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Record W2144903671 · doi:10.1109/issse.2007.4294512

Adaptive Microwave Beamforming with Low Weighting Rate Requirement

2007· article· en· W2144903671 on OpenAlexaff
Abdel-Razik Sebak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsBeamformingWeightingAdaptive beamformerBandwidth (computing)Computer scienceMicrowaveSIGNAL (programming language)Signal-to-interference-plus-noise ratioInterference (communication)AlgorithmElectronic engineeringControl theory (sociology)AcousticsTelecommunicationsPhysicsEngineeringPower (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

A new structure for single-port adaptive microwave beamforming is proposed. In this structure the array signal vector is estimated for gradient vector estimation based on temporally-correlated array signal samples. The temporal correlation is provided by lowering the overall receiver bandwidth instead of high weighting rate which makes the structure realizable with slower control elements. The structure is simulated in time domain including the beam switching transients. Adaptive constrained least mean square (CLMS) algorithm is implemented with the proposed structure. It is observed that the proposed single-port structure achieves higher signal to interference plus noise ratio (SINR) gain than the corresponding multi-port structure.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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