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Record W2547374654 · doi:10.1109/ccece.2016.7726660

Design of digital modules for capacitive proximity sensing system applications

2016· article· en· W2547374654 on OpenAlexaff
Fan Xia, Behraad Bahreyni, Fabio Campi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCapacitive sensingProximity sensorComputer scienceObstacleComputer hardwareProcess (computing)Digital signal processingRobotSIGNAL (programming language)Field-programmable gate arrayEmbedded systemElectronic engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Design of an intelligent controlling module as well as the signal processing module for a novel capacitive proximity sensing system for robotic applications are proposed. The capacitive sensor is composed of a 4×4 matrix of electrodes. The intelligent controlling module is mapped on an FPGA device embedded in the robotic unit; and is utilized to realize various different sensing arrangements within the same electrode matrix. The signal processing module is realized on an external computer and is used to properly process and classify the results produced by the sensor. By involving these two digital components, a rough estimation of obstacles facing the robot unit carrying the sensor and relative accurate distance information from the obstacle can be obtained.

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 score0.970
Threshold uncertainty score0.160

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 head0.020
GPT teacher head0.200
Teacher spread0.180 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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