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Technologies, Volume 7, Issue 3 (September 2019)

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Open AccessArticle
ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things
Technologies 2019, 7(3), 50; https://doi.org/10.3390/technologies7030050
Received: 15 June 2019 / Revised: 11 July 2019 / Accepted: 14 July 2019 / Published: 16 July 2019
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Abstract
Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, [...] Read more.
Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, posing a significant challenge in the study and research of the interaction of indoor positioning and IoT. This paper proposes a new indoor positioning platform, called ThingsLocate, that fills this gap by building upon the popular and flexible ThingSpeak cloud service for IoT, leveraging its data input and data processing capabilities and, most importantly, its native support for cloud execution of Matlab code. ThingsLocate provides a flexible, user-friendly WiFi fingerprinting indoor positioning service for IoT devices, based on Received Signal Strength Indicator (RSSI) information. The key components of ThingsLocate are introduced and described: RSSI channels used by IoT devices to provide WiFi RSSI data, an Analysis app estimating the position of the device, and a Location channel to publish such estimate. A proof-of-concept implementation of ThingsLocate is then introduced, and used to show the possibilities offered by the platform in the context of graduate studies and academic research on indoor positioning for IoT. Results of an experiment enabled by ThingsLocate with limited setup and no coding effort are presented, focusing on the impact of using different devices and different positioning algorithms on positioning accuracy. Full article
(This article belongs to the Special Issue Technology Advances on IoT Learning and Teaching)
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Open AccessArticle
Analysis, Optimization, and Characterization of Magnetic Photonic Crystal Structures and Thin-Film Material Layers
Technologies 2019, 7(3), 49; https://doi.org/10.3390/technologies7030049
Received: 20 May 2019 / Revised: 2 July 2019 / Accepted: 4 July 2019 / Published: 5 July 2019
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Abstract
The development of magnetic photonic crystals (MPC) has been a rapidly evolving research area since the late 1990s. Magneto-optic (MO) materials and the techniques for their characterization have also continually undergone functional and property-related improvements. MPC optimization is a feature-rich Windows software application [...] Read more.
The development of magnetic photonic crystals (MPC) has been a rapidly evolving research area since the late 1990s. Magneto-optic (MO) materials and the techniques for their characterization have also continually undergone functional and property-related improvements. MPC optimization is a feature-rich Windows software application designed to enable researchers to analyze the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. We report on a set of computational algorithms which aim to optimize the design and the optical or magneto-optical spectral analysis of 1D MPC, together with a Windows software implementation. Relevant material property datasets (e.g., the spectral dispersion data for the refractive index, absorption, and gyration) of several important optical and MO materials are included, enabling easy reproduction of the previously published results from the field of MPC-based Faraday rotator development, and an effective demonstration-quality introduction of future users to the multiple features of this package. We also report on the methods and algorithms used to obtain the absorption coefficient spectral dispersion datasets for new materials, where the film thickness, transmission spectrum, and refractive index dispersion function are known. Full article
(This article belongs to the Special Issue Reviews and Advances in Materials Processing)
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Open AccessArticle
Formaldehyde Detection by a Combination of Formaldehyde Dehydrogenase and Chitosan on a Sensor Based on an Organic Field-Effect Transistor
Technologies 2019, 7(3), 48; https://doi.org/10.3390/technologies7030048
Received: 9 June 2019 / Revised: 30 June 2019 / Accepted: 1 July 2019 / Published: 4 July 2019
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Abstract
Formaldehyde is utilized for the preservation of materials due to its strong bactericidal effects. As formaldehyde is also a harmful substance that causes health hazards, the quantitative monitoring of formaldehyde in natural and living environments is desirable. For the rapid and easy detection [...] Read more.
Formaldehyde is utilized for the preservation of materials due to its strong bactericidal effects. As formaldehyde is also a harmful substance that causes health hazards, the quantitative monitoring of formaldehyde in natural and living environments is desirable. For the rapid and easy detection of formaldehyde, in this study we applied an organic field-effect transistor (OFET)-based sensor that can function as a potentiometric device for electrochemical measurements. A polyion-complex gel of formaldehyde dehydrogenase (FDH) and chitosan (CT) was constructed on a gold electrode. When the FDH/CT gel-coated electrode was connected to an OFET device it could detect formaldehyde in an aqueous solution, in which the amino groups of chitosan would protonate during the enzymatic reaction. The limit of detection was calculated to be 3.1 µM (93 ppb), demonstrating the applicability of the film-type OFET sensor to environmental monitoring. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2018))
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Open AccessArticle
Internet of Energy Training through Remote Laboratory Demonstrator
Technologies 2019, 7(3), 47; https://doi.org/10.3390/technologies7030047
Received: 15 May 2019 / Revised: 17 June 2019 / Accepted: 26 June 2019 / Published: 27 June 2019
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Abstract
In this paper, a new learning tool is proposed to train professional figures, such as entrepreneurs, engineers, and technicians, who need to improve their skills in the field of Internet of Energy. The proposed tool aims to cover the lack of experimental knowledge [...] Read more.
In this paper, a new learning tool is proposed to train professional figures, such as entrepreneurs, engineers, and technicians, who need to improve their skills in the field of Internet of Energy. The proposed tool aims to cover the lack of experimental knowledge on new energy systems and to layer proper skills, which are useful to deal with challenges required by smart energy management in the new complex distributed configuration of the electric power systems, characterized by demand response services. This tool is based on a small-scale laboratory demonstrator, representative of a smart rural house, equipped with a measurement and control system. This demonstrator can be remotely accessed, through web server applications based on a low cost single-board computer. Trainers can have direct experience on the main concepts related to smart grids, renewable energy sources, electrochemical storage systems, and electric vehicles, through the use of the proposed tool managed by the web software interface. Full article
(This article belongs to the Special Issue Technology Advances on IoT Learning and Teaching)
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Technologies EISSN 2227-7080 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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