Automated taxiing for unmanned aircraft systems

Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation

Formal Semantics of a Subset of the Paderborn's BSPlib

PUB (Paderborn University BSPLib) is a C library supporting the development of Bulk-Synchronous Parallel (BSP) algorithms. The BSP model allows an estimation of the execution time, avoids deadlocks and indeterminism. This paper presents a formal operational semantics for a C+PUB subset language using the Coq proof assistant and a certified N-body computation as example of using this for-mal semantics. 1

Toward a trans-regional vulnerability assessment for Alps. A methodological approach to land cover changes over alpine landscapes, supporting urban adaptation

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Spectral and spatial methods for the classification of urban remote sensing data

Lors de ces travaux, nous nous sommes intéressés au problème de la classification supervisée d'images satellitaires de zones urbaines. Les données traitées sont des images optiques à très hautes résolutions spatiales: données panchromatiques à très haute résolution spatiale (IKONOS, QUICKBIRD, simulations PLEIADES) et des images hyperspectrales (DAIS, ROSIS). Deux stratégies ont été proposées. La première stratégie consiste en une phase d'extraction de caractéristiques spatiales et spectrales suivie d'une phase de classification. Ces caractéristiques sont extraites par filtrages morphologiques : ouvertures et fermetures géodésiques et filtrages surfaciques auto-complémentaires. La classification est réalisée avec les machines à vecteurs supports (SVM) non linéaires. Nous proposons la définition d'un noyau spatio-spectral utilisant de manière conjointe l'information spatiale et l'information spectrale extraites lors de la première phase. La seconde stratégie consiste en une phase de fusion de données pre- ou post-classification. Lors de la fusion postclassification, divers classifieurs sont appliqués, éventuellement sur plusieurs données issues d'une même scène (image panchromat ique, image multi-spectrale). Pour chaque pixel, l'appartenance à chaque classe est estimée à l'aide des classifieurs. Un schéma de fusion adaptatif permettant d'utiliser l'information sur la fiabilité locale de chaque classifieur, mais aussi l'information globale disponible a priori sur les performances de chaque algorithme pour les différentes classes, est proposé. Les différents résultats sont fusionnés à l'aide d'opérateurs flous. Les méthodes ont été validées sur des images réelles. Des améliorations significatives sont obtenues par rapport aux méthodes publiées dans la litterature

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Largeeddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue

Geochemical Characterisation of Oils and Sediments from Cuba and Jamaica; Implications for the Northern Petroleum System

Master's thesis in Petroleum Geosciences EngineeringThe Caribbean plate has a complex tectonic history and its petroleum potential is relatively unexplored. Jamaica and a large part of Cuba were once part of the Great Arc of the Caribbean (GAC), which was later deformed as a result of two collisional events; Jamaica with the Chortis block during Latest Cretaceous and Cuba with the North American plate during the Paleocene. Due to the collision in the Paleocene, the stratigraphy of Cuba comprises rocks of both Caribbean and North American origins. The majority of the petroleum occurrences in northern Cuba are proposed to belong to a Gulf of Mexico (GOM) petroleum system. This study presents detailed organic geochemical observations of fourteen crude oils and nineteen extracts from Cuba, as well as one crude oil, four extracts, and thirteen potential source rocks from Jamaica. The main goal is to investigate the petroleum system in the northern part of Caribbean. Analyses of oils and extracts by gas chromatography (GC), GC-mass spectrometry (GC-MS), GC-tandem mass spectrometry (GC-MSMS), and isotope analyses revealed alteration, facies, maturity, and age of the generating source rock. The geochemical results obtained were compared with published geochemical data from GOM and oils from the southern rim of the Caribbean plate (Barbados, Venezuela, and southeastern South America). In addition, Rock-Eval analysis gave information about source rock richness and maturity. Organic geochemical data suggests that the Cuban and Jamaican oils can be divided into five oil families based on facies and age dependent biomarker ratios. Family I consists of oils derived from shales in northwestern Cuba, whereas Family II comprises oils originating from marls in the same area. The third (Family III) represents the oils generated by carbonates in central Cuba, Family IV includes the carbonate derived oils from southern Cuba, and Family V consists of the oils originating from marls in Jamaica. Furthermore, the presence of light oil fraction in biodegraded oils, as well as differences in maturity and facies between the oil fractions suggest the existence of at least two petroleum system in northwestern and central Cuba. The regional geochemical comparison showed similarities in lithofacies and age with oils from GOM, suggests that the GOM petroleum system is working on northwestern Cuba. On the other hand, the Cuban and Jamaican oils appear to belong to different petroleum system than the oils from the southern margin of the Caribbean plate. Finally, the potential source rocks collected from Jamaica showed to have petroleum potential when buried sufficiently deep to attain the temperatures needed to generate petroleum.submittedVersio

Image Processing and Classification Applications in Aerospace NDT and Honey Bee Health Monitoring

Fast development in image processing and classification techniques brings many new solutions to the challenges in engineering. In this thesis, image processing and classification techniques are introduced as a powerful inspection tool into non-destructive testing on aircraft parts and honey bee disease detection to improve inspection speed and accuracy of human inspectors. Safety and reliability are the most important issues in aerospace industry, especially in high temperature and pressure turbine engine parts. Fluorescent Penetrant Inspection (FPI) is widely used in Non-Destructive Testing (NDT) on aircraft parts as an easy and powerful method. To improve efficiency and robustness in human inspection in FPI, an Advanced Automatic Inspection System (AAIS) is developed by using image processing and classification techniques in this thesis. The system can automatically detect, measure and classify the discontinuities from turbine blade FPI images. As the world’s twelfth-largest honey producer, Canada’s honey bee suffers from big losses in the past few years. The detection of disease and disorder of bee colony in an early stage is critical to prevent more loss for the bee industry. To greatly improve the speed of the inspection while retain accuracy, an automatic health monitoring system is developed to inspect honey bee colony and detect disease and disorder. The system can monitor colony development and measure the proportion of unhealthy cells. And thus it can improve the efficiency and accuracy of bee colony inspection significantly

Instrumentation, Data, And Algorithms For Visually Understanding Haptic Surface Properties

Autonomous robots need to efficiently walk over varied surfaces and grasp diverse objects. We hypothesize that the association between how such surfaces look and how they physically feel during contact can be learned from a database of matched haptic and visual data recorded from various end-effectors\u27 interactions with hundreds of real-world surfaces. Testing this hypothesis required the creation of a new multimodal sensing apparatus, the collection of a large multimodal dataset, and development of a machine-learning pipeline. This thesis begins by describing the design and construction of the Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short), an untethered handheld sensing device that emulates the capabilities of the human senses of vision and touch. Its sensory modalities include RGBD vision, egomotion, contact force, and contact vibration. Three interchangeable end-effectors (a steel tooling ball, an OptoForce three-axis force sensor, and a SynTouch BioTac artificial fingertip) allow for different material properties at the contact point and provide additional tactile data. We then detail the calibration process for the motion and force sensing systems, as well as several proof-of-concept surface discrimination experiments that demonstrate the reliability of the device and the utility of the data it collects. This thesis then presents a large-scale dataset of multimodal surface interaction recordings, including 357 unique surfaces such as furniture, fabrics, outdoor fixtures, and items from several private and public material sample collections. Each surface was touched with one, two, or three end-effectors, comprising approximately one minute per end-effector of tapping and dragging at various forces and speeds. We hope that the larger community of robotics researchers will find broad applications for the published dataset. Lastly, we demonstrate an algorithm that learns to estimate haptic surface properties given visual input. Surfaces were rated on hardness, roughness, stickiness, and temperature by the human experimenter and by a pool of purely visual observers. Then we trained an algorithm to perform the same task as well as infer quantitative properties calculated from the haptic data. Overall, the task of predicting haptic properties from vision alone proved difficult for both humans and computers, but a hybrid algorithm using a deep neural network and a support vector machine achieved a correlation between expected and actual regression output between approximately ρ = 0.3 and ρ = 0.5 on previously unseen surfaces