MétaCan
Menu
Back to cohort
Record W3034332226 · doi:10.22215/etd/2017-11744

Determination of Bioimpedance Using Multi-Frequency Bio-electrical Impedance Analysis

2017· dissertation· en· W3034332226 on OpenAlexaff
Satyanarayana Brahmandlapalli

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectronic engineeringElectrical impedanceTransient (computer programming)Electronic circuitAmplifierElectrical engineeringCMOSVoltageFrequency responseTransient responseEngineeringComputer science

Abstract

fetched live from OpenAlex

A current source circuit and a voltage acquisition circuit along with a postprocessing rectifier circuit are designed to determine bioimpedance of a biological subject.An operational amplifier based current source with a simulated output impedance of 6.7MΩ and operational at frequencies under 100kHz is designed to inject current into the subject.An instrumentation amplifier operating at unity gain with a simulated CMRR of -111.4dB and DC power consumption of 477.7µW is used to acquire the voltage response of the subject.A diode-less rectifier circuit that eliminates the loss in voltage due to diode threshold voltage during rectification is designed.Layouts for the sub-circuits have been designed and developed using the Cadence layout tool, Vitruoso, and integrated to develop the IC layout.When tested, the operational amplifier is found to oscillate due to inadequate phase margin.i Researching a topic, designing an integrated circuit and the IC layout, taping it out, testing the chips upon their fabrication, writing the thesis and defending the thesis has been a fierce and profound journey.Having very little background on IC design and very limited experience in circuits, this thesis couldn't have been possible without the support of a few people in my life.With all humility, I'd like take this opportunity to acknowledge the people and appreciate their support.First of all, I'd like to thank my parents, Mr. B Rama Rao and Ms. B N S Prasanna Laxmi.It can be categorically stated that without their financial and emotional support, this thesis would've remained an un-true dream.Second of all, I'd like to thank my supervisor, Dr. Leonard MacEachern, for his support and confidence in me through out the course of the thesis.His professional expertise and personality taught me more than just designing circuits.It is an honor to have worked with him under his supervision and would like to continue to learn from him in the future too.Thirdly, I'd like thank my friend, Mr. Niranjan B Ramesh, for sharing his knowledge of integrated circuits

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicElectrical and Bioimpedance TomographyFrench-language works237,207