Dr. Eric J. Mahoney Profile

Dr. Eric J. Mahoney

at McMaster Univ

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Proceedings Article | 2 July 2019 Open Access

Experiential learning of data acquisition and sensor networks with a cloud computing platform

Eric Mahoney, Colleen Chau, Qiyin Fang

Proceedings Volume 11143, 111433X (2019) https://doi.org/10.1117/12.2535399

KEYWORDS: Data acquisition, Sensors, Humidity, Statistical analysis, Clouds, Temperature metrology, Temperature sensors, Data communications, Environmental sensing, Sensing systems

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Experimental data acquisition and statistical data analysis are core components in photonics undergraduate curriculum. Although it focuses on experimental data, the content is usually delivered by a lecture-based format. This is partially because the contents are delivered at the beginning of the program when experimental data acquisition techniques have not yet been introduced. In a second-year data acquisition and applied statistics course, we have designed an experiential learning module that covers the fundamental content of data acquisition and statistical analysis. This module uses a single physical experimental setup that is continuously measuring environmental parameters (temperature, humidity, light, imaging, etc.) using a set of multiple modality sensors in an Internet-of-things (IoT) big data platform (Pi Vision, OSI Soft). Different types of sensors measuring the same parameters are also used for cross-validation purposes. The data is streamed to a cloud computing platform, allowing each student to acquire their own subset of data, and then perform processing and analysis. The capability of remote access a physical sensing experiment provide the students hands-on learning opportunities on a managed complex data acquisition system. The platform provides a set of powerful visualization tools to allow a multi-dimension view of complex data streams (e.g. time-lapse of statistical distribution). Such IoT data acquisition platform allows key concepts to be demonstrated, applied, and tested including error propagation, distribution and test of distribution, correlation and cross-validation, data rejection, and signal processing. This experiential learning module has been demonstrated to be more effective in achieving related learning objectives through quantitative graduate attribute measurements as well as qualitative feedback.

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