Communication behavior recognition using CNN-based signal analysis.

Hao Meng, Yingke Lei, Fei Teng, Jin Wang, Changming Liu, Caiyi Lou

Abstract read

In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hao MengSchool of Electronic Countermeasures, National University of Defense Technology, Anhui, China.

Yingke LeiSchool of Electronic Countermeasures, National University of Defense Technology, Anhui, China.

Fei TengSchool of Electronic Countermeasures, National University of Defense Technology, Anhui, China.

Jin WangSchool of Electronic Countermeasures, National University of Defense Technology, Anhui, China.

Changming LiuElectronic Countermeasures Division, Tongfang Electronic Technology Co., Ltd., Jiangxi, China.

Caiyi LouJiaxing Communication Information Security Control Laboratory, Technology Research Institute, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article explores the technology of recognizing non-cooperative communication behavior, with a specific emphasis on analyzing communication station signals. Conventional techniques for analyzing signal data frames to determine their identity, while precise, do not have the ability to operate in real-time. In order to tackle this issue, we developed a pragmatic architecture for recognizing communication behavior and a system based on polling. The method utilizes a one-dimensional convolutional neural network (CNN) to segment data, hence improving its ability to recognize various communication activities. The study assesses the reliability of CNN in several real-world scenarios, examining its accuracy in the presence of noise interference, varying lengths of interception signals, interferences at different frequency points, and dynamic changes in outpost locations. The experimental results confirm the efficacy and dependability of the convolutional neural network in recognizing communication behavior in various contexts.

Indexed as

Actual environmental adaptabilityCommunication behavior cognitionConvolutional neural networkDeep learningNon cooperative communication behavior cognition

Identifiers

PMID38855248

DOI10.7717/peerj-cs.2036

PMCPMC11157550

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LicenceCC BY

Read underepoch 390

Registered trials

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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.