S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Copyright(C) MYU K.K.

Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

Xinyu Hao, Yu Luo, Qiubo Ye, Qi He, Chin-Cheng Chen, and Guangsong Yang

(Received June 9, 2021; Accepted September 16, 2021)

Keywords: convolutional neural networks, global average pooling, gate recurrent units, automatic modulation recognition

With the application of various wireless communication technologies, the electromagnetic environment has become more complex, and the recognition of signal modulation has become increasingly difficult. In this paper, a hybrid model based on deep learning, which aims to quickly classify received modulated signals and help to plan spectrum resources, is proposed. The model is designed by considering the characteristics of convolutional neural networks (CNNs), global average pooling (GAP), gate recurrent units (GRUs), and other structures. Firstly, signal spatial features are extracted by convolution using a CNN, the dimension of the high-dimensional feature map is reduced by GAP, then the signal temporal correlation is extracted using GRUs. Finally, modulation modes are classified in the softmax layer to classify and recognize the modulation modes of the received signal. Experimental results show that the average recognition rate of the model was 60.64% and the maximum recognition rate was 90%. The proposed method not only improves the recognition performance, but also enhances the interpretability of the network.

Corresponding author: Chin-Cheng Chen, Guangsong Yang

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on Intelligent Manufacturing and Application Technology Part 1
Guest editor, Cheng-Chi Wang (National Chin-Yi University of Technology)
Call for paper

Special Issue on Smart Sensing Technologies and Their Application in Forest Management and Engineering
Guest editor, Byoungkoo Choi (Kangwon National University)
Call for paper

Special Issue on Sensors, Materials, and Computational Intelligence Algorithms in Robotics and AI Engineering
Guest editor, Pitikhate Sooraksa (King Mongkut’s Institute of Technology Ladkrabang)
Call for paper

Special Issue on Microfluidics and Related Nano/Microengineering for Medical and Chemical Applications
Guest editor, Yuichi Utsumi (University of Hyogo)
Call for paper

Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2019 (Bio4Apps 2019) (2)
Guest editor, Hirofumi Nogami and Masaya Miyazaki (Kyushu University)
Conference website

Special Issue on High-sensitivity Sensors and Sensors for Difficult-to-measure Objects
Guest editor, Ki Ando (Chiba Institute of Technology)
Call for paper

Copyright(C) MYU K.K. All Rights Reserved.