arXiv subjects

Christian Vogt

Publications and source records attributed to Christian Vogt.

At least 19 recordsLinked to original sources

arXiv2026

SkyEV: RGB-Event UAV detection and tracking dataset and baseline

Detecting UAVs in air spaces has become increasingly important due to UAVs widespread availability and easy usage. However, due to their small size, they are typically difficult to detect at a sufficient range. For the training of optimized detection algorithms, datasets have been published, covering optical sensing methods ranging from infrared to regular RGB to event-sensor-based. However, these datasets often fail to reflect realistic counter-UAV scenarios, lacking critical factors such as camera ego-motion, extremely small target scales, and diverse lens configurations, and introduce compression artefacts on the frame images. To address this gap, we introduce SkyEV, an open-source dataset featuring highly synchronized uncompressed RGB and event-based data. SkyEV distinguishes itself by capturing complex real-world conditions, including significant camera motion and varied optical setups, which are essential for testing the fundamental trade-off between Field of View and detection range. Furthermore, we provide a unified data loader and establish an experimental baseline using a multi-modal architecture, demonstrating the dataset's efficacy in detecting challenging, small-scale targets.

cs.CV↗

arXiv2026

A Miniaturized In-Mouth pH Sensing System for Real-Time Intraoral Telemetry

Dental caries is one of the most common chronic diseases worldwide, caused by acid production from bacterial metabolism of fermentable carbohydrates and affecting people of all ages. To evaluate the cariogenic and erosive properties of widely consumed food products, such as energy drinks, intraoral pH changes are measured during consumption. The gold standard for such measurements is miniaturized silicon-lithium-barium glass membrane electrodes. These electrodes allow dental plaque to form on their surface, thereby enabling in situ monitoring of pH changes in a biologically relevant environment. Due to their high impedance and susceptibility to external interference, they can currently only be measured using a large analog amplification and recording unit, which is highly limiting for study design and participant comfort, as individual measurements can take upwards of an hour. This work presents the first battery-powered, low power wireless and wearable pH telemetry evaluation system designed for real time intraoral pH monitoring with glass electrodes. The system comprises a miniaturized pH telemetry frontend, a neck-worn Bluetooth Low Energy (BLE) node, and software tools for data acquisition, visualization, and reporting. The front end integrates with a custom dental prosthesis, directly digitizing the pH signal in the mouth and minimizing noise. The data is transmitted over BLE to a host computer, and analyzed using dedicated software that supports calibration, drift compensation, region marking, and PDF report generation. The system integrates an 8.6 by 3.3 mm, 0.2 g pH front-end and a 37.6 g neck-worn BLE node which consume 8.89 mW to transmit data at 10 Hz to a host computer during a measurement.

eess.SP↗

arXiv2026

LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices

Accurate, infrastructure-less sensor systems for motion tracking are essential for mobile robotics and augmented reality (AR) applications. The most popular state-of-the-art visual-inertial odometry (VIO) systems, however, are too computationally demanding for resource-constrained hardware, such as micro-drones and smart glasses. This work presents LEVIO, a fully featured VIO pipeline optimized for ultra-low-power compute platforms, allowing six-degrees-of-freedom (DoF) real-time sensing. LEVIO incorporates established VIO components such as Oriented FAST and Rotated BRIEF (ORB) feature tracking and bundle adjustment, while emphasizing a computationally efficient architecture with parallelization and low memory usage to suit embedded microcontrollers and low-power systems-on-chip (SoCs). The paper proposes and details the algorithmic design choices and the hardware-software co-optimization approach, and presents real-time performance on resource-constrained hardware. LEVIO is validated on a parallel-processing ultra-low-power RISC-V SoC, achieving 20 FPS while consuming less than 100 mW, and benchmarked against public VIO datasets, offering a compelling balance between efficiency and accuracy. To facilitate reproducibility and adoption, the complete implementation is released as open-source.

cs.CV↗

arXiv2026

Validation of a Software-Defined 100-Gb/s RDMA Streaming Architecture for Ultrafast Optoacoustic and Ultrasound Imaging

Optoacoustic (OA) imaging has emerged as a powerful investigation tool, with demonstrated applicability in oncology, neuroscience, and cardiovascular biology. However, its clinical translation is limited with the existing OA systems, which often rely on bulky and expensive acquisition hardware mainly optimized for pulse-echo ultrasound (US) imaging. Despite the fact that OA imaging has different requirements for receive bandwidths and timing synchronization with external laser sources, there is a strong need for unified OA-US imaging platforms, as pulse-echo US remains the standard tool for visualizing soft tissues. To address these challenges, we propose a new data acquisition architecture for ultrafast OA and US imaging that fully covers the requirements for large channel counts, wide bandwidth, and software-defined operation. LtL combines state-of-the-art wideband analog front-ends, a Zynq UltraScale+ MPSoC integrating FPGA fabric with an Application Processing Unit, and a 100 GbE Remote Direct Memory Access (RDMA) backend enabling raw-data streaming at up to 95.6 Gb/s. The architecture avoids local buffers followed by burst transfers, which commonly constrain sustainable frame rate and recording intervals, thus achieving true continuous and sustained streaming of raw data. We validate the core elements of the LtL architecture using a 16-channel demonstration system built from commercial evaluation boards. We further verify the signal chain for up to 256-channel scalability, confirming the wide bandwidth capabilities to support state-of-the-art data transmission speeds.

eess.SY↗

arXiv2026

Eco-WakeLoc: An Energy-Neutral and Cooperative UWB Real-Time Locating System

Indoor localization systems face a fundamental trade-off between efficiency and responsiveness, which is especially important for emerging use cases such as mobile robots operating in GPS-denied environments. Traditional RTLS either require continuously powered infrastructure, limiting their scalability, or are limited by their responsiveness. This work presents Eco-WakeLoc, designed to achieve centimeter-level UWB localization while remaining energy-neutral by combining ultra-low power wake-up radios (WuRs) with solar energy harvesting. By activating anchor nodes only on demand, the proposed system eliminates constant energy consumption while achieving centimeter-level positioning accuracy. To reduce coordination overhead and improve scalability, Eco-WakeLoc employs cooperative localization where active tags initiate ranging exchanges (trilateration), while passive tags opportunistically reuse these messages for TDOA positioning. An additive-increase/multiplicative-decrease (AIMD)-based energy-aware scheduler adapts localization rates according to the harvested energy, thereby maximizing the overall performance of the sensor network while ensuring long-term energy neutrality. The measured energy consumption is only 3.22mJ per localization for active tags, 951uJ for passive tags, and 353uJ for anchors. Real-world deployment on a quadruped robot with nine anchors confirms the practical feasibility, achieving an average accuracy of 43cm in dynamic indoor environments. Year-long simulations show that tags achieve an average of 2031 localizations per day, retaining over 7% battery capacity after one year -- demonstrating that the RTLS achieves sustained energy-neutral operation. Eco-WakeLoc demonstrates that high-accuracy indoor localization can be achieved at scale without continuous infrastructure operation, combining energy neutrality, cooperative positioning, and adaptive scheduling.

cs.NI↗

arXiv2025

Efficient and Accurate Downfacing Visual Inertial Odometry

Visual Inertial Odometry (VIO) is a widely used computer vision method that determines an agent's movement through a camera and an IMU sensor. This paper presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UAVs. The proposed design incorporates state-of-the-art feature detection and tracking methods (SuperPoint, PX4FLOW, ORB), all optimized and quantized for emerging RISC-V-based ultra-low-power parallel systems on chips (SoCs). Furthermore, by employing a rigid body motion model, the pipeline reduces estimation errors and achieves improved accuracy in planar motion scenarios. The pipeline's suitability for real-time VIO is assessed on an ultra-low-power SoC in terms of compute requirements and tracking accuracy after quantization. The pipeline, including the three feature tracking methods, was implemented on the SoC for real-world validation. This design bridges the gap between high-accuracy VIO pipelines that are traditionally run on computationally powerful systems and lightweight implementations suitable for microcontrollers. The optimized pipeline on the GAP9 low-power SoC demonstrates an average reduction in RMSE of up to a factor of 3.65x over the baseline pipeline when using the ORB feature tracker. The analysis of the computational complexity of the feature trackers further shows that PX4FLOW achieves on-par tracking accuracy with ORB at a lower runtime for movement speeds below 24 pixels/frame.

cs.CV↗

arXiv2025

Release and Recapture of Silica Nanoparticles from an Optical Trap in Weightlessness

Optically trapped Silica nanoparticles are a promising tool for precise sensing of gravitational or inertial forces and fundamental physics, including tests of quantum mechanics at 'large' mass scales. This field, called levitated optomechanics can greatly benefit from an application in weightlessness. In this paper we demonstrate the feasibility of such setups in a microgravity environment for the first time. Our experiment is operated in the GraviTower Bremen that provides up to 2.5 s of free fall. System performance and first release-recapture experiments, where the particle is no longer trapped are conducted in microgravity. This demonstration should also be seen in the wider context of preparing space missions on the topic of levitated optomechanics.

physics.optics↗

arXiv2025

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems

The demand for hundreds of tightly synchronized channels operating at tens of MSPS in ultrasound systems exceeds conventional low-voltage differential signaling links' bandwidth, pin count, and latency. Although the JESD204B serial interface mitigates these limitations, commercial FPGA IP cores are proprietary, costly, and resource-intensive. We present ListenToJESD204B, an open-source receiver IP core released under a permissive Solderpad 0.51 license for AMD Xilinx Zynq UltraScale+ devices. Written in synthesizable SystemVerilog, the core supports four GTH/GTY lanes at 12.8 Gb/s and provides cycle-accurate AXI-Stream data alongside deterministic Subclass~1 latency. It occupies only 107 configurable logic blocks (approximately 437 LUTs), representing a 79\% reduction compared to comparable commercially available IP. A modular data path featuring per-lane elastic buffers, SYSREF-locked LMFC generation, and optional LFSR descrambling facilitates scaling to high lane counts. We verified protocol compliance through simulation against the Xilinx JESD204C IP in JESD204B mode and on hardware using TI AFE58JD48 ADCs. Block stability was verified by streaming 80 MSPS, 16-bit samples over two 12.8 Gb/s links for 30 minutes with no errors.

cs.AR↗

arXiv2025

RGB-Event Fusion with Self-Attention for Collision Prediction

Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network framework for predicting the time and collision position of an unmanned aerial vehicle with a dynamic object, using RGB and event-based vision sensors. The proposed architecture consists of two separate encoder branches, one for each modality, followed by fusion by self-attention to improve prediction accuracy. To facilitate benchmarking, we leverage the ABCD [8] dataset collected that enables detailed comparisons of single-modality and fusion-based approaches. At the same prediction throughput of 50Hz, the experimental results show that the fusion-based model offers an improvement in prediction accuracy over single-modality approaches of 1% on average and 10% for distances beyond 0.5m, but comes at the cost of +71% in memory and + 105% in FLOPs. Notably, the event-based model outperforms the RGB model by 4% for position and 26% for time error at a similar computational cost, making it a competitive alternative. Additionally, we evaluate quantized versions of the event-based models, applying 1- to 8-bit quantization to assess the trade-offs between predictive performance and computational efficiency. These findings highlight the trade-offs of multi-modal perception using RGB and event-based cameras in robotic applications.

cs.RO↗

arXiv2025

WakeLoc: An Ultra-Low Power, Accurate and Scalable On-Demand RTLS using Wake-Up Radios

For future large scale robotic moon missions, the availability of infrastructure-less, cheap and low power real-time locating systems (RTLSs) is critical. Traditional RTLS face significant trade-offs between power consumption and localization latency, often requiring anchors to be connected to the power grid or sacrificing speed for energy efficiency. This paper proposes WakeLoc, an on-demand RTLS based on ultra-wideband (UWB), enabling both low-latency and ultra-low power consumption by leveraging UWB wake-up radios (WuRs). In WakeLoc, tags independently start a localization procedure by sending a wake-up call (WuC) to anchors, before performing the actual localization. Distributed tags equipped with WuRs listen to the WuC and use passive listening of the UWB messages to determine their own position. Experimental measurements demonstrate that the localization accuracy in a 2D setup achieves less than 12.9cm error, both for the active and the passive tag. Additional power simulations based on real-world measurements were performed in a realistic environment, showing that anchors can achieve a power consumption as low as 15.53{\mu}W while the RTLS performs one on-demand localization per minute for 5 tags, thus operate up to 5.01 years on a single coin cell battery (690mWh).

cs.NI↗

arXiv2025

Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone

This work quantitatively evaluates the performance of event-based vision systems (EVS) against conventional RGB-based models for action prediction in collision avoidance on an FPGA accelerator. Our experiments demonstrate that the EVS model achieves a significantly higher effective frame rate (1 kHz) and lower temporal (-20 ms) and spatial prediction errors (-20 mm) compared to the RGB-based model, particularly when tested on out-of-distribution data. The EVS model also exhibits superior robustness in selecting optimal evasion maneuvers. In particular, in distinguishing between movement and stationary states, it achieves a 59 percentage point advantage in precision (78% vs. 19%) and a substantially higher F1 score (0.73 vs. 0.06), highlighting the susceptibility of the RGB model to overfitting. Further analysis in different combinations of spatial classes confirms the consistent performance of the EVS model in both test data sets. Finally, we evaluated the system end-to-end and achieved a latency of approximately 2.14 ms, with event aggregation (1 ms) and inference on the processing unit (0.94 ms) accounting for the largest components. These results underscore the advantages of event-based vision for real-time collision avoidance and demonstrate its potential for deployment in resource-constrained environments.

cs.CV↗

arXiv2025

An experimental platform for levitated mechanics in space

Conducting levitated mechanical experiments in extreme conditions has long been the aim of researchers, as it allows for the investigation of new fundamental physics phenomena. One of the great frontiers has been sending these experiments into the micro-g environment of space, with multiple proposals calling for such a platform. At the same time, levitated sensors have demonstrated a high sensitivity to external stimuli which will only improve in low-vibrational conditions. conditions This paper describes the development of a technology demonstrator for optical and magnetic trapping experiments in space. Our payload represents the first concrete step towards future missions with aims of probing fundamental physical questions: matter-wave interferometry of nanoparticles to probe the limits of macroscopic quantum mechanics, detection of Dark Matter candidates and gravitational waves to test physics beyond the Standard Model, and accelerometry for Earth-observation.

physics.ins-det↗

arXiv2025

BodySense: An Expandable and Wearable-Sized Wireless Evaluation Platform for Human Body Communication

Wearable, wirelessly connected sensors have become a common part of daily life and have the potential to play a pivotal role in shaping the future of personalized healthcare. A key challenge in this evolution is designing long-lasting and unobtrusive devices. These design requirements inherently demand smaller batteries, inevitably increasing the need for energy-sensitive wireless communication interfaces. Capacitive Human Body Communication (HBC) is a promising, power-efficient alternative to traditional RF-based communication, enabling point-to-multipoint data and energy exchange. However, as this concept relies on capacitive coupling to the surrounding area, it is naturally influenced by uncontrollable environmental factors, making testing with classical setups particularly challenging. This work presents a customizable, wearable-sized, wireless evaluation platform for capacitive HBC, designed to enable realistic evaluation of wearable-to-wearable applications. Comparative measurements of channel gains were conducted using classical grid-connected and wireless Data Acquisition (DAQ) across various transmission distances within the frequency range of 4 MHz to 64 MHz and revealed an average overestimation of 18.15 dB over all investigated distances in the classical setup.

eess.SP↗

arXiv2024

A Proximity-Based Approach for Dynamically Matching Industrial Assets and Their Operators Using Low-Power IoT Devices

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling assetuser matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This paper introduces a novel approach to address this gap by leveraging existing Bluetooth Low Energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: i) a wearable device, ii) an IoT device attached to or embedded in the assets, iii) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an Extended Kalman Filter (EKF), and iv) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.

eess.SP↗

arXiv2024

PuLsE: Accurate and Robust Ultrasound-based Continuous Heart-Rate Monitoring on a Wrist-Worn IoT Device

This work explores the feasibility of employing ultrasound (US) US technology in a wrist-worn IoT device for low-power, high-fidelity heart-rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large vessels and the surrounding tissue, potentially improving robustness and accuracy compared to PPG. We present an IoT wearable system prototype utilizing a commercial microcontroller MCU employing the onboard ADC to capture high frequency US signals and an innovative low-power US pulser. An envelope filter lowers the bandwidth of the US signal by a factor of >5x, reducing the system's acquisition requirements without compromising accuracy (correlation coefficient between HR extracted from enveloped and raw signals, r(92)=0.99, p<0.001). The full signal processing pipeline is ported to fixed point arithmetic for increased energy efficiency and runs entirely onboard. The system has an average power consumption of 5.8mW, competitive with PPG based systems, and the HR extraction algorithm requires only 68kB of RAM and 71ms of processing time on an ARM Cortex-M4 MCU. The system is estimated to run continuously for more than 7 days on a smartwatch battery. To accurately evaluate the proposed circuit and algorithm and identify the anatomical location on the wrist with the highest accuracy for HR extraction, we collected a dataset from 10 healthy adults at three different wrist positions. The dataset comprises roughly 5 hours of HR data with an average of 80.6+-16.3 bpm. During recording, we synchronized the established ECG gold standard with our US-based method. The comparisons yields a Pearson correlation coefficient of r(92)=0.99, p<0.001 and a mean error of 0.69+-1.99 bpm in the lateral wrist position near the radial artery. The dataset and code have been open-sourced at https://github.com/mgiordy/Ultrasound-Heart-Rate

eess.SP↗

arXiv2024

A Lora-Based and Maintenance-Free Cattle Monitoring System for Alpine Pastures and Remote Locations

The advent of the Internet of Things (IoT) is boosting the proliferation of sensors and smart devices in industry and daily life. Continuous monitoring IoT systems are also finding applications in agriculture, particularly in the realm of smart farming. The adoption of wearable sensors to record the activity of livestock has garnered increasing interest. Such a device enables farmers to locate, monitor, and constantly assess the health status of their cattle more efficiently and effectively, even in challenging terrain and remote locations. This work presents a maintenance-free and robust smart sensing system that is capable of tracking cattle in remote locations and collecting activity parameters, such as the individual's grazing- and resting time. To support the paradigm of smart farming, the cattle tracker is capable of monitoring the cow's activity by analyzing data from an accelerometer, magnetometer, temperature sensor, and Global Navigation Satellite System (GNSS) module, providing them over Long Range Wide Area Network (LoRaWAN) to a backend server. By consuming 511.9 J per day with all subsystems enabled and a data transmission every 15 minutes, the custom-designed sensor node achieves a battery lifetime of 4 months. When exploiting the integrated solar energy harvesting subsystem, this can be even increased by 40% to up to 6 months. The final sensing system's robust operation is proven in a trial run with two cows on a pasture for over three days. Evaluations of the experimental results clearly show behavior patterns, which confirms the practicability of the proposed solution.

eess.SY↗

arXiv2024

A LoRa-based Energy-efficient Sensing System for Urban Data Collection

Nowadays, cities provide much more than shopping opportunities or working spaces. Individual locations such as parks and squares are used as meeting points and local recreation areas by many people. To ensure that they remain attractive in the future, the design of such squares must be regularly adapted to the needs of the public. These utilization trends can be derived using public data collection. The more diverse and rich the data sets are, the easier it is to optimize public space design through data analysis. Traditional data collection methods such as questionnaires, observations, or videos are either labor intensive or cannot guarantee to preserve the individual's privacy. This work presents a privacy-preserving, low-power, and low-cost smart sensing system that is capable of anonymously collecting data about public space utilization by analyzing the occupancy distribution of public seating. To support future urban planning the sensor nodes are capable of monitoring environmental noise, chair utilization, and their position, temperature, and humidity and provide them over a city-wide Long Range Wide Area Network (LoRaWAN). The final sensing system's robust operation is proven in a trial run at two public squares in a city with 16 sensor nodes over a duration of two months. By consuming 33.65 mWh per day with all subsystems enabled, including sitting detection based on a continuous acceleration measurement operating on a robust and simple threshold algorithm, the custom-designed sensor node achieves continuous monitoring during the 2-month trial run. The evaluation of the experimental results clearly shows how the two locations are used, which confirms the practicability of the proposed solution. All data collected during the field trial is publicly available as open data.

eess.SY↗

arXiv2024

ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware

Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, custom hardware and software, and usually focus on Time-Trials rather than full unrestricted Head-to-Head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well-resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head-to-Head autonomous racing competition, which simplifies replication by using commercial off-the-shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout. Capable of handling both Time-Trials and Head-to-Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times.

cs.RO↗