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Record W2129351666 · doi:10.1109/tuffc.2008.867

Inline SAW RFID tag using time position and phase encoding

2008· article· en· W2129351666 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

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2008
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEncoding (memory)Position (finance)Phase (matter)Computer scienceAcousticsElectrical engineeringPhysicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Surface acoustic wave (SAW) radio-frequency identification (RFID) tags are encoded according to partial reflections of an interrogation signal by short metal reflectors. The standard encryption method involves time position encoding that uses time delays of response signals. However, the data capacity of a SAW RFID tag can be significantly enhanced by extracting additional phase information from the tag responses. In this work, we have designed, using FEM-BEM simulations, and fabricated, on 128 degrees -LiNbO3, inline 2.44-GHz SAW RFID tag samples that combine time position and phase encoding. Each reflective echo has 4 possible time positions and a phase of 0 degrees , -90 degrees , -180 degrees , or -270 degrees. This corresponds to 16 different states, i.e., 4 bits of data, per code reflector. In addition to the enhanced data capacity, our samples also exhibit a low loss level of -38 dB for code reflections.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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