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Record W6986685161

16 QAM Radio Link on the 5.7 GHz ISM Band

2003· dissertation· en· W6986685161 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2003
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsISM bandBandwidth (computing)Quadrature amplitude modulationModulation (music)WirelessFrequency modulationDigital radioBasebandAmplitude modulation
DOInot available

Abstract

fetched live from OpenAlex

A wireless data distribution system could be more cost effective than retrofitting cable to a finished building. The radio channel has the disadvantage of being lossy, noisy, and frequency selective. To compensate, the modulator/demodulator needs to be more robust. This thesis investigates the feasibility of implementing a modern wireless data distribution in an in—building environment. The cost of FPGA's is constantly decreasing, making this solution more and more cost—effective as time goes on.\n\nThe ISM band is used in order to avoid licensing issues. This band requires that spread spectrum modulation with a spreading gain greater than or equal to 10 be used. To get a full complement of music channels on the ISM bandwidth requires a high order modulation scheme. In this study, the feasibility of implementing a 16— QAM modem is investigated. The resulting system is a hybrid modulation scheme with direct sequence spread spectrum modulation in tandem with 16-QAM.\n\nThe entire system, excluding the RF portion, is implemented on three Altera 100,000 gate FLEX 10KA series FPGA's. The design is specified using Verilog HDL. This facilitates future design changes and ASIC development.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.004

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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 designBench or experimental
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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

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