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Record W2702722880 · doi:10.1109/ccece.2017.7946638

Reconfigurable self-calibrated multi-sensing RFID-based platform

2017· article· en· W2702722880 on OpenAlexaff
Mohamed Zgaren, Abbes Amira, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInterfacingComputer scienceTroubleshootingPressure sensorWireless sensor networkEmbedded systemElectronicsElectro-optical sensorISM bandSensor nodeIntegrated circuitComputer hardwareElectrical engineeringElectronic engineeringWirelessEngineeringKey distribution in wireless sensor networksWireless networkTelecommunications

Abstract

fetched live from OpenAlex

This paper concerns an RFID tag platform using an integrated temperature sensor and external pressure sensor targeted for the Electronics Product Code (EPC) Gen-2 standard operating in the 902-928 MHz ISM band. The developed system is part of a low-cost wireless sensors network node. This work demonstrates the study, development, and troubleshooting of battery-assisted sensing platform compliant to the RFID standard electronic product code Gen-2. Two main scenarios are tested to connect the sensors; passive and semi-passive tags. The two architectures allow collecting data based on either the RFID reader command or a signal processing program to save the sensing data using the integrated circuit memory. Temperature and pressure measurements are performed based on the integrated and external sensors combined in the RFID circuit board. Besides the fully integrated temperature sensor with a sensing precision of 0.5 C over the target temperature range, external pressure sensor is connected to provide a flexible way for additional sensors interfacing. Sensor are connected to a single system as the design is intended for a reconfigurable self-calibrated multi-sensing platform. The proposed sensing tags are fully verified through measurements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.652

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 head0.028
GPT teacher head0.250
Teacher spread0.223 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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