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Record W2140531470 · doi:10.1109/cnsr.2011.49

Performance of a Frequency-Hopped Real-Time Remote Control System in a Multiple Access Scenario

2011· article· en· W2140531470 on OpenAlexafffund
Frank Cervantes, Marc St‐Hilaire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsCarleton University
FundersFederation for the Humanities and Social Sciences
KeywordsFrequency-hopping spread spectrumSpread spectrumComputer scienceAsynchronous communicationSynchronization (alternating current)Real-time computingNetwork packetDuty cycleElectronic engineeringComputer networkEngineeringChannel (broadcasting)Electrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The ubiquitous presence of powerful low-power wireless systems on chip (SoC) able to operate in the Industrial, Scientific and Medical (ISM) band has brought a new enhanced operational choice for real-time Radio Control (RC) applications such as aircrafts and cars in the hobby grade category. Frequency Hopping Spread Spectrum (FHSS) has become the dominant transmission technique for the previously mentioned hardware platform. Even though, FHSS provides for resilience to noise and interference, partial-band type of interference could be specially harmful with regards to the overall system performance. This is critical in real-time RC applications as it could increase system latency. The present paper characterizes the performance of a single real-time RC application, which operates in a realistic multi-user ISM environment by means of two main metrics: System Lag Occurrence Probability (SLOP) and Probability of Losing a Packet (PoLP). Both Synchronous FHSS Multiple Access (SFHSS-MA) and Asynchronous FHSS Multiple Access (AFHSSMA) environments have been modeled. Simulation results show the level of impact on system performance of key engineering parameters such as clock drift, number of co-located users, and variable data packet duty cycle.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.518

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 communication0.0000.001
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 head0.037
GPT teacher head0.234
Teacher spread0.198 · 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
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
Published2011
Admission routes2
Has abstractyes

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