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.

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.

This study is focused on optimizing electromagnetic acoustic transducer (EMAT) sensors for enhanced ultrasonic guided wave signal generation in steel cables using CAD and modern manufacturing to enable contactless ultrasonic signal transmission and reception. A lab test rig with advanced measurement and data processing was set up to test the sensors’ ability to detect cable damage, like wire breaks and abrasion, while also examining the effect of potential disruptors such as rope soiling. Machine learning algorithms were applied to improve the damage detection accuracy, leading to significant advancements in magnetostrictive measurement methods and providing a new standard for future development in this area. The use of the Vision Transformer Masked Autoencoder Architecture (ViTMAE) and generative pre-training has shown that reliable damage detection is possible despite the considerable signal fluctuations caused by rope movement.

The study presents a unifying methodology for characterizing micromixers, integrating both experimental and simulation techniques. Focusing on Dean mixer designs, it employs an optical evaluation for experiments and a modified Sobolev norm for simulations, yielding a unified dimensionless characteristic parameter for the whole mixer at a given Reynolds number. The results demonstrate consistent mixing performance trends across both methods for various operation points. This paper also proposes enhancements in the evaluation process to improve accuracy and reduce noise impact. This approach provides a valuable framework for optimizing micromixer designs, essential in advancing microfluidic technologies.

Particularly in medical technology, biotechnology or the pharmaceutical sector, very small quantities of fluids often have to be transported or dosed. Noninvasive measuring methods for flow rate or volume flow measurement that work without direct contact to the fluid and thus meeting the high hygiene standards of these industries hardly exist. Sensors available on the market are either not suitable for precise measurement of the smallest flow rates in the microliter range or are very expensive. For this field of applications, a retrofittable ultrasound-based flow sensor was developed in cooperation with the company ibidi GmbH, which can be integrated into an existing system consisting of very thin tubes or cannulas or capillaries as well as thin flexible tubes.

The loosening of an artificial joint is a frequent and critical complication in orthopedics and trauma surgery. Due to a lack of accuracy, conventional diagnostic methods such as projection radiography cannot reliably diagnose loosening in its early stages or detect whether it is associated with the formation of a biofilm at the bone–implant interface. In this work, we present a non-invasive ultrasound-based interferometric measurement procedure for quantifying the thickness of the layer between bone and prosthesis as a correlate to loosening. In principle, it also allows for the material characterization of the interface. A well-known analytical model for the superposition of sound waves reflected in a three-layer system was combined with a new method in data processing to be suitable for medical application at the bone–implant interface. By non-linear fitting of the theoretical prediction of the model to the actual shape of the reflected sound waves in the frequency domain, the thickness of the interlayer can be determined and predictions about its physical properties are possible. With respect to determining the layer’s thickness, the presented approach was successfully applied to idealized test systems and a bone–implant system in the range of approx. 200 µm to 2 mm. After further optimization and adaptation, as well as further experimental tests, the procedure offers great potential to significantly improve the diagnosis of prosthesis loosening at an early stage and may also be applicable to detecting the formation of a biofilm.

Material characterization using contactless excited and detected guided acoustic waves is a well proven, but overall expensive approach, since a cheap method of excitation is missing so far. Ignition-spark-excitation of Lamb waves could be such a method but has not shown to be suitable for material characterization yet. Covering various thicknesses and materials of metal plates, systematic dependencies of the spectral amplitude of the ignition-spark-excited Lamb waves are presented in this work. This enables the approach to be an alternative for exciting Lamb waves for material characterization.