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Battery-Less Approach to Wireless Water Leak Detection

2024· article· en· W4402979590 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLeak detectionBattery (electricity)Computer scienceLeakWirelessEmbedded systemEnvironmental scienceTelecommunicationsEnvironmental engineeringPower (physics)

Abstract

fetched live from OpenAlex

This study introduces a transformative approach to water leak detection systems, overcoming the limitations of conventional battery-dependent models. Our innovative design integrates advanced nanomaterials with dual-purpose monopole antennas, fulfilling critical roles in Radio Frequency (RF) energy harvesting and Bluetooth Low Energy (BLE) data transmission. The BLE system issues prompt leak alerts, while RF -harvested energy provides the system with enough power to send daily “heartbeat” pings, affirming continuous system functionality. The sensor unit, employing an innovative use of a metal sheet in the electricity-generating nanomaterials module, functions dually as an efficient water-leak sensor and an RF ground for antennas, demonstrating a synergy that enhances overall system efficiency. This approach eliminates the environmental concerns associated with battery usage like the need for frequent replacements, recycling complexities, and associated operational costs. This combination of RF energy harvesting and fluid-responsive nanomaterials represents a significant advancement in water leak detection technology, offering a more sustainable, reliable, and cost-effective solution.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.009
GPT teacher head0.183
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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