Kondaveeti, Hari Kishan, Kumaravelu, Nandeesh Kumar, Vanambathina, Sunny Dayal, Mathe, Sudha Ellison, and Vappangi, Suseela
Computer Science Review. May, 2021, Vol. 40
Keywords Raspberry Pi; BeagleBone; Sharks Cove; Waspmote Abstract Arduino, an open-source electronics platform, has become the go-to option for anyone working on interactive hardware and software projects. An Arduino board (such as the Uno) connected to a breadboard with plugins such as inputs, sensors, lights, and displays can be controlled by a code written in the Arduino development environment. How to achieve this is by prototyping with Arduino. Prototyping with Arduino has grown in popularity with the increased use of the Arduino platform. Prototyping with Arduino, however, is not an easy task for nonprogrammers with interest in the field. With increased public interest in the field will come a need for accessible information. This paper presents a methodical literature review intended to intensively analyze and compare existing primary studies on prototyping with Arduino. We found about 130 of such studies, all peer-reviewed and published within the last 15 years, including these years (2015--2020). These studies were tediously and carefully chosen through a three-step process. In this paper, a cautious analysis of selected studies was followed by a clear description of the methods applied. The methods were categorized according to the success rate of the studied prototypes. Results obtained can be used in researches on the best technique to adopt while prototyping with Arduino. They can also be used in electronics researches and by individuals who wish to obtain a guide on prototyping with Arduino despite lacking grounded knowledge of the subject matter. Author Affiliation: (a) School of Computer Science & Engineering, VIT-AP University, Beside AP Secretariat, Near Vijayawada, Andhra Pradesh, India (b) School of Electronics Engineering, VIT-AP University, Beside AP Secretariat, Near Vijayawada, Andhra Pradesh, India * Corresponding author. Article History: Received 22 September 2020; Accepted 13 January 2021 Byline: Hari Kishan Kondaveeti [firstname.lastname@example.org] (a,*), Nandeesh Kumar Kumaravelu (b), Sunny Dayal Vanambathina (b), Sudha Ellison Mathe (b), Suseela Vappangi (b)
Journal of Information Systems Education. Summer, 2020, Vol. 31 Issue 3, p179, 8 p.
Teaching -- Usage, Teaching -- Methods, and Teaching -- Study and teaching
Given the ubiquity of interfaces on computing devices, it is essential for future Information Systems (IS) professionals to understand the ramifications of good user interface (UI) design. This article provides instructions on how to efficiently and effectively teach IS students about "fit," a Human-Computer Interaction (HCI) concept, through a paper prototyping activity. Although easy to explain, the concept of "fit" can be difficult to understand without repeated practice. Practically, designing "fit" into UIs can be cost-prohibitive because working prototypes are often beyond students' technical skillset. Accordingly, based on principles of active learning, we show how to use paper prototyping to demonstrate "fit" in a hands-on class exercise. We provide detailed stepby-step instructions to plan, setup, and present the exercise to guide students through the process of "fit" in UI design. As a result of this activity, students are better able to employ both theoretical and practical applications of "fit" in UI design and implementation. This exercise is applicable in any course that includes UI design, such as principles of HCI, systems analysis and design, software engineering, and project management. Keywords: Human-computer interaction (HCI), Paper prototyping, Active learning, Constructionism, Teaching tip 1. INTRODUCTION With computing devices peppering nearly every aspect of our lives, how people interact with these technologies is critically important to all computing fields. In fact, failure to properly [...]
Procedia Computer Science. Jan 1, 2021, Vol. 180, p649.
Machine learning -- Analysis
Keywords Production; Lead Time Prediction; AutoML; Machine Learning Abstract Many Small and Medium Enterprises in the domain of Make-To-Order- and Small-Series-Production struggle with accurately predicting lead times of highly customisable orders. This paper investigates an approach using AutoML integrated into existing enterprise systems in order to enable Lead Time Prediction based on Machine Learning models. This prediction is based on both order data from an ERP system as well as real-time factory state informed by an IIoT platform. We used simulation data to feed the AutoML model generation and developed a lightweight web-based microservice around it to infer lead times of incoming orders during live production. Using industry standards, this microservice can be seamlessly integrated into existing system landscapes. The simplicity of AutoML systems allows for swift (re)training and benchmarking of models but potentially comes at the cost of overall lower model quality. Author Affiliation: (a,b) FZI Research Center for Information Technology, Haid-und-Neu-Strasse 10-14, 76135 Karlsruhe, Germany Byline: Janek Bender [email@example.com] (a), Jivka Ovtcharova (b)