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机译：免费获得国际在线工程教育技术

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Open source technology plays a vital role in a cost-effective and robustly accessible online engineering education. This role becomes even more vital in some developing countries where there are much engineering education demand and where the supply of accessible technology may be cost prohibitive and not as accessible. This paper encapsulates some of the freely accessible state-of-the-art open source technology and their applications in online engineering education and addresses some key features of the current technology. Open source technology is not very useful without the appropriate tools. Thus, discussions also include some open source tools to facilitate the access and the cost-effectiveness.

机译：开源技术在具有成本效益和强大的可访问的在线工程教育中起着至关重要的作用。这种角色在一些发展中国家在有很多工程教育需求以及可接近技术供应可能是持有的且不可访问的情况下，这一角色变得更加重要。本文封装了一些自由访问的最先进的开源技术及其在在线工程教育中的应用，并解决了当前技术的一些关键特征。如果没有合适的工具，开源技术并不是很有用。因此，讨论还包括一些开源工具，以便于访问和成本效益。

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《American Society For Engineering Education Annual Conference and Exposition》|2011年||共6页
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作者
Ali Mehrabian; Walter W. Buchanan; Alireza Rahrooh;
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原文格式 PDF
正文语种
中图分类 TB1-53;
关键词
Distance learning; Pedagogy; Engineering education; Open source technology; Freeware; Learning management system;

机译：远程学习;教育学;工程教育;开源技术;自由用品;学习管理系统;

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7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

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