The LAB@FUTURE Project - Moving Towards the Future of E-Learning

This paper presents Lab@Future, an advanced e-learning platform that uses novel Information and Communication Technologies to support and expand laboratory teaching practices. For this purpose, Lab@Future uses real and computer-generated objects that are interfaced using mechatronic systems, augmented reality, mobile technologies and 3D multi user environments. The main aim is to develop and demonstrate technological support for practical experiments in the following focused subjects namely: Fluid Dynamics - Science subject in Germany, Geometry - Mathematics subject in Austria, History and Environmental Awareness – Arts and Humanities subjects in Greece and Slovenia. In order to pedagogically enhance the design and functional aspects of this e-learning technology, we are investigating the dialogical operationalisation of learning theories so as to leverage our understanding of teaching and learning practices in the targeted context of deployment

FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

To enhance collaborative learning and practice network knowledge with a virtualization laboratory and online synchronous discussion

This work is licensed under a Creative Commons Attribution 4.0 Internatinal License.Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux operating system can be used to operate network devices and the virtualization technique can include multiple OSs for supporting a significant number of students. In previous research, the virtualization application was successfully applied in a laboratory, but focused only on individual assignments. The present study extends previous research by designing the Networking Virtualization-Based Laboratory (NVBLab), which requires collaborative learning among the experimental students. The students were divided into an experimental group and a control group for the experiment. The experimental group performed their laboratory assignments using NVBLab, whereas the control group completed them on virtual machines (VMs) that were installed on their personal computers. Moreover, students using NVBLab were provided with an online synchronous discussion (OSD) feature that enabled them to communicate with others. The laboratory assignments were divided into two parts: Basic Labs and Advanced Labs. The results show that the experimental group significantly outperformed the control group in two Advanced Labs and the post-test after Advanced Labs. Furthermore, the experimental group's activities were better than those of the control group based on the total average of the command count per laboratory. Finally, the findings of the interviews and questionnaires with the experimental group reveal that NVBLab was helpful during and after laboratory class

Co-doping red-emitting Sr2Si5N8:Eu2+ into yellow-emitting phosphor-packaging for enhancing the optical properties of the 8500 K remote-phosphor packaging wleds

In the last decades, WLEDs attract more and more consideration in both academic and industrial purposes because of its advantages such as fast response time, environment friendliness, small size, long lifetime, and high efficiency. In this research, by doping the red-emitting Sr2Si5N8:Eu2+ phosphor particles into yellow-emitting YAG:Ce phosphor-packaging, a new recommendation for enhancing the optical properties (color uniformity, color rendering index, and lumen output) of the 8500 K remote-phosphor packaging WLEDs is presented, investigated, and demonstrated. By using Mat Lab and Light Tools software based on Mie Theory, the obtained results show that the optical properties of the 8500 K remote-phosphor packaging WLEDs significantly depended on Sr2Si5N8:Eu2+ concentration. The results have provided a potential practical recommendation for manufacturing remote-phosphor W-LEDs.Web of Science1341034102

IREEL: remote experimentation with real protocols and applications over emulated network

This paper presents a novel e-learning platform called IREEL. IREEL is a virtual laboratory allowing students to drive experiments with real Internet applications and end-to-end protocols in the context of networking courses. This platform consists in a remote network emulator offering a set of predefined applications and protocol mechanisms. Experimenters configure and control the emulation and the end-systems behavior in order to perform tests, measurements and observations on protocols or applications operating under controlled specific networking conditions. A set of end-to-end mechanisms, mainly focusing on transport and application level protocols, are currently available. IREEL is scalable and easy to use thanks to an ergonomic web interface

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Chemistry by Mobile Phone (or how to justify more time at the bar)

By combining automatic environment monitoring with Java smartphones a system has been produced for the real-time monitoring of experiments whilst away from the lab. Changes in the laboratory environment are encapsulated as simple XML messages, which are published using an MQTT compliant broker. Clients subscribe to the MQTT stream, and produce a user display. An MQTT client written for the Java MIDP platform, can be run on a smartphone with a GPRS Internet connection, freeing us from the constraints of the lab. We present an overview of the technologies used, and how these are helping chemists make the best use of their time