UND professor is ‘featured engineer’ in Web-based electrical engineering mag
Bibliographic record
Abstract
University of North Dakota Electrical Engineering Associate Professor Saleh Faruque was highlighted as the “feature engineer” in a recent interview with the Web-based Electrical Engineering magazine, EEWeb. Faruque, a native of Bangladesh who has been with UND’s College of Engineering & Mines since 2002, teaches undergraduate and graduate courses via traditional and distance-learning means in UND’s Department of Electrical Engineering. His current research addresses challenges presented by the rapid growth of wireless communication use. Here are excerpts from the online interview: Faruque on his current research: Cellular communication has brought the world community closer than ever before; it is indispensable in even our everyday lives. Its use is increasing every day. However, it is unfortunate that existing technology cannot keep pace with this because of customer growth and high speed data communication. Recently, it has come to our attention that cellular technology may also contribute to Global CO2 pollution, which is expected to rise because of current explosion in wireless data. Almost all countries around the globe face this dilemma, giving rise to ever-increasing Global RF& CO2 pollution. In an effort to address these issues, I am currently conducting a graduate research program, in which my students are looking at various spectrum management techniques currently in progress, to identify limitations if any, and to propose future research areas to improve spectral efficiency and reduce RF and CO2 pollution. We believe that transition to green cellular technology could be an effective solution to the spectrum crunch we are facing today. On teaching: I believe teaching is a comprehensive effort that brings students up-to-date in key concepts, underlying principles and practical applications of the respective subjects. I consider myself as a surrogate parent to my students, who bring theory and practice into the classroom. When I find that they have grasped this, my spirit soars with joy and I feel that my efforts have been amply rewarded. More about Faruque: Faruque received his bachelors and master’s degrees in Physics in applied physics from Dhaka University in Bangladesh in 1969 and 1970, respectively. He also received a master’s and Ph.D. in electrical engineering from the University of Waterloo in Ontario, Canada, in 1976 and 1980, respectively. Upon completion of his degrees, Faruque worked in the telecom industry for more than 20 years in various capacities and contributed extensively in cellular communications and related areas. In 2011-12, Faruque was the recipient of the prestigious U.S. Fulbright scholarship. In addition, the UND College of Engineering & Mines awarded him its Outstanding Professor of the Year in 2008 and 2012. Faruque has authored a book book and contributed several chapters to three others. He has published more than 70 papers for various technical journals and conferences. In addition, he has secured 15 U.S. and three Canadian patents, and has several U.S. patents pending.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.064 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".