Open Source Solutions Research Articles

Toward an Intelligent Campus: IoT Platform for Remote Monitoring and Control of Smart Buildings

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort.

Interactive, in-browser cinematic volume rendering of medical images

ABSTRACT The diversity and utility of cinematic volume rendering (CVR) for medical image visualisation have grown rapidly in recent years. At the same time, volume rendering on augmented and virtual reality systems is attracting greater interest with the advance of the WebXR standard. This paper introduces CVR extensions to the open-source visualisation toolkit (vtk.js) that supports WebXR. This paper also summarises two studies that were conducted to evaluate the speed and quality of various CVR techniques on a variety of medical data. This work is intended to provide the first open-source solution for CVR that can be used for in-browser rendering as well as for WebXR research and applications. This paper aims to help medical imaging researchers and developers make more informed decision when selecting CVR algorithms for their applications. Our software and this paper also provide a foundation for new research and product development at the intersection of medical imaging, web visualisation, XR and CVR.

No cost but high performance-An alternative open source solution for 3D-visualizations in morphology.

3D-visualization has become common courtesy in science and also found its way into teaching in schools and universities. Nevertheless, the way to high performance 3D-visualization and analyses remains difficult and is often also a matter of budget. Due to the obvious advantages of presenting morphological and anatomical datasets with the help of 3D-figures, all in one software solutions usually come along with high rental and maintenance fees. For that reason, it is more than overdue to establish and use open source software solutions with all their obvious advantages - as other disciplines already do. Here we provide a modular, highly adaptive and freely available software pipeline for high performance 3D-visualizations of (not only) morphological datasets by combining features of ImageJ, MeshLab and Blender, without any additional costs. Exemplarily using serial-block face SEM data as well as serial AZAN-stained histological sections, the herein presented step-by-step protocol allows for a fast and efficient analysis, visualization and animation of large, anatomical datasets. Regardless which type of serial, morphological datasets needs to be analyzed, our open source guide provides an easy to handle and promptly adaptable solution. Therefore, our pipeline for 3D-visualization represents a valuable alternative to conventional, commercial packages. RESEARCH HIGHLIGHTS: We provide a highly modular and easy to learn open source solution for multiple 3D-visualizations. The step-by-step-guide make it easy to start, and advanced users can replace software, add others and such build your own individual software pipeline.

Auto-scoring of Student Speech: Proprietary vs. Open-source Solutions

This paper compares the speaking scores generated by two online systems that are designed to automatically grade student speech and provide personalized speaking feedback in an EFL context. The first system, Speech Assessment for Moodle (SAM), is an open-source solution developed by the author that makes use of Google’s speech recognition engine to transcribe speech into text which is then automatically scored using a phoneme-based algorithm. SAM is designed as a custom quiz type for Moodle, a widely adopted open-source course management system. The second auto-scoring system, EnglishCentral, is a popular proprietary language learning solution which utilizes a trained intelligibility model to automatically score speech. Results of this study indicated a positive correlation between the speaking scores generated by both systems, meaning students who scored higher on the SAM speaking tasks also tended to score higher on the EnglishCentral speaking tasks and vice versa. In addition to comparing the scores generated from these two systems against each other, students’ computer-scored speaking scores were compared to human-generated scores from small-group face-to-face speaking tasks. The results indicated that students who received higher scores with the online computer-graded speaking tasks tended to score higher on the human-graded small-group speaking tasks and vice versa.

AI-Based Autonomic and Scalable Security Management Architecture for Secure Network Slicing in B5G

The vital importance of securing 5G and beyond networks while meeting their stringent performance requirements has promoted the recent shift towards fully automated and smart security management. In this article, we introduce a novel autonomic and cognitive security management framework that empowers fine-grained zero-touch security management through different levels (i.e., network functions, sub-slice, and slice) and different administrative and technological domains. We showcase the compliance of the proposed framework with the ongoing standards (e.g., ZSM, 3GPP, and NFV) and demonstrate its feasibility by advocating for potential open source solutions to implement its functional blocks in a cloud-native service-based environment.

The SpacePy space science package at 12 years

For over a decade, the SpacePy project has contributed open-source solutions for the production and analysis of heliophysics data and simulation results. Here we introduce SpacePy’s functionality for the scientific user and present relevant design principles. We examine recent advances and the future of SpacePy in the broader scientific Python ecosystem, concluding with some of the work that has used SpacePy.

3D DATA INTEGRATION FOR WEB BASED OPEN SOURCE WebGL INTERACTIVE VISUALISATION

Abstract. Recent advances in open-source geospatial technologies in WebGIS allowed the visualization of a 3D complex environment on the web, exploiting realistic Globe reproduction of the real territorial asset. At the same time, in the field of gaming technologies, the new possibilities offered by open-source WebGL JavaScript libraries allowed the creation of Virtual Reality navigation models on the web. The integration between 3D GIS globe navigation models and VR environment navigation is a solution that offers a further level of detail in web navigation, exploiting the capabilities of web browsers in the best way. This research further contributes to this field, showing a workflow to integrate different 3D data in a VR and 3D WebGIS navigation model. The case study for this research is the new building of the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC) of Enschede (The Netherlands). This work tests the online integration of variety of 3D input data that can lead to different Levels of Details (LoD) of the buildings inside the Globe-based WebGIS platform. The developed solution works on desktop and mobile devices using the capabilities of the most common web browsers, avoiding any software installation. The result of this work is based on completely open-source solutions that offers the possibility to navigate within a 3D model, which is useful for citizens, governmental or private institutions in decision-making processes. This work represents a first step towards the ambition to generate a web Digital Twin platform to combine datasets from different sources in a unique open-source solution.

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Objective. De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria. Approach. Towards this end, this manuscript describes the Federated Tumor Segmentation (FeTS) tool, in terms of software architecture and functionality. Main results. The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data. Significance. Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced at https://github.com/FETS-AI/Front-End.

Evaluating the Performance of Airborne and Ground Sensors for Applications in Precision Agriculture: Enhancing the Postprocessing State-of-the-Art Algorithm.

The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time.

Low-cost fabrication of flexible tactile sensor arrays

While for vision and audio the same mass-produced units can be embedded in many different systems from smartphones to robots, tactile sensors have to be built in application-specific shapes and sizes. To use a commercially available tactile sensor, it can be necessary to develop the entire system around an existing sensor model. We present a set of open-source solutions for designing, manufacturing, reading and integrating custom application-specific tactile matrix sensors. Our manufacturing process only requires an off-the-shelf cutting plotter and widely available plastic and metal foils. This allows creating sensors of diverse sizes, shapes, and layouts, which can be adapted to various specific use cases as demonstrated with exemplary robot integrations. For interfacing and readout, we develop an Arduino-like prototype board (Tacduino) with amplifier circuits to ensure good resolution and to suppress crosstalk. As an example, we give step-by-step instructions to build tactile fingertips for the RobotiQ 3-Finger Gripper, and we provide design files for the readout circuit board together with Arduino firmware and driver software. Both, wired and wireless communication between the sensors and a host PC are supported by this system. The hardware was originally presented and investigated in [1].

Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.

Open source SIEM solutions for an enterprise

PurposeThe security of applications, systems and networks has always been the source of great concern for both enterprises and common users. Different security tools like intrusion detection system/intrusion prevention system and firewalls are available that provide preventive security to the enterprise networks. However, security information and event management (SIEM) systems use these tools in combination to collect events from diverse data sources across the network. SIEM is a proactive tool that processes the events to present a unified security view of the whole network at one location. SIEM system has, therefore, become an essential component of an enterprise network security architecture. However, from various options available, the selection of a suitable and cost-effective open source SIEM solution that can effectively meet most of the security requirements of small-to-medium-sized enterprises (SMEs) is not simple because of the lack of strong analysis.Design/methodology/approachIn this work, the authors first review the security challenges faced by different SME sectors and then consider a comprehensive comparative analysis of the capabilities of well-known open source SIEM solutions. Based on this, the authors provide requirements based recommendations of open source SIEM solutions for SMEs. This paper aims to provide a valuable resource that can be referred to by SMEs for the selection of a SIEM system best suited to their organization’s security posture.FindingsSecurity requirements of SMEs vary according to their network infrastructure; therefore, every open source SIEM solution would not be suitable for an SME. Selection of a SIEM solution from available open source solutions based upon the security requirements of an SME network is a critical task. Therefore, in this work, a meaningful insight for the selection of an appropriate SIEM solution for SMEs is provided.Originality/valueMajor contribution of this work is the mapping of the security requirements of the SME sectors under consideration, against the open source SIEM options to provide meaningful insight for SMEs in the selection of an appropriate solution.

HBIM Open Source: A Review

Historic Building Information Modelling (HBIM) methodology has revolutionized the entire cultural heritage documentation panorama since 2009. At the same time, the possibility of creating and managing HBIM projects by using open source solutions opened new research paths in 2016. Different reasons can drive the utilisation of free and open source software (FOSS), however the accessibility of a tailor-made project should be the main purpose. After six years of research on open source HBIM, this paper will review the actual panorama of designed and operative programmes on informative models of historic architecture built with FOSS solutions. Different aspects will be analysed, from open source software setup to parametric modelling and from semantic dimension to data exchange and cloud accessibility. Then, the advantages and drawbacks of open source protocols will be highlighted. Lastly, the next updates, future scenarios and developments on open source HBIM will be estimated.

Object classification on video data of meteors and meteor-like phenomena: algorithm and data

ABSTRACT Every moment, countless meteoroids enter our atmosphere unseen. The detection and measurement of meteors offer the unique opportunity to gain insights into the composition of our solar systems’ celestial bodies. Researchers therefore carry out a wide-area-sky-monitoring to secure 360-degree video material, saving every single entry of a meteor. Existing machine intelligence cannot accurately recognize events of meteors intersecting the earth’s atmosphere due to a lack of high-quality training data publicly available. This work presents four reusable open source solutions for researchers trained on data we collected due to the lack of available labelled high-quality training data. We refer to the proposed data set as the NightSkyUCP data set, consisting of a balanced set of 10 000 meteor- and 10 000 non-meteor-events. Our solutions apply various machine-learning techniques, namely classification, feature learning, anomaly detection, and extrapolation. For the classification task, a mean accuracy of 99.1 per cent is achieved. The code and data are made public at figshare with DOI 10.6084/m9.figshare.16451625.

Ontology-based Meta AutoML

Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted user groups, support for ML libraries, and degrees of maturity. Depending on the AutoML solution, a user may be locked into one specific ML library technology or one product ecosystem. Additionally, the user might require some expertise in data science and programming for using the AutoML solution. We propose a concept called OMA-ML (Ontology-based Meta AutoML) that combines the features of existing AutoML solutions by integrating them (Meta AutoML). OMA-ML can incorporate any AutoML solution allowing various user groups to generate ML pipelines with the ML library of choice. An ontology is the information backbone of OMA-ML. OMA-ML is being implemented as an open source solution with currently third-party 7 AutoML solutions being integrated.

Open Source Solutions in Experimental Design: An Introduction to the Symposium.

The Open Science movement has increased dramatically in popularity with deserved calls to action around transparency, access to resources, and inclusion in our field. However, its practical applications within experimental design have been slow to uptake, with researchers unsure where to even start with the dizzying array of open source hardware and software solutions available. The perceived time investment and unknown cost, especially in implementing open source hardware, has stagnated the implementation of inexpensive experimental solutions, but we sought to increase awareness to lower the barrier to participation in this space. While there are countless technical and financial advantages to integrating open source solutions into every biologist's experimental design, we put an emphasis on the "people" part of the equation in our symposium. This symposium championed innovative experimental designs by early career Society for Integrative and Comparative Biology researchers across all fields of biology, from plants to animals, in the lab or in the field, or even virtually engaging with the public and students. The Open Science movement operates within community norms that champion transparency, continuous development, and collaboration. These values are congruent with the priorities of reducing barriers to participation in science, and we hope our symposium's collection of open source solutions encourages readers to adopt these or other innovative designs into their own experimentation.

EDALoCo: Enhancing the accessibility of blockchains through a low-code approach to the development of event-driven applications for smart contract management

Blockchain is a cutting-edge technology based on a distributed, secure and immutable ledger that facilitates the registration of transactions and the traceability of tangible and intangible assets without requiring central governance. The agreements between the nodes participating in a blockchain network are defined through smart contracts. However, the compilation, deployment, interaction and monitoring of these smart contracts is a barrier compromising the accessibility of blockchains by non-expert developers. To address this challenge, in this paper, we propose a low-code approach, called EDALoCo, that facilitates the development of event-driven applications for smart contract management. These applications make blockchain more accessible for software developers who are non-experts in this technology as these can be modeled through graphical flows, which specify the communications between data producers, data processors and data consumers. Specifically, we have enhanced the open-source Node-RED low-code platform with blockchain technology, giving support for the creation of user-friendly and lightweight event-driven applications that can compile and deploy smart contracts in a particular blockchain. Additionally, this platform extension allows users to interact with and monitor the smart contracts already deployed in a blockchain network, hiding the implementation details from non-experts in blockchain. This approach was successfully applied to a case study of COVID-19 vaccines to monitor and obtain the temperatures to which these vaccines are continuously exposed, to process them and then to store them in a blockchain network with the aim of making them immutable and traceable to any user. As a conclusion, our approach enables the integration of blockchain with the low-code paradigm, simplifying the development of lightweight event-driven applications for smart contract management. The approach comprises a novel open-source solution that makes data security, immutability and traceability more accessible to software developers who are non-blockchain experts.

LITOS: a versatile LED illumination tool for optogenetic stimulation

Optogenetics has become a key tool to manipulate biological processes with high spatio-temporal resolution. Recently, a number of commercial and open-source multi-well illumination devices have been developed to provide throughput in optogenetics experiments. However, available commercial devices remain expensive and lack flexibility, while open-source solutions require programming knowledge and/or include complex assembly processes. We present a LED Illumination Tool for Optogenetic Stimulation (LITOS) based on an assembled printed circuit board controlling a commercially available 32 × 64 LED matrix as illumination source. LITOS can be quickly assembled without any soldering, and includes an easy-to-use interface, accessible via a website hosted on the device itself. Complex light stimulation patterns can easily be programmed without coding expertise. LITOS can be used with different formats of multi-well plates, petri dishes, and flasks. We validated LITOS by measuring the activity of the MAPK/ERK signaling pathway in response to different dynamic light stimulation regimes using FGFR1 and Raf optogenetic actuators. LITOS can uniformly stimulate all the cells in a well and allows for flexible temporal stimulation schemes. LITOS’s affordability and ease of use aims at democratizing optogenetics in any laboratory.

A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country.

ContextAccess to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making.AimTo demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran.MethodThis COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients’ data, as well as a pool of health data for analytical and decision-making purposes.OutcomesUsing this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres.ConclusionElectronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19.ImplicationsImproved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.

PyPore3D: An Open Source Software Tool for Imaging Data Processing and Analysis of Porous and Multiphase Media.

In this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.).