Drone Assist Indoor Locating System for Trapped Victim Using Smartphone Application in a 3D Space
Bibliographic record
Abstract
When there are large and unaccounted-for numbers of victims in events, fires, or disasters, they are frequently trapped in buildings or wreckage and need to be found and saved as quickly as possible. Given that most people carry smartphones with them when calamities hit, this study turns handsets into interior location tools without requiring further modifications or difficult application installation. This study proposes a method to turn cellphones to a lifesaving tool for stranded victims. In this work, the low power consumption feature of Bluetooth beacons in cellphones is used to deliver Bluetooth signals. For a longer period of time, the signal can keep transmitting, helping rescuers find victims who are stuck. Rescuers might find and pinpoint the location of numerous trapped victims by using a drone and a smartphone app for BLE (Bluetooth Low Energy) detection. By putting this approach in place, the search and rescue team will be able to save more victims who are trapped faster and with less need to visit dangerous locations. In addition, the calling feature on a smartphone might not function during a disaster, and the victim who is trapped might not be strong enough to call for assistance. This technique would make it easier to find them even if they're hidden by falling debris like wood piles or tiles. By reducing the amount of time search and rescue personnel must spend in dangerous areas, this initiative will improve the likelihood of discovering trapped people during the window of opportunity for rescue operations.
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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 | 0.000 | 0.000 |
| 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) | 0.008 | 0.002 |
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".