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Record W2557840842 · doi:10.1109/iemcon.2016.7746288

A data acquisition system based on Raspberry Pi: Design, construction and evaluation

2016· article· en· W2557840842 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsData acquisitionMicrocomputerSignal conditioningComputer scienceBenchmark (surveying)Computer hardwareRaspberry piReading (process)Analog signalSIGNAL (programming language)Range (aeronautics)Embedded systemReal-time computingEngineeringOperating systemPower (physics)

Abstract

fetched live from OpenAlex

This paper presents the construction of a low-cost data acquisition system (DAS) prototype based on Raspberry Pi-2 microcomputer. The prototype is designed to operate as a standalone system without the need for an additional personal computer (PC). It performs the data acquisition, online plotting and data logging, simultaneously. This is a general-purpose setup, as it is capable of reading any analog sensor giving output in the designed range, or through appropriate signal conditioning. The system is tested in a Mechanical laboratory by collecting data which are compared to a benchmark DAS. Statistical analyses are also performed on the acquired data. It is proved that both signals are identical with only minor differences.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.243
Teacher spread0.204 · 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

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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