Function Virtualization Research Articles

Optimal resource allocation in practical quantum key distribution optical networks

In the application research of large-scale quantum communication network, one generally realizes resource allocation by constructing virtual service network and mapping it to actual physical space. In this mapping process, some assumptions are often made to simplify the model. For example, the key resource in the physical topology is assumed to be a fixed value, that is, the actual physical conditions and the performance differences of key supply caused by different protocols are ignored. This assumption may lead the network to fail to run appropriately in practical applications. In order to solve the above problems, from the perspective of link mapping, this paper proposes an improved virtual service mapping model and virtual service mapping algorithm with the quantum key distribution optical network as the underlying network, which makes it closer to the actual application scenario. On the one hand, by increasing the constraints of geographical location, the range from virtual nodes to the mappable physical nodes is reasonably restricted. On the other hand, from the perspective of hardware cost and actual key generation rate, the cost performance evaluation index is proposed to allocate and manage resources. In addition, by combining three mainstream quantum key distribution protocols (BB84, measurement-device-independent, and twin-field), we construct a universal virtual service mapping model in the quantum key distribution optical network, and realize the recommendation of the optimal protocol and the optimal allocation and management of resources.

Preliminary Research and Practical Application of BIM Standard System Framework for Underground Engineering

The intelligent transformation has put forward new requirements for the digitization and informatization of underground engineering. BIM can effectively support real feature perception, high-fidelity modelling, virtual mapping, and multi-physical simulation of underground engineering. However, the research on BIM in underground engineering is still in the preliminary exploration stage, and the standard system framework, key theories and technical directions are still not clear enough. Based on literature research, the current status of BIM standard system was analysed, and the preliminary framework for the BIM standard system combined with the characteristics of underground engineering and the needs of intelligent transformation has been established. According to the compilation principles, the standard system framework was divided into three parts: data standards, application standards, and management standards, and the composition of each part has been studied. The applicability of this framework has been demonstrated through an underground engineering example. The aim is to provide reference for the development of BIM theory and technology in underground engineering, and promote the full lifecycle digital information application of underground engineering.

Cyber-Physical Production and Multi-Sensor Fusion Systems, 3D Digital Twin Modeling and Image-based Visual Computing Technologies, and Virtual Machine and Environment Mapping Algorithms in the Immersive Industrial Metaverse

Cyber-Physical Production and Multi-Sensor Fusion Systems, 3D Digital Twin Modeling and Image-based Visual Computing Technologies, and Virtual Machine and Environment Mapping Algorithms in the Immersive Industrial Metaverse

Crosstalk-Aware Virtual Network Mapping Algorithm in Space Division Multiplexing Elastic Optical Networks

Crosstalk-Aware Virtual Network Mapping Algorithm in Space Division Multiplexing Elastic Optical Networks

Virtual Grid Mapping for Visual Size Measurements

This paper proposes a novel, more robust method for single-image visual size measurements, named Virtual Grid Mapping (VGM). VGM requires just a single image as input; however, it does not require any prior information concerning the scene like the horizon line or reference objects. Unlike current methods, the VGM approach uses a grid of virtual 3D points projected to the 2D image plane, along with the fusion of probable depth values indicated by 2D-3D point correspondences, it manages to reduce the uncertainty originating from the calibration and the positioning of the camera; thus, providing more accurate measurements. Given the geometric properties of the camera, a novel approach of VGM is that it automatically generates and projects a grid of virtual 3D points to the 2D image plane, enabling the establishment of approximative correspondences between 3D points of the real world and 2D points of the image plane. Then, by considering these initial 3D-2D point correspondences as known, a range of possible depth values is estimated, and through an adaptive fusion process, the size of an object of interest can be accurately approximated. Another advantage of VGM over the state-of-the-art deep learning -based methods, is that it requires a simple training process. Experiments performed on simulated and real image datasets, captured in controlled conditions and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-the-wild</i> , show that the mean absolute percentage error (MAPE) of the proposed method ranges between 3.13% and 11.66%.

Machine Learning-based Predictive and Virtual Mapping Algorithms, Immersive Metaverse and Holographic Telepresence Technologies, and 3D Generative Modeling and Multiscale Spatial Data Processing Tools in Extended Reality Environments

Machine Learning-based Predictive and Virtual Mapping Algorithms, Immersive Metaverse and Holographic Telepresence Technologies, and 3D Generative Modeling and Multiscale Spatial Data Processing Tools in Extended Reality Environments

Comunicação e pandemia: interlocuções criativas de populações vulnerabilizadas no Distrito Federal

Abstract The article aims to present results of a research that mapped communication technologies and arrangements designed and directed toward vulnerable populations in the territory of the Federal District (DF), Brazil, in the context of the COVID-19 pandemic. The research, which integrated a multicentric investigation, has a qualitative approach and was structured in five stages: virtual mapping of experiences; interviews with their leaders; individual and cross-sectional analysis of empirical material; collective validation of the results by virtual sessions and construction of a thematic map of the experiences. Fourteen initiatives on COVID-19 were mapped in the DF that had a communicational component and were aimed at the population of peripheral territories, with ten being selected for in-depth analysis.The respective leaders were interviewed, composing a thematic cartographic panorama of technologies and communication arrangements: life stories; “word of mouth”; network joints; in-person and organic communication; cards; advocacy; digital social networks; use of territorial equipment as communication tools; independent digital community newspaper; apps; solidarity network and community TV. The commitment of communication strategies and technologies regarding local realities, respecting the characteristics of communities, is highlighted, pointing to a crucial difference in relation to the predominant communication about the pandemic in public health institutions.

Generating Digital Twins for Path-Planning of Autonomous Robots and Drones Using Constrained Homotopic Shrinking for 2D and 3D Environment Modeling

A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. However, common path algorithms are often limited to two tasks and to finding shortest paths. In real applications, not only a short path but also the width of the passage with a path as centered as possible are crucial, since robotic systems are not ideal and require recalibration frequently. In this work, so-called homotopic shrinking is used to generate the digital twin, which can be used to extract all possible path proposals including their passage widths for 2D and 3D environments and multiple tasks and robots. The erosion of the environment is controlled by constraints such that the task stations, the robot or drone positions, and the topology of the environment are considered. Such a deterministic path algorithm can flexibly respond to changing environmental conditions and consider multiple tasks simultaneously for path generation. A distinctive feature of these paths is the central orientation to the non-passable areas, which can have significant benefits for worker and patient safety. The method is tested on 2D and 3D maps with different tasks, obstacles, and multiple robots. For example, the robust generation of the digital twin for a maze and also the dynamic adaptation in case of sudden changes in the environment is covered. This variety of use cases and the comparison with alternative methods result in significant advantages, such as high robustness, consideration of multiple targets, and high safety distances to obstacles and areas that cannot be traversed. Finally, it was shown that the environment for the digital twin can be reduced to reasonable paths by constrained shrinking, both for real 2D maps and for complex virtual 2D and 3D maps.

1921. Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing in Healthcare Settings during the COVID-19 Pandemic

Abstract Background COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coin-sized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1System configuration Results Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers. Disclosures M Cristina Vazquez Guillamet, MD, AUPH: Stocks/Bonds|BNGO: Stocks/Bonds|OCGN: Stocks/Bonds|SESN: Stocks/Bonds.

Decoding the Virtual 2D Map of the Chloroplast Proteomes

BackgroundThe chloroplast is a semi-autonomous organelle having its own genome and corresponding proteome. Although chloroplast genomes have been reported, no reports exist on their corresponding proteomes. Therefore, a proteome-wide analysis of the chloroplast proteomes of 2893 species was conducted, and a virtual 2D map was constructed.ResultsThe resulting virtual 2D map of the chloroplast proteome exhibited a bimodal distribution. The molecular mass of the chloroplast proteome ranged from 0.448 to 616.334 kDa, and the isoelectric point (pI) ranged from 2.854 to 12.954. Chloroplast proteomes were dominated by basic pI proteins with an average pI of 7.852. The molecular weight and isoelectric point of chloroplast proteome were found to show bimodal distribution. Leu was the most abundant and Cys the least abundant amino acid in the chloroplast proteome. Notably, Trp amino acid was absent in the chloroplast protein sequences of Pilostyles aethiopica. In addition, Selenocysteine (Sec) and Pyrrolysine (Pyl) amino acids were also found to be lacking in the chloroplast proteomes.ConclusionThe virtual 2D map and amino acid composition of chloroplast proteome will enable the researchers to understand the biochemistry of chloroplast protein in detail. Further, the amino acid composition of the chloroplast proteome will also allow us to understand the codon usage bias. The codon usage bias and amino acid usage bias of chloroplast will be crucial to understanding their relationship.

Creating the Map of Interactive Services Aiding and Assisting Persons With Disabilities (MSAADA) Project: Tutorial for the Novel Use of a Store Locator App.

An estimated 15% of the global population is living with a disability. In Kenya, children with disabilities remain among the most vulnerable populations, experiencing substantial barriers to wellness and inclusion. Smartphone ownership and internet access have been increasing across sub-Saharan Africa, including in Kenya. Despite these advances, online or mobile resources remain limited and difficult to find and navigate. This paper aims to describe the novel use of a store locator app to develop an interactive map of organizations that provide medical, educational, and socioeconomic resources to individuals with disabilities in Kenya. The target audience is individuals with disabilities, medical professionals, and organization leaders. A comprehensive list of organizations, government county offices, educational assessment and resource centers, and institutions was compiled. Organizations were contacted via email, WhatsApp, or in person for semistructured interviews. Based on the services offered, each organization was assigned categorical search tags. The data were entered into a third-party store locator app. The resulting map was inserted into a page on the Academic Model Providing Access to Healthcare (AMPATH) website. The Map of Interactive Services Aiding and Assisting Persons With Disabilities (MSAADA; this abbreviation is also Swahili for "help") was launched in July 2020 in both English and Swahili. The map included 89 organizations across Kenya. Of these, 51 were reached for an interview (for a 57% response rate). Interviewees cited limited paid staff and dependence on grant-based funding as primary challenges to growth and sustainability. MSAADA is an interactive, virtual map that aims to connect individuals with disabilities, medical professionals, and organization leaders to resources in Kenya. The novel use of a store locator app to compile resources in remote settings has the potential to improve access to health care for a wide variety of specialties and patient populations. Innovators in global health should consider the use of store locator apps to connect individuals to resources in regions with limited mapping.

Optimizing FANET Lifetime for 5G Softwarized Network Provisioning

Recently, Flying Ad Hoc Networks (FANET) have been proposed to empower 5G networks to support complex missions and provide ubiquitous connectivity to heterogeneous devices. However, it is needed to cope with the limited UAV capabilities (e.g. limited available energy to supply engines and computing elements, limited computing capabilities), as well as with the need to provide network and application services as foreseen in highly dynamic and time varying 5G ecosystems. This paper presents for the first time a comprehensive framework that integrates a FANET with a 5G network, with the aim of providing services that can be even chained with each other. This model is comprehensive in the sense that it takes into account physical constraints of the devices, as well as features and requirements of traffic flows. For this framework, the paper proposes a mathematical optimization model, allowing Virtual Function (VF) placement and chaining, aimed at minimizing energy consumption and service unsatisfaction probabilities of the FANET as a whole without employing heuristics for the solution of the problem. Two placement strategies named MLP and WMP are introduced and compared with the standard placement strategy named NoShP. An extensive numerical analysis shows that MLP and WMP allow us to well catch network dynamics and to reduce the number of virtual functions needed while decreasing the power consumption, so increasing UAV flight time and network lifetime.

Dental niche cells directly contribute to tooth reconstitution and morphogenesis.

Mammalian teeth develop from the inductive epithelial-mesenchymal interaction, an important mechanism shared by many organs. The cellular basis for such interaction remains elusive. Here, we generate a dual-fluorescence model to track and analyze dental cells from embryonic to postnatal stages, in which Pitx2+ epithelium and Msx1+ mesenchyme are sufficient for tooth reconstitution. Single-cell RNA sequencing and spatial mapping further revealed critical cellular dynamics during molar development, where tooth germs are organized by Msx1+Sdc1+ dental papilla and surrounding dental niche. Surprisingly, niche cells are more efficient in tooth reconstitution and can directly regenerate papilla cells through interaction with dental epithelium. Finally, from the dental niche, we identify a group of previously unappreciated migratory Msx1+ Sox9+ cells as the potential cell origin for dental papilla. Our results indicate that the dental niche cells directly contribute to tooth organogenesis and provide critical insights into the essential cell composition for tooth engineering.

Engaging clinical end users in the development of an outcome measurement protocol for paediatric communicative health systems

Purpose To develop a conceptual framework of the factors likely to influence clinicians’ use of a new participation-focused outcome measurement protocol in a large paediatric speech-language pathology program. Method A convenience sample of 27 end users (clinicians, managers) were recruited from Ontario, Canada’s Preschool Speech and Language Program. Participants engaged in one virtual concept mapping session in groups of five to six during which they learned about the new protocol, and generated statements in response to a prompt asking them to identify factors that would influence their use of the protocol. Following all sessions, participants asynchronously sorted and rated all statements, and data were analysed using multidimensional scaling and hierarchical cluster analyses. Result Six themes were identified: (1) response from families; (2) use of resources; (3) feasibility and clinical utility; (4) relevance and value-added for clinicians; (5) streamlining policies and guidelines; and (6) delivery, administration, and modification of tool. Response from families, feasibility and clinical utility, and use of resources received the highest importance ratings. Conclusion Concept mapping methodology was used to engage clinicians and managers to identify the barriers to a new implementation protocol for outcome measurement. Results will support future research and implementation efforts.

Double-DQN-Based Path-Tracking Control Algorithm for Orchard Traction Spraying Robot

The precise path-tracking control of tractors and trailers is the key to realizing agricultural automation. In order to improve the path-tracking control accuracy and driving stability of orchard traction spraying robots, this study proposed a navigation path-tracking control algorithm based on Double Deep Q-Network (Double DQN). Drawing on the driver’s driving experience and referring to the principle of radar scanning and the principle of image recognition, a virtual radar model was constructed to generate a virtual radar map. The virtual radar map was used to describe the position relationship between the traction spraying robot and the planned path. Combined with the deep reinforcement learning method, all possible robot driving actions under the current virtual radar map were scored, and the best driving action was selected as the output of the network. In this study, a path-tracking algorithm was self-developed with a deep Q-network trained by driving the traction spraying robot in a simulated virtual environment. The algorithm was tested in both simulations and in a field to follow a typical ‘U’-shaped path. The simulation results showed that the proposed algorithm was able to achieve accurate path-tracking control of the spraying trailer. The field tests showed that when the vehicle speed was 0.36 m/s and 0.75 m/s, the maximum lateral deviation of the algorithm was 0.233 m and 0.266 m, the average lateral deviation was 0.071 m and 0.076 m, and the standard deviation was 0.051 m and 0.057 m, respectively. Compared with the algorithm based on the virtual radar model, the maximum lateral deviation was reduced by 56.37% and 51.54%, the average lateral deviation was reduced by 7.8% and 5.0%, and the standard deviation was reduced by 20.31% and 8.1%, respectively. The results showed that the Double-DQN-based navigation path-tracking control algorithm for the traction spraying robot in the orchard had higher path-tracking accuracy and driving stability, which could meet the actual operational requirements of traditional orchards.

Exploring the Design of a Mixed-Reality 3D Minimap to Enhance Pedestrian Satisfaction in Urban Exploratory Navigation

The development of ubiquitous computing technology and the emergence of XR could provide pedestrian navigation with more options for user interfaces and interactions. In this work, we aim investigate the role of a mixed-reality map interface in urban exploration to enhance pedestrians’ mental satisfaction. We propose a mixed-reality 3D minimap as a part of the navigation interface which pedestrians could refer to and interact during urban exploration. To further explore the different levels of detail of the map interface, we conducted a user study (n = 28, two groups with two tasks). We designed two exploratory activities as experimental tasks with two map modes (a normal one and a simplified one) to discuss the detailed design of the minimap interface. The results indicated that participants showed a positive attitude toward our method. The simplified map mode could result in a lower perceived workload in both tasks while enhancing performance in specific navigation, such as wayfinding. However, we also found that pedestrians’ preference for the level of detail of the minimap interface is dynamic in navigation. Thus, we suggest discussing the different levels of detail further in specific scenarios. Finally, we also summarize some findings observed during user study for inspiring the study of virtual map interface of future mixed-reality navigation for urban exploration in various scenarios.

Genetic algorithm enabled virtual multicast tree embedding in Software-Defined Networks

The recent network virtualization technology enables the multi-tenancy, where various virtual network requests can share the same physical network by decoupling network services from the underlying hardware architecture. The process of virtual node and link mapping onto a shared Substrate Network (SN) by satisfying the requested network resources (i.e., bandwidth, computing capacity, etc.) is referred to as Virtual Network Embedding (VNE), which is known as an NP-Hard problem. The problem of VNE aims to exhibit one-to-one (unicast) communication. However, the motivation of this research is to explore how to efficiently map virtual networks with one-to-many (multicast) communications, which are in the form of Virtual Multicast Trees (VMTs), onto an SN. This problem differs from the traditional VNE problem and has not been well-studied by the research community. To this end, we propose a novel algorithm, Modified Genetic Algorithm for Virtual Multicast Tree Embedding (MGA-VMTE), to embed VMTs onto a shared SN in this research. The proposed MGA-VMTE algorithm focuses on minimizing the network resource consumption (i.e., bandwidth) under end-to-end delay constraint in the SN while satisfying the computing request of virtual nodes. Our extensive simulations demonstrate that the MGA-VMTE algorithm outperforms the dynamic impact factor and traditional greedy-based virtual multicast tree embedding approaches regarding bandwidth consumption, acceptance ratio, and resource depletion ratio metrics on NSFNET, USNET, Random-60 node, and Random-120 node network topologies.

Building façade element extraction based on multidimensional virtual semantic feature map ensemble learning and hierarchical clustering

• Definition of multidimensional semantic features of the building façade. • Multidimensional virtual semantic feature map are constructed based on Multiple View Stereo techniques. • Ensemble-learning are used for multi-semantic feature block pre-segmentation. • Color&Texture, orientation and curvature semantics segmentation. • Different virtual semantic feature map and clustering strategies are combined to accurately extract of diverse building façade elements. Building façade elements are an important foundation for smart cities. As buildings exhibit an array of textures and geometric forms, the process of image acquisition is easily affected, although the robustness of texture in scenes (e.g., dilapidated buildings) is poor, with high point cloud data, and low recognition efficiency; therefore, the accuracy of building element extraction based on a single data source remains limited. In this research, a method for building façade element extraction based on multidimensional virtual semantic feature map ensemble learning and hierarchical clustering is proposed. Point clouds were obtained by multi-view images, and then the multidimensional virtual semantic feature maps, including color, texture, orientation, and curvature semantics, were acquired via reprojection. The multi-semantic feature block pre-segmentation, considering multiple features, was obtained by ensemble learning, and a hierarchical clustering strategy was established for to achieve fine extraction of building façade elements. Experiments were conducted across multiple building types, and the results showed that: 1) The method can use different virtual semantic feature map and clustering strategies to achieve accurate extraction of diverse building façade elements; 2) The method achieved joint learning tasks in both 2D and 3D space; and, 3) The proposed method achieved fine extraction of building elements with pixel accuracy (PA) over 70% in all experiments and mean intersection over union (mIoU) up to 95%, which were better than the image based method. In summary, this method offers a novel, more reliable method for segmenting and extracting building façade elements, which has important theoretical and practical significance.

Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization

Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round communications of multi-dimensional model updates between local participants and the central parameter server. With an elastic edge-assisted aggregation procedure, the burden of backbone congestion can be handled for multi-service model communications. To provide an adaptive virtual resource orchestration, the integration of software-defined networking (SDN), network functions virtualization (NFV), EC, and double deep q-networks is emerged, which accurately gathers the state observation from local and edge nodes in data plane and NFV infrastructure (NFVI) for applying actions of allocated resource predetermination and recommended offloading policy. The reward formulation considers the minimization of completion time, energy consumption, and round communications between local participants and the selected NFV-enabled EC (NFVeEC) node until the global model is constructed with satisfying accuracy. In this paper, we proposed a decentralized SDN controller as an agent to enable a softwarization mechanism and interact with the virtualization-based EFL environment. The architecture adoption, agent controller, and orchestrator allow the centralized view for virtual network functions placement with efficient virtual machine mapping to perform edge update procedures and create the forwarding path effectively. The recommended NFVeEC for local-EFL updates is based on reward output and proposed agent valuation.

Asset mapping 2.0; contextual, iterative, and virtual mapping for community development

ABSTRACT We argue a re-appraisal of asset mapping is needed based on revisiting the concept of assets. Asset mapping is useful for inter/trans-disciplinary work involving complex systems: organizations, administrations, governance systems, social-ecological systems, etc. Asset mapping can be an integrative method, allowing a combination of different disciplinary insights and knowledge types; co-defining what is valuable in and for a system. We propose a new version of asset mapping that combines contextual, iterative, and virtual asset mapping in different manners depending on the system and situation. The unpredictable character of co-evolution makes iterative asset mapping important, contextual asset mapping allows different delineations of relevant contexts, and virtual asset mapping entails recognizing assets in different futures, either scenario-based or as strategy options. We argue that this novel approach is particularly important for planning, in the broad sense, because it provides a bridging opportunity with other fields, connecting discourses and policy.