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Record W2060129820 · doi:10.1109/apccas.2014.7032804

An energy-efficient baseband transmitter design for implantable biotelemetry applications

2014· article· en· W2060129820 on OpenAlexafffund
Deyasini Majumdar, Mithun Ceekala, Kamal El‐Sankary, Christian Schlegel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsDalhousie University
FundersAlberta Innovates - Health SolutionsCMC Microsystems
KeywordsBiotelemetryBasebandTransmitterCMOSComputer scienceAsynchronous communicationEfficient energy useElectronic engineeringWirelessElectrical engineeringEnergy harvestingEmbedded systemPower (physics)EngineeringTelemetryTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Design of implantable wireless telemetry is challenging due to strictly-limited allowable power budgets. The design of wireless prototypes becomes even more challenging due to the need for rice-grain-sized autonomous devices with enhanced useful life. This paper elaborates on the design and implementation of a second-generation prototype of a baseband (BB) transmitter (TX) proposed for use in next-generation body area networks (BANs), with an implementation targeted in TSMC's 65 nm Complementary Metal-Oxide Semiconductor (CMOS) process. The proposed BB TX utilizes a preamble-based asynchronous packet transmission methodology that can potentially meet the energy-efficiency demands of next-generation BANs (below 500pJ/bit). A comparison of the implemented integrated circuit (IC) with the first-generation prototype reveals an improvement of 88% in power consumption. The proposed design requires 1.3mW of power at a supply voltage of 0.9V with an energy efficiency of 144pJ/bit.

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 score0.960
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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