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Peer-Review Record

Radiation Emitter Classification and Identification Approach Based on Radiation Emission Components†

Appl. Sci. 2022, 12(16), 8193; https://doi.org/10.3390/app12168193
by Fan Zhang 1, Wang Wang 1, Dongrong Zhang 2, Aixin Chen 1 and Donglin Su 1,3,*
Reviewer 1:
Reviewer 2: Anonymous
Appl. Sci. 2022, 12(16), 8193; https://doi.org/10.3390/app12168193
Submission received: 15 July 2022 / Revised: 9 August 2022 / Accepted: 12 August 2022 / Published: 16 August 2022

Round 1

Reviewer 1 Report

The mathematical model need to be explained in more details. More simulations are required.

Author Response

Please see the attachment

Author Response File: Author Response.doc

Reviewer 2 Report

Traditional EMC testing is based on FFT, WPA and neural networks. In this paper, authors using the EMC testing, obtain the unintentional radiated emission spectral data between 30 MHz and 200 MHz. Based on their previously presented theory which states that many signals are generated by four basic signals  (Sine, square, damped oscillation, spike waves) and produce high frequencies (by coupling, mixing, modulation), in this paper authors verify their proposed method by using different kinds of electronic equipment. 

In my point of view the manuscript is suitable for publication in Applied Sciences journal, as this study confirms with accuracy 99% their method to decompose the radiation data into broadband, peak and trend components.

Author Response

Please see the attachment

Author Response File: Author Response.doc

Round 2

Reviewer 1 Report

Accepted in present form.

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