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Assessing Visualization and Interaction Techniques to Support Comparison Tasks in Virtual Reality
No full textDesktop screens are effective for supporting comparison tasks, but as the scale increases to room-sized or larger structures, context is lost. Users are forced to focus on isolated details through panning, zooming, and scrolling, making it difficult to maintain an overview while exploring finer details. Virtual Reality (VR) potentially offers a solution to this problem by immersing users in 3D spaces and enabling more intuitive comparisons. While related work has proposed many solutions for visualizing and interacting for comparison tasks in desktop environments, knowledge regarding the efficacy of supporting such tasks in VR environments is still lacking. We investigated varying visualization and interaction techniques in a controlled experiment with 24 participants. Our findings provide valuable insights for designing VR systems that improve usability, reduce workload, and enhance performance in comparison tasks
Oxygen-vacancy-induced Raman softening in the catalyst Fe2(MoO4)3
No full textAlthough iron molybdate (Fe2(MoO4)3) has been commercially utilized for the production of formaldehyde from methanol via oxidative dehydrogenation, the detailed mechanism during the catalytic process remains unclear. Recent operando Raman and impedance measurements of the reaction suggested that the bulk region of Fe2(MoO4)3 acts as a reservoir of oxygen atoms that can migrate to the surface to participate in catalysis. This conclusion was drawn from the observed significant reduction in Raman intensity during the catalytic process which implies the formation of atomic defects. However, the microscopic origin of this reduction remains to be clarified. In this work, we performed density functional theory (DFT) calculations to elucidate the origin of the experimentally observed Raman intensity variation. Our phonon analysis reveals that oxygen-dominated vibrational modes, with a small Mo contribution, occur near 782 cm-1 the same frequency region where the Raman intensity reduction was measured. Using an effective frozen-phonon approach, we further demonstrate that oxygen vibrations are primarily responsible for the decrease in calculated Raman intensity. Moreover, structural relaxation of Fe2(MoO4)3 including an oxygen vacancy suggests that oxygen diffusion from the bulk to the surface should occur without significant alteration of the local symmetry, consistent with the absence of measurable peak shifts or broadening in the experimental Raman spectra
Breaking the Barrier to Downlink Packet Capture in OFDMA Wi-Fi Networks
No full textOrthogonal frequency-division multiple access (OFDMA) is considered as one of the main enablers for scaling Wi-Fi throughput. Although the technology was formally introduced with the publication of the IEEE 802.11ax amendment in May 2021, the first commercially available products had already appeared in 2018, and since then millions of access points and client devices have been sold. Still, researchers lack a practical way to observe the OFDMA-based multi-user frame exchanges in the wild. We close this gap by designing and implementing the first open-source complete toolkit that captures every downlink frame of an 802.11ax OFDMA network using only commodity hardware augmented with lightweight firmware patches. An extensive experimental campaign across diverse access points quantifies capture and timing accuracy, error rates, and reveals scheduler behaviors that are opaque to conventional sniffers. By exposing both the capabilities and the limits of COTS-based monitoring, our work provides a foundation for future research on next-generation Wi-Fi MAC protocols and performance tuning
Spectroscopic cell for coupled in situ and operando synchrotron-based Mössbauer and Raman spectroscopy of catalytic reactions
No full textSpeculative Recursion Unrolling
No full textIn this paper, we propose a novel, annotation based approach to optimize recursive functions in C/C++ codes. It can be used to fully remove recursive structures, optimize the working pattern and gives the developer fine grain control over inlining behavior, leading to speedups of up to 40x. To achieve this we use Clang annotations and have developed an LLVM compiler pass that parses the developer given annotation and transforms the code accordingly. We demonstrate the effectiveness of this approach on 5 well known recursive algorithms. Our experiments show, that recursive functions that are not limited by memory bandwidth but computation intensity benefit the most from this optimization. Our code is available at https://github.com/tudasc/SpecRecUnroll
Athena: An Autonomous Open-Hardware Tracked Rescue Robot Platform
No full textIn disaster response and situation assessment, robots have great potential in reducing the risks to the safety and health of first responders.
As the situations encountered and the required capabilities of the robots deployed in such missions differ wildly and are often not known in advance, heterogeneous fleets of robots are needed to cover a wide range of mission requirements.
While UAVs can quickly survey the mission environment, their ability to carry heavy payloads such as sensors and manipulators is limited.
UGVs can carry required payloads to assess and manipulate the mission environment, but need to be able to deal with difficult and unstructured terrain such as rubble and stairs.
The ability of tracked platforms with articulated arms (flippers) to reconfigure their geometry makes them particularly effective for navigating challenging terrain.
In this paper, we present Athena, an open-hardware rescue ground robot research platform with four individually reconfigurable flippers and a reliable low-cost remote emergency stop (E-Stop) solution.
A novel mounting solution using an industrial PU belt and tooth inserts allows the replacement and testing of different track profiles.
The manipulator with a maximum reach of 1.54m can be used to operate doors, valves, and other objects of interest.
Full CAD & PCB files, as well as all low-level software, are released as open-source contributions
SPUR: enhancing cyclist safety through digital city twins – a data-driven infrastructure planning and VR simulation approach
No full textCyclist safety in urban areas is a growing concern that requires innovative planning and citizen participation approaches. This research introduces SPUR, a serious gaming application designed to evaluate cyclist safety on a city scale through a data-driven traffic infrastructure analysis and immersive evaluation of the current state of the traffic network and planned alternatives. The concept of SPUR introduces a digital city twin that uses data from city models, road infrastructure, point clouds, geo-referenced statistics, and citizen feedback to identify vulnerabilities and guide towards safer planning alternatives. Users are given assistance for interactively generating planning scenarios in an editor and can create simulations that are made tangible with a VR setup with a functional bicycle and a traffic simulation based on SUMO, ensuring realistic scenarios for the evaluation. While this approach focuses on cyclist safety, the workflow is generally applicable to other use cases in urban and traffic infrastructure planning
Hybrid sensor fusion approach for robust perception
No full textModern automated vehicles are equipped with a variety of sensors, including cameras, radar, and LiDAR, each with unique strengths and weaknesses. To leverage these complementary capabilities, perception algorithms fuse inputs from multiple sensors to build a comprehensive environmental representation. Among fusion-based approaches, deep fusion algorithms, which combine features extracted from raw sensor signals, demonstrate superior performance on various dataset benchmarks. However, these approaches are inherently vulnerable to sensor failures, as their detection pipelines rely on inputs from multiple sensor modalities. To address this limitation, we propose a novel architecture that combines a single-sensor detection pipeline with a deep fusion-based object detector. Our approach improves robustness in scenarios with sensor signal loss while maintaining high performance under normal operating conditions
Learned Cost Models for Query Optimization: From Batch to Streaming Systems
No full textLearned cost models (LCMs) have recently gained traction as a promising alternative to traditional cost estimation techniques in data management, offering improved accuracy by capturing complex interactions between queries, data, and runtime behavior. While initially developed for batch systems, LCMs are now increasingly applied to stream processing as well, where real-time demands pose new challenges. This tutorial presents the first unified overview of LCMs across both batch and stream processing systems, examining their role as essential components in modern query optimizers. We explore key aspects of LCM design—including input representations and model architectures—and highlight how these models deal with query optimization tasks