The social needs that require the support of robotics are increasingly evident. Tasks such as caring for children and the elderly in isolated, unsupervised homes have become evident following the demands of social isolation caused by the COVID-19 pandemic. Service robotics is presented as a current and readily available solution for the remote monitoring of people, particularly those with medical restrictions or in education processes. However, the development of these applications requires a critical mass of professional personnel trained in the design of these robots and their navigation and manipulation schemes. Current robotic systems tend to be expensive, so their availability at the university level is limited. Its use aims to be dedicated to research, leaving aside the processes of training at the undergraduate level. A very efficient way to develop a specialized training in robotics for young researchers is through the use of software tools that simulate existing robotic models in the laboratory. This article presents the formulation and development of a software tool designed to train young researchers within the research group. The tool is the first of a knowledge management system that the research group has proposed to encourage and accelerate research in service robotics. This first tool focuses on the problem of autonomous planning of movement in globally observable environments, similar to those normally foreseen for service robots. Specifically, it implements the algorithm of visibility graphs to define the navigation route of a robot from a point of origin to a point of destination. As result, it is presented the final operation of the tool, its advantages in terms of configuration and manipulation, and its capacity to evaluate the performance in front of different variables of the problem. This article is developed as part of the group's research in mobile robotics and image processing, specifically related to the training of junior researchers. © 2021 Little Lion Scientific.
Spectrum valuation is significant in helping policymakers prepare new technology regulations to address unique circumstances, such as location-specific services in industrial areas with larger spectrum bands. This paper presents the results of a study on the economic valuation of 5G spectrum at millimeter wave (mmWave) for accelerating broadband development. It uses a case study of industrial areas in Indonesia, focusing on the effects of frequency bands of 26 GHz and 28 GHz on three factors of an engineering-economic model: maximal cellular coverage, cost per square kilometer (in terms of capital expenditures (CAPEX) and Operational expenditures (OPEX)), and spectrum value per MHz population. The results showed that the mmWave utilization for 5G services requires higher infrastructure expenses. Population density also has a significant influence on spectrum valuation in both lower and higher frequency bands.
The novel coronavirus (SARS-CoV-2) pandemic has resulted in the worldwide closure of educational institutions such as universities. Accordingly, teaching in most countries now takes place remotely via digital platforms. As education moves from conventional to online instruction, student performance is a key concern for management because numerous factors could affect learning. This study proposes to evaluate the attributes of distance learning that could affect student performance and then adopt a classification algorithm to recommend improvements to higher education organizations. The relevant educational data for the algorithm were gathered in the 2020-2021 academic year from student surveys in four classes at the College of Sciences and Arts at Qassim University (Unaizah, Kingdom of Saudi Arabia). The data were then subjected to decision tree analysis, which generated a model based on 66 classification rules. The results of the present study showed that the model developed using data-mining rule-based classifiers is efficient for predicting a student's final course grade. In order to evaluate the accurate of the correctly classified instances;three different classification methods were tested, i.e., Bayes Net-D, naive Bayes, and J48. As a percentage of the correctly identified cases using the three separate algorithms, the overall accuracies of the evaluation results were 90.2439%, 87.8049% and 95.0617 % respectively. © 2021 Little Lion Scientific. All rights reserved.
BCI has been an alternative method of communication between a user and a system
The COVID-19 pandemic has brought much challenge and disruption to the lives of students in elementary through high schools, their families, and their communities. Most students cannot attend schools due to the increased spread of COVID-19, and they learn online from home. This paper aims to develop an augmented reality learning model that delivers information on Komodo dragons that can be accessed using smartphones. The developing augmented reality application uses the Augmented Reality Development Method. It can be that the augmented reality learning model would be easier, more fun, and interesting for students in conducting online learning the New Normal of COVID-19 Pandemic. The research findings show that the learning model is useful to help teachers since it can be used for independent learning and motivates children to learn in COVID-19 pandemic. © 2021 Little Lion Scientific. All rights reserved.
