Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM.

Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker

The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident.

SwiVR-Car-Seat

Autonomous vehicles provide new input modalities to improve interaction with in-vehicle information systems. However, due to the road and driving conditions, the user input can be perturbed, resulting in reduced interaction quality. One challenge is assessing the vehicle motion effects on the interaction without an expensive high-fidelity simulator or a real vehicle. This work presents SwiVR-Car-Seat, a low-cost swivel seat to simulate vehicle motion using rotation. In an exploratory user study (N=18), participants sat in a virtual autonomous vehicle and performed interaction tasks using the input modalities touch, gesture, gaze, or speech. Results show that the simulation increased the perceived realism of vehicle motion in virtual reality and the feeling of presence. Task performance was not influenced uniformly across modalities; gesture and gaze were negatively affected while there was little impact on touch and speech. The findings can advise automotive user interface design to mitigate the adverse effects of vehicle motion on the interaction.

The visualization of a graph semantics of imperative languages

This work aims to present the software support for teaching in the field of formal semantics of imperative programming languages. The main part focuses on a software tool that provides a visual representation of the individual steps of the calculation in categorical semantics, which can also be referred to as graph semantics. The use of software tools in teaching to visually represent computational steps considerably facilitates understanding by students and can also serve as a good basis for supporting distance learning. Our program works in the standard form: after reading the correct user input, a visual representation of the meaning of the program is generated in the form of a category of states, which is displayed as an oriented graph. For better extensibility, the program is implemented as a web application.

A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.

Discover, Sample and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Over the last few decades enhanced sampling methods have made great strides. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and characterization of reaction paths. Central to our strategy is the use of the recently developed explore variant of the on-the-fly probability enhanced sampling method. Like metadynamics, this method is based on the identification of appropriate collective variables. Our first step is the discovery of new chemical reactions and it is performed biasing a one dimensional collective variable derived from spectral graph theory. Once new reaction pathways are detected, we construct ad-hoc tailored neural-network based collective variables to improve sampling of specific reactions and finally we refine the results using free energy perturbation theory. Our workflow has been successfully applied to both intramolecular and intermolecular reactions. Without any chemical hypothesis, we discovered several possible products, computed the free energy surface at semiempirical level, and finally refined it with a more accurate Hamiltonian. Our workflow requires minimal user input, and thanks to its modularity and flexibility, can extend the scope of ab initio molecular dynamics for the exploration and characterization of reaction space.

High Resolution Powder Electron Diffraction in Scanning Electron Microscopy

A modern scanning electron microscope equipped with a pixelated detector of transmitted electrons can record a four-dimensional (4D) dataset containing a two-dimensional (2D) array of 2D nanobeam electron diffraction patterns; this is known as a four-dimensional scanning transmission electron microscopy (4D-STEM). In this work, we introduce a new version of our method called 4D-STEM/PNBD (powder nanobeam diffraction), which yields high-resolution powder diffractograms, whose quality is fully comparable to standard TEM/SAED (selected-area electron diffraction) patterns. Our method converts a complex 4D-STEM dataset measured on a nanocrystalline material to a single 2D powder electron diffractogram, which is easy to process with standard software. The original version of 4D-STEM/PNBD method, which suffered from low resolution, was improved in three important areas: (i) an optimized data collection protocol enables the experimental determination of the point spread function (PSF) of the primary electron beam, (ii) an improved data processing combines an entropy-based filtering of the whole dataset with a PSF-deconvolution of the individual 2D diffractograms and (iii) completely re-written software automates all calculations and requires just a minimal user input. The new method was applied to Au, TbF3 and TiO2 nanocrystals and the resolution of the 4D-STEM/PNBD diffractograms was even slightly better than that of TEM/SAED.

AquaScouts: ePROs Implemented as a Serious Game for Children With Cancer to Support Palliative Care

MyPal is a European initiative focusing on the use of the electronic patient reported outcome (ePRO) measures to enhance patient engagement in palliative cancer care via digital self-reporting palliative care for patients with cancer. As a part of its approach, MyPal also focuses on pediatric patients, implementing a specific digital health platform including a serious game to facilitate the reporting of the symptoms and overall status regarding their quality of life (QoL). To this end, the reduction of psychological burden related to frequent reporting, a.k.a. as “reporting fatigue” has been identified as a priority. In this study, we present the MyPal-CHILD platform, emphasizing on the serious game named AquaScouts and its key design decisions, while also emphasizing on the respective challenges. More specifically, we provide insights on the participatory design approach applied during the design of the platform and the high-level goals defined based on end-user input. In addition, the validation process applied before the use of the platform under real-world conditions is also presented. Finally, we discuss a number of challenges and the prospects of deploying eHealth interventions to support palliative care.

Perception and Attitudes of Residents Towards Green Spaces in Croatia – an Exploratory Study

Green spaces are important parts of urban infrastructure. COVID-19 pandemic and lockdown periods around the world have confirmed benefits that people derive from using green spaces for their physical and mental health. Green spaces need to meet the needs of users so that people can use them and benefit over time. It is important to consider users' perceptions and attitudes. User input proves beneficial in improving management practices. We investigated the differences in attitudes and perceptions of respondents from different large settlements in Croatia towards green spaces. Data on the use and perception of green spaces were collected in the first lockdown period in Europe and processed the part of the questionnaire on attitudes and perceptions towards green spaces. People have similar, mostly positive perceptions of green spaces regardless of the size of the settlement. Differences were found in the perception of disadvantages and needs related to the management of green spaces. This is the first study of the attitudes and perceptions on a large spatial scale in Croatia, so the results are exploratory and important. This study contributes to research on the social aspects of green spaces by investigating the influence of environmental context on perceptions and attitudes.

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.