Evidence map›Paper›PMID 38813222›Full record

ArticleHeliyon2024

Optical frequency multiplication using residual network with random forest regression.

Qi Zhang, Xu Han, Xinyu Fang, Ming Liu, Ke Ge, Hong Jiang

Abstract read
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In one paragraph

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

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0citing papers in PubMed
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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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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qi ZhangCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.
Xu HanCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.
Xinyu FangCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.
Ming LiuCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.
Ke GeCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.
Hong JiangCollege of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

Indexed as

Deep learning algorithmFrequency multiplication modulationRandom forest regressionResidual network

Identifiers

PMID38813222
PMCPMC11133761

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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.