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Record W2133673269 · doi:10.1109/rws.2009.4957387

An energy-efficient transceiver architecture for short range wireless sensor applications

2009· article· en· W2133673269 on OpenAlexafffund
Reza Yousefi, R. Mason

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsCarleton University
FundersCMC Microsystems
KeywordsTransceiverTransmitterCMOSElectrical engineeringPhase noisedBcFrequency synthesizerElectronic engineeringLocal oscillatorBandwidth (computing)Radio frequencyFrequency offsetPhase-locked loopComputer scienceEngineeringTelecommunicationsChannel (broadcasting)Orthogonal frequency-division multiplexing

Abstract

fetched live from OpenAlex

A low-power, energy-efficient and configurable transceiver architecture is introduced. It is implemented on a single chip intended for use in short range radios based on WPAN (IEEE802.15.4) at 2.4 GHz. The circuitry employed in the dual conversion receiver with the tunable LNA is re-used in the dual loop frequency synthesizer to form a constant envelope modulator and function as the transmitter; techniques are presented for tuning the center frequency and bandwidth of the LNA, as well as generating the required local oscillator frequencies using injection locking mechanism. The transceiver is fabricated in a 0.18-mum standard CMOS process. The receiver achieves -83-dBm sensitivity and -25 dBm 1-dB compression point. The transmitter outputs -7-dBm QPSK signal, while carrier phase noise is better than -108-dBc/Hz at 5-MHz offset. Active mode power consumption is 11-mW and 14-mW in receive and transmit modes, respectively, on a 1.6-V 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 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: none
Teacher disagreement score0.981
Threshold uncertainty score0.717

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 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

Citations7
Published2009
Admission routes2
Has abstractyes

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