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Record W2113400945 · doi:10.1109/ccece.2009.5090259

RF power harvesting analog front-end circuit for implants

2009· article· en· W2113400945 on OpenAlexaff
Haizheng Guo, Robert Sobot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrical engineeringAnalog front-endAmplifierCMOSWirelessFront and back endsElectronic engineeringLow-noise amplifierVoltageComputer scienceRF front endVoltage regulatorRadio frequencyEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Advances in hardware technology, and wireless network have led to widely usage of implantable wireless micro-systems. However, they still present many challenges in different applications, mainly due to the size constrains and limited available power. In this work, the simulation results of a low voltage CMOS 180nm passive analog front-end circuit intended for powering implants are presented. The 2.4GHz ISM band frequency is employed for transmitting power. The RF signal is converted to a DC power by a charge pump circuit and stabilized at 1V by a voltage regulator. At the same time, the data signals are transmitted through the Medical Implant Communication Service (MICS) band. Subsequently, a low noise amplifier (LNA) with a voltage gain of 16.48dB at 1V is implemented to recover the data signals.

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: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.484

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 head0.062
GPT teacher head0.283
Teacher spread0.221 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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