Communication-aided Terahertz Sensing: A Novel Indoor People Counting System Via Deep Learning
Bibliographic record
Abstract
Indoor people counting systems are used in security monitoring, energy management, room resources adjustment, market research, and smart homes. However, the existing radio-frequency-based indoor people counting systems use a different frequency than the utilized radio frequency communication signal, which adds more costs for system deployment and wastes the radio frequency resources. This paper introduces a novel communication-aided terahertz (THz) sensing approach for robust indoor people counting that utilizes the THz communication downlink signal to sense the number of indoor people. Leveraging the wireless THz communication downlink signal, we propose a device-free, cost-effective, and non-intrusive indoor people counting system. Our method employs a 1D convolutional neural network (CNN) to process historical THz downlink channel gain data and accurately estimate the number of indoor occupants. The numerical results demonstrate the effectiveness of the proposed approach, achieving a remarkable 99.5% accuracy in people counting indoors up to eight people. The proposed model's ability to maintain high accuracy in indoor people counting across different numbers of users demonstrates its effectiveness and robustness in capturing the occupancy signature from the wireless THz downlink communication signal in indoor environments. Also, the accuracy of the proposed CNN time series classifier outperforms the random forest times series classifier with the catch22 feature extractor by more than 10% without needing any feature extraction methods. To the best of the authors' knowledge, this study represents the first investigation into indoor people counting in the THz frequency band utilizing the THz downlink communication signal for sensing the number of indoor occupants.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".