Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

An Approach to Air-to-Surface Mission Planner on 3D Environments for an Unmanned Combat Aerial Vehicle

Recently, interest in mission autonomy related to Unmanned Combat Aerial Vehicles(UCAVs) for performing highly dangerous Air-to-Surface Missions(ASMs) has been increasing. Regarding autonomous mission planners, studies currently being conducted in this field have been mainly focused on creating a path from a macroscopic 2D environment to a dense target area or proposing a route for intercepting a target. For further improvement, this paper treats a mission planning algorithm on an ASM which can plan the path to the target dense area in consideration of threats spread in a 3D terrain environment while planning the shortest path to intercept multiple targets. To do so, ASMs are considered three sequential mission elements: ingress, intercept, and egress. The ingress and egress elements require a terrain flight path to penetrate deep into the enemy territory. Thus, the proposed terrain flight path planner generates a nap-of-the-earth path to avoid detection by enemy radar while avoiding enemy air defense threats. In the intercept element, the shortest intercept path planner based on the Dubins path concept combined with nonlinear programming is developed to minimize exposure time for survivability. Finally, the integrated ASM planner is applied to several mission scenarios and validated by simulations using a rotorcraft model.

An Occupancy Mapping Method Based on K-Nearest Neighbours

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.

Push or pull: how cytoskeletal crosstalk facilitates nuclear movement through 3D environments

As cells move from two-dimensional (2D) surfaces into complex 3D environments, the nucleus becomes a barrier to movement due to its size and rigidity. Therefore, moving the nucleus is a key step in 3D cell migration. In this review, we discuss how coordination between cytoskeletal and nucleoskeletal networks is required to pull the nucleus forward through complex 3D spaces. We summarize recent migration models which utilize unique molecular crosstalk to drive nuclear migration through different 3D environments. In addition, we speculate about the role of proteins that indirectly crosslink cytoskeletal networks and the role of 3D focal adhesions and how these protein complexes may drive 3D nuclear migration.

3D environment approach to teaching and learning business process management concepts: a systematic literature review

Although efficient process management is a factor that impacts the competitiveness of organizations, undergraduate students entering the employment market still have difficulties to understand and perform functions related to BPM, which indicates obstacles in teaching and learning these concepts. Using innovative approaches such as 3D environments can bring pedagogical resources to complement BPM training. This work performs a systematic literature review to investigate approaches using 3D environments in BPM courses and training to find their impact on learning and teaching. Studies suggest that 3D environments can positively affect the BPM teaching and learning process and impact student motivation, keeping them connected to the study material.

Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments, Multiscale Models, and Spatial Validation

Throughout the years, mathematical models of cancer growth have become increasingly more accurate in terms of the description of cancer growth in both space and time. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In this study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows the calibration of multiscale-spatiotemporal models of cancer growth using 3D cell culture data, and their validation based on the morphological patterns. The implementation of this framework enables us to pursue two goals; first, the quantitative description of the morphology of cancer progression in 3D cultures, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth. We apply this framework to the study of the spatiotemporal progression of Triple Negative Breast Cancer (TNBC) cells cultured in 3D Matrigel scaffolds, under the hypothesis of chemotactic migration using a multiscale Keller-Segel model. The results reveal transient, non-random spatial distributions of cancer cells that consist of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the proposed model was able to describe the general characteristics of the experimental observations and suggests that cancer cells exhibited chemotactic migration and cell accumulation, as well as random motion throughout the period of development. To our knowledge, this is the first time a framework attempts to quantify the relationship of the spatial patterns and the underlying mechanisms of cancer growth in 3D environments.

Investigation of SARS-CoV-2 inactivation using UV-C LEDs in public environments via ray-tracing simulation

This paper proposes an investigating SARS-CoV-2 inactivation on surfaces with UV-C LED irradiation using our in-house-developed ray-tracing simulator. The results are benchmarked with experiments and Zemax OpticStudio commercial software simulation to demonstrate our simulator's easy accessibility and high reliability. The tool can input the radiant profile of the flexible LED source and accurately yield the irradiance distribution emitted from an LED-based system in 3D environments. The UV-C operating space can be divided into the safe, buffer, and germicidal zones for setting up a UV-C LED system. Based on the published measurement data, the level of SARS-CoV-2 inactivation has been defined as a function of UV-C irradiation. A realistic case of public space, i.e., a food court in Singapore, has been numerically investigated to demonstrate the relative impact of environmental UV-C attenuation on the SARS-CoV-2 inactivation. We optimise a specific UV-C LED germicidal system and its corresponding exposure time according to the simulation results. These ray-tracing-based simulations provide a useful guideline for safe deployment and efficient design for germicidal UV-C LED technology.

A scalable coaxial bioprinting technology for mesenchymal stem cell microfiber fabrication and high extracellular vesicle yield

Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising candidates for regenerative medicine; however, the lack of scalable methods for high quantity EV production limits their application. In addition, signature EV-derived proteins shared in 3D environments and 2D surfaces, remain mostly unknown. Herein, we present a platform combining MSC microfiber culture with ultracentrifugation purification for high EV yield. Within this platform, a high quantity MSC solution (~3x10^8 total cells) is encapsulated in a meter-long hollow hydrogel-microfiber via coaxial bioprinting technology. In this 3D core-shell microfiber environment, MSCs express higher levels of stemness markers (Oct4, Nanog, Sox2) than in 2D culture, and maintain their differentiation capacity. Moreover, this platform enriches particles by ~1009-fold compared to conventional 2D culture, while preserving their pro-angiogenic properties. Liquid chromatography-mass spectrometry characterization results demonstrate that EVs derived from our platform and conventional 2D culturing have unique protein profiles with 3D-EVs having a greater variety of proteins (1023 vs 605), however, they also share certain proteins (536) and signature MSC-EV proteins (10). This platform, therefore, provides a new tool for EV production using microfibers in one culture dish, thereby reducing space, labor, time, and cost.

Opal: An open source ray-tracing propagation simulator for electromagnetic characterization

Accurate characterization and simulation of electromagnetic propagation can be obtained by ray-tracing methods, which are based on a high frequency approximation to the Maxwell equations and describe the propagating field as a set of propagating rays, reflecting, diffracting and scattering over environment elements. However, this approach has been usually too computationally costly to be used in large and dynamic scenarios, but this situation is changing thanks the increasing availability of efficient ray-tracing libraries for graphical processing units. In this paper we present Opal, an electromagnetic propagation simulation tool implemented with ray-tracing on graphical processing units, which is part of the Veneris framework. Opal can be used as a stand-alone ray-tracing simulator, but its main strength lies in its integration with the game engine, which allows to generate customized 3D environments quickly and intuitively. We describe its most relevant features and provide implementation details, highlighting the different simulation types it supports and its extension possibilites. We provide application examples and validate the simulation on demanding scenarios, such as tunnels, where we compare the results with theoretical solutions and further discuss the tradeoffs between the simulation types and its performance.