Make space for the Pi

Bruce, Rebecca; Brock, J. Dean; Reiser, Susan

doi:10.1109/secon.2015.7132994

Cited by 14 publications

(13 citation statements)

References 4 publications

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“…Educators have exploited Raspberry Pi either as a single device [10,18] or as the basis of more sophisticated learning systems [7,8,[19][20][21] for their IoT education. Bruce et al [10] and Jamieson et al [18] described valuable experiences and case studies of using Raspberry Pi in undergraduate study. Sobota et al [19] coupled a Raspberry Pi with an Arduino to build an inexpensive platform for students to run control algorithms.…”

Section: An Overview Of Raspberry Pimentioning

confidence: 99%

“…It is of critical importance to provide the next generation of computer scientists and engineers an opportunity to not only understand the concepts and principles of IoT, but also to study the practical development of IoT solidly, so that students can learn how to apply theories to real applications. Many universities have started to introduce IoT courses into their undergraduate curriculum [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

Zhong

Liang

2016

Electronics

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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 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.

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Section: An Overview Of Raspberry Pimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

Zhong

Liang

2016

Electronics

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“…Among the variety of vendors providing these single-board computers (SBCs), maybe the most renowned ones are Raspberry Pi and Odroid. Although the initial aim of these devices was to promote the teaching of basic computer science in schools [3,4] and developing countries [5][6][7], recent appearance of single-board computers with multicore ARM CPU chips and several gigabytes of main memory also provides a desirable hardware platform for the project-based learning paradigm in computer science and engineering education [8][9][10][11] and have attracted interest of a multitude of projects trying to take advantage of their very low-cost performance ratio (i.e., for scientific computing [12][13][14]) in contrast with other energyefficient but which are alternatives of higher cost [15].…”

Section: Introductionmentioning

confidence: 99%

On the Parallelization of Stream Compaction on a Low-Cost SDC Cluster

Bernabé

Acacio

2018

Scientific Programming

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Many highly parallel algorithms usually generate large volumes of data containing both valid and invalid elements, and high-performance solutions to the stream compaction problem reveal extremely important in such scenarios. Although parallel stream compaction has been extensively studied in GPU-based platforms, and more recently, in the Intel Xeon Phi platform, no study has considered yet its parallelization using a low-cost computing cluster, even when general-purpose single-board computing devices are gaining popularity among the scientific community due to their high performance per $ and watt. In this work, we consider the case of an extremely low-cost cluster composed by four Odroid C2 single-board computers (SDCs), showing that stream compaction can also benefit—important speedups can be obtained—from this kind of platforms. To do so, we derive two parallel implementations for the stream compaction problem using MPI. Then, we evaluate them considering varying number of processes and/or SDCs, as well as different input sizes. In general, we see that unless the number of elements in the stream is too small, the best results are obtained when eight MPI processes are distributed among the four SDCs that conform the cluster. To add value to the obtained results, we also consider the execution of the two parallel implementations for the stream compaction problem on a very high-performance but power-hungry 18-core Intel Xeon E5-2695 v4 multicore processor, obtaining that the Odroid C2 SDC cluster constitutes a much more efficient alternative when both resulting execution time and required energy are taken into account. Finally, we also implement and evaluate a parallel version of the stream split problem to store also the invalid elements after the valid ones. Our implementation shows good scalability on the Odroid C2 SDC cluster and more compensated computation/communication ratio when compared to the stream compaction problem.

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“…Another trend is the usage of single board computer (Bruce et al, 2013(Bruce et al, , 2015 or microcontroller (Furman and Wertz, 2010;Gajic et al, 2004;Giurgiutiu et al, 2005) with strong positive feedback from the students related to the hands-on experience. Another approach is to directly set exercises in the machine shop, to let students design CNC programs and review the results of their programs directly with a working piece (Munro, 2013).…”

Section: Introductionmentioning

confidence: 99%

Integrating Lab-size Automation Plants into a Web-based E-learning Environment for Teaching C Programming in Teams

et al. 2015

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Teaching computer science and particularly programming in the curriculum of a mechanical engineering bachelor poses a difficult task due to motivational factors of the students. In this paper we describe the measures we have taken to reduce barriers and motivate the freshmen students to deal with the subject by introduction of an e-learning course and further by offering an associated lab course with practical application of cyclic programming of a real-time system in teams. Further we present an adapted architecture how the lab course is enhanced to act as a remote lab. Finally we evaluate the impact of the course by measuring the success factors of different gender combined groups and give an outlook on future scenarios for lab courses to improve the learning experience and success of the students for both gender.

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Make space for the Pi

Cited by 14 publications

References 4 publications

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

On the Parallelization of Stream Compaction on a Low-Cost SDC Cluster

Integrating Lab-size Automation Plants into a Web-based E-learning Environment for Teaching C Programming in Teams

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