MétaCan
Menu
Back to cohort
Record W2914133272 · doi:10.1109/mwscas.2018.8624051

Design and Implementation of an Ultrasonic Link for Concurrent Telemetry of Multiple Data Streams to Implantable Biomedical Microsystems

2018· article· en· W2914133272 on OpenAlexaff
Keivan Keramatzadeh, Amir M. Sodagar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsYork University
Fundersnot available
KeywordsSubcarrierComputer scienceBandwidth (computing)TelemetryMultiplexingData linkElectronic engineeringFrequency-division multiplexingData stream miningFrequency modulationComputer hardwareReal-time computingTelecommunicationsOrthogonal frequency-division multiplexingEngineering

Abstract

fetched live from OpenAlex

This paper reports on the design, implementation, and test of an ultrasonic data telemetry link. The link benefits from the combination of on-off keying (00K) modulation and frequency-division multiplexing (FDM) in order to enhance the telemetry data rate, while using regular transducers. As a result of using the FDM scheme, multiple data streams will be telemetered via the link concurrently. Each one of the data streams is OOK-modulated with a specific sub-carrier frequency. Covering the breadth of the effective bandwidth of the link, the subcarrier frequencies are chosen to be located at the local maxima of the link frequency response. To further increase the data rate, the OOK-modulated data streams are amplitude-equalized to compensate for the non-flat frequency response of the link. Designed using two ultrasonic transducers with a resonance frequency of 1MHz placed with a spacing of 3cm, a prototype link with 8 concurrent sub-channels was developed, characterized, and tested for a bit rate of 120kbps.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.309
Teacher spread0.277 · 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
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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicWireless Power Transfer SystemsFrench-language works237,207