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Record W2421721583 · doi:10.1109/maes.2016.150043

Design and implementation of a low-cost SoC-based software GNSS receiver

2016· article· en· W2421721583 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 Aerospace and Electronic Systems Magazine · 2016
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsYork University
Fundersnot available
KeywordsGNSS applicationsField-programmable gate arrayComputer scienceEmbedded systemSoftwareFlexibility (engineering)Satellite navigationComputer hardwareGNSS augmentationSatellite systemGlobal Positioning SystemSoftware-defined radioReal-time computingElectronic engineeringEngineeringTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

A global navigation satellite system (GNSS) receiver processes signals transmitted from the satellites to determine user position, velocity, and time. Compared to conventional receivers, a software GNSS receiver offers better design flexibility and requires fewer dedicated hardware components [1]. An ideal software receiver typically processes all signals in a processor; however, this method is not efficient practically, because it becomes a computational burden for the processor. For this reason, frequent multiplications and operations are offloaded to hardware elements such as a field-programmable gate array (FPGA).

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 score0.252
Threshold uncertainty score0.437

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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