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Electronics
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Pattern Recognition and Applications
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Pattern Recognition and Applications

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 23266

Special Issue Editor

Dr. Jorge Igual

E-Mail Website
Guest Editor
Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Departamento de Comunicaciones, Universitat Politècnica de València, València, Spain
Interests: classification; pattern recognition; statistical signal processing; photography; independent component analysis; machine learning; non negative matrix factorization; biomedical signal processing; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In the last decade, we have experienced an unprecedented increase in the number of pattern recognition applications. This is a consequence of the big data era that we are currently living in. The large amount of data available for a vast range of different application fields provides the basic element for any machine learning/pattern recognition algorithm. These data come in different forms (supervised, unsupervised) and sizes (one or multidimensional, short episodes or continuous data streams), depending on the problem and nature of the signals. 

The huge amount of different machine learning methods allows for almost any new specific pattern recognition problem to be easily matched with the more appropriate machine learning approach. In addition, most of these machine learning algorithms are already implemented and their corresponding code is publicly available, so a deep understanding of the theory in order is not required to apply these methods to the specific new problems; the only requirements are the input data, a clear statement of the problem (detection, classification, prediction, description, …), and to choose the correct machine learning tool. 

In this Special Issue, we look for new contributions, especially, but not limited, to new research fields where pattern recognition methods are relatively new. The approach can be a traditional one, based on the extraction of some features using parametric or non-parametric methods, or using deep learning techniques exploiting the availability of a large amount of data or transfer learning.

A few of the popular applications this Special Issue will make reference to include computer vision, speech signals, energy industry, and biomedical applications. 

Prof. Dr. Jorge Igual
Guest Editor

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
  • Deep learning
  • Big data
  • Classification
  • Detection
  • Signal Processing
  • Artificial Intelligence
  • Computer Science
  • Computer vision
  • Biomedicine

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Published Papers (7 papers)

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Research

15 pages, 5132 KiB  
Article
TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance
by Yue Tao, Zhiwei Jia, Runze Ma and Shugong Xu
Electronics 2021, 10(22), 2780; https://doi.org/10.3390/electronics10222780 - 13 Nov 2021
Cited by 5 | Viewed by 1876
Abstract
Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN [...] Read more.
Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN to capture global dependencies to solve the inductive bias and strengthen the relationship between text features. Recently, the transformer has been proposed as a promising network for global context modeling by self-attention mechanism, but one of the main short-comings, when applied to recognition, is the efficiency. We propose a 1-D split to address the challenges of complexity and replace the CNN with the transformer encoder to reduce the need for a context modeling module. Furthermore, recent methods use a frozen initial embedding to guide the decoder to decode the features to text, leading to a loss of accuracy. We propose to use a learnable initial embedding learned from the transformer encoder to make it adaptive to different input images. Above all, we introduce a novel architecture for text recognition, named TRansformer-based text recognizer with Initial embedding Guidance (TRIG), composed of three stages (transformation, feature extraction, and prediction). Extensive experiments show that our approach can achieve state-of-the-art on text recognition benchmarks. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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14 pages, 481 KiB  
Article
Improving the Performance of an Associative Classifier in the Context of Class-Imbalanced Classification
by Carlos Alberto Rolón-González, Rodrigo Castañón-Méndez, Antonio Alarcón-Paredes, Itzamá López-Yáñez and Cornelio Yáñez-Márquez
Electronics 2021, 10(9), 1095; https://doi.org/10.3390/electronics10091095 - 6 May 2021
Viewed by 2003
Abstract
Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative [...] Read more.
Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative memories are models used for pattern recall; however, they can also be employed for pattern classification. In this paper, a novel method for improving the classification performance of a hybrid associative classifier with translation (better known by its acronym in Spanish, CHAT) is presented. The extreme center points (ECP) method modifies the CHAT algorithm by exploring alternative vectors in a hyperspace for translating the training data, which is an inherent step of the original algorithm. We demonstrate the importance of our proposal by applying it to imbalanced datasets and comparing the performance to well-known classifiers by means of the balanced accuracy. The proposed method not only enhances the performance of the original CHAT algorithm, but it also outperforms state-of-the-art classifiers in four of the twelve analyzed datasets, making it a suitable algorithm for classification in imbalanced class scenarios. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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15 pages, 426 KiB  
Article
A Tandem Feature Extraction Approach for Arrhythmia Identification
by Javier Tejedor, David G. Marquez, Constantino A. Garcia and Abraham Otero
Electronics 2021, 10(8), 976; https://doi.org/10.3390/electronics10080976 - 19 Apr 2021
Cited by 1 | Viewed by 2117
Abstract
Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for [...] Read more.
Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for the identification of arrhythmias. With the advances in electronic technology, there are nowadays sensors that enable the recording of the ECG during the daily life of the patient and its wireless transmission to healthcare facilities. This type of information has a great potential to detect cardiac diseases in their early stages and to permit early interventions before the patient’s health deteriorates. However, to usefully exploit the large volume of information obtained from ambulatory ECG, pattern recognition techniques that are capable of automatically analyzing it are required. Tandem feature extraction techniques have proven to be useful for the processing of physiological parameters such as the electroencephalogram (EEG) and speech. However, to the best of our knowledge, they have never been applied to the ECG. In this paper, the utility of tandem feature extraction for the identification of arrhythmias is studied. The coefficients of a regression using Hermite functions are used to create a feature vector that represents the heartbeat. A multiple-layer perceptron (MLP) is trained using these features and its posterior probability outputs are used to extend the original feature vector. Finally, a Gaussian mixture model (GMM) is trained on the extended feature vectors, which is then used in a GMM-based arrhythmia identification system. This approach has been validated using the MIT-BIH Arrhythmia database. The accuracy of the Gaussian mixture model increased by 15.8% when applied over the extended feature vectors, compared to its application over the original feature vectors, showing the potential of tandem feature extraction for ECG analysis and arrhythmia identification. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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19 pages, 5411 KiB  
Article
A Multi-Position Approach in a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection
by Javier Tejedor, Javier Macias-Guarasa, Hugo F. Martins, Sonia Martin-Lopez and Miguel Gonzalez-Herraez
Electronics 2021, 10(6), 712; https://doi.org/10.3390/electronics10060712 - 18 Mar 2021
Cited by 16 | Viewed by 3053
Abstract
We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (ϕ-OTDR) and pattern recognition for event classification. The proposal incorporates a [...] Read more.
We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (ϕ-OTDR) and pattern recognition for event classification. The proposal incorporates a multi-position approach in a Gaussian Mixture Model (GMM)-based pattern classification system which operates in a real-field scenario with a thorough experimental procedure. The objective is exploiting the availability of vibration-related data at positions nearby the one actually producing the main disturbance to improve the robustness of the trained models. The system integrates two classification tasks: (1) machine + activity identification, which identifies the machine that is working over the pipeline along with the activity being carried out, and (2) threat detection, which aims to detect suspicious threats for the pipeline integrity (independently of the activity being carried out). For the machine + activity identification mode, the multi-position approach for model training obtains better performance than the previously presented single-position approach for activities that show consistent behavior and high energy (between 6% and 11% absolute) with an overall increase of 3% absolute in the classification accuracy. For the threat detection mode, the proposed approach gets an 8% absolute reduction in the false alarm rate with an overall increase of 4.5% absolute in the classification accuracy. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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16 pages, 2148 KiB  
Article
Protein Subnuclear Localization Based on Radius-SMOTE and Kernel Linear Discriminant Analysis Combined with Random Forest
by Liwen Wu, Shanshan Huang, Feng Wu, Qian Jiang, Shaowen Yao and Xin Jin
Electronics 2020, 9(10), 1566; https://doi.org/10.3390/electronics9101566 - 24 Sep 2020
Cited by 3 | Viewed by 2319
Abstract
Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. [...] Read more.
Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. In this work, a novel method is therefore proposed to predict the protein subnuclear localization of unbalanced datasets. First, the position-specific score matrix is used to extract the feature vectors of two benchmark datasets and then the useful features are selected by kernel linear discriminant analysis. Second, the Radius-SMOTE is used to expand the samples of minority classes to deal with the problem of imbalance in datasets. Finally, the optimal feature vectors of the expanded datasets are classified by random forest. In order to evaluate the performance of the proposed method, four index evolutions are calculated by Jackknife test. The results indicate that the proposed method can achieve better effect compared with other conventional methods, and it can also improve the accuracy for both majority and minority classes effectively. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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21 pages, 15215 KiB  
Article
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis
by Shanshan Huang, Yikun Yang, Xin Jin, Ya Zhang, Qian Jiang and Shaowen Yao
Electronics 2020, 9(9), 1531; https://doi.org/10.3390/electronics9091531 - 18 Sep 2020
Cited by 9 | Viewed by 3015
Abstract
Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of [...] Read more.
Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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14 pages, 539 KiB  
Article
Intelligent Indexing—Boosting Performance in Database Applications by Recognizing Index Patterns
by Alberto Arteta Albert, Nuria Gómez Blas and Luis Fernando de Mingo López
Electronics 2020, 9(9), 1348; https://doi.org/10.3390/electronics9091348 - 20 Aug 2020
Cited by 1 | Viewed by 6906
Abstract
An issue that most databases face is the static and manual character of indexing operations. This old-fashioned way of indexing database objects is proven to affect the database performance to some degree, creating downtime and a possible impact in the performance that is [...] Read more.
An issue that most databases face is the static and manual character of indexing operations. This old-fashioned way of indexing database objects is proven to affect the database performance to some degree, creating downtime and a possible impact in the performance that is usually solved by manually running index rebuild or defrag operations. Many data mining algorithms can speed up by using appropriate index structures. Choosing the proper index largely depends on the type of query that the algorithm performs against the database. The statistical analyzers embedded in the Database Management System are neither always accurate enough to automatically determine when to use an index nor to change its inner structure. This paper provides an algorithm that targets those indexes that are causing performance issues on the databases and then performs an automatic operation (defrag, recreation, or modification) that can boost the overall performance of the Database System. The effectiveness of proposed algorithm has been evaluated with several experiments developed and show that this approach consistently leads to a better resulting index configuration. The downtime of having a damaged, fragmented, or inefficient index is reduced by increasing the chances for the optimizer to be using the proper index structure. Full article
(This article belongs to the Special Issue Pattern Recognition and Applications)
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