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Record W1928684552 · doi:10.24908/pceea.v0i0.5931

TEACHING DELTA-SIGMA SIGNAL CONVERSION IN AN INTERFACING COURSE

2015· article· en· W1928684552 on OpenAlexafffundvenue
Witold Kinsner

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsInterfacingSIGNAL (programming language)Delta-sigma modulationDigital signalComputer scienceAnalog signalSigmaDigital signal processingData conversionSignal processingDigital filterComputer hardwareTelecommunicationsPhysicsBandwidth (computing)Programming language

Abstract

fetched live from OpenAlex

Conversion of signals is fundamental to theinterfacing of embedded systems. Such signal conversionsinclude (i) analog-to digital (A/D) in order to translate ananalog form of the signal to its sampled and quantized formfor digital signal processing, (ii) digital-to-analog (D/A) inorder to translate the digital samples to a correspondingboxcar signal for further low-pass filtering and recovery ofthe original signal, and (iii) digital-to-digital (D/D) toachieve new desired properties of the data.This paper focuses on teaching the delta-sigma (ΔΣ)A/D conversion that is often omitted from an interfacingcourse because it appears to be a difficult topic tocomprehend and to teach. This new approach links the newΔΣ conversion to the other classes of A/D conversiontechniques explicitly, thus unifying and simplifying theteaching of signal conversions.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0440.021

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designNot applicable
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
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207