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Record W4388386139 · doi:10.30880/ijie.2021.13.02.003

Development of a Lock Biometric Authentication System for a Battery Powered Locking Device

2021· article· en· W4388386139 on OpenAlexaff
Iman Fitri Ismail, Mas Fawzi, Wan Akashah Wan Jamaludin, Rais Hanizam Madon, Ahmad Fauzan Abdullah, Mohamad Ashik Abdullah

Bibliographic record

VenueInternational Journal of Integrated Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsReach Technologies (Canada)
FundersUniversiti Tun Hussein Onn Malaysia
KeywordsFingerprint (computing)BiometricsFingerprint recognitionLock (firearm)Battery (electricity)ActuatorAuthentication (law)Computer hardwareArduinoComputer scienceDC motorEmbedded systemEngineeringPower (physics)Electrical engineeringArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This paper describes the development of a biometric authentication system in a battery-powered locking device. It differs from the conventional locking system, which connected to an external power supply. The system focuses on a fingerprint-based identification with single button operation. A fingerprint sensor readily available in the market was integrated with an Arduino as the processing unit to unify the button operation as means to trigger functions on the fingerprint sensor and actuator motors, as well as indicating lights for user feedback. The aim of the seamless fingerprint authentication requires a brief matching of users’ fingerprints. A prototype of the system was built and tested. It was found that the device is able to match a fingerprint in less than 1s for up to 50 registered fingerprints. Using a nominal battery capacity of the 12V lithium battery pack with 5000mAh, the amount of current supply to the actuator is sufficient for 400 activations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations2
Published2021
Admission routes1
Has abstractyes

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