3D Engine Research Articles

A 3D simulation framework with ray-tracing propagation for LoRaWAN communication

The adoption of Low-Power Wide-Area Networks (LP-WAN) for interconnecting remote wireless sensors has become a reality in smart scenarios, covering communications needs of large Internet of Things (IoT) deployments. The correct operation and expected performance of such network scenarios, which can range hundreds or thousands of nodes and tens of squared kilometres, should be assessed before carrying out the deployment to save installation and maintenance costs. Common network planning tools can help to roughly study potential coverage, but network simulation offers fine-grained information about network performance. Nevertheless, current simulation frameworks include limited propagation models based on statistical and empirical measurements that do not consider scenario particularities, such as terrain elevation, buildings or vegetation. This is critical in urban settings. In this line, this paper presents a simulation framework including a network simulator, a 3D engine and a ray-tracing tool, which models realistically the performance of Long-Range Wide-Area Network (LoRaWAN) communication technology. We have evaluated the performance of the solution taking as reference experimental campaigns in the city of Cartagena (Spain), comparing data obtained when simulating with the commonly employed propagation models such as Okumura–Hata or path loss. Results indicate that our framework, set-up with data from open geographical information systems, accurately fits experimental values, reporting improvements between 10% and 50% in the error committed when estimating signal strength in challenging urban streets with signal obstruction, as compared with the better performing classical model, Okumura–Hata.

An integrated 0D/1D/3D numerical framework to predict performance, emissions, knock and heat transfer in ICEs fueled with NH3–H2 mixtures: The conversion of a marine Diesel engine as case study

In the maritime transportation, e-fuels represent a valid alternative to fossil energy sources, in order to accomplish the European Union goals in terms of climate neutrality. Among the e-fuels, the ammonia-hydrogen mixtures can play a leading role, as the combination of the two allows to exploit the advantages of each one, simultaneously compensating their gaps.The main goal of the present publication is the proposal of a robust numerical framework based on 0D, 1D and 3D tools for CFD analyses of internal combustion engines fueled with ammonia-hydrogen mixtures.The 1D engine model provides boundary conditions for the multi-dimensional investigations and estimates the overall engine performance. 3D in-cylinder detailed analyses are proficiently used to predict combustion efficiency (via the well-established G-equation model supported by laminar flame speed correlations for both ammonia and hydrogen) and emissions (with a detailed chemistry based approach). Heat transfer and knock tendency are evaluated as well, by in-house developed models. As for the 0D/1D chemical kinetics calculations, firstly they support 3D analyses (for example via the generation of ignition delay time tables). Moreover, they allow insights on aspects such as NOx formation, to individuate mixture qualities able to strongly reduce the emissions.The present paper offers also indications for designers on the effectiveness of NH3–H2 as valid substitutes of traditional fuels in marine applications. In fact, the proposed framework is preliminary tested to convert an existing marine Diesel engine to ammonia-hydrogen mixtures. Different proportions (among the two fuels) are investigated, included pure ammonia and pure hydrogen and the NH3–H2 80mol%-20mol% mixture is selected as the best compromise. Thanks to both the anti-knock quality of the ammonia and the (limited) addition of hydrogen as combustion enhancer, the engine supplied with NH3–H2 mixtures is able to operate free of knock and keep the same performance as the original one. Simultaneously (neglecting the presence of lubricant oil in the combustion chamber) unburned hydrocarbons, carbon monoxide and carbon dioxide are eliminated. Still in comparison with the Diesel version, the heat transfer is similar, the NOx formation is much greater and the engine range (here evaluated in terms of working hours) noticeably reduces.

A multi-scale geological–engineering sweet spot evaluation method for tight sandstone gas reservoirs

Exploration and development practices have proved that staged volumetric fracturing stimulation in horizontal wells is a key technology for tight sandstone gas development, and reservoir sweet spot is an important basis for the perforation position selection and staged fracturing in the process of well location deployment and reservoir stimulation. Tight sandstone reservoirs are usually characterized by sandstone and mudstone interlayers with different thicknesses, and complex natural fracture distribution and geostress state. It is hard to predict “geological-engineering” dual sweet spots, and these two kinds of sweet spots are usually in different zones. As a result, there lacks a basis for the optimization of fracturing parameters to stimulate tight sandstone reservoirs. This paper establishes a geological sweet spot prediction model which takes into account total hydrocarbon content, reservoir porosity and other factors, then puts forward a 3D multi-scale engineering sweet spot evaluation method which takes into account lithology, fracture morphology, fracture mechanical behavior, and dilatation and shear dilation effect, and finally a “geological-engineering” dual sweet spot evaluation model for tight sandstone reservoirs. Two wells in the tight sandstone gas field in the Linxing Block of the Ordos Basin were selected as a case, and the dual sweet spot profiles, fracturing pressure and SRV were compared and analyzed. The results show that: 1) shear dilation angle influences the distribution of engineering sweet spots at the most in the study area, followed by dissipated energy, elastic modulus and fracture energy; 2) the geological sweet spot zone with a high coefficient is not necessarily the pay zone with high shale gas production; 3) the engineering sweet spot zone with a high coefficient needs lower fracture pressure and can be stimulated relatively sufficiently; 4) high-quality geological sweet spots and high-quality engineering sweet spots are poorly consistent in spatial location. In conclusion, the stimulation of tight sandstone gas reservoirs shall take geological sweet spot as the basis and engineering sweet spot as the guarantee, and the distribution of dual sweep spots should be considered comprehensively. The multi-scale “geological-engineering” dual sweet spot evaluation method proposed in this paper provides important technical support for the prediction of sweet spots of the tight sandstone gas and the optimization of development schemes in the study area.

Teaching Reform of WSN Laboratory Class based on a Virtual Simulation Platform

Wireless Sensor Networks (WSNs) are considered as one of the most rapidly developing technologies in recent years. The WSN course, as a core curriculum of the Internet of Things (IoT) major, also plays an important role in higher education teachings. However, the inadequate facilities, equipment and spaces have been main constraints of universities’ WSN laboratory classes. In this paper, we designed a specific situation and scene for smart home, one of the commonly used applications of WSN, and developed a Virtual Simulation Experimental Platform (VSEP) based on Unity 3D engine, aiming to enhance students’ learning interest and improve their practical skills. In particular, we firstly analyzed the current limitations in WSN experimental teachings, with design concepts and objectives and experiment teaching content of VSEP introduced thereafter. In addition, we discussed systems architecture and functions design, and experimental methods and steps. Finally, an anonymous survey was utilized to examine the teaching reform result. The study showed that most students have found the virtual platform helped them understand the theoretical concepts and experimental processes better, and is an effective way to learn the course.

Multi-objective optimization of a compression ignition engine using on-board methanol reforming

The dual-fuel combustion technology utilizing hydrogen and diesel fuel shows potential in enhancing the performance of compression ignition engines and reducing carbon emissions. However, this approach is accompanied by several significant challenges, such as hydrogen transportation and storage, as well as low combustion efficiency at low loads. To address these challenges, this study integrates low-temperature combustion technology with online hydrogen production through methanol steam reforming to achieve efficient and clean combustion. The results of methanol steam reforming, mainly consisting of hydrogen, carbon dioxide, and unreacted methanol, is premixed via port injection as a low-reactivity fuel. Simultaneously, diesel fuel is directly injected into the cylinder as a high-reactivity fuel to achieve reactivity-controlled compression ignition mode. The primary fuels employed in compression ignition engines comprise reformate gas (mainly hydrogen), unreacted methanol, and diesel. This combination is collectively termed as tri-fuel combustion. This study utilizes a combination of genetic algorithms and 3D engine simulation to determine the optimal strategy for the methanol/reformed gas/diesel tri-fuel engine under different loads. The results of the optimization reveal that the tri-fuel combustion mode has the highest indicated thermal efficiency, surpassing the methanol/diesel dual-fuel mode and the reformed gas/diesel dual-fuel mode. Meanwhile, the optimized engine demonstrates significantly lower NOx and soot emissions than those required by the Euro VI emission regulations. Additionally, the study summarizes engine operating parameters impact on performance. Moreover, the tri-fuel combustion mode has a distinct advantage in increasing the methanol energy substitution rate at all three different loads. The dual-fuel mode leads to increased unburned hydrogen and methanol concentration near cylinder liner due to lower combustion temperature. In contrast, tri-fuel mode substantially reduces unburned hydrogen and methanol due to high combustion temperature and methanol reactivity.

Evaluation of thermal management technologies for fuel efficient cold-start emissions reduction in diesel engines

The ultra-low NOx emission limits for heavy and mid-duty vehicles, which are expected to be imposed in both Europe and the USA by 2027, together with the enforcement of continuous decrease in CO2 emission levels, make the need for adoption of newer technologies in the automotive industry imperative. To achieve low tailpipe NOx emissions, the accelerated warm-up of the exhaust aftertreatment system is of great importance. On the other hand, the thermal management strategies applied for this purpose result in fuel consumption penalty and consequently higher CO2 as it can be widely found in the literature. Thus, the use and optimization of advanced thermal management technologies is critical to decrease the NOx– fuel penalty trade-off on the basis of complete driving cycles. In the present work an integrated modeled-based approach is implemented by coupling advanced, predictive 1D engine, and aftertreatment simulation models which are extensively validated via dedicated experimental data. The main goal of this approach is to define and investigate multiple engine or aftertreatment-based thermal management technologies, as well as a combination of them, creating a more efficient warm-up mode for the engine. Thermal measures such as cylinder deactivation, retarded start of the main injection, late intake valve closing (Miller cycle), intake throttling, elevated idle speed, and secondary fuel injection upstream the DOC are evaluated for their effect on the exhaust system heat-up time, the fuel consumption penalty and more importantly, for their impact on the tailpipe NOx emissions calculated by this holistic simulation approach.

A convolution-superposition fluence model for the Siemens HD120 multi leaf collimator with application to a 3D VMAT dose engine

Purpose. To construct a fast-calculating fluence modelfor the Siemens HD120 multi leaf collimator (MLC) using convolution-superposition techniques, and to develop a 3D VMAT dose engine using this fluence model. This work offers analternative to time-consuming open-source Monte Carlo simulations for thosedeveloping in-house dose-calculating software for research or clinical needs.Methods. EPID-acquired images of sweeping-window and sweeping-checker field profiles were used to commission transmission, 2 Dinterleaf leakage, and tongue-and-groove maps specific to the HD120 MLC. These maps, along with a 2D head-scattermodel were incorporated into a convolution-superposition algorithm to provide a fluence model for the HD120 MLC. This fluence model was used to develop a 3D VMAT dose engine, where 3D pre-computed 6MV dose kernels (EGSnrc) and a 3D fluence curvature-correction map were incorporatedto calculate 3D VMAT doses in a 22 cm diameter cylindrical phantom. Four VMAT patient plans witha large range of PTV sizes (36 cc to 604 cc) were chosen to test the fluence model and dose engine. Results. Excellent agreement was observed between the simulated commissioning fields and measured EPID-responses. 2D 2%/2 mm gamma analysis yielded a 98.9% pass rate for 1 cm, 2 cm, and 4 cm sweeping-window fields. 2D 2%/2mm gamma analysis for outer/inner MLC leaves yielded 89.1%/77.0% and 95.2%/91.1% pass rates from 1 cm and 2 cm sweeping-checker fields. Mean 3%/3 mm gamma analysis showed excellent agreement between our dose engine and Eclipse (Acuros) regardless of PTV size: 98.7% pass rate, with 95.1% pass rate in the high-dose volume. Fluence calculation times were13.6 seconds per dynamic MLC field and 1.4 minutes/arc for 3D VMAT dose on a standard PC. Conclusions. A fast-calculating convolution-superposition fluence model has been commissioned for the Siemens HD120 MLC and incorporatedinto a 3D VMAT dose engine. This work can be used to facilitate the development of fast in-house dose-calculating software.

Application of Virtual Avatar using Motion Capture in Immersive Virtual Environment

This study proposes a method for providing various experiential environments and experiences by applying virtual avatars based on motion capture to produce immersive virtual environment. It aims to increase the immersion of virtual environment and provide new experiences, whereby both non-immersive users (using PCs and mobile devices) and immersive users (using virtual reality (VR)) can participate and perform various experiences. To this end, an integrated development environment based on the Unity 3D engine is built. In this environment, immersive content allowing for multi-user participation on cross-platforms and virtual avatars using motion capture can work together flexibly. Further, it defines the real-time streaming processes of movements in a real space as calculated from actors (skeleton data) and motion capture cameras for avatars in a virtual environment. Immersive content that users in the various participating environments can experience together is created and virtual avatars based on actor are applied according to the motion capture. In addition, through survey experiments, the experiential environment that interacts with virtual avatars based on human actor using motion capture and non-player character (NPC) avatars operating according to a predetermined pattern within the produced virtual environment is compared and analyzed. This virtual avatar application method using motion capture provides a high-immersion, and an interesting and positive experience owing to the irregular and unpredictable variety of experiences.

Innovative marketing strategies in dentistry: The potential of the doll as a brand image

The creation of 3D-printed corporate mascots is a complex scientific and social process. It requires the use of 3D printing technologies, engineering, and design, while taking into account ethical, legal, and moral considerations. Creating a unique and personalized mascot allows for an emotional connection with patients. Mascots have the potential to have a significant impact on businesses, including dental clinics. In this sense, they help to create a strong and memorable brand identity. This potential represents an opportunity to use corporate mascots as a marketing element, especially on social media as a distinctive element from the competition.

The 3D model construction method of overhead transmission line based on GIM

With the continuous progress of power grid digitization, Building Information Model (building information modeling, BIM) technology can no longer meet the application display requirements of power grid engineering. Power grid information model (grid information model, GIM) technology uses parameterization to build a 3D model, which reduces redundancy and is conducive to the digital transfer of the project. This paper briefly analyzes the structure of the GIM data model. It carries out research on 3D parametric modeling of overhead transmission line ground wire, tower, foundation, and insulator string based on GIM. It uses a 3D graphics engine to realize the visual rendering of ontology 3D models. On this basis, a 3D scene construction method of overhead transmission line engineering based on GIM is proposed, which provides reliable technical support for digital power grid applications.

Three-dimensional oxygen concentration monitoring in hydrogels using low-cost phosphorescence lifetime imaging for tissue engineering.

Oxygen concentration measurement in 3D hydrogels is vital in 3D cell culture and tissue engineering. However, standard 3D imaging systems capable of measuring oxygen concentration with adequate precision are based on advanced microscopy platforms, which are not accessible in many laboratories due to the system's complexity and the high price. In this work, we present a fast and low-cost phosphorescence lifetime imaging design for measuring the lifetime of oxygen-quenched phosphorescence emission with 0.25 µs temporal precision and sub-millimeter spatial resolution in 3D. By combining light-sheet illumination and the frequency-domain lifetime measurement using a commercial rolling-shutter CMOS camera in the structure of a conventional optical microscope, this design is highly customizable to accommodate application-specific research needs while also being low-cost as compared to advanced instruments. As a demonstration, we made a fluidic device with a gas-permeable film to create an artificial oxygen gradient in the hydrogel sample. Dye-embedded beads were distributed in the hydrogel to conduct continuous emission lifetime monitoring when nitrogen was pumped through the fluidic channel and changed oxygen distribution in the sample. The dynamics of the changes in lifetime co-related with their location in the gel of size 0.5 mm×1.5 mm×700 µm demonstrate the ability of this design to measure the oxygen concentration stably and precisely in 3D samples.

In Vitro and In Vivo Biological Assessments of 3D-Bioprinted Scaffolds for Dental Applications.

Three-dimensional (3D) bioprinting is a unique combination of technological advances in 3D printing and tissue engineering. It has emerged as a promising approach to address the dilemma in current dental treatments faced by clinicians in order to repair or replace injured and diseased tissues. The exploration of 3D bioprinting technology provides high reproducibility and precise control of the bioink containing the desired cells and biomaterial over the architectural and dimensional features of the scaffolds in fabricating functional tissue constructs that are specific to the patient treatment need. In recent years, the dental applications of different 3D bioprinting techniques, types of novel bioinks, and the types of cells used have been extensively explored. Most of the findings noted significant challenges compared to the non-biological 3D printing approach in constructing the bioscaffolds that mimic native tissues. Hence, this review focuses solely on the implementation of 3D bioprinting techniques and strategies based on cell-laden bioinks. It discusses the in vitro applications of 3D-bioprinted scaffolds on cell viabilities, cell functionalities, differentiation ability, and expression of the markers as well as the in vivo evaluations of the implanted bioscaffolds on the animal models for bone, periodontal, dentin, and pulp tissue regeneration. Finally, it outlines some perspectives for future developments in dental applications.

Development of a real-time noise estimation model for construction sites

As construction noise negatively affects the health and quality of life of stakeholders, field managers need to properly monitor and manage noise. Thus, the authors developed a model that estimates real-time noise levels at a construction site and the surroundings to enable preemptive responses to noise-related issues. To accurately estimate noise, necessary field data were collected using an unmanned aerial vehicle (UAV) and noise sensors. The noise estimation model was composed of two sub-models: the noise-customized spatial interpolation model and the noise propagation model. The noise-customized spatial interpolation model was developed to estimate the internal noise of the construction site using a few sensor noise levels. Meanwhile, the noise propagation model was developed to estimate the noise level outside the construction site using internal noise estimation results, obstacles, weather information, and noise sources information. The model was evaluated through field tests at a construction technology demonstration center, environments identical to real construction sites in South Korea. The model showed satisfactory performance, with an accuracy of 96.71% and a root mean square error (RMSE) of 2.62 for the internal construction site noise and an accuracy of 96.03% and an RMSE of 2.70 for outside the construction site. To facilitate the usage of the noise estimation results for field managers, the research team visualized the results using the Unity 3D Engine. The results will enable field managers to assess workers’ long-term noise exposure and respond to potential civil complaints, gearing up to realize environmental, social, and governance (ESG) goals in the construction industry.

Enumeration and Identification of Unique 3D Spatial Topologies of Interconnected Engineering Systems Using Spatial Graphs

Abstract Systematic enumeration and identification of unique 3D spatial topologies (STs) of complex engineering systems (such as automotive cooling systems, electric power trains, satellites, and aero-engines) are essential to navigation of these expansive design spaces with the goal of identifying new spatial configurations that can satisfy challenging system requirements. However, efficient navigation through discrete 3D ST options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. This article presents a new, efficient, and scalable design framework that leverages mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for a generic 3D engineering system, given its system architecture (SA)—its components and their interconnections. First, spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum number of interconnect crossings. Then, corresponding Yamada polynomials for all the planar SGDs are generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, within each topological class, 3D geometric models are generated using the SGDs having different numbers of interconnect crossings. Selected case studies are presented to illustrate the different features of our proposed framework, including an industrial engineering design application: ST enumeration of a 3D automotive fuel cell cooling system (AFCS). Design guidelines are also provided for practicing engineers to aid the application of this framework to different types of real-world problems such as configuration design and spatial packaging optimization.

Overcoming Data Scarcity in Human Activity Recognition.

Wearable sensors have become increasingly popular in recent years, with technological advances leading to cheaper, more widely available, and smaller devices. As a result, there has been a growing interest in applying machine learning techniques for Human Activity Recognition (HAR) in healthcare. These techniques can improve patient care and treatment by accurately detecting and analyzing various activities and behaviors. However, current approaches often require large amounts of labeled data, which can be difficult and time-consuming to obtain. In this study, we propose a new approach that uses synthetic sensor data generated by 3D engines and Generative Adversarial Networks to overcome this obstacle. We evaluate the synthetic data using several methods and compare them to real-world data, including classification results with baseline models. Our results show that synthetic data can improve the performance of deep neural networks, achieving a better F1-score for less complex activities on a known dataset by 8.4% to 73% than state-of-the-art results. However, as we showed in a self-recorded nursing activity dataset of longer duration, this effect diminishes with more complex activities. This research highlights the potential of synthetic sensor data generated from multiple sources to overcome data scarcity in HAR.

Boosting electro-Fenton performance by constructing a large-scale 3D-architected amorphous–nanocrystalline structure

Metallic glasses (MGs) catalysts have shown promise in Fenton-like reactions for environmental remediation. However, the challenge of balancing catalytic activity and recyclability has hindered their practical application. Here, we present a novel approach to fabricate a three-dimensional (3D) amorphous-nanocrystalline catalyst on Cu-foam using thermal spraying, which shows excellent catalytic activity-recyclability synergy. This method enables large-scale production of a 3D porous MG composite (referred to as 3DP MGC) without the need of post-treatment. The architected 3DP MGC exhibits exceptional electro-Fenton activity, surpassing the performance of most Fe-based catalysts reported to date, with a kinetic rate constant 1.4 times higher than that of the amorphous FeSiB MG ribbon. Furthermore, the 3DP MGC catalyst demonstrates excellent reusability, maintaining efficiency for over 60 cycles without decay. The excellent catalytic activity of the 3D-architected MGC is attributed to the galvanic coupling between the MG and α-Fe phases with a low grain boundary effect and accelerated electronic transfer kinetics, facilitating the generation of multiple reactive oxygen species. Additionally, the catalyst benefits from a sustainably active surface enriched with Fe(II) species, resulting in ultrahigh reusability. Finally, we demonstrate the exceptional environmental tolerance of the architected 3DP MGC catalyst, showcasing its high efficiency in degrading real oilfield wastewater and suggesting its potential for the 3D engineering of MG materials in catalytic applications.

3D Printable Gelatin Methacryloyl (GelMA)-Dextran Aqueous Two-Phase System with Tunable Pores Structure and Size Enables Physiological Behavior of Embedded Cells In Vitro.

The restricted porosity of most hydrogels established for in vitro 3D tissue engineering applications limits embedded cells with regard to their physiological spreading, proliferation, and migration behavior. To overcome these confines, porous hydrogels derived from aqueous two-phase systems (ATPS) are an interesting alternative. However, while developing hydrogels with trapped pores is widespread, the design of bicontinuous hydrogels is still challenging. Herein, an ATPS consisting of photo-crosslinkable gelatin methacryloyl (GelMA) and dextran is presented. The phase behavior, monophasic or biphasic, is tuned via the pH and dextran concentration. This, in turn, allows the formation of hydrogels with three distinct microstructures: homogenous nonporous, regular disconnected-pores, and bicontinuous with interconnected-pores. The pore size of the latter two hydrogels can be tuned from ≈4 to 100µm. Cytocompatibility of the generated ATPS hydrogels is confirmed by testing the viability of stromal and tumor cells. Their distribution and growth pattern are cell-type specific but are also strongly defined by the microstructure of the hydrogel. Finally, it is demonstrated that the unique porous structure is sustained when processing the bicontinuous system by inkjet and microextrusion techniques. The proposed ATPS hydrogels hold great potential for 3D tissue engineering applications due to their unique tunable interconnected porosity.

Fabrication and Training of 3D Conductive Polymer Networks for Neuromorphic Wetware

AbstractThe human brain possesses an exceptional information processing capability owing to the 3D and dense network architecture of numerous neurons and synapses. Brain‐inspired neuromorphic hardware can also benefit from 3D architectures, such as high integration of circuits and acquisition of highly complex dynamical systems. In this study, for future 3D neuromorphic engineering, 3D conductive polymer networks consisting of poly(3,4‐ethylenedioxy‐thiophene) doped with poly(styrene sulfonate) anions (PEDOT:PSS) are successfully and stably fabricated between multiple electrodes from scratch in precursor solution by electropolymerization. The networks efficiently emulate the 3D local connections between neighboring neurons observed in the cortex. This novel technology, which allows 3D conductive wiring only between desired electrodes, is unprecedented and has potential as an underlying technology for 3D integration. Furthermore, the experimental results also conclusively prove that conductance modification can be performed by manipulating the physical and chemical properties of 3D branch‐wired conductive polymer wires, thus demonstrating for the first time the feasibility of neuromorphic wetware with enhanced biological plausibility in the subsequent post‐Moore era.

Graph MADDPG with RNN for multiagent cooperative environment.

Multiagent systems face numerous challenges due to environmental uncertainty, with scalability being a critical issue. To address this, we propose a novel multi-agent cooperative model based on a graph attention network. Our approach considers the relationship between agents and continuous action spaces, utilizing graph convolution and recurrent neural networks to define these relationships. Graph convolution is used to define the relationship between agents, while recurrent neural networks define continuous action spaces. We optimize and model the multiagent system by encoding the interaction weights among agents using the graph neural network and the weights between continuous action spaces using the recurrent neural network. We evaluate the performance of our proposed model by conducting experimental simulations using a 3D wargame engine that involves several unmanned air vehicles (UAVs) acting as attackers and radar stations acting as defenders, where both sides have the ability to detect each other. The results demonstrate that our proposed model outperforms the current state-of-the-art methods in terms of scalability, robustness, and learning efficiency.

WebMRI: Brain extraction and linear registration in the web browser

AbstractBackground and AimSince the initial release of the World Wide Web, the capabilities of web browsers have grown from presenting formatted documents to running complex programs, such as 3D game engines. The medical imaging community started to adopt technologies that came with the fifth major version of the HyperText Markup Language (HTML5). It led to the creation of various web-based radiological applications such as cornerstone.js or BrainBrowser. BrainBrowser supports both 3D and 2D rendering of neuroimaging data. However, it cannot run important image processing algorithms, such as brain extraction and linear registration, which are essential in most neuroimaging workflows. The most commonly used library that supports these algorithms is the FMRIB Software Library (FSL). We aim to build a web-based cross-platform neuroimaging platform that combines data visualization with image processing.MethodsWe built our system as an extension of BrainBrowser. We developed WebMRI in JavaScript and designed the user interface using HTML, CSS, and Bootstrap. We used Emscripten to port the brain extraction and linear registration tools of FSL to the web.ResultsWe built WebMRI, a fully web-based extensible neuroimaging platform that combines the visualization capabilities of BrainBrowser with the brain extraction and linear registration tools of FSL by porting them from C++ to WebAssembly. We extended BrainBrowser with a plugin system that makes it easy to bring other processing algorithms into the platform. We released the WebMRI source code on Github: https://github.com/wpmed92/WebMRI.ConclusionsWe developed and released WebMRI, a web-based cross-platform open-source neuroimaging platform.