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Record W3046677084 · doi:10.11159/icbes20.112

Sensitivity Analysis of a Portable Microwave Breast Cancer Detection System

2020· article· en· W3046677084 on OpenAlexafffundvenue
Muhammad Masud Rana, Debarati Nath, Stephen Pistorius

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsResearch Institute in Oncology and HematologyUniversity of Manitoba
FundersUniversity of ManitobaCancerCare Manitoba Foundation
KeywordsSensitivity (control systems)Breast cancerMicrowaveComputer scienceCancerElectronic engineeringTelecommunicationsMedicineEngineeringInternal medicine

Abstract

fetched live from OpenAlex

A prototype portable breast microwave sensing (BMS) system for early breast cancer detection has been developed in our lab.In this paper, we provide preliminary results for the response of the antenna sensor array to a point scatterer, using both simulation and experiment.The portable system uses a horn antenna to transmit frequencies from 1.5 GHz to 6 GHz and a sensor array using thirteen patch antennas.The optimal separation of each antenna in the receiver array was calculated to be 4 mm based on an envelope correlation coefficient of 0.37.The BMS system, including the horn antenna, was designed in CST Microwave Studio to mimic the experimental setup.This study compares the E-field characteristics and DC voltages for each sensor, using simulation and experimental results, for both a free air system and with an Aluminum rod placed at different positions in the scanning plane.A 13×10 array of geometric correction constants was calculated from the simulated E-field.The range of difference between the simulated and experimental results was -4% to 3% for open space conditions and ±20% when an Aluminum rod was placed at different positions in the scanning plane.The preliminary results are promising and provide some insight as to where improvements must be made to enhance the detection ability of the portable system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.005
GPT teacher head0.180
Teacher spread0.176 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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