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Pattern Recognition and Machine Learning Applications, 2nd Edition

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 2932

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Special Issue Editors

Prof. Dr. Junhui Zhao

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Guest Editor

School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: AI application in ICT and transportation; 5G/6G mobile communications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lisheng Fan

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Guest Editor

School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 511370, China
Interests: wireless communications; security; edge computing; deep learning; federated learning; IoT networks
Special Issues, Collections and Topics in MDPI journals

Dr. Shengrong Bu

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Guest Editor

Department of Engineering, Brock University, St. Catharines, ON L2S 3A1, Canada
Interests: smart grids; multi-vector energy microgrids; energy systems; deep reinforcement learning; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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.

Keywords

pattern recognition
machine learning
communication

Related Special Issue

Pattern Recognition and Machine Learning Applications in Electronics (15 articles)

Published Papers (4 papers)

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Research

16 pages, 1724 KiB

Open AccessArticle

WaveletDFDS-Net: A Dual Forward Denoising Stream Network for Low-Dose CT Noise Reduction

by Yusheng Zhou, Zhengmin Kong, Tao Huang, Euijoon Ahn, Hao Li and Li Ding

Electronics 2024, 13(10), 1906; https://doi.org/10.3390/electronics13101906 - 13 May 2024

Viewed by 400

Abstract

The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods. Full article

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

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25 pages, 10188 KiB

Open AccessArticle

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

by Xuru Li, Kun Wang, Xiaoqin Xue and Fuzhong Li

Electronics 2024, 13(10), 1868; https://doi.org/10.3390/electronics13101868 - 10 May 2024

Viewed by 200

Abstract

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms. Full article

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

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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 711

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 1038

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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