ABSTRACT
In recent years, Internet of Things(IoT) has become popular, the requirements for sharing the operation/mechanisms of IoT devices, such as Arduino and M5Stack are increasing. Moreover, owing to the coronavirus pandemic, many educational institutions have adopted online lectures, such as on-demand classes and online classes using video conference systems. For IoT programming education, these methods have challenges, such as a lack of linkage with real-world devices and source codes. In this study, we propose a system called "IoTeach", which supports the learning of IoT programming by attaching a scripting language to sequential contents, such as videos and slides shared on the Web. The IoTeach can link videos and slides with real-world IoT devices and source codes. We describe the concept and implementation of the system in this study. © 2023 Owner/Author.
ABSTRACT
In this study, we explore the use of echelon analysis and its software named EcheScan for spatial lattice data. EcheScan is developed as a web application via an internet browser in R language and Shiny server for echelon analysis. The technique of echelon is proposed to analyze the topological structure for spatial lattice data. The echelon tree provides a dendrogram representation. Regional features, such as hierarchical spatial data structure and hotspots clusters, are shown in an echelon dendrogram. In addition, we introduce the conception of echelon with the values and neighbors for lattice data. We also explain the use of EcheScan for one- and two-dimensional regular lattice data. Furthermore, coronavirus disease 2019 death data corresponding to 50 US states are illustrated using EcheScan as an example of geospatial lattice data. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Data: Types and Structure > Image and Spatial Data. © 2022 The Authors. WIREs Computational Statistics published by Wiley Periodicals LLC.
ABSTRACT
In this study, we explore the use of echelon analysis and its software named EcheScan for spatial lattice data. EcheScan is developed as a web application via an internet browser in R language and Shiny server for echelon analysis. The technique of echelon is proposed to analyze the topological structure for spatial lattice data. The echelon tree provides a dendrogram representation. Regional features, such as hierarchical spatial data structure and hotspots clusters, are shown in an echelon dendrogram. In addition, we introduce the conception of echelon with the values and neighbors for lattice data. We also explain the use of EcheScan for one- and two-dimensional regular lattice data. Furthermore, coronavirus disease 2019 death data corresponding to 50 US states are illustrated using EcheScan as an example of geospatial lattice data. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Data: Types and Structure > Image and Spatial Data. © 2022 The Authors. WIREs Computational Statistics published by Wiley Periodicals LLC.