Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping.

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.

Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT.

Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols.

Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces.

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed.

Standing strong amid a pandemic: How a global online team project stands up to the public health crisis.

The annual instructional virtual team Project X brings together professors and students from across the globe to engage in client projects. The 2020 project was challenged by the global disruption of the COVID-19 pandemic. This paper draws on a quantitative dataset from a post-project survey among 500 participating students and a qualitative narrative inquiry of personal experiences of the faculty members.

Risk management via contemporaneous and temporal dependence structures with applications.

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models.

Mapping the audit traces of interdisciplinary collaboration: bridging and blending between choreography and cognitive science.

Two long-term sci-art research projects are described and positioned in the broader conceptual landscape of interdisciplinary collaboration. Both projects were aimed at understanding and augmenting choreographic decision-making and both were grounded in research conducted within a leading contemporary dance company. In each case, the work drew upon methods and theory from the cognitive sciences, and both had a direct impact on the way in which the company made new work.