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1. EER --> MLN}: {EER} Approach for Modeling, Mapping, and Analyzing Complex Data Using Multilayer Networks (MLNs)

   https://doi.org/10.1007/978-3-030-62522-1_41  

   Kanthi Sannappa Komar, Abhishek Santra ( November 2020 , Conceptual Modeling - 39th International Conference, {ER})

   null (Ed.)

   Algorithm for converting Enhanced Entity Relationship diagrams into Multilayer Networks for analysis and Relational schema for drill down. 

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2. A switched-capacitor controlled digital-current modulated class-E EER transmitter

   https://doi.org/10.1109/NEWCAS.2015.7182014  

   Yuan, Wen ; Walling, Jeffrey S. ( June 2015 , New Circuits and Systems Conference (NEWCAS), 2015 IEEE 13th International)

   An envelope elimination and restoration (EER) transmitter that comprises a class-E power amplifier and a digitally controlled current DAC modulator is presented. A switched capacitor DAC is designed to control an open-loop transconductor that operates as a current modulator, modulating the amplitude of the current supplied to a class-E PA. Such a topology allows for increased filtering of the quantization noise that is problematic in most digital PAs (DPA). The system measurements yield a peak output power and power added efficiency (PAE) of 22.5 dBm and 23.6%, respectively. When applying a WCDMA signal, the measured EVM is 1.32% and the adjacent channel power ratio (ACPR) is -37.9 dBc, while outputting 19.9 dBm at 14.3% PAE. For an LTE signal, the measured EVM is 3.72% and the ACLR is -30.2 dBc, while outputting 18.1 dBm at 10.6% PAE. 

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3. Work in Progress: Developing an Ethnographic Observation and Reflection Template: An Example from Studying Epistemic Differences within EER Teams

   Boyd, I. ; Chen, C. ; Treffert, L. ; Gillmore, A. ; Faber, C. ( January 2023 , Proceedings of the American Society for Engineering Education Annual Meeting)
4. Changing the Paradigm: Developing a Framework for Secondary Analysis of EER Qualitative Datasets

   Case, J. ; Matusovich, H. ; Paretti, M. ; Sochacka, N. ; Walther, J. ( August 2022 , 2022 ASEE Annual Conference & Exposition)

   This paper reports on a project funded through the Engineering Education and Centers (EEC) Division of the National Science Foundation. Since 2010, EEC has funded more than 500 proposals totaling over $150 million through engineering education research (EER) programs such as Research in Engineering Education (REE) and Research in the Formation of Engineers (RFE), to enhance understanding and improve practice. The resulting archive of robust qualitative and quantitative data represents a vast untapped potential to exponentially increase the impact of EEC funding and transform engineering education. But tapping this potential has thus far been an intractable problem, despite ongoing calls for data sharing by public funders of research. Changing the paradigm of single-use data collection requires actionable, proven practices for effective, ethical data sharing, coupled with sufficient incentives to both share and use existing data. To that end, this project draws together a team of experts to overcome substantial obstacles in qualitative data sharing by building a framework to guide secondary analysis in engineering education research (EER), and to test this framework using pioneering data sets. Herein, we report on accomplishments within the first year of the project during which time we gathered a group of 13 expert qualitative researchers to engage in the first of a series of working meetings intended to meet our project goals. We came into this first workshop with a potentially limiting definition of secondary data analysis and the idea that people would want to share existing datasets if we could find ways around anticipated hurdles. However, the workshop yielded a broader definition of secondary data analysis and revealed a stronger interest in creating new datasets designed for sharing rather than sharing existing datasets. Thus, we have reconceived our second phase as one that is a cohesive effort based on an inclusive “open cohort model” to pilot projects related to secondary data analysis. 

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