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Record W2024935826 · doi:10.1109/access.2014.2357422

Novel UWB and Spread Spectrum System Using Time Compression and Overlap-Add Techniques

2014· article· en· W2024935826 on OpenAlex
Stephen Harrison, Peter F. Driessen

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2014
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMultipath propagationWirelessRobustness (evolution)Spread spectrumAir interfaceUltra-widebandData compressionCode division multiple accessReal-time computingAlgorithmElectronic engineeringTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

We present a UWB and spread spectrum communications method based on the idea of time compression where a sampled message signal is transmitted at a higher sampling rate. Robustness is achieved by dividing the signal into overlapping segments, transmitting each segment fast enough so that the segments no longer overlap, receiving these segments and reconstructing the message by overlap-adding the segments. A key feature of this scheme is that an exact sample rate match is not required to recover the signal. This method is implemented in a custom wideband software defined radio, with good results in the presence of interference and multipath. This method, referred to as time compression overlap-add (TC-OLA), represents a new concept and design approach and an advance in fundamental technology of the air interface physical layer that may be relevant to 5G wireless technologies.

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 score0.342
Threshold uncertainty score0.503

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 head0.015
GPT teacher head0.246
Teacher spread0.232 · 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