based calibration technique which does not require knowledge of the ADC input signal for calibration. For that, a test signal and a scaled version of it are fed into the ADC sequentially, while only the corresponding output samples are used for calibration. Several test signal sources are possible, such as a signal generator (SG) or the system application (SA) itself. For the latter case, the presented method corresponds to a background calibration technique. Thus, slowly changing errors are tracked and calibrated continuously. Because of the low computational complexity of the calibration technique, it is suitable for an on-chip implementation. Ultimately, this work contains an analysis of the stability and convergence behavior as well as simulation results.

eess.SP↗

arXiv2018

A Robust Nonlinear RLS Type Adaptive Filter for Second-Order-Intermodulation Distortion Cancellation in FDD LTE and 5G Direct Conversion Transceivers

Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive least-squares (RLS) type adaptive filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select-, and DC-notch filter at the output of the proposed adaptive filter ensure that the provided IMD2 replica includes the receiver front-end filtering. A second, robust version of the nonlinear RLS algorithm is derived which provides numerical stability for highly correlated input signals which arise in e.g. LTE-A intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.

eess.SP↗

arXiv2007

Classical and ab initio preparation of reliable structures for polymeric coordination compounds

The detailed investigation of electronic and magnetic properties of polymeric coordination materials with accurate ab initio quantum mechanical methods is often computationally extremely demanding because of the large number of atoms in the unit cell. Moreover, usually the available structural data are insufficient or poorly determined, especially when the structure contains hydrogen atoms. In order to be able to perform controlled ab initio calculations on reliable structures, we use a two-step approach to systematically prepare model structures for polymeric coordination compound systems and to relax them to their equilibrium configuration. First, a structure is constructed on the basis of a crystallographic database and optimized by force field methods; in the second step, the structure is relaxed by ab initio quantum mechanical molecular dynamics. With this structure, we perform accurate electronic structure calculations. We will apply this procedure to a Fe(II) triazole compound and to a coordination polymer of Cu(II) ions with 2,5-bis(pyrazol-1-yl)-1,4-dihydroxybenzene.

cond-mat.mtrl-sci↗