Advances in Signal Detecting and Processing

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 2789

Special Issue Editors

Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China
Interests: radar, communication and sensor signal detection and processing
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Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: silicon photonics; microwave photonics; intelligent photonics
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Guest Editor
School of Automation, China University of Geosciences, Wuhan, China
Interests: machine vision and its applications in industry; image recognition, and processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: intelligent spectrum control; 3D reconstruction and measurement; photoelectric instrument

Special Issue Information

Dear Colleagues,

Signal detection and processing have been widely applied in various fields such as radar, communication, sonar, navigation, biomedical, geophysical, speech processing, image processing, and deep learning. With the development of technology, especially artificial intelligence, new signal detection and processing technologies continue to emerge, and the application fields of some traditional technologies have also been expanded. This Special Issue aims to provide a platform for researchers in the field of signal detection and processing technology, and to promote communication and innovation in this field. In this Special Issue, we invite research papers presenting advanced methods and novel applications of signal detection and processing technologies.

Dr. Wei Xue
Prof. Dr. Li Liu
Dr. Yue Yang
Dr. Jingjing Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • advanced signal detection and processing theory
  • new applications of signal detection and processing
  • implementation of signal detection and processing methods
  • the combination of signal detection and processing methods and deep learning
  • improvement in signal detection and processing methods

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Published Papers (1 paper)

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Research

17 pages, 2255 KB  
Article
Electromyography-Based Sign Language Recognition: A Low-Channel Approach for Classifying Fruit Name Gestures
by Kudratjon Zohirov, Mirjakhon Temirov, Sardor Boykobilov, Golib Berdiev, Feruz Ruziboev, Khojiakbar Egamberdiev, Mamadiyor Sattorov, Gulmira Pardayeva and Kuvonch Madatov
Signals 2025, 6(4), 50; https://doi.org/10.3390/signals6040050 - 25 Sep 2025
Cited by 3 | Viewed by 1976
Abstract
This paper presents a method for recognizing sign language gestures corresponding to fruit names using electromyography (EMG) signals. The proposed system focuses on classification using a limited number of EMG channels, aiming to reduce classification process complexity while maintaining high recognition accuracy. The [...] Read more.
This paper presents a method for recognizing sign language gestures corresponding to fruit names using electromyography (EMG) signals. The proposed system focuses on classification using a limited number of EMG channels, aiming to reduce classification process complexity while maintaining high recognition accuracy. The dataset (DS) contains EMG signal data of 46 hearing-impaired people and descriptions of fruit names, including apple, pear, apricot, nut, cherry, and raspberry, in sign language (SL). Based on the presented DS, gesture movements were classified using five different classification algorithms—Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, and neural networks—and the algorithm that gives the best result for gesture movements was determined. The best classification result was obtained during recognition of the word cherry based on the RF algorithm, and 97% accuracy was achieved. Full article
(This article belongs to the Special Issue Advances in Signal Detecting and Processing)
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