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Record W4387379978 · doi:10.7716/aem.v12i3.2083

Phased array antenna controlled by FPGA-ARM Cortex-M Processor

2023· article· en· W4387379978 on OpenAlex
Wided Amara, Ridha Ghayoula, A. Hammami, Amor Smida, Issam El Gmati, Jaouhar Fattahi

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

VenueAdvanced Electromagnetics · 2023
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversité LavalUniversité de Moncton
FundersUmm Al-Qura University
KeywordsField-programmable gate arrayGate arrayComputer sciencePhased arrayAntenna (radio)Digital signal processorComputer hardwareDigital signal processingDirection of arrivalSIGNAL (programming language)Antenna arraySignal processingElectronic engineeringARM architectureEmbedded systemEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper discusses the architecture of an adaptive transmitting antenna array that allows highly flexible beamsteering. Antenna arrays are used in many digital signal processing applications due to their ability to locate signal sources. The Direction of Arrival (DOA) estimation is a key task of array signal processing, with the Taguchi method and Multiple Signal Classification (MUSIC). Although various algorithms have been developed for DOA estimation, their high complexity prevents their use in real-time applications. In this paper, we design and develop an implementation using Field Programmable Gate Array (FPGA Artix-7), which is the most widely used ARM processor in embedded systems. The antenna array is digitally controlled with the phases synthesized by the Taguchi method.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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