MétaCan
Menu
Back to cohort
Record W1761004645 · doi:10.1002/dac.2568

Hybrid MUSIC Dolph–Chebyshev algorithm for a smart antenna system

2013· article· en· W1761004645 on OpenAlexaff
Ridha Ghayoula, Amor Smida, Ali Gharsallah, Dominic Grenier

Bibliographic record

VenueInternational Journal of Communication Systems · 2013
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSmart antennaComputer scienceChebyshev filterAntenna arrayAntenna (radio)Sensor arrayGate arrayBeamformingField-programmable gate arrayElectronic engineeringAlgorithmComputer hardwareTelecommunicationsDirectional antennaEngineering

Abstract

fetched live from OpenAlex

ABSTRACT This paper presents practical design of a smart antenna system based on direction‐of‐arrival estimation and Dolph–Chebyshev beam forming. Direction‐of‐arrival estimation is based on the multiple signal classification algorithm for identifying the directions of the source signals incident on the sensor array comprising the smart antenna system. The smart antenna system design involves a hardware part, which provides real data measurements of the incident signals received by the sensor array. This paper presents the Dolph–Chebyshev method for the synthesis and design of antenna arrays with periodic element spacing. A Field‐Programmable Gate Array implementation is presented for an antenna array application employing digital beamforming. The array comprises 10 elements, equal in number receiving radio frequency and intermediate frequency components, as well as a Spartan‐3E Field‐Programmable Gate Array‐based unit, which is responsible for the control of the array. Low‐cost and switched‐beam, and fully adaptive antenna array suitable for 2‐GHz applications are proposed in this paper. Copyright © 2013 John Wiley & Sons, Ltd.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.554

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 communication0.0000.001
Open science0.0030.000
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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Communication SystemsSame topicDirection-of-Arrival Estimation TechniquesFrench-language works237,207