Using Robus In Electrical And Computer Engineering Education
Bibliographic record
Abstract
ROBUS (ROBot University of Sherbrooke) is an autonomous mobile robot designed to facilitate interdisciplinary engineering design in Electrical Engineering (EE) and Computer Engineering (CE).Its primary purpose is to serve as an integrated platform for a project called INGÉNIUS that introduces electrical and computer engineering simultaneously to a large group of first-year undergraduate students registered in these two distinct programs.Divided in thirty-five teams of six or seven, these students are being initiated to various aspects of electrical and computer engineering such as electric circuits, electronics, sensors and actuators, logic circuits and CPLD, microprocessors, real-time C programming, robotics, technical drawing and communication.This way, ROBUS gives hands-on technical and teamwork experiences early in the curriculum.The robot is used in six different courses, and an interdisciplinary team of professors also work together to coordinate these activities.At the end of the second semester, teams participate in a robot competition where the objective is to design an entertainment robot for children with learning disorders.For fourth-year students in EE and CE, ROBUS is used in more advanced undergraduate courses such as Microprocessor Interfaces, Real-Time Systems, Robotics Projects and also in one graduate course on Artificial Intelligence.The projects done in these courses are oriented toward giving more advanced capabilities to ROBUS, help developed complete autonomous robots and to teach specific concepts.This paper gives a description of ROBUS and how it is used in these activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.199 | 0.113 |
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".