Sensor-based allocation for path planning and area coverage using UGSs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of the project is to incorporate situational awareness methods and performance evaluation using distributed unattended ground sensor (UGS) wireless sensor networks, area coverage, and path planning to monitor targets. Generalized measures of performance are utilized for determining which sensors to allocate to observe a moving target through obscurations. Using a variety of techniques in the literature, we propose a method of analysis for dynamic resource management for decision support. In this paper, we simulate a path planning object that moves to goal in the presence of obstacles. The distributed sensors must maintain area coverage and perform sensor management to maintain the path of the object. We investigated the sensor management functions of direct search, cueing, the sequential probability ratio test (SPRT), and information theoretical methods of KL divergence (or discrimination gain) and mutual information. These methods afford sensor hand-off and can be used to optimal place sensors to maintain are coverage and target tracking.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it