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Record W2063421261 · doi:10.15866/irease.v7i4.4468

A Transmission Line Modeling for IR-UWB Radars in Human Body Sensing and Detection

2014· article· en· W2063421261 on OpenAlexaff
Tao Wang, Anh Dinh, Li Chen, Daniel Teng, Yang Shi, Seok‐Bum Ko, Vanina Dal Bello‐Haas, Jenny Basran, Carl McCrosky

Bibliographic record

VenueInternational Review of Aerospace Engineering (IREASE) · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTransmission lineTransmission (telecommunications)Electronic engineeringTransceiverAcousticsAntenna (radio)Multipath propagationComputer scienceElectric power transmissionCapacitanceAttenuationChannel (broadcasting)WirelessElectrical engineeringEngineeringTelecommunicationsPhysicsOptics

Abstract

fetched live from OpenAlex

For the applications of IR-UWB in human body’s sensing and detection, researchers and industries concentrate on the circuit design of the UWB transceiver and antenna. Very few work focuses on the significant part, the human body’s physical dimension through which the UWB signal propagate. This paper presents an approach to model the multi-tissue layer from the skin to the heart of a human body in a wideband of spectrum. The tissues act as a two-wire transmission line carrying a high frequency signal. Some of critical transmission line parameters such as characteristic impedance, propagation constant as well as its resistance, inductance, conductance, and capacitance per unit length are derived based on the transmission line theory and the human body featured from a cellular view. The distributed circuit of this transmission line model is implemented and simulated in a CAD tool. Simulation results provide the reflection and transmission factors at the boundary between two neighbor tissues (for example, between the lung and the heart) along with S-parameter analysis and attenuation in the tissues. The “body channel” model is very useful in the design of IR-UWB human sensing devices.

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: Empirical · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.895

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Review of Aerospace Engineering (IREASE)Same topicWireless Body Area NetworksFrench-language works237,207