Special Issue "Raspberry Pi Technology"

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editors

Guest Editor
Prof. Simon J. Cox

Faculty of Engineering and the Environment University of Southampton, UK
Website | E-Mail
Interests: High Performance Computing and Data; Internet of Things; computational methods; algorithms; commodity computing
Guest Editor
Dr. Steven J Johnston

Faculty of Engineering and the Environment University of Southampton, UK
Website | E-Mail
Interests: Internet of Things; Cloud computing; Embedded devices and sensors

Special Issue Information

Dear Colleagues,

The Raspberry Pi has been very influential in introducing embedded systems to a more general audience, resulting in their novel use in a wide range of applications and areas. The aim of this Special Issue is to capture and present feature and scholarly papers from a wide range of topics related to Electronics.

These topics include but are not limited to: Internet of things devices and gateways; low cost and disposable compute; sensor networks; remote sensing; low power solutions; non-traditional security devices; industrial and scientific/engineering applications and any other related novel applications of the Raspberry Pi eco system.

We invite scientists and researchers from all fields of electronics, computer science, and applied science and engineering fields to submit papers for this Special Issue of Electronics. Case studies, reviews, and research papers on all topics related to the novel use of the Raspberry Pi are invited.

Prof. Simon J. Cox
Dr. Steven J Johnston
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things applications and drivers
  • Low cost devices
  • Sensor networks
  • Remote sensing
  • Low power solutions
  • Evaluation and testbeds
  • Novel and unique ways of using a Raspberry Pi

Published Papers (19 papers)

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Editorial

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Open AccessEditorial The Raspberry Pi: A Technology Disrupter, and the Enabler of Dreams
Electronics 2017, 6(3), 51; doi:10.3390/electronics6030051
Received: 4 July 2017 / Revised: 4 July 2017 / Accepted: 6 July 2017 / Published: 12 July 2017
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(This article belongs to the Special Issue Raspberry Pi Technology)
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Research

Jump to: Editorial

Open AccessArticle A Miniature Data Repository on a Raspberry Pi
Electronics 2017, 6(1), 1; doi:10.3390/electronics6010001
Received: 22 September 2016 / Revised: 14 December 2016 / Accepted: 15 December 2016 / Published: 28 December 2016
Cited by 1 | PDF Full-text (8109 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This work demonstrates a low-cost, miniature data repository proof-of-concept. Such a system needs to be resilient to power and network failures, and expose adequate processing power for persistent, long-term storage. Additional services are required for interoperable data sharing and visualization. We designed and
[...] Read more.
This work demonstrates a low-cost, miniature data repository proof-of-concept. Such a system needs to be resilient to power and network failures, and expose adequate processing power for persistent, long-term storage. Additional services are required for interoperable data sharing and visualization. We designed and implemented a software tool called Airchive to run on a Raspberry Pi, in order to assemble a data repository for archiving and openly sharing timeseries data. Airchive employs a relational database for storing data and implements two standards for sharing data (namely the Sensor Observation Service by the Open Geospatial Consortium and the Protocol for Metadata Harvesting by the Open Archives Initiative). The system is demonstrated in a realistic indoor air pollution data acquisition scenario in a four-month experiment evaluating its autonomy and robustness under power and network disruptions. A stress test was also conducted to evaluate its performance against concurrent client requests. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle On the Application of the Raspberry Pi as an Advanced Acoustic Sensor Network for Noise Monitoring
Electronics 2016, 5(4), 74; doi:10.3390/electronics5040074
Received: 12 July 2016 / Revised: 6 October 2016 / Accepted: 20 October 2016 / Published: 27 October 2016
Cited by 2 | PDF Full-text (5028 KB) | HTML Full-text | XML Full-text
Abstract
The concept of Smart Cities and the monitoring of environmental parameters is an area of research that has attracted scientific attention during the last decade. These environmental parameters are well-known as important factors in their affection towards people. Massive monitoring of this kind
[...] Read more.
The concept of Smart Cities and the monitoring of environmental parameters is an area of research that has attracted scientific attention during the last decade. These environmental parameters are well-known as important factors in their affection towards people. Massive monitoring of this kind of parameters in cities is an expensive and complex task. Recent technologies of low-cost computing and low-power devices have opened researchers to a wide and more accessible research field, developing monitoring devices for deploying Wireless Sensor Networks. Gathering information from them, improved urban plans could be carried out and the information could help citizens. In this work, the prototyping of a low-cost acoustic sensor based on the Raspberry Pi platform for its use in the analysis of the sound field is described. The device is also connected to the cloud to share results in real time. The computation resources of the Raspberry Pi allow treating high quality audio for calculating acoustic parameters. A pilot test was carried out with the installation of two acoustic devices in the refurbishment works of a neighbourhood. In this deployment, the evaluation of these devices through long-term measurements was carried out, obtaining several acoustic parameters in real time for its broadcasting and study. This test has shown the Raspberry Pi as a powerful and affordable computing core of a low-cost device, but also the pilot test has served as a query tool for the inhabitants of the neighbourhood to be more aware about the noise in their own place of residence. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle AgPi: Agents on Raspberry Pi
Electronics 2016, 5(4), 72; doi:10.3390/electronics5040072
Received: 4 June 2016 / Revised: 23 August 2016 / Accepted: 30 September 2016 / Published: 19 October 2016
Cited by 1 | PDF Full-text (3525 KB) | HTML Full-text | XML Full-text
Abstract
The Raspberry Pi and its variants have brought with them an aura of change in the world of embedded systems. With their impressive computation and communication capabilities and low footprint, these devices have thrown open the possibility of realizing a network of things
[...] Read more.
The Raspberry Pi and its variants have brought with them an aura of change in the world of embedded systems. With their impressive computation and communication capabilities and low footprint, these devices have thrown open the possibility of realizing a network of things in a very cost-effective manner. While such networks offer good solutions to prominent issues, they are indeed a long way from being smart or intelligent. Most of the currently available implementations of such a network of devices involve a centralized cloud-based server that contributes to making the necessary intelligent decisions, leaving these devices fairly underutilized. Though this paradigm provides for an easy and rapid solution, they have limited scalability, are less robust and at times prove to be expensive. In this paper, we introduce the concept of Agents on Raspberry Pi (AgPi) as a cyber solution to enhance the smartness and flexibility of such embedded networks of physical devices in a decentralized manner. The use of a Multi-Agent System (MAS) running on Raspberry Pis aids agents, both static and mobile, to govern the various activities within the network. Agents can act autonomously or on behalf of a human user and can collaborate, learn, adapt and act, thus contributing to embedded intelligence. This paper describes how Tartarus, a multi-agent platform, embedded on Raspberry Pis that constitute a network, can bring the best out of the system. To reveal the versatility of the concept of AgPi, an application for a Location-Aware and Tracking Service (LATS) is presented. The results obtained from a comparison of data transfer cost between the conventional cloud-based approach with AgPi have also been included. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Easy as Pi: A Network Coding Raspberry Pi Testbed
Electronics 2016, 5(4), 67; doi:10.3390/electronics5040067
Received: 19 July 2016 / Revised: 18 September 2016 / Accepted: 28 September 2016 / Published: 13 October 2016
Cited by 1 | PDF Full-text (362 KB) | HTML Full-text | XML Full-text
Abstract
In the near future, upcoming communications and storage networks are expected to tolerate major difficulties produced by huge amounts of data being generated from the Internet of Things (IoT). For these types of networks, strategies and mechanisms based on network coding have appeared
[...] Read more.
In the near future, upcoming communications and storage networks are expected to tolerate major difficulties produced by huge amounts of data being generated from the Internet of Things (IoT). For these types of networks, strategies and mechanisms based on network coding have appeared as an alternative to overcome these difficulties in a holistic manner, e.g., without sacrificing the benefit of a given network metric when improving another. There has been recurrent issues on: (i) making large-scale deployments akin to the Internet of Things; (ii) assessing and (iii) replicating the obtained results in preliminary studies. Therefore, finding testbeds that can deal with large-scale deployments and not lose historic data in order to evaluate these mechanisms are greatly needed and desirable from a research perspective. However, this can be hard to manage, not only due to the inherent costs of the hardware, but also due to maintenance challenges. In this paper, we present the required key steps to design, setup and maintain an inexpensive testbed using Raspberry Pi devices for communications and storage networks with network coding capabilities. This testbed can be utilized for any applications requiring results replicability. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi
Electronics 2016, 5(4), 66; doi:10.3390/electronics5040066
Received: 30 June 2016 / Revised: 16 August 2016 / Accepted: 29 August 2016 / Published: 13 October 2016
Cited by 1 | PDF Full-text (3148 KB) | HTML Full-text | XML Full-text
Abstract
Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense,
[...] Read more.
Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense, the Raspberry Pi has been a popular network node choice due to its reduced size, processing capabilities, low cost and its support by widely-used operating systems. For information transport, network coding is a new paradigm for fast and reliable data processing in networking and storage systems, which overcomes various limitations of state-of-the-art routing techniques. Therefore, in this work, we provide an in-depth performance evaluation of Random Linear Network Coding (RLNC)-based schemes for the Raspberry Pi Models 1 and 2, by showing the processing speed of the encoding and decoding operations and the corresponding energy consumption. Our results show that, in several scenarios, processing speeds of more than 80 Mbps in the Raspberry Pi Model 1 and 800 Mbps in the Raspberry Pi Model 2 are attainable. Moreover, we show that the processing energy per bit for network coding is below 1 nJ or even an order of magnitude less in these scenarios. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle A New Power Quality Instrument Based on Raspberry-Pi
Electronics 2016, 5(4), 64; doi:10.3390/electronics5040064
Received: 30 June 2016 / Revised: 14 September 2016 / Accepted: 19 September 2016 / Published: 27 September 2016
Cited by 2 | PDF Full-text (5458 KB) | HTML Full-text | XML Full-text
Abstract
This article describes a new instrument for power quality (PQ) measurements based on the Raspberry-Pi. This is the latest step of a long study started by the Electric and Electronic Measurements Laboratory of “Roma Tre” University 12 years ago. During this time, the
[...] Read more.
This article describes a new instrument for power quality (PQ) measurements based on the Raspberry-Pi. This is the latest step of a long study started by the Electric and Electronic Measurements Laboratory of “Roma Tre” University 12 years ago. During this time, the Laboratory developed and refined instrumentation for high accuracy power quality measurements. Through its own architecture, the new instrument allows the use of the Raspberry instead of a personal computer (PC). The data acquired and locally processed are then sent to a remote server where they can be shown to users. Imagines of the system and of the data prove the activity of the system. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement
Electronics 2016, 5(4), 61; doi:10.3390/electronics5040061
Received: 30 April 2016 / Revised: 9 September 2016 / Accepted: 13 September 2016 / Published: 23 September 2016
Cited by 2 | PDF Full-text (507 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Power consumption has become an increasingly important metric when building large supercomputing clusters. One way to reduce power usage in large clusters is to use low-power embedded processors rather than the more typical high-end server CPUs (central processing units). We investigate various power-related
[...] Read more.
Power consumption has become an increasingly important metric when building large supercomputing clusters. One way to reduce power usage in large clusters is to use low-power embedded processors rather than the more typical high-end server CPUs (central processing units). We investigate various power-related metrics for seventeen different embedded ARM development boards in order to judge the appropriateness of using them in a computing cluster. We then build a custom cluster out of Raspberry Pi boards, which is specially designed for per-node detailed power measurement. In addition to serving as an embedded cluster testbed, our cluster’s power measurement, visualization and thermal features make it an excellent low-cost platform for education and experimentation. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Low Delay Video Streaming on the Internet of Things Using Raspberry Pi
Electronics 2016, 5(3), 60; doi:10.3390/electronics5030060
Received: 29 April 2016 / Revised: 7 September 2016 / Accepted: 13 September 2016 / Published: 20 September 2016
Cited by 1 | PDF Full-text (967 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist of various multimedia contents, such as live audio
[...] Read more.
The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist of various multimedia contents, such as live audio and video. This article presents a solution for the communication of high definition video in low-delay scenarios (<200 ms) under the constraints of devices with limited hardware resources, such as the Raspberry Pi. We verify that it is possible to enable low delay video streaming between Raspberry Pi devices using a distributed Internet of Things system called the SensibleThings platform. Specifically, our implementation transfers a 6 Mbps H.264 video stream of 1280 × 720 pixels at 25 frames per second between devices with a total delay of 181 ms on the public Internet, of which the overhead of the distributed Internet of Things communication platform only accounts for 18 ms of this delay. We have found that the most significant bottleneck of video transfer on limited Internet of Things devices is the video coding and not the distributed communication platform, since the video coding accounts for 90% of the total delay. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Monitoring and Analyzing of Circadian and Ultradian Locomotor Activity Based on Raspberry-Pi
Electronics 2016, 5(3), 58; doi:10.3390/electronics5030058
Received: 1 June 2016 / Revised: 24 August 2016 / Accepted: 12 September 2016 / Published: 15 September 2016
Cited by 2 | PDF Full-text (9290 KB) | HTML Full-text | XML Full-text
Abstract
A new device based on the Raspberry-Pi to monitor the locomotion of Arctic marine invertebrates and to analyze chronobiologic data has been made, tested and deployed. The device uses infrared sensors to monitor and record the locomotor activity of the animals, which is
[...] Read more.
A new device based on the Raspberry-Pi to monitor the locomotion of Arctic marine invertebrates and to analyze chronobiologic data has been made, tested and deployed. The device uses infrared sensors to monitor and record the locomotor activity of the animals, which is later analyzed. The software package consists of two separate scripts: the first designed to manage the acquisition and the evolution of the experiment, the second designed to generate actograms and perform various analyses to detect periodicity in the data (e.g., Fourier power spectra, chi-squared periodograms, and Lomb–Scargle periodograms). The data acquisition hardware and the software has been previously tested during an Arctic mission with an arctic marine invertebrate. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering
Electronics 2016, 5(3), 56; doi:10.3390/electronics5030056
Received: 1 May 2016 / Revised: 1 September 2016 / Accepted: 8 September 2016 / Published: 13 September 2016
Cited by 3 | PDF Full-text (3342 KB) | HTML Full-text | XML Full-text
Abstract
The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in
[...] Read more.
The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students’ learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Building IoT Applications with Raspberry Pi and Low Power IQRF Communication Modules
Electronics 2016, 5(3), 54; doi:10.3390/electronics5030054
Received: 16 May 2016 / Revised: 18 August 2016 / Accepted: 1 September 2016 / Published: 8 September 2016
Cited by 1 | PDF Full-text (5394 KB) | HTML Full-text | XML Full-text
Abstract
Typical Internet of Things (IoT) applications involve collecting information automatically from diverse geographically-distributed smart sensors and concentrating the information into more powerful computers. The Raspberry Pi platform has become a very interesting choice for IoT applications for several reasons: (1) good computing power/cost
[...] Read more.
Typical Internet of Things (IoT) applications involve collecting information automatically from diverse geographically-distributed smart sensors and concentrating the information into more powerful computers. The Raspberry Pi platform has become a very interesting choice for IoT applications for several reasons: (1) good computing power/cost ratio; (2) high availability; it has become a de facto hardware standard; and (3) ease of use; it is based on operating systems with a big community of users. In IoT applications, data are frequently carried by means of wireless sensor networks in which energy consumption is a key issue. Energy consumption is especially relevant for smart sensors that are scattered over wide geographical areas and may need to work unattended on batteries for long intervals of time. In this scenario, it is convenient to ease the construction of IoT applications while keeping energy consumption to a minimum at the sensors. This work proposes a possible gateway implementation with specific technologies. It solves the following research question: how to build gateways for IoT applications with Raspberry Pi and low power IQRF communication modules. The following contributions are presented: (1) one architecture for IoT gateways that integrates data from sensor nodes into a higher level application based on low-cost/low-energy technologies; (2) bindings in Java and C that ease the construction of IoT applications; (3) an empirical model that describes the consumption of the communications at the nodes (smart sensors) and allows scaling their batteries; and (4) validation of the proposed energy model at the battery-operated nodes. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Wearable Multimodal Skin Sensing for the Diabetic Foot
Electronics 2016, 5(3), 45; doi:10.3390/electronics5030045
Received: 29 January 2016 / Revised: 27 May 2016 / Accepted: 4 July 2016 / Published: 28 July 2016
Cited by 2 | PDF Full-text (2266 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Ulceration of the diabetic foot is currently difficult to detect reliably in a timely manner causing undue suffering and cost. Current best practice is for daily monitoring by those living with diabetes coupled to scheduled monitoring by the incumbent care provider. Although some
[...] Read more.
Ulceration of the diabetic foot is currently difficult to detect reliably in a timely manner causing undue suffering and cost. Current best practice is for daily monitoring by those living with diabetes coupled to scheduled monitoring by the incumbent care provider. Although some metrics have proven useful in the detection or prediction of ulceration, no single metric can currently be relied upon for diagnosis. We have developed a prototype multivariate extensible sensor platform with which we demonstrate the ability to gather acceleration, rotation, galvanic skin response, environmental temperature, humidity, force, skin temperature and bioimpedance signals in real time, for later analysis, utilising low cost Raspberry Pi and Arduino devices. We demonstrate the utility of the Raspberry Pi computer in research which is of particular interest to this issue of electronics—Raspberry Pi edition. We conclude that the hardware presented shows potential as an adaptable research tool capable of gathering synchronous data over multiple sensor modalities. This research tool will be utilised to optimise sensor selection, placement and algorithm development prior to translation into a sock, insole or platform diagnostic device at a later date. The combination of a number of clinically relevant parameters is expected to provide greater understanding of tissue state in the foot but requires further volunteer testing and analysis beyond the scope of this paper which will be reported in due course. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle A Raspberry Pi Based Portable Endoscopic 3D Measurement System
Electronics 2016, 5(3), 43; doi:10.3390/electronics5030043
Received: 29 April 2016 / Revised: 15 July 2016 / Accepted: 18 July 2016 / Published: 26 July 2016
Cited by 2 | PDF Full-text (12981 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Geometry measurements are very important to monitor a machine part’s health and performance. Optical measurement system have several advantages for the acquisition of a parts geometry: measurement speed, precision, point density and contactless operation. Measuring parts inside of assembled machines is also desirable
[...] Read more.
Geometry measurements are very important to monitor a machine part’s health and performance. Optical measurement system have several advantages for the acquisition of a parts geometry: measurement speed, precision, point density and contactless operation. Measuring parts inside of assembled machines is also desirable to keep maintenance cost low. The Raspberry Pi is a small and cost efficient computer that creates new opportunities for compact measurement systems. We have developed a fringe projection system which is capable of measuring in very limited space. A Raspberry Pi 2 is used to generate the projection patterns, acquire the image and reconstruct the geometry. Together with a small LED projector, the measurement system is small and easy to handle. It consists of off-the-shelf products which are nonetheless capable of measuring with an uncertainty of less than 100 μ m . Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Developing an Affordable and Portable Control Systems Laboratory Kit with a Raspberry Pi
Electronics 2016, 5(3), 36; doi:10.3390/electronics5030036
Received: 10 May 2016 / Revised: 26 June 2016 / Accepted: 27 June 2016 / Published: 4 July 2016
Cited by 1 | PDF Full-text (4311 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Instructional laboratories are common in engineering programs. Instructional laboratories should evolve with technology and support the changes in higher education, like the increased popularity of online courses. In this study, an affordable and portable laboratory kit was designed to replace the expensive on-campus
[...] Read more.
Instructional laboratories are common in engineering programs. Instructional laboratories should evolve with technology and support the changes in higher education, like the increased popularity of online courses. In this study, an affordable and portable laboratory kit was designed to replace the expensive on-campus equipment for two control systems courses. The complete kit costs under $135 and weighs under 0.68 kilograms. It is comprised of off-the-shelf components (e.g., Raspberry Pi, DC motor) and 3D printed parts. The kit has two different configurations. The first (base) configuration is a DC motor system with a position and speed sensor. The second configuration adds a Furuta inverted pendulum attachment with another position sensor. These configurations replicate most of the student learning outcomes for the two control systems courses for which they were designed. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Erica the Rhino: A Case Study in Using Raspberry Pi Single Board Computers for Interactive Art
Electronics 2016, 5(3), 35; doi:10.3390/electronics5030035
Received: 4 May 2016 / Revised: 17 June 2016 / Accepted: 24 June 2016 / Published: 30 June 2016
Cited by 1 | PDF Full-text (18738 KB) | HTML Full-text | XML Full-text
Abstract
Erica the Rhino is an interactive art exhibit created by the University of Southampton, UK. Erica was created as part of a city wide art trail in 2013 called “Go! Rhinos”, curated by Marwell Wildlife, to raise awareness of Rhino conservation. Erica
[...] Read more.
Erica the Rhino is an interactive art exhibit created by the University of Southampton, UK. Erica was created as part of a city wide art trail in 2013 called “Go! Rhinos”, curated by Marwell Wildlife, to raise awareness of Rhino conservation. Erica arrived as a white fibreglass shell which was then painted and equipped with five Raspberry Pi Single Board Computers (SBC). These computers allowed the audience to interact with Erica through a range of sensors and actuators. In particular, the audience could feed and stroke her to prompt reactions, as well as send her Tweets to change her behaviour. Pi SBCs were chosen because of their ready availability and their educational pedigree. During the deployment, ‘coding clubs’ were run in the shopping centre where Erica was located, and these allowed children to experiment with and program the same components used in Erica. The experience gained through numerous deployments around the country has enabled Erica to be upgraded to increase reliability and ease of maintenance, whilst the release of the Pi 2 has allowed her responsiveness to be improved. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessFeature PaperArticle Educational Programming on the Raspberry Pi
Electronics 2016, 5(3), 33; doi:10.3390/electronics5030033
Received: 3 May 2016 / Revised: 16 June 2016 / Accepted: 16 June 2016 / Published: 24 June 2016
Cited by 4 | PDF Full-text (3611 KB) | HTML Full-text | XML Full-text
Abstract
The original aim when creating the Raspberry Pi was to encourage “kids”—pre-university learners—to engage with programming, and to develop an interest in and understanding of programming and computer science concepts. The method to achieve this was to give them their own, low cost
[...] Read more.
The original aim when creating the Raspberry Pi was to encourage “kids”—pre-university learners—to engage with programming, and to develop an interest in and understanding of programming and computer science concepts. The method to achieve this was to give them their own, low cost computer that they could use to program on, as a replacement for a family PC that often did not allow this option. With the original release, the Raspberry Pi included two programming environments in the standard distribution software: Scratch and IDLE, a Python environment. In this paper, we describe two programming environments that we developed and recently ported and optimised for the Raspberry Pi, Greenfoot and BlueJ, both using the Java programming language. Greenfoot and BlueJ are both now included in the Raspberry Pi standard software distribution, and they differ in many respects from IDLE; they are more graphical, more interactive, more engaging, and illustrate concepts of object orientation more clearly. Thus, they have the potential to support the original aim of the Raspberry Pi by creating a deeper engagement with programming. This paper describes these two environments and how they may be used, and discusses their differences and relationships to the two previously available systems. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessFeature PaperArticle Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data
Electronics 2016, 5(2), 29; doi:10.3390/electronics5020029
Received: 30 April 2016 / Revised: 20 May 2016 / Accepted: 31 May 2016 / Published: 6 June 2016
Cited by 2 | PDF Full-text (1245 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such
[...] Read more.
Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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Open AccessArticle Universal Safety Distance Alert Device for Road Vehicles
Electronics 2016, 5(2), 19; doi:10.3390/electronics5020019
Received: 25 January 2016 / Revised: 12 April 2016 / Accepted: 26 April 2016 / Published: 29 April 2016
Cited by 1 | PDF Full-text (4356 KB) | HTML Full-text | XML Full-text
Abstract
Driving with too short of a safety distance is a common problem in road traffic, often with traffic accidents as a consequence. Research has identified a lack of vehicle-mountable devices for alerting the drivers of trailing vehicles about keeping a sufficient safe distance.
[...] Read more.
Driving with too short of a safety distance is a common problem in road traffic, often with traffic accidents as a consequence. Research has identified a lack of vehicle-mountable devices for alerting the drivers of trailing vehicles about keeping a sufficient safe distance. The principal requirements for such a device were defined. A conceptual study was performed in order to select the components for the integration of the device. Based on the results of this study, a working prototype of a flexible, self-contained device was designed, built and tested. The device is intended to be mounted on the rear of a vehicle. It uses radar as the primary distance sensor, assisted with a GPS receiver for velocity measurement. A Raspberry Pi single-board computer is used for data acquisition and processing. The alerts are shown on an LED-matrix display mounted on the rear of the host vehicle. The device software is written in Python and provides automatic operation without requiring any user intervention. The tests have shown that the device is usable on almost any motor vehicle and performs reliably in simulated and real traffic. The open issues and possibilities for future improvements are presented in the Discussion. Full article
(This article belongs to the Special Issue Raspberry Pi Technology)
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