1,720,996 research outputs found
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes
No full textCollection of left and right RGB view as well as disparity for the left view in Airsim Building_99 simulation environment. Made by Benjamin Keltjens, Tom van Dijk and Guido de Croon using Microsoft Airsim. Used for training for Filled Disparity Monodepth
Preface for the special issue on selected software artifacts from DisCoTec 2023 – the 18th International Federated Conference on Distributed Computing Techniques
No full textThis special issue includes a selection of the artefacts presented at the 18th International Federated Conference on Distributed Computing Techniques (DiScoTec 2023), held at the NOVA University Lisbon (Lisbon, Portugal), in June 18-23, 2023. The federated conference included: COORDINATION 2023, the 25th International Conference on Coordination Models and Languages); DAIS 2023, the 23rd International Conference on Distributed Applications and Interoperable Systems; and FORTE 2023, the 43rd International Conference on Formal Techniques for Distributed Objects, Components, and Systems. All the three conferences welcomed submissions describing technological artefacts, including innovative prototypes supporting the modelling, development, analysis, simulation, or testing of systems in the broad spectrum of distributed computing subjects. The artefact evaluation chairs have selected a subset of high-quality accepted artefacts to be invited for submission to this special issue. Following the revision process, nine artefacts have been accepted to be part of this special issue. The published contributions include different types of artefacts, including programming libraries, frameworks, as well as tools for the analysis, verification, and simulation of distributed systems
Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments
No full textThis thesis presents a visual route following method that minimizes memory consumption to the point that even Micro Aerial Vehicles (MAV) equipped with only a simple microcontroller can traverse distances of a few hundred meters. Existing Simultaneous Localization and Mapping (SLAM) algorithms are too complex for use on a microcontroller. Instead, the route is modeled by a sequence of snapshots that can be followed back using a combination of visual homing and odometry. Three visual homing methods are evaluated to find and compare their memory efficiency. Of these methods, Fourier-based homing performed best: it still succeeds when snapshots are compressed to less than twenty bytes. Visual homing only works from a small region surrounding the snapshot, therefore odometry is used to travel longer distances between snapshots. The proposed route following technique is tested in simulation and on a Parrot AR.Drone 2.0. The drone can successfully follow long routes with a map that consumes only 17.5 bytes per meter.Mechanical Engineering | BioMechanical DesignMechanical Engineering | Systems and Contro
Self-Supervised Learning for Visual Obstacle Avoidance
No full textWith a growing number of drones, the risk of collision with other air traffic or fixed obstacles increases. New safety measures are required to keep the operation of Unmanned Aerial Vehicles (UAVs) safe. One of these measures is the use of a Collision Avoidance System (CAS), a system that helps the drone autonomously detect and avoid obstacles. The design of a Collision Avoidance System is a complex task with many smaller subproblems, as illustrated by Albaker and Rahim [1]. How should the drone sense nearby obstacles? When is there a risk of collision? What should the drone do when a conflict is detected? All of these questions need to be answered to develop a functional Collision Avoidance System. However, all of these subproblems – except the sensing of obstacles – only concern the behavior of the vehicle. They can be solved independently of the target platform as long as it can perform the required maneuvers; it does not matter whether it is a UAV or a larger vehicle. The sensing of the environment, on the other hand, is the only subproblem that places requirements on the hardware, specifically the sensors that should be carried by the UAV. It is the hardware that sets UAVs apart from other vehicles. Unlike autonomous cars, other groundbased vehicles or larger aircraft, UAVs have only a small payload capacity. It is therefore not practical to carry large or heavy sensors such as LIDAR or radar for obstacle avoidance. Instead, obstacle avoidance on UAVs requires clever use of lightweight sensors: cameras, microphones or antennae. This research will therefore focus on the sensing of the environment. Out of the sensors mentioned above – cameras, microphones and antennae – cameras are the only ones that can detect nearly all groundbased obstacles and other air traffic; microphones and antennae are limited to detection of sources of noise or radio signals1. Therefore, this research will focus on the visual detection of obstacles. The field of computer vision is welldeveloped; it may already be possible to find an adequate solution for visual obstacle detection using existing stereo vision methods like Semiglobal Matching (SGM) [23]. These methods, however, only use a fraction of the information present in the images to estimate depth – the disparity. Other cues such as the apparent size of known objects are completely ignored. The use of appearance cues for depth estimation is a relatively new development driven largely by the advent of Deep Learning, which allows these cues to be learned from large, labeled datasets. As long as the UAV’s operational environment is similar to this training dataset it should be possible to use appearance cues in a CAS. However, this is difficult to guarantee and may require a prohibitively large training set. SelfSupervised Learning may provide a solution to this problem. After training on an initial dataset, the UAV will continue to collect new training samples during operation. This allows it to ‘adapt’ to its operational environment and to learn new depth cues that are relevant in that environment. SelfSupervised Learning for depth map estimation is a young field, the first practical examples started to appear around 2016 (e.g. [17]). Most of the current literature is focused on automotive applicationsControl & Simulatio
Progress Measures and Tangle Learning algorithm implementation and benchmarks
No full text<p>The <code>pmtl.cpp</code> and <code>pmtl.hpp</code> files implement the PMTL solver in the <a href="https://github.com/trolando/oink">Oink framework</a>. The <code>benchmarks</code> folder contains the <code>run_benchmarks.sh</code> shell script which performs all the benchmarks automatically using <a href="https://github.com/sharkdp/hyperfine">hyperfine</a>. The games in the <code>benchmarks/real_world_games</code> folder are a selection from the <a href="https://github.com/SYNTCOMP/benchmarks/releases/tag/v2023.4">SYNTCOMP 2023 HOA files</a> which were converted to parity games using <a href="https://github.com/trolando/knor">Knor</a>. The games in the folders <code>benchmarks/rngames{25,50,100,200,2000}</code> are sparse games generated using Oink with 25, 50, 100, 200, and 2000 vertices respectively. These games can also be regenerated with a random seed using the <code>benchmarks/prepare_games.sh</code>.</p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Hear-and-avoid for UAVs using convolutional neural networks
No full textWe investigate how an Unmanned Air Vehicle (UAV) can detect manned aircraft with a single microphone. In particular, we create an audio data set in which UAV ego-sound and recorded aircraft sound can be mixed together, and apply convolutional neural networks to the task of air traffic detection. Due to restrictions on flying UAVs close to aircraft, the data set has to be artificially produced, so the UAV sound is captured separately from the aircraft sound. The aircraft data set is collected at Lelystad airport by capturing flyovers with a microphone array. It is mixed with UAV recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The mixed recordings are the input for a model that determines whether an aircraft is present or not. The model is a CNN which uses the features MFCC, spectrogram or Mel spectrogram as input. For each feature the effect of UAV/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings is explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the UAV/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. It is not desirable to train the model on distant approaches and test them on nearby approaches as the performance then drops. The results also prove that the performance increases the closer the aircraft is. Although the currently presented approach has a number of false positives and false negatives, that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. In addition, the data set is provided as open access, allowing the community to contribute to the improvement of the detection task.Aerospace Engineerin
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