MétaCan
Menu
Back to cohort
Record W2440845537 · doi:10.1109/tmtt.2016.2572677

Soil Moisture Scatter Radio Networking With Low Power

2016· article· en· W2440845537 on OpenAlexfundno aff
Spyridon-Nektarios Daskalakis, Stylianos D. Asimonis, Eleftherios Kampianakis, Aggelos Bletsas

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
FundersAlexander S. Onassis Public Benefit FoundationGeneral Secretariat for Research and TechnologyQueen's UniversityQueen's University BelfastEuropean Commission
KeywordsCapacitive sensingWireless sensor networkSoftware-defined radioComputer scienceElectronic engineeringScalabilityRadio frequencyDuty cycleModulation (music)Electrical engineeringEngineeringTelecommunicationsComputer networkAcousticsPhysicsVoltage

Abstract

fetched live from OpenAlex

A low-cost (6 Euro per sensor), low-power (in the order of 200 μW per sensor), with high communication range (on the order of 250 m), scatter radio sensor network is presented, for soil moisture monitoring at multiple locations. The proposed network utilizes analog frequency modulation in a bistatic network architecture (i.e., the emitter and reader are not colocated), while the sensors operate simultaneously, using frequency-division multiple access. In contrast to prior art, this paper utilizes an ultralow-cost software-defined radio reader and offers custom microstrip capacitive sensing with simple calibration, as well as modulation pulses for each scatter radio sensor with 50% duty cycle; the latter is necessary for scalable network designs. The overall root mean squared error below 1% is observed, even for the range of 250 m. This is another small (but concrete) step for the adoption of scatter radio technology as a key enabling technology for scalable, large-scale, low-power, and cost environmental sensor networking.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.708

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)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations66
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Microwave Theory and TechniquesSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207