Development and Performance Verification of the GANDALF High-Resolution Transient Recorder System

With present-day detectors in high energy physics one often faces fast analog pulses of a few nanoseconds length which cover large dynamic ranges. In many experiments both amplitude and timing information have to be measured with high accuracy. Additionally, the data rate per readout channel can reach several MHz, which leads to high demands on the separation of pile-up pulses. For an upgrade of the COMPASS experiment at CERN we have designed the GANDALF transient recorder with a resolution of 12bit@1GS/s and an analog bandwidth of 500\:MHz. Signals are digitized with high precision and processed by fast algorithms to extract pulse arrival times and amplitudes in real-time and to generate trigger signals for the experiment. With up to 16 analog channels, deep memories and a high data rate interface, this 6U-VME64x/VXS module is not only a dead-time free digitization unit but also has huge numerical capabilities provided by the implementation of a Virtex5-SXT FPGA. Fast algorithms implemented in the FPGA may be used to disentangle possible pile-up pulses and determine timing information from sampled pulse shapes with a time resolution better than 50 ps.Comment: 5 pages, 9 figure

Map-based Height Above Ground Estimation for Safe Operation Monitoring of Unmanned Aircraft in Very Low Level Airspaces

According to current European regulations, most common drone operations are limited to a maximum altitude of 120m above ground. However, a direct measurement of the height above ground is usually not available for small drones. Consequently, ensuring compliance to height above ground constraints may prove to be difficult for many scenarios, especially when flying over complex terrain or beyond the visual line-of-sight of a remote pilot. In this work, we investigate the use of a satellite-based navigation and digital terrain maps to estimate the height above ground. We propose to integrate this estimation into a runtime assurance architecture with a safe operation monitor ensuring compliance to the maximum height above ground imposed by regulatory or operational constraints. We assess the feasibility and limitations of the approach, by analyzing sources of errors including navigation uncertainty and elevation data accuracy. We present the design and implementation details of a height above ground estimation and monitoring system and show results from flight tests with a multicopter drone. The presented results indicate the practicability and current limitations of a map-based height above ground estimation for drones operated in very low level airspaces

Build Your Own Training Data -- Synthetic Data for Object Detection in Aerial Images

Machine learning has become one of the most widely used techniques in artificial intelligence, especially for image processing. One of the biggest challenges in developing an accurate image processing model is to collect large amounts of data that are sufficiently close to the real-world scenario. Ideally, real-world data is therefore used for model training. Unfortunately, real-world data is often insufficiently available and expensive to generate. Therefore, models are trained using synthetic data. However, there is no standardized method of how training data is generated and which properties determine the data quality. In this paper, we present first steps towards the generation of large amounts of data for human detection based on aerial images. To create labeled aerial images, we are using Unreal Engine and AirSim. We report on first impressions of the generated labeled aerial images and identify future challenges-current simulation tools can be used to create realistic and diverse images including labeling, but native support would be beneficial to ease their usage

Ensuring Safety of Machine Learning Components Using Operational Design Domain

The introduction of machine learning in the aviation domain is an ongoing process. This is also true for safety-critical domains, especially for the area of Urban Air Mobility. A significant growth in number of air taxis and an increasing level of autonomy is to be expected allowing for operating a large number of air taxis in complex urban environments. Due to the complexity of the tasks and the environment, key autonomy functions will be realized using machine learning, for example the camera-based detection of objects. However, the safety assurance for avionics systems using machine learning components is challenging. This work investigates safety and verification aspects of machine learning components. A camera-based detection of humans on the ground, e.g. to assess a potential landing area, serves as an example for an machine learning-based autonomy functio and was integrated into an Unmanned Aircraft. In the context of this exemplary machine learning component, the concept of Operational Design Domain as recently adapted European Aviation Safety Agency in the context of machine learning assurance is described along with other key concepts of machine learning assurance. Furthermore, runtime assurance is used to monitor conformance to the Operational Design Domain during flight. The presented flight test results indicate that monitoring the Operational Design Domain can support performance as well as the safety of the operation

Collins and Sivers asymmetries in muonproduction of pions and kaons off transversely polarised protons

Measurements of the Collins and Sivers asymmetries for charged pions and charged and neutral kaons produced in semi-inclusive deep-inelastic scattering of high energy muons off transversely polarised protons are presented. The results were obtained using all the available COMPASS proton data, which were taken in the years 2007 and 2010. The Collins asymmetries exhibit in the valence region a non-zero signal for pions and there are hints of non-zero signal also for kaons. The Sivers asymmetries are found to be positive for positive pions and kaons and compatible with zero otherwise. © 2015

A Line-Graph Path Planner for Performance Constrained Fixed-Wing UAVs in Wind Fields

We present a runtime efficient approach to sampling-based path planning for fixed-wing unmanned aerial vehicles (UAV) based on line-graphs. Accounting for flight performance limits and the local prevailing wind, path planning is computationally expensive especially in 3D obstacle environments. A common approach is to solve the problem successively, i.e. to plan collision free paths, which are then transformed into feasible paths. However, this may compromise planning completeness if path smoothing fails. We show that line-graphs based on 3D probabilistic roadmaps can be used to effectively decouple the planning problem. The roadmap serves as a persistent free space representation of the environment and the corresponding linegraph is used to incorporate kinematic constraints to respect the fixed-wing flight performance limits in wind. Applying the A* graph-search on the line-graph instead of the 3D roadmap allows to efficiently find paths that respect these kinematic constraints without relying on path smoothing. Our results show that the presented approach can be used for near-realtime multi-query planning with varying wind conditions and flight performance constraints