Dual‐band sensor–antenna design for low energy consumption/cost wireless sensor nodes
Bibliographic record
Abstract
Wireless sensor networks (WSNs) consist of nodes with a limited power source. Reducing the energy consumption is an effective way to extend the lifespan of the sensor node. In this contribution, a new approach of designing of a new class radio frequency sensor–antenna for low energy consumption and low‐cost wireless sensor nodes is proposed. Unlike the architecture of a conventional wireless sensor node, the proposed approach does not require any processor device that consumes power. It consists of integrating a capacitive sensor in a narrowband antenna that can send raw information, through its operating frequency which can be tuned depending on the value of the physical parameter to measure. As a result, a corresponding table between the resonant frequency of the sensor–antenna and the measured physical parameter is obtained. Furthermore, by using a dual‐band antenna, the proposed configuration is able to measure, simultaneously, two physical parameters. A prototype of the sensor–antenna has been simulated and its behaviour has been validated with measurements. From the obtained results, it can be noted that the use of the proposed sensor–antenna can be a good alternative to reduce considerably the complexity and hence the cost of wireless sensor nodes for WSN applications.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".