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Compact, eye-safe polarization light detection and ranging system at 1550 nm wavelength for aerosol anomaly detection
No full textThe study of atmospheric aerosols, which are defined as fine particles suspended in the atmosphere, using light detection and ranging (lidar) techniques is well-established. This work presents the real-time measurement of aerosol anomalies (i.e., concentration hotspots) using an eye-safe, portable polarization lidar system operating at a wavelength of 1550 nm. The system, mainly consisting of commercial off-the-shelf components, has a spatial resolution of about 3 m and a weight of less than 10 kg. As a proof of concept, this paper discusses the classification of aerosols and their physical characteristics, as well as the implications of their presence in the atmosphere. Therefore, the aerosols water mist, sand, and Arizona road dust were measured in a distance of 100 m
Assessing residential location and commuting preferences
No full textUnderstanding the interplay between residential location choices and commuting behavior is crucial for addressing urban mobility
and housing policies. This paper introduces a comprehensive survey conducted in Germany, integrating three discrete choice experiments to simultaneously measure preferences for residential locations, commuting distances, and transportation modes. The sample
comprises 1,169 employed respondents, recruited through a professional service provider with quotas based on age, gender, and
residential location. Experiment 1 focused on residential location preferences without considering commuting characteristics, utilizing a Multiple Discrete-Continuous Extreme Value (MDCEV) model to assess budget allocations for various dwelling attributes.
Results indicate that the quality of the residential area holds greater significance than the specific place of residence. Experiments
2 and 3 explored the trade-offs between dwelling characteristics and commuting time, alongside mode choice preferences. These
experiments revealed that while dwelling attributes are prioritized over commuting duration, commuting time remains a significant factor in residential decisions. Notably, teleworking scenarios were associated with a reduction in the Value of Travel Time
Savings (VTTS) for commuting, highlighting the diminishing burden of travel time for remote workers. The three experiments
build progressively from housing preferences to commuting trade-offs and mode choices. Together, they reveal how residential
and mobility decisions interact. The findings contribute valuable insights for integrating residential location and travel demand
models, emphasizing the impact of teleworking on future commuting patterns. Future work will focus on joint model estimations
and further integration into predictive models to inform integrated land-use and transport planning policies
Design of a rotary kiln solar particle receiver for pyrolysis application
No full textDesign of solar rotary kiln receiver for heating of particle heat carriers, which are coupled in a biomass pyrolysis proces
Design and Implementation of an alternative Azimuth Interpolation Scheme for Staggered-SAR FPGA Processing
No full textMulti-Sensor Vigilance Detection: Sensor Reliability and Comfort in Automated Train Operations
No full textThe increasing levels of automation in rail transport, such as Grade of Automation Level 2 (GOA2), introduce new challenges for train driver vigilance. Despite several operational advantages of partial automation, human operators are required to maintain a high level of attention to ensure safe operations. Prolonged monitoring tasks can lead to mental fatigue and reduced vigilance, increasing the risk of errors. One potential solution to this issue lies in multi-sensor systems that can monitor the driver’s physiological state and detect signs of low vigilance in real time. The HMI4Rail project addresses this challenge by investigating the reliability and feasibility of various sensor types for detecting low vigilance in train drivers.
To explore this, a study was conducted using a high-fidelity train simulator with 14 professional train drivers. Each driver completed three simulator drive sessions with the train driving fully automated. All drives required no train operation besides activation of the dead man’s button. In the second drive, a mental fatigue induction task was administered. This task involved a 55-minute n-back task with four varying difficulty levels, designed to induce mental workload and simulate the mental fatigue experienced during prolonged real-world train driving.
Throughout the study, several body-worn sensors were employed to measure physiological signals associated with mental fatigue. These included an electrocardiogram (ECG) to track heart activity, a 26-channel wet electroencephalogram (EEG) to monitor brain waves, a breathing belt to measure respiration, eye-tracking glasses to measure blink rates, and skin conductance sensors to capture changes in sweat production, a known indicator of stress and arousal. Each of these sensors was selected based on previous research demonstrating their efficacy in detecting changes in mental state, particularly under conditions of mental fatigue.
In this talk, we will present the initial findings from our study, focusing on the comparison of sensor measurements between the baseline and post-intervention simulator drives. These results will provide insights into how the drivers' physiological states changed after the mental fatigue-inducing task, offering key information on the reliability of each sensor type for monitoring vigilance in real-world applications. Additionally, we will discuss feedback from the participants regarding the comfort and practicality of wearing these sensors during their driving tasks. The wearability and comfort of these sensors are crucial factors in their potential integration into smart garments for train drivers.
Our findings will inform future developments in smart garments for train drivers, with the goal of enhancing safety in automated rail systems by providing real-time monitoring of driver vigilance. By identifying the most reliable sensors and addressing comfort concerns, the HMI4Rail project takes an important step towards implementing wearable technology that supports the performance of train operators in increasingly automated environments. In a next step, the same sensor system will be tested during a real-world automated drive
Experience Atlas: Travel experience in intermodal public transport for service innovation
No full textThe decision to travel by public transport often involves navigating intermodal journeys, requiring travelers to switch between various modes like trains, trams, buses, and walking. These intermodal journeys are frequently associated with lower travel satisfaction due to unmet needs for safety, comfort, accessibility, efficiency, reliability, or information. The project Experience Atlas (German original title: Erlebensatlas) aims to address these issues by mapping geo-tagged collective travel experiences onto the public transport map to identify locations of collective positive and negative travel experience. This can help to identify where and why pain points occur along intermodal journeys and how they might be alleviated.
The project explores various data sources for measuring travel experience. These include subjective traveler feedback during the journey, physiological data (e.g., heart rate monitoring), and contextual information like weather and traffic conditions. These datasets can offer a dynamic, real-time picture of the travel experience, enabling operators to spot potential service disruptions or traveler discomfort as they happen.
This expo session talk will present results from such geo-tagged travel experience data collected in public transport of Hamburg and Berlin. In addition, it will discuss different approaches for visualizing such spatial travel experience data as an experience atlas. By this, we aim to provide a more nuanced understanding of the travel experience by visualizing hot and cold spots of public transport experience. This offers the potential to improve service offerings, adapt information given to customers, and ultimately enhance traveler satisfaction across intermodal journeys.
One of the key users of these visualizations will be mobility providers, who currently have no feasible option to understand dynamics in travelers’ experience. To ensure the visualizations are meaningful and actionable, we conducted a series of interviews with key decision-makers responsible for customer adaptation at a major mobility provider. By this, we identified requirements for the functionality of the experience atlas. These will be presented alongside the travel experience results and provide insights into what types of data need to be captured and how best to represent them to support service innovation.
Overall, the Experience Atlas will enable mobility providers to adopt more passenger-focused innovations in public transport services. This, in turn, is expected to lead to more positive travel experiences and encourage greater adoption of public transport
EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response
No full textWith growing concerns about climate change, increasing natural hazards, and extreme weather events, monitoring Earth’s surface parameters has become a critical area of interest for both the scientific community and society. Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative and low-cost technique that exploits existing Global Navigation Satellite System (GNSS) signals after reflection from Earth’s surface. GNSS-R constellations offer unique observations with unprecedented data volume, temporal resolution, and spatial coverage across the entire globe under all-weather conditions. As the data volumes are continuously accumulating, the trend in applying Artificial Intelligence (AI) is expanding. However, current AI models rely heavily on labelled data, feature engineering, and extra fine-tuning, leading to high computational and labor costs. To address these issues, we propose the project EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response.
EcoGEM develops cutting-edge Earth observation foundation models using GNSS-R measurements and integrates them with other remote sensing data. It pioneers the first general-purpose GNSS-R foundation models and curated multimodal datasets to support climate science, hazard detection, and environmental monitoring. Unlike task-specific methods, the proposed models adapt across applications such as soil moisture, vegetation water content, and ocean wind speed. Uniquely, EcoGEM emphasizes energy-efficient AI through model pruning, knowledge distillation, and dynamic architectures, enabling deployment on edge devices and small satellite platforms. This collaborative project of GFZ and DLR advances sustainable AI and promotes novel and open-access tools for Earth scientists, environmental policymakers, and global users
How to Integrate Vertiport Operations Into the Airspace? Introducing the EUREKA Project
Get PDFInnovative Air Mobility (IAM), as envisioned by the European Aviation Safety Agency, is "the safe, secure and sustainable air mobility of passengers and cargo enabled by new-generation technologies integrated into multimodal transportation system." Vertiports, new aircraft and new air traffic management systems are essential for implementing IAM. The European project EUREKA, running from 2023 to 2026, aims at enabling key solutions for vertiports. Solutions for arrival and departure for vertiports, collaborative traffic management, how to deal with emergencies and disruptions also the network flow, capacity and operational management are developed in the project. This paper introduces the EUREKA project and reports on the early progress
Optimization of Hybrid Quantum-Classical Algorithms
Get PDFQuantum computers do not run in isolation; rather, they are embedded in quantum-classical hybrid architectures.
In these setups, a quantum processing unit communicates with a classical device in near-real time.
To enable efficient hybrid computations, it is mandatory to optimize quantum-classical hybrid code.
To the best of our knowledge, no previous work on the optimization of hybrid code nor on metrics for which to optimize such code exists.
In this work, we take a step towards optimization of hybrid programs by introducing seven optimization routines and three metrics to evaluate the effectiveness of the optimization.
We implement these routines for the hybrid quantum language Quil and show that our optimizations improve programs according to our metrics.
This lays the foundation for new kinds of hybrid optimizers that enable real-time collaboration between quantum and classical devices