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A Real-Time Acoustic Spectrum Analyzer on FPGA for the Internet of Underwater Things

2022· article· en· W4292070380 on OpenAlexafffund
Mojgan Mirzaei Hotkani, Jean‐François Bousquet, Bruce Martin, Laird Bolt, Blair Macdonald

Bibliographic record

Venue2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) · 2022
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
FundersMitacs
KeywordsSpectrum analyzerUnderwaterField-programmable gate arrayComputer scienceUnderwater acoustic communicationThe InternetInternet of ThingsSpectrum (functional analysis)Embedded systemTelecommunicationsPhysicsWorld Wide WebGeology

Abstract

fetched live from OpenAlex

The deployment of a low-power broadband acoustic spectrum analyzer in the ocean is of substantial interest, particularly for underwater monitoring applications. This paper presents a real-time spectrum analyzer implemented on a Field Programmable Gate Array. The resource requirement is reduced by using a novel implementation of the Hann window at the input of the Fast Fourier Transform algorithm. Also, using a logarithmic scale, the frequency range represented is equal to 32,768 Hz. The design is demonstrated using Xilinx’s Artix-7 System-on-Chip. To verify the accuracy of the implemented circuit, it is compared against the performance of an ideal simulated circuit in Matlab.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.256
Teacher spread0.213 · 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.

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
Published2022
Admission routes2
Has abstractyes

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