Open Source Solutions Research Articles

Real-time GNSS precise positioning: RTKLIB for ROS

The global navigation satellite system (GNSS) constitutes an effective and affordable solution to the outdoor positioning problem. When combined with precise positioning techniques, such as the real time kinematic (RTK), centimeter-level positioning accuracy becomes a reality. Such performance is suitable for a whole new range of demanding applications, including high-accuracy field robotics operations. The RTKRCV, part of the RTKLIB package, is one of the most popular open-source solutions for real-time GNSS precise positioning. Yet the lack of integration with the robot operating system (ROS), constitutes a limitation on its adoption by the robotics community. This article addresses this limitation, reporting a new implementation which brings the RTKRCV capabilities into ROS. New features, including ROS publishing and control over a ROS service, were introduced seamlessly, to ensure full compatibility with all original options. Additionally, a new observation synchronization scheme improves solution consistency, particularly relevant for the moving-baseline positioning mode. Real application examples are presented to demonstrate the advantages of our rtkrcv_ros package. For community benefit, the software was released as an open-source package.

QGIS, pyarchinit and blender: Surveying and Management ofArchaeological data with Open Source Solutions

The goal of this article is to provide several practical procedures for working within the GIS environment in the archaeological sector, with specific reference to the excavation site, through open source methodologies and software such as Qgis and PyArchinit. It will also demonstrate how to use the data derived from the survey, processed and managed through Qgis and PyArchinit for enhancement projects such as 3d modeling and 3d mapping through Blender software.

Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.

Biomedical data frequently contain imbalance characteristics which make achieving good predictive performance with data-driven machine learning approaches a challenging task. In this study, we investigated the impact of re-sampling techniques for imbalanced datasets in PET radiomics-based prognostication model in head and neck (HNC) cancer patients. Radiomics analysis was performed in two cohorts of patients, including 166 patients newly diagnosed with nasopharyngeal carcinoma (NPC) in our centre and 182 HNC patients from open database. Conventional PET parameters and robust radiomics features were extracted for correlation analysis of the overall survival (OS) and disease progression-free survival (DFS). We investigated a cross-combination of 10 re-sampling methods (oversampling, undersampling, and hybrid sampling) with 4 machine learning classifiers for survival prediction. Diagnostic performance was assessed in hold-out test sets. Statistical differences were analysed using Monte Carlo cross-validations by post hoc Nemenyi analysis. Oversampling techniques like ADASYN and SMOTE could improve prediction performance in terms of G-mean and F-measures in minority class, without significant loss of F-measures in majority class. We identified optimal PET radiomics-based prediction model of OS (AUC of 0.82, G-mean of 0.77) for our NPC cohort. Similar findings that oversampling techniques improved the prediction performance were seen when this was tested on an external dataset indicating generalisability. Our study showed a significant positive impact on the prediction performance in imbalanced datasets by applying re-sampling techniques. We have created an open-source solution for automated calculations and comparisons of multiple re-sampling techniques and machine learning classifiers for easy replication in future studies.

Automation of cleaning and reconstructing residential address histories to assign environmental exposures in longitudinal studies.

BackgroundWe have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10 µm (PM10) concentrations.MethodsALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility.ResultsAverage PM10 exposure was 32.6 µg/m3 [standard deviation (S.D.) 3.0 µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6 µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3 µg/m3 to 12.4 µg/m3 (up to 26% difference) during pregnancy and -7.22 µg/m3 to 7.64 µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy.ConclusionsALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification.

CERN AppStore: Development of a multi-platform application management system for BYOD devices at CERN

The number of BYOD continuously grows at CERN. Additionally, it is desirable to move from a centrally managed model to a distributed model where users are responsible for their own devices. Following this strategy, the new tools have to be provided to distribute and - in case of licensed software - also track applications used by CERN users. The available open source and commercial solutions were analyzed and none of them proved to be a good fit for CERN use cases. Therefore, it was decided to develop a system that could integrate various open source solutions and provide desired functionality for multiple platforms, both mobile and desktop. This paper presents the architecture and design decisions made to achieve a platform-independent, modern, maintainable and extensible system for software distribution at CERN.

OpenDaylight and OpenNebula integration: Testing traffic management

Software-Defined Networking (SDN) and Cloud Computing are now two of the most adopted technologies, on which many organizations are working to enhance every day. For instance, SDN is particularly emerging to solve networking complexity in cloud data centers, so we see many attempts to integrate Network and Cloud Managers. In this paper, we address an integration of these two technologies, particularly a yet undiscussed combination of two popular frameworks: OpenNebula and OpenDaylight. These open source solutions are widely used for cloud management and network management, yet there are no developed modules for communication between the two. Therefore, we propose a simple way for OpenDaylight to manage OpenNebula's compute nodes, using a common component they both support: OpenvSwitch. We compared OpenNebula with the popular OpenStack cloud manager, as it is attracting more attention in both academia and industry, by evaluating some relevant time metrics and discussing the differences of the proposed technologies. Then, we deployed a test topology to conduct some traffic management techniques in this integration. Our results show that OpenNebula's deployment time as well as clean-up time is significantly lower than OpenStack, but OpenStack takes less time to the running state, besides proving the simplicity of traffic management in OpenNebula using OpenDaylight.

WEB GIS SOLUTION FOR URBAN PLANNING DOCUMENTATION WORKFLOW

The goal of this work is to study the use of WEB GIS open source solution for workflow organizing of urban planning documentation: zoning permit, building permit, occupancy permit, utilities connection. A web GIS solution Giscuit.Com developed by a moldavian company VEC.MD was used as a framework for implementation of test version of a specialized website Primaria.Giscuit.Com. Giscuit is a cost-effective web mapping platform built on powerful, cutting-edge, open source geospatial components. The goal is to make it as easy as possible to build more secure, reliable and modern web GIS applications. With Giscuit users stay in control of their content through centralized management of vector and raster spatial data. Giscuit allows users to visualize, share, edit and analyze geospatial data. It has powerful web-based administrator panel with features like data import, layer styling, user management, permissions management, publishing data and more. It is compliant with the Open Geospatial Consortium standards, this is achieved through OpenLayers or PHP MapScript supporting several OGC standards like WMS, WFS, WMC etc. Giscuit provides a scalable GIS server platform that can be deployed on a single Linux or Microsoft Windows machine, it can be distributed across multiple servers or deployed on cloud infrastructure.

Evolution of the CERNBox platform to support the Malt project

CERNBox is the CERN cloud storage hub for more than 25,000 users at CERN. It allows synchronising and sharing files on all major desktop and mobile platforms (Linux, Windows, MacOSX, Android, iOS) providing universal, ubiquitous, online- and offline access to any data stored in the CERN EOS infrastructure. CERNBox also provides integration with other CERN services for big science: visualisation tools, interactive data analysis and real-time collaborative editing.Over the last two years, CERNBox has evolved from a pure cloud sync and share platform into a collaborative service, to support new applications such as DrawIO for diagrams and organigrams sketching, OnlyOffice and Collabora Online for documents editing, and DXHTML Gantt for project management, as alternatives to traditional desktop applications. Moving to open source applications has the advantage to reduce licensing costs and enables easier integration within the CERN infrastructure. This move from commercial software to open source solutions is part of the MALT project, led by the IT department at CERN to reduce the dependencies on commercial solutions.As part of the MALT project, CERNBox is the chosen solution to replace Home directories of the Windows DFS file system. Access to storage from Windows managed devices for end-users is largely covered by synchronization clients. However, online access using standard CIFS/SMB protocol is required for shared use-cases, such as central login services (Terminal Services) and visitor desktop computers. We present recent work to introduce a set of Samba gateways running in High Availability cluster mode to enable direct access to the CERNBox backend storage (EOS).

ESCAPE prototypes a data infrastructure for open science

The European-funded ESCAPE project will prototype a shared solution to computing challenges in the context of the European Open Science Cloud. It targets Astronomy and Particle Physics facilities and research infrastructures and focuses on developing solutions for handling Exabyte scale datasets. The DIOS work package aims at delivering a Data Infrastructure for Open Science. Such an infrastructure would be a non HEP specific implementation of the data lake concept elaborated in the HSF Community White Paper and endorsed in the WLCG Strategy Document for HL-LHC. The science projects in ESCAPE are in different phases of evolution. While HL-LHC can leverage 15 years of experience of distributed computing in WLCG, other sciences are building now their computing models. This contribution describes the architecture of a shared ecosystem of services fulfilling the needs in terms of data organisation, management and access for the ESCAPE community. The backbone of such a data lake will consist of several storage services operated by the partner institutes and connected through reliable networks. Data management and organisation will be orchestrated through Rucio. A layer of caching and latency hiding services, supporting various access protocols will serve the data to heterogeneous facilities, from conventional Grid sites to HPC centres and Cloud providers. The authentication and authorisation system will be based on tokens. For the success of the project, DIOS will integrate open source solutions which demonstrated reliability and scalability as at the multi petabyte scale. Such services will be configured, deployed and complemented to cover the use cases of the ESCAPE sciences which will be further developed during the project.

Processing Radio Astronomical Data Using the PROCESS Software Ecosystem

In this paper we discuss our efforts in unlocking the Long Term Archive (LTA) of the LOFAR radio telescope using the software ecosystem developed in the PROCESS project. The LTA is a large (>50 PB) archive that expands with about 7 PB per year by the ingestion of new observations. It consists of coarsely calibrated visibilities, i.e. correlations between signals from LOFAR stations. Converting these observations into sky maps (images), which are needed for astronomy research, can be challenging due to the data sizes of the observations and the complexity and compute requirements of the software involved. Using the PROCESS software environment and testbed, we enable a simple point-and-click-reduction of LOFAR observations into sky maps for users of this archive. This work was performed as part of the PROCESS project which aims to provide generalizable open source solutions for user friendly exascale data processing.

Automated Insulin Delivery: The Artificial Pancreas Technical Challenges.

The automation of glucose control has been an important goal of diabetes treatment for many decades. The first artificial pancreas experiences were in-hospital, closely supervised, small-scale, and short-term studies that demonstrated their superiority over continuous subcutaneous insulin infusion therapy. At present, long-term outpatient studies are being conducted in free-living scenarios. The integration of multiple devices increases patients' burden and the probability of technical risks. Control algorithms must be robust to manage disturbance variables, such as physical exercise, meal composition, stress, illness, and circadian variations in insulin sensitivity. Extra layers of safety could be achieved through remote supervision. Dual-hormone systems reduce the incidence and duration of hypoglycemia, but the availability of stable pumpable glucagon needs to be solved. Faster insulin analogues are expected to improve all types of artificial pancreas. Artificial pancreas safety and feasibility are being demonstrated in outpatient studies. Artificial pancreas use increases the time of sensor-measured glucose in near-normoglycemia and reduces the risk of hyperglycemia and hypoglycemia. The benefits are observed both in single- and dual-hormone algorithms and in full- or semi-closed loop control. A recent meta-analysis including 41 randomized controlled trials showed that artificial pancreas use achieves a reduction of time in hyperglycemia (2 hours less than control treatment) and in hypoglycemia (20 minutes less); mean levels of continuous glucose sensor fell by 8.6 mg/dL over 24 hours and by 14.6 mg/dL overnight. The OpenAPS community uses Do It Yourself artificial pancreas in the real world since 2013, and a recent retrospective cross-over study (n = 20) compared continuous glucose sensor readings before and after initiation: mean levels of blood glucose fell by 7.4 mg/dL over 24 hours and time in range increased from 75.8% to 82.2% (92 minutes more). The outpatient use of artificial pancreas is safe and improves glucose control in outpatients with type 1 diabetes compared with the use of any type of insulin-based treatment. The availability of open-source solutions and data sharing is needed to foster the development of new artificial pancreas approaches and to promote the wide use of Big Data tools for knowledge discovery, decision support, and personalization.

Using open-source data to explore distribution of built environment characteristics across Kerala, India.

Built environment characteristics in the neighborhood are of utmost priority for a healthy lifestyle in the fast-urbanizing countries. These characteristics are closely linked to the disease burden and challenges in low- and middle-income countries (LMICs), which have been unexplored using open-source data. The present technology offers online resources and open source software that enable researchers to explore built environment characteristics with health and allied phenomena. This article intends to delineate methods to capture available and accessible objective built environment variables for a state in India and determine their distribution across the state. Built environment variables such as population density and residential density were collated from the Census of India. Safety from crime and traffic were captured as crime rates and pedestrian accident rates, respectively, acquired from State Crime Records Bureau. Greenness, built-up density, and land slope were gathered from open-source satellite imagery repository. Road intersection density was derived from OpenStreetMap. Processing and analysis differed for each dataset depending on its source and nature. Each variable showed a distinct pattern across the state. Population and residential density were found to be closely related to each other across both districts and subdistricts. They were both positively related to crime rates, pedestrian accident rates, built-up density, and intersection density, whereas negatively related to land slope and greenness across the subdistricts. Delineating the distribution of built environment variables using available and open-source data in resource-poor settings is a first in public health research among LMICs. Cost-effectiveness and reproducible nature of open-source solutions could equip researchers in resource-poor settings to identify built environment characteristics and patterns across regions.

Managing ICT solutions for training and evaluation of C++ programming skills in e-learning ecosystem

А set of functional upgrades to the existing e-learning ecosystem for the C++ programming language, which provides a solution where students can use their own computers during training and practical exams, is presented in this article. The ICT solutions applied in this case offer a supervised and protected the online environment, where each student has their own working account. The rights and privileges for accessing and using various software services are customised according to the educational tasks (training or exam). Depending on the purpose, the described software solutions are realised as web services for education and training and network services for collection and evaluation of the students’ solutions during the practical exam. During the training, web services are used, and each account has its own folder, in which the solutions are stored, and if necessary, those can be opened, modified, tested and shared for collaboration or evaluation in an integrated C++ compiler. During practical exams, network services installed on Raspberry Pi are used, and for security reasons, there is no online access. Ready student solutions according to defined test cases are placed on the server. The principle of evaluation consists in comparing the solutions of each student separately with the pre-designed solutions set in the system for the same test cases. The e-learning ecosystem is developed and upgraded by managing various ICT open source solutions described in this work.&#x0D; Keywords: E-learning ecosystem, C++ programming, Raspberry Pi.

Measuring Selective Exposure in Mock Website Experiments: A Simple, Free, and Open-source Solution

ABSTRACT The mock website paradigm has been established as a methodological standard for experimental selective exposure research. To date, despite the amount of empirical research utilizing mock website experiments, no openly accessible practical implementations tailored to the needs of communication scholars exist. In this paper, we first review extant implementations of mock website experiments in selective exposure research and outline the methodological shortcomings. We then outline a procedure to conduct mock website experiments that addresses these shortcomings using only freely available software and that does not restrict participants to specific devices nor require additional software to be installed. Additionally, it can be employed and easily adapted without further programming knowledge as all scripts are published open-source on GitHub. We also demonstrate an application of the procedure by outlining a study on selective exposure to search engine content (N = 1,187). Further applications and limitations of the procedure are discussed.

CyBy2: a strongly typed, purely functional framework for chemical data management

We present the development of CyBy2, a versatile framework for chemical data management written in purely functional style in Scala, a modern multi-paradigm programming language. Together with the core libraries we provide a fully functional example implementation of a HTTP server together with a single page web client with powerful querying and visualization capabilities, providing essential functionality for people working in the field of organic and medicinal chemistry. The main focus of CyBy2 are the diverse needs of different research groups in the field and therefore the flexibility required from the underlying data model. Techniques for writing type level specifications giving strong guarantees about the correctness of the implementation are described, together with the resulting gain in confidence during refactoring. Finally we talk about the advantages of using a single code base from which the server, the client and the software’s documentation pages are being generated. We conclude with a comparison with existing open source solutions. All code described in this article is published under version 3 of the GNU General Public License and available from GitHub including an example implementation of both backend and frontend together with documentation how to download and compile the software (available at https://github.com/stefan-hoeck/cyby2).

OPEN-SOURCE IMAGE-BASED 3D RECONSTRUCTION PIPELINES: REVIEW, COMPARISON AND EVALUATION

Abstract. State-of-the-art automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) methods commonly used to produce 3D information from 2D images can generate 3D results – such as point cloud or meshes – of varying geometric and visual quality. Pipelines are generally robust and reliable enough, mostly capable to process even large sets of unordered images, yet the final results often lack completeness and accuracy, especially while dealing with real-world cases where objects are typically characterized by complex geometries and textureless surfaces and obstacles or occluded areas may also occur. In this study we investigate three of the available commonly used open-source solutions, namely COLMAP, OpenMVG+OpenMVS and AliceVision, evaluating their results under diverse large scale scenarios. Comparisons and critical evaluation on the image orientation and dense point cloud generation algorithms is performed with respect to the corresponding ground truth data. The presented FBK-3DOM datasets are available for research purposes.

CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT

Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

Comparing DInSAR and PSI Techniques Employed to Sentinel-1 Data to Monitor Highway Stability: A Case Study of a Massive Dobkovičky Landslide, Czech Republic

Single-pair differential synthetic aperture radar interferometry (DInSAR) as well as more advanced methods, such as persistent scatterer interferometry (PSI), allow vertical displacements to be detected at the sub-centimeter level. Since 2014 free SAR data—Sentinel-1—have been collected systematically under the COPERNICUS program at a high temporal resolution and with global coverage. Such an open-access policy greatly helps build a wide user-community and develop diverse SAR-based applications. In this study conventional single-pair DInSAR and the PSI techniques were employed to monitor the vertical displacements of the newly constructed D8 highway, more specifically the part passing through the České Středohoří Mountains, where, during highway construction, a massive landslide occurred in June 2013. For both DInSAR and PSI, free Sentinel-1 radar data were used; moreover, the conventional single-pair DInSAR workflow was processed using freely available SNAP software. Results from the radar interferometry were validated using in situ techniques, such as geodetic measurements, 3D inclinometers, and laser scanning. Both approaches proved their ability to achieve reliable results in detecting vertical displacement “hotspots”. Additionally, in terms of absolute values, the PSI interferometry corresponds very well with the in situ measurements. This study also shows that open-source solutions (free data and SW) provided under the COPERNICUS program bring a great potential for monitoring vertical displacements.

Assembling cheap, high-performance microphones for recording terrestrial wildlife: the Sonitor system.

Passive acoustic monitoring of wildlife requires sound recording systems. Several cheap, high-performance open-source solutions currently exist for recording soundscapes, but all of them are still reliant on commercial microphones. Commercial microphones are relatively expensive, specialized for particular taxa, and often have incomplete technical specifications. We designed Sonitor, an open-source microphone system to address all needs of ecologists that sample terrestrial wildlife acoustically. We evaluated the cost and durability of our system and measured trade-offs that are seldom acknowledged but which universally limit microphones' functions: weatherproofing versus sound attenuation, windproofing versus transmission loss after rain, signal loss in long cables, and analog sound amplification versus directivity with acoustic horns. We propose five microphone configurations suiting different budgets (from 8 to 33 EUR per unit), and fulfilling different sound quality and flexibility requirements. The Sonitor system consists of sturdy acoustic sensors that cover the entire sound frequency spectrum of sonant terrestrial wildlife at a fraction of the cost of commercial microphones.

Implementation and user acceptance of research information systems

PurposeThe purpose of this paper is to present empirical evidence on the implementation, acceptance and quality-related aspects of research information systems (RIS) in academic institutions.Design/methodology/approachThe study is based on a 2018 survey with 160 German universities and research institutions.FindingsThe paper presents recent figures about the implementation of RIS in German academic institutions, including results on the satisfaction, perceived usefulness and ease of use. It contains also information about the perceived data quality and the preferred quality management. RIS acceptance can be achieved only if the highest possible quality of the data is to be ensured. For this reason, the impact of data quality on the technology acceptance model (TAM) is examined, and the relation between the level of data quality and user acceptance of the associated institutional RIS is addressed.Research limitations/implicationsThe data provide empirical elements for a better understanding of the role of the data quality for the acceptance of RIS, in the framework of a TAM. The study puts the focus on commercial and open-source solutions while in-house developments have been excluded. Also, mainly because of the small sample size, the data analysis was limited to descriptive statistics.Practical implicationsThe results are helpful for the management of RIS projects, to increase acceptance and satisfaction with the system, and for the further development of RIS functionalities.Originality/valueThe number of empirical studies on the implementation and acceptance of RIS is low, and very few address in this context the question of data quality. The study tries to fill the gap.