UNIVERSITY OF CALGARY Performance Evaluation of Localization Techniques for Wireless Sensor Networks
Bibliographic record
Abstract
Localising people can be achieved by using wearable sensor nodes or by listening to speech. Receiver Signal Strength Indicator (RSSI), Time of Arrival (TOA) and Time Difference of Arrival (TDOA) are three main techniques that have been used in sensor node localization. Since lack of restrictions, the TDOA based methods have become more popular. It can be used for both wearable sensor nodes and for localizing speech. Using a wireless sensor network instead a wired microphone array is more convenient and easily deployable. However, performance degrades rapidly with acoustic reverberation and packet loss. Though robust beamforming techniques like SRP-PHAT exist, they are computationally exhaustive. In this study, performance of both localization using wearable sensor nodes and speech are experimentally evaluated. Effect of packet loss is analysed and ways of minimizing the effects are proposed. Variance of TDOA estimate is derived and a new search algorithm to minimize computational cost of SRP-PHAT is proposed. ii
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".