Board 103: Solar-Powered Car Speed Radar Measurement, Display, and Logging System
Bibliographic record
Abstract
Studies have shown that there is a 10% fatality rate when a pedestrian is struck by a vehicle moving at the speed of 20 (mph), and the rate scales up to 90% at the speed of 40 (mph).Moreover, the residential areas with radar speed monitors are shown to be safer in terms of accident probability.Motivated by these statistics, in this National Science Foundation sponsored senior design project a speed radar system is designed and developed.The components, functionalities, and objectives of the project are listed as follows: (i) A camera will detect and identify a vehicle and distinguish it from other objects; (ii) a radar sensor will measure the speed of the vehicle; (iii) a microprocessor (Raspberry Pi) will acquire the speed data, send it to the display, and analyze and log it in a server; and (iv) a stand-alone solar Photovoltaic system will provide electrical power to and guarantee the continuous operation of the entire system.This senior design project was conducted by a group of undergraduate students in the electrical and computer engineering technology program at New Jersey Institute of Technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".