Berliner Hochschule für Technik

Interacting with smart environments through gestures faces the challenge of false positives, also known as the Midas touch problem. To enable seamless and robust gesture activation, we selected activation microgestures performed simultaneously on a ring during gesture execution. A user study revealed a clear preference for activating the ring with the thumb over the middle finger, based on usability and physical demand. Interestingly, while activating a push button requires more force than pressing a smooth pressure sensor, the soft button caused significantly higher physical demand than pressing a push button, which provides clear passive haptic force feedback indicating when the button has been pushed. The lack of force feedback from the pressure sensor appears to cause users to continue pressing after the sensor has been activated. In summary, activating index-finger gestures using a thumb press is a promising technique for easy, usable, and robust ubiquitous gesture interaction.

Mechanochemistry enables selective access to fcu and hcp UiO-66 phases starting from PET bottles and coloured textile waste.

Pressure control forms the foundation for operating soft pneumatic actuators (SPAs). For effective motion or force control, however, the underlying pressure control must be both fast and accurate. This can be achieved either by placing the valve close to the actuator or by compensating for long pneumatic tubes through dynamic modeling. Tube compensation, however, is complex and difficult to implement, while direct valve mounting is often impractical because conventional proportional valves are large and heavy. To overcome these limitations, a compact, custom-designed 3/3-valve unit (CVU) based on Festo’s VEAE piezoelectric valves is developed. The CVU supports pressures up to 6 bar, flow rates up to 70 l/min and control bandwidths exceeding 9 Hz. It is controlled using the presented data-driven approach, which eliminates the need for classical system identification and automatically adapts to different actuator volumes, resulting in high accuracy and simple deployment. The control scheme employs a two-stage, data-driven architecture based on single-shot Gaussian process (GP) regression. First, the inverse static flow characteristics of each valve are modeled, compensating for valve-to-valve variability without manual mass-flow identification. Second, the CVU is adapted to the actuator’s state-dependent volume, improving accuracy and robustness to external disturbances. In both stages, only the pressure derivative is used, avoiding the need for additional flow sensors or external test benches and keeping the approach lightweight and low-cost. The CVU with the data-driven control method was validated on an antagonistic pneumatic arm with pneumatic artificial muscles (PAMs) and benchmarked against a manually tuned PID controller, a feedback-linearized controller based on analytical system inversion and a commercially available VEAB valve unit. Across all tests, the CVU with GP-based control achieved highly accurate pressure tracking and disturbance rejection. All hardware (CAD) and development code (m-code) are released as open source. • GP-based pressure controller for SPAs without classical modeling. • Compact piezo 3/3-valve mounts on the SPA to eliminate tube dynamics. • Controller uses LUTs on microcontrollers and is validated on a PAM testbed. • Open-source CAD and code: https://github.com/apaws-collab/piezo_valve_unit .

Abstract The REGIS and REThM data provide a valuable basis for geospatial analysis, even for users new to GIS. This chapter demonstrates how these datasets can be applied in research and teaching through practical examples. In historical research, REGIS and REThM enable the visualization of administrative boundaries over time, facilitating comparative studies. In education, the data support interactive learning, allowing students to explore spatial relationships and thematic mapping. By integrating external datasets, users can analyze socioeconomic trends or demographic changes. Through some step-by-step guidance and practical application examples, this chapter highlights the potential of REGIS/REThM for enhancing GIS-based research and teaching.

Effective bone regeneration requires coordinated remodeling of vascular and extracellular matrix (ECM) networks, yet their spatiotemporal interplay remains poorly understood. Here, we leverage two-photon microendoscopy to longitudinally image collagen and blood vessels within the femoral bone in vivo during bone healing post-osteotomy. Using second-harmonic generation (SHG) and fluorescence angiography, we acquired datasets from the periosteum and medullary cavity at postoperative days 7, 10, and 14. To robustly quantify structural dynamics, we developed a Radon Transform-based algorithm that automatically detects orientations in noisy in vivo imaging data without manual annotation. Our analysis revealed that vascular invasion precedes the formation of aligned collagen bundles, with collagen organization progressively increasing over time. By day 14, orientation histograms showed that both networks aligned predominantly within the 0–90° angular range, especially in the medullary cavity. Notably, we observed considerable inter-individual variability across all animals, underscoring the biological complexity of fracture healing. Together, these findings establish a new framework for quantifying ECM-vascular interactions in vivo and reveal remodeling dynamics that may drive successful bone repair.

Errors in data are a key challenge in modern data management and processing systems. Monitoring and mitigating risks associated with errors in data transformations and downstream applications, such as Machine Learning (ML) model training, requires a profound understanding of error generation and impact of errors on data pipelines. Unfortunately, scientific progress in the field is facing two main challenges: For one, research on data errors often does not adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, which impedes reproducibility and comparisons. Second, existing data error models are oversimplified and fail to capture the complex statistical dependencies underlying the types and distributions of errors observed in real-world data. Building on prior work in the database management systems and statistics literature, we extend the theory on missing values to encompass a broader range of errors in tables and provide an overview of relevant error types. Combining error sampling mechanisms often observed in real data with a comprehensive categorization of errors, we introduce a latent factor model for tabular data errors that is simple to implement and can effectively model realistic error dependencies. Error sampling is decoupled from error types, which allows for simple extensions with more error types or sampling mechanisms. Using established benchmarks, we evaluate our model in two application scenarios, data cleaning and tabular ML tasks. In a comprehensive suite of experiments we demonstrate the impact of realistic error models on data cleaning benchmarks. Our results also show that a simple generative error model captures a wide range of error mechanisms and offers a convenient formalization of data perturbations to improve the generalizability, robustness and reproducibility of data cleaning research.