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A Picosecond Pulse Transmission and Reception System for Next Generation Wireless Sensing and Imaging Applications

2023· article· en· W4388095531 on OpenAlexaff
MuhibUr Rahman, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsVivaldi antennaElectronic engineeringComputer scienceAntenna (radio)Transmission (telecommunications)TelecommunicationsElectrical engineeringEngineeringRadiation pattern

Abstract

fetched live from OpenAlex

This paper proposes and presents a complete ultrafast wireless system in support of transmission and reception of picosecond ultrashort pulses by estimating and incorporating antenna effects. The impact on pulse order, system fidelity factor, pulse distortion, stretch ratio, detailed ringing level, and figure of merit is studied on the receiver side with reference to wideband antipodal Vivaldi antenna. A comprehensive analysis is conducted in such a way that a Gaussian pulse is first generated and then transformed into its derivative for transmission. For both transmission and reception in this case study, a wideband antenna is considered to operate over a 2-to-20 GHz frequency range. An experimental demonstration is made, and a complete system model is derived while presenting the radiated pulse characteristics. This demonstration is set to open a new horizon in the field of future wireless sensing and imaging technologies and applications, specifically related to vital signs monitoring, UWB radars, breast tumor imaging and detections.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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
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

Citations2
Published2023
Admission routes1
Has abstractyes

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