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Record W4318573557 · doi:10.1088/2058-8585/acb722

Enhanced RF response of 3D-printed wireless LC sensors using dielectrics with high permittivity

2023· article· en· W4318573557 on OpenAlex
Amirhossein Hassanpoor Kalhori, Taeil Kim, Woo Soo Kim

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

VenueFlexible and Printed Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsSimon Fraser University
FundersNational Research Council
KeywordsWirelessWearable computerElectrical engineeringInductorMaterials scienceCapacitorWireless sensor networkComputer scienceElectronic engineeringEngineeringTelecommunicationsEmbedded systemComputer networkVoltage

Abstract

fetched live from OpenAlex

Abstract The development of wireless sensing technologies paves the way for advances in the fields of wearable devices, prosthetics and robotics. Wireless communication between sensors and readers plays an important role in recent Internet of Things technologies. Among many types of wireless sensing devices, wireless passive radio frequency devices including inductor-capacitor (LC) resonators have been spotlighted. However, passive LC sensors suffer from short-range wireless detection, and their fabrication requires several processes. Here, we design a 3D integrated wireless compact LC location sensor fabricated using the 3D printing method for multi-layered devices. The fabricated wireless sensing system shows an increased wireless readout distance of up to 10 cm. In addition, a dielectric material with high dielectric permittivity has been applied to enhance the quality factor of the sensors by 2.5 times with improved wireless detection.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
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
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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