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Record W1974087819 · doi:10.1109/icuwb.2007.4381058

A Low Power CMOS Transmitter Design for IR-UWB Communication Systems

2007· article· en· W1974087819 on OpenAlexaff
Yanjie Wang, Sai Mohan Kilambi, Vincent Gaudet, Kris Iniewski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransmitterCMOSUltra low powerComputer scienceLow-power electronicsElectronic engineeringElectrical engineeringPower (physics)TelecommunicationsEngineeringPower consumptionPhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

A low power CMOS impulse-based transmitter with on-off keying (OOK) modulation scheme, for ultra-wideband (UWB) impulse radio (IR) system has been designed and laid out in a standard TSMC 0.18 mum CMOS technology. A novel design of Gaussian mono-pulse generator employing simple and robust first-derivative capacitive current-voltage characteristic has been presented. On-chip pulse shaping using an LC Band-Pass Filter (BPF) is developed to meet the Federal Communications Commission (FCC) spectrum requirement. The post-layout simulation results of the proposed UWB transmitter are analyzed. A Gaussian mono-pulse of less than 100 ps falling/rising time with a 130 mVppamplitude is obtained, at a clock frequency of 500 MHz. The total power dissipation of the transmitter is 1.2 mW with only 316 muW consumed by impulse generator. The output of the transmitter before antenna is 48 mVppunder a 1.2 V power supply.

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 score0.008

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 communication0.0010.000
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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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