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Dear Colleagues,

Nowadays, the applications of pattern recognition and artificial intelligence technologies are proliferating rapidly in our daily life. Among the various fields of artificial intelligence, machine learning has certainly been one of those most studied in recent years. There has been an enormous shift in machine learning, illuminating unprecedented theoretic and application-based opportunities. Examples of the application of pattern recognition and machine learning technologies include communication, self-driving vehicles, gaming, and image recognition, amongst others. Despite the significant success of machine learning and pattern recognition methods in the past decade, their ability to address real-world problems remains unsatisfactory. There remains much to be studied in the related areas.

This Special Issue aims to provide a platform via which researchers may share their recent advancements in different areas of pattern recognition and artificial intelligence, with an emphasis on new approaches and techniques for machine learning applications. We encourage the submission of papers with an innovative concept or research results that address all aspects of pattern recognition and machine learning applications.

Topics of interest include (but are not limited to) the following:

Statistical and structural pattern recognition methods and applications;
Signal and image processing;
Computer vision and pattern recognition;
Data analytics, data mining, and computing in big data;
Machine learning algorithms, model selection, clustering, and classification;
Methodologies, frameworks, and models for pattern recognition;
Machine learning applications:
Image analysis;
communications (such as V2X, IoT, MEC);
information processing;
biometrics analysis;
healthcare and medical image analysis;
natural language processing;
scenario fusion and classification.

Prof. Dr. Junhui Zhao
Prof. Dr. Lisheng Fan
Dr. Shengrong Bu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Published Papers (2 papers)

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Research

17 pages, 4952 KiB

Open AccessArticle

Spectral-Kurtosis and Image-Embedding Approach for Target Classification in Micro-Doppler Signatures

by Ji-Hyeon Kim, Soon-Young Kwon and Hyoung-Nam Kim

Electronics 2024, 13(2), 376; https://doi.org/10.3390/electronics13020376 - 16 Jan 2024

Viewed by 659

Abstract

Micro-Doppler signature represents the micromotion state of a target, and it is used in target recognition and classification technology. The micro-Doppler frequency appears as a transition of the Doppler frequency due to the rotation and vibration of an object. Thus, tracking and classifying targets with high recognition accuracy is possible. However, it is difficult to distinguish the types of targets when subdividing targets with the same micromotion or classifying different targets with similar velocities. In this study, we address the problem of classification of three different targets with similar speeds and segmentation of the same type of targets. A novel signature extraction procedure is developed to automatically recognize drone, bird, and human targets by exploiting the different micro-Doppler signatures exhibited by each target. The developed algorithm is based on a novel adaptation of the spectral kurtosis technique of the radar echoes reflected by the three target types. Further, image-embedding layers are used to classify the spectral kurtosis of objects with the same micromotion. We apply a ResNet34 deep neural network to micro-Doppler images to analyze its performance in classifying objects performing micro-movements on the collected bistatic radar data. The results demonstrate that the proposed method accurately differentiates the three targets and effectively classifies multiple targets with the same micromotion. Full article

(This article belongs to the Special Issue Pattern Recognition and Machine Learning Applications, 2nd Edition)

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12 pages, 28124 KiB

Open AccessArticle

Substation Personnel Fall Detection Based on Improved YOLOX

by Xinnan Fan, Qian Gong, Rong Fan, Jin Qian, Jie Zhu, Yuanxue Xin and Pengfei Shi

Electronics 2023, 12(20), 4328; https://doi.org/10.3390/electronics12204328 - 18 Oct 2023

Cited by 2 | Viewed by 977

Abstract

With the continuous promotion of smart substations, staff fall detection has become a key issue in automatic detection of substations. The injuries and safety hazards caused by falls among substation personnel are numerous. If a timely response can be made in the event of a fall, the injuries caused by falls can be reduced. In order to address the issues of low accuracy and poor real-time performance in detecting human falls in complex substation scenarios, this paper proposes an improved algorithm based on YOLOX. A customized feature extraction module is introduced to the YOLOX feature fusion network to extract diverse multiscale features. A recursive gated convolutional module is added to the head to enhance the expressive power of the features. Meanwhile, the SIoU(Soft Intersection over Union) loss function is utilized to provide more accurate position information for bounding boxes, thereby improving the model accuracy. Experimental results show that the improved algorithm achieves an mAP value of 78.45%, which is a 1.31% improvement over the original YOLOX. Compared to other similar algorithms, the proposed algorithm achieves high accuracy prediction of human falls with fewer parameters, demonstrating its effectiveness. Full article

(This article belongs to the Special Issue Pattern Recognition and Machine Learning Applications, 2nd Edition)

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