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Record W2128801939 · doi:10.1109/wddc.2007.4339442

Radar signal design using chaotic signals

2007· article· en· W2128801939 on OpenAlexaff
Ali Ashtari, Gabriel Thomas, Héctor Garcés, Benjamin C. Flores

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChaoticSIGNAL (programming language)Computer scienceRadarNoise (video)Frequency modulationSpectral densityModulation (music)Electronic engineeringAcousticsPhysicsTelecommunicationsRadio frequencyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The use of chaotic signals in radar imaging applications present particular advantages as they behave like pseudo noise, have a wide band, and are easy to generate. A chaotic frequency modulated (FM) sine wave is an example of a chaotic signal that can yield higher transmitted mean power when peak-power limited transmitters are used. Unlike the random FM signal, the behavior of chaotic FM signals is not fully understood. In this paper, two approaches for analyzing the spectrum of chaotic FM signals are discussed. The first approach approximates the chaotic signal with noise and the second one, deals with the condition for the chaotic signal to remain chaotic after frequency modulation and consequently have a wide band spectrum.

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 categoriesInsufficient payload (model declined to judge)
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 score0.832
Threshold uncertainty score0.999

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)0.0020.000

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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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.

Study designSimulation or modeling
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

Citations23
Published2007
Admission routes1
Has abstractyes

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