Nonlinear optimisation techniques are commonly employed to minimise complex cost functions, with their effectiveness determined largely by the structure of the underlying error landscape. These methods require initial parameter values, and in the presence of multiple local minima, they are prone to becoming trapped in suboptimal regions. The likelihood of locating the global minimum increases substantially when the initialisation lies within its corresponding basin of attraction. Consequently, high-quality initial parameters are critical for successful optimisation. This technical report outlines a new strategy for selecting suitable initial parameters for a trigonometric model and unevenly sampled data, ensuring that the optimisation procedure starts sufficiently close to the global minimum. The proposed parameter estimation approach is strictly NI-based, interpretable, and explainable. It targets at complicated cases which include: samples with strong random noise, samples with only few covered periods, and samples which cover only a fraction of one period. Special attention is put on the frequency estimation. It can be shown that an estimation of initial parameters with sufficient accuracy is possible down to a signal-noise-ratio of 1.4 dB at much lower computational costs than the Lomb-Scargle-periodogram method requires.

In this paper, a novel sensing structure to be used for real-time monitoring of soil movements in construction sites is proposed. The structure integrates an array of sensor nodes, to be deployed at different depths according to a tree-shaped structure. Each sensor node measures temperature, Volumetric Water Content (VWC) and soil movement, by exploiting the measurement of pressure variations exerted by a column of water on pressure sensors positioned in the soil. The structure manages the acquisition of data from each sensor node every 30 minutes and transmits it to a remote data management centre using the Long Range Wide Area Network (LoRaWAN) protocol. A prototype of the structure was designed, developed and installed at a test site in Coburg. The results acquired across several months of experimentation demonstrate the accuracy of the measurements as well as the reliability of the overall sensing structure.

We investigated the adaptation and performance of Masked Autoencoders (MAEs) with Vision Transformer (ViT) architectures for self-supervised representation learning on one-dimensional (1D) ultrasound signals. Although MAEs have demonstrated significant success in computer vision and other domains, their use for 1D signal analysis, especially for raw ultrasound data, remains largely unexplored. Ultrasound signals are vital in industrial applications such as nondestructive testing (NDT) and structural health monitoring (SHM), where labeled data are often scarce and signal processing is highly task-specific. We propose an approach that leverages MAE to pre-train on unlabeled synthetic ultrasound signals, enabling the model to learn robust representations that enhance performance in downstream tasks, such as time-of-flight (ToF) classification. This study systematically investigated the impact of model size, patch size, and masking ratio on pre-training efficiency and downstream accuracy. Our results show that pre-trained models significantly outperform models trained from scratch and strong convolutional neural network (CNN) baselines optimized for the downstream task. Additionally, pre-training on synthetic data demonstrates superior transferability to real-world measured signals compared with training solely on limited real datasets. This study underscores the potential of MAEs for advancing ultrasound signal analysis through scalable, self-supervised learning.

A German translation of the Human-Robot Interaction Evaluation Scale (HRIES) from Spatola et al. 2021 is presented and estimates of reliability from previous studies are provided.

New quantitative data processing methods could enable ultrasound as a potential diagnostic method for hip implant integration monitoring. For development of such methods, suitable acoustic simulation tools are essential. In this work, a novel 1D FDTD simulation tool for multilayer structures, considering frequency-dependent properties, is introduced, particularly meeting the special needs of this application. Simulation results show excellent agreement with experimental data, confirming accurate prediction of wave propagation in multilayer systems.

Since COVID-19, clean indoor air has become more of a focus due to airborne viruses. Conventional filters often fail to capture ultrafine particles. This work investigates how standing ultrasonic fields manipulate aerosols for more efficient cleaning. Gor’kov theory and FEM simulations used to evaluate the acoustic forces on particles. Experiments by light refractive vibrometry and high-speed camera observations confirm the model quality.

This paper explores an alternative approach to ultrasonic flow measurement using guided acoustic waves in cylindrical modes. Unlike conventional methods with diagonal sound propagation, the entire pipe including the fluid is excited to vibrate, reducing path-dependent correction factors. A ring-shaped sensor was developed for a DN15 steel pipe. Results show a signal time shift 2.5 times greater than with Lamb wave-based sensors, adjustable over distance. This approach enables precise, non-invasive flow measurement across various pipe diameters.

Background, Motivation and Objective

Hip joint prostheses (HJP) are increasingly common with an aging population. The most frequent complication is aseptic loosening, linked to bone resorption and a growing soft tissue gap between bone and implant. However, integration monitoring and loosening diagnosis still rely on expensive, static X-ray imaging. Ultrasound, despite cheaper, dynamic, and radiation-free, is not yet viable due to its limits in resolving tissue beyond the bone.

This work presents how a novel quantitative ultrasound (QUS) data processing approach could improve HJP monitoring by quantitatively assessing osteointegration. While the basic concept was already tested on artificial models, we now show first clinical results for ultrasonic thickness measurements of the bone-implant gap at hip implant patients compared to X-ray imaging.

Statement of Contribution/Methods

Our approach is based on an analysis of raw (RF) beamformed ultrasonic data. A scan line perpendicular to the bone surface is extracted and a certain signal range following the dominant bone reflection is transformed to the frequency domain using a Fast Fourier Transform (FFT) (a). The gap thickness, indicating local osteointegration quality and potential loosening signs, is then determined by evaluating the frequency spacing of minima in the amplitude spectrum.

To demonstrate the potential of our QUS method, we analyzed ultrasonic scans from six HJP patients at one fixed position each (sagittal and transversal) using a handheld scanner (C3 HD3, Clarius, Canada) and compared the measured gap thicknesses with x-ray images.

Results/Discussion

(b) shows the gap thicknesses determined using our QUS method in comparison with the visual assessment of corresponding X-ray images. Despite the small sample size and some simplifying assumptions used for this first feasibility test, the clear trend highlights the approach’s potential for assessing local implant integration. The cases where no gap could be seen in the x-ray image illustrate its potential for gap detection beyond X-ray resolution limits.

Besides gap thickness, our QUS approach could also reveal elasticity changes in the soft-tissue gap, potentially indicating critical biofilm formation. Further steps also include extending our method to an automated thickness detection during dynamic scanning and integrating results into B-mode images, e. g., using color coding.

Zusammenfassung

Geführte Akustische Wellen (GAW) haben sich im Themengebiet des Structural Health Monitoring (SHM) etabliert. Neben ihren Vorteilen bei der Überwachung von Objekten und Detektion von Fehlstellen, gibt es jedoch auch einige Herausforderungen. Zu diesen zählt die dispersive Natur der häufig eingesetzten Lambwellen. Dispersion führt zu Signalverzerrung und reduziert dadurch die räumliche Auflösung und erschwert die Erkennung von schwach reflektierenden Fehlstellen. In diesem Beitrag wird der Einsatz eines Phased-Array-Systems zur Delaminationserkennung bei einem Mehrschichtsystem demonstriert, bei dem dispersive Lambwellen zum Einsatz kommen. Durch das Kompensieren der Dispersionseffekte kann die Sign Coherence Factor (SCF) Erweiterung des Total Focusing Method (TFM) Algorithmus eingesetzt und so auch schwach reflektierende Fehlstellen erkannt werden. Des Weiteren wird auf das Entstehen von Modenüberlagerungen bei Mehrschichtsystemen eingegangen, die bei der Auswahl der Arbeitsfrequenz und Sendesignallänge des Phased-Array-Systems berücksichtigt werden müssen.

Guided Acoustic Waves (GAW) are well established in the field of Structural Health Monitoring (SHM). However, in addition to their advantages in monitoring objects and detecting defects, there are also several challenges. These include the dispersive nature of the commonly used Lamb waves. Dispersion leads to signal distortion that reduces spatial resolution and makes it difficult to detect weakly reflecting defects. This paper demonstrates the use of a phased array system for delamination detection in a multilayer system using dispersive Lamb waves. By compensating for the dispersion effects, the Sign Coherence Factor (SCF) extension of the Total Focusing Method (TFM) algorithm can be used to detect even weakly reflective defects. Furthermore, the occurrence of mode superposition in multilayer systems is discussed, which must be taken into account when selecting the operating frequency and transmit signal length of the phased array system.