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Record W4388520053 · doi:10.1109/jsen.2023.3328193

Wide Bandwidth Signals for Joint Time–Frequency Characterization of Nonlinear and Time-Varying Circuits

2023· article· en· W4388520053 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

VenueIEEE Sensors Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Electrical Measurement Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWidebandCrest factorBandwidth (computing)Time domainElectronic engineeringElectronic circuitNonlinear systemResistorLinearitySIGNAL (programming language)Computer scienceAcousticsVoltageEngineeringElectrical engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this work, we generate and use a total of six different wideband signals for joint time–frequency characterization of nonlinear time-invariant [N-shaped differential resistor (NDR)] and linear time-varying (thermistor) circuits. A data acquisition board is used for applying the signals in the form of a voltage excitation and reading the induced current. The input signals have flat power spectra, thus avoiding the need for iterative calibration loops required to obtain signals with low crest factor. Such iterative loops are unavoidable when using random, pseudorandom, or chaotic signals all of which have a wideband spectrum but not necessarily a flat one. Flat spectra help in controlling the maximum voltage within the generated signal, thus eliminating large spikes in the obtained signal. The accuracy of the measurements is compared with the measurements acquired on a Biologic VSP-300 electrochemical station. An average relative error margin of 3% at most is obtained.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.580

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 head0.026
GPT teacher head0.244
Teacher spread0.219 · 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