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Record W2971609188 · doi:10.1109/jsen.2019.2938971

Binaural Sonar System for Simultaneous Sensing of Distance and Direction of Extended Barriers

2019· article· en· W2971609188 on OpenAlexafffund
Payman Rajai, Matthew Straeten, Shahpour Alirezaee, Mohammed Jalal Ahamed

Bibliographic record

VenueIEEE Sensors Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WindsorUniversity of Ottawa
KeywordsBinaural recordingHuman echolocationAcousticsComputer scienceCommon emitterSonarTransducerOrientation (vector space)Range (aeronautics)TransmitterComputer visionArtificial intelligenceElectronic engineeringEngineeringPhysicsTelecommunicationsMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

Bat navigation via echolocation has inspired numerous acoustic-based obstacle sensing techniques. In this paper, we use a low-cost, small size, single frequency active binaural system for simultaneous detection of distance and direction of extended obstacles using the time of flight cue. For a binaural system and given size of the transducer, the minimum tolerable separation distance between the emitter and receiver as well as best frequency range for effectively sensing a barrier of any orientation will be assessed by simulation. With the given size of transducers of 6.30 mm in radius and for a working distance of 1 m, the minimum separation distance between the emitter and receiver should not be less than 5 cm and the emitter frequency should not exceed higher than 20 kHz. It will be shown in this article that higher frequency limits the lateral distance detection ability of the system. We experimentally verified the technique for detecting a wall of any orientation with respect to the system’s axis. The approach developed in this paper could be useful for mobile robotics, indoor navigation, and personalized navigation, where a compact configuration is of interest.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.144

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 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

Citations6
Published2019
Admission routes2
Has abstractyes

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