Evidence map›Paper›PMID 35336448›Full record

ArticleSensors (Basel, Switzerland)2022

Adaptive Recognition of Bioacoustic Signals in Smart Aquaculture Engineering Based on r-Sigmoid and Higher-Order Cumulants.

Tianyu Cao, Xiaoqun Zhao, Yichen Yang, Caiyun Zhu, Zhongwei Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tianyu CaoDepartment of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.ORCID 0000-0003-3188-8818
Xiaoqun ZhaoDepartment of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.
Yichen YangDepartment of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.
Caiyun ZhuDepartment of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.ORCID 0000-0001-8241-460X
Zhongwei XuDepartment of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.

Funding

Tongji University kh0080020192283
6 · The paper itself

Abstract

In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.

Indexed as

AcousticsAlgorithmsAquacultureNoiseRecordsHOCrecognition-sigmoid functionSRCunderwater acoustic signals

Identifiers

PMID35336448
PMCPMC8953075

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.