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Record W2533411565 · doi:10.1109/itict.2005.1609632

Ultra-Low Power Circuit and System Design Trade-Offs for Smart Sensor Network Applications

2006· article· en· W2533411565 on OpenAlexaff
K. Iniewski, C. Siu, Sai Mohan Kilambi, Saifur R. Khan, B. Crowley, Patrick P. Mercier, Christian Schlegel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransceiverCMOSWireless sensor networkWirelessComputer scienceEmbedded systemElectrical engineeringEngineeringTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

The presented analysis shows that a system approach, that targets heterogeneous technologies, is clearly needed to address ultra-low power operation of hardware built for wireless sensor networks. CMOS design efforts have to be aided by a clear understanding of network requirements and protocol implementation. A very talented CMOS designer, or even dozen of them, working in isolation are not going to get far ahead in designing useful RF transceivers for "smart dust" applications. Although impressive progress has been made in the last decade in CMOS RF design in order to build state-of-the-art ultra low power wireless nodes CMOS designers need to pair with battery chemists, antenna physicists, and communication experts to execute on the exciting vision of "ambient intelligence"

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.821

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 head0.185
Teacher spread0.175 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations14
Published2006
Admission routes1
Has abstractyes

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