MétaCan
Menu
Back to cohort
Record W2062678695 · doi:10.1109/iccis.2010.5518539

RFIDMania extensible and adaptable RFID middleware and specifications

2010· article· en· W2062678695 on OpenAlexaff
Eyan Aboulouz, Dwight Deugo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceVendorMiddleware (distributed applications)Radio-frequency identificationSoftware deploymentSoftware engineeringScalabilitySoftwarePortingProcess (computing)StandardizationExtensibilityBusiness logicWorld Wide WebEmbedded systemOperating systemDatabase

Abstract

fetched live from OpenAlex

Radio Frequency Identification technology is an emerging technology that allows objects to be electronically tagged and identified wirelessly. In recent years, many organizations have started to show interest in porting this technology to their existing business processes. With the increase in popularity of the technology, many vendor-specific RFID readers are being manufactured and sold to interested organizations. Deployment of such RFID readers to existing business processes is difficult as software developers need to understand each vendor-specific RFID reader, due to lack of standardization among RFID readers. This paper describes a scalable and adaptable middleware, called RFIDMania, designed to allow software developers to interact with any RFID reader without having to know the specifics of the reader. RFIDMania provides a framework to process data received from RFID tags, and translate, decode, filter and route the data to the application in the form of an event. RFIDMania is able to offer software developers a framework to deliver portable generic code that is not tightly coupled with hardware specific commands.

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 score0.519
Threshold uncertainty score0.338

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 head0.014
GPT teacher head0.184
Teacher spread0.170 · 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicRFID technology advancementsFrench-language works237,207