Development of signal processing online labs using HTML5 and mobile platforms

Dixit, Aparna; Katoch, Sameeksha; Spanias, Photini; Banavar, Mahesh K.; Song, Huan; Spanias, Andreas

doi:10.1109/fie.2017.8190657

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…The HTML simulation platform is a better platform that needs to be used in the future because of its simplicity: directly running on the browser. Moreover, as java-based simulation is also vulnerable to cyber-attacks and is no longer compatible with secure browsers, recently many web-based simulations are being moved to HTML5 framework [17]. These findings suggest that HTML-based simulation is currently the best choice of simulation platform for reducing technical problems faced by the users.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Web-based Simulations as Learning Media for Teaching Quantum Wave Packet Concept

Aminoto¹,

Pujaningsih²,

Purwaningsih³

et al. 2020

ujer

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Quantum wave packet is one of the well-known topics which is difficult to the students because of its abstract, mathematical and complex characteristics. Web-based simulations, as learning media of quantum physics course, are widely available on the Internet, this research aimed to review and evaluate several versions of quantum wave-packet web-based simulations. This paper was part of the preliminary study in developing ICT-based quantum physics learning modules at Jambi University. It was found that there were 4 websites that provided a high-quality web-based simulation for teaching and learning of quantum wave packet. The simulations were evaluated based on the hierarchical framework and evaluation of educational software method including proposed additional criteria for more specific evaluation. The quantum wave-packet simulation of PhET version gets the highest score, followed by the Schroeder with a slightly different score, Quvis and Physlet version are in the lower position. However, in terms of convenience for users to run directly in the browser, the ease to perform modifications according to user needs, and having another supporting simulation, the Schroeder version is a better learning media. In addition, as the java platform often has compatible issues with many popular browsers, Schroeder version which is HTML-based simulation is more preferable. The performance highlights of each simulation and its specific comparison results are also discussed.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Web-based Simulations as Learning Media for Teaching Quantum Wave Packet Concept

Aminoto¹,

Pujaningsih²,

Purwaningsih³

et al. 2020

ujer

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“…The program required students to first attend a boot camp and receive online training in signal processing, ML and sensors. To accelerate learning we started the training in signal processing and ML using the object oriented J-DSP software [21][22][23][24] which has built in modules for k-means (Fig. 4) and other clustering methods.…”

Section: Reu In Sensors Devices and ML Algorithmsmentioning

confidence: 99%

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias

2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

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This paper accompanies the keynote speech at IISA-2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.

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“…It has extensive applications in IoT systems [24], and more recently solar analytics [3]. The introduction of k-means in education, using our award-winning J-DSP was described in [11,16]. In this paper, we discuss how k-means is introduced in our classes in the context of solar energy monitoring and control.…”

Section: Module and Exercise On Fault Detection Using Machine Learningmentioning

confidence: 99%

“…We describe in the paper online machine learning software that we developed specifically for online education purposes. This software is based on the HTML 5 J-DSP environment [16] which can run on any browser and is accessible freely by students and faculty. We also describe the content that accompanies the J-DSP software modules and computer exercises.…”

Section: Introductionmentioning

confidence: 99%

Board 162: Online Modules to Introduce Students to Solar Array Control using Neural Nets

Narayanaswamy

Shanthamallu²,

Dixit

et al.

2019 ASEE Annual Conference &Amp; Exposition Proceedings

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He is currently a graduate student in the school of electrical, computer and energy engineering at ASU, Tempe, AZ. He completed an internship with Qualcomm R&D this summer. His current research interests include developing automated techniques for topology reconfiguration in PV panels to maximize power and to investigate other possible metrics and deep learning frameworks that can be used for speaker diarization. Mr. Uday Shankar Shanthamallu "I received my B.S degree in Electronics and Communications from the National Institute of Engineering, India in 2011. I am currently pursuing my Master's and PhD program in Electrical Engineering at Arizona State University(ASU). I am advised by Dr. Andreas Spanias. I joined Sensor, Signal and Information Processing Center (SenSIP) at ASU in Jan 2016. My research interests lie at the overlap of sensors and Machine learning and Big Data including, but not limited to Pattern recognition and Anomaly detection. In summer 2016, I did a summer internship at NXP Semiconductors where I worked on sensor data analytics for anomaly detection. I worked on integrating machine learning algorithms on an embedded sensor systems for Internet of Things applications, which can identify anomalies in real time. Before joining ASU, I worked as Systems engineer for 4 years at

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Development of signal processing online labs using HTML5 and mobile platforms

Cited by 14 publications

References 21 publications

Evaluation of Web-based Simulations as Learning Media for Teaching Quantum Wave Packet Concept

Evaluation of Web-based Simulations as Learning Media for Teaching Quantum Wave Packet Concept

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Board 162: Online Modules to Introduce Students to Solar Array Control using Neural Nets

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