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Electronics
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Ultrasonic Pattern Recognition by Machine Learning
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Ultrasonic Pattern Recognition by Machine Learning

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

Deadline for manuscript submissions: closed (22 May 2023) | Viewed by 22119

Special Issue Editors

Dr. Alfredo Arcos Jiménez

E-Mail Website
Guest Editor
Ingenium Researh Group, Universidad de Castilla La Mancha, 13071 Ciudad Real, Spain
Interests: signal processing; guided waves; pattern recognition by machine learning; wind turbine blade
Prof. Dr. Fausto Pedro García Márquez

E-Mail Website
Guest Editor
Ingneium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain
Interests: artificial intelligence; maintenance; management; advanced analytics; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Caroline Leonore König

E-Mail Website
Guest Editor
Department of Computer Science, Universitat Politècnica de Catalunya, UPC BarcelonaTech, 08034 Barcelona, Spain
Interests: artificial intelligence; machine learning; condition monitoring; process control; bioinformatics

Special Issue Information

Dear Colleagues,

Pattern recognition is being increasingly applied to many research fields where there is a need to simplify highly complex models into reference patterns that solve, in many cases, problems that occur in engineering processes, computing, mathematics and medicine, among others. Ultrasonic technologies, being a non-intrusive technology for object detection and distance measurement, have a wide range of applications, ranging from medical imaging and robotic navigation systems to structural health monitoring systems, to name a few. Pattern recognition, machine learning and ultrasonic waves form a trinomial of success, reflected in the existing literature and increasing number of applications. A wide variety of machine learning techniques have been used in different applications for pattern recognition, providing solutions with a high success rate, and consequently providing a wide range of research possibilities.

The purpose of this Special Issue is to publish high-quality articles that contribute to pattern recognition through binomial ultrasonic waves and machine learning, with application in any area of research. New models with deep learning, as well as the implementation of new advances in ultrasonic wave collection, will be accepted in this Issue. Similarly, in this Issue, we will include reviews of the latest advances in ultrasonic wave pattern recognition by machine learning.

Prof. Alfredo Arcos Jiménez
Prof. Dr. Fausto Pedro García Márquez
Dr. Caroline Leonore König
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
  • Ultrasonic waves
  • Supervised learning
  • Unsupervised learning
  • Deep learning
  • Data preprocessing
  • Mapping

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Published Papers (4 papers)

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Research

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26 pages, 2969 KiB  
Article
Image Formation Algorithms for Low-Cost Freehand Ultrasound Scanner Based on Ego-Motion Estimation and Unsupervised Clustering
by Ayusha Abbas, Jeffrey Neasham and Mohsen Naqvi
Electronics 2023, 12(17), 3634; https://doi.org/10.3390/electronics12173634 - 28 Aug 2023
Cited by 2 | Viewed by 1569
Abstract
This paper describes the application of unsupervised learning techniques to improve ego-motion estimation for a low-cost freehand ultrasound probe. Echo decorrelation measurements, which are used to estimate the lateral velocity of a scanning probe as it is passed over the skin, are found [...] Read more.
This paper describes the application of unsupervised learning techniques to improve ego-motion estimation for a low-cost freehand ultrasound probe. Echo decorrelation measurements, which are used to estimate the lateral velocity of a scanning probe as it is passed over the skin, are found to be sensitive to varying tissue types and echogenicity in the imaged scene, and this can impact the geometric accuracy of the generated images. Here, we investigate algorithms to cluster the collated 1D echo data into regions of different echogenicity by applying a Gaussian mixture model (GMM), spatial fuzzy c-means (SFCM) or k-means clustering techniques, after which the decorrelation measurements can focus on the regions that yield the most accurate velocity estimates. A specially designed mechanical rig is used to provide the ground truth for the quantitative analysis of probe position estimation on phantom and in vivo data using different clustering techniques. It is concluded that the GMM is the most effective in classifying regions of echo data, leading to the reconstruction of the most geometrically correct 2D B-mode ultrasound image. Full article
(This article belongs to the Special Issue Ultrasonic Pattern Recognition by Machine Learning)
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19 pages, 911 KiB  
Article
Automatic Estimation of Food Intake Amount Using Visual and Ultrasonic Signals
by Ki-Seung Lee
Electronics 2021, 10(17), 2153; https://doi.org/10.3390/electronics10172153 - 3 Sep 2021
Cited by 7 | Viewed by 2453
Abstract
The continuous monitoring and recording of food intake amount without user intervention is very useful in the prevention of obesity and metabolic diseases. I adopted a technique that automatically recognizes food intake amount by combining the identification of food types through image recognition [...] Read more.
The continuous monitoring and recording of food intake amount without user intervention is very useful in the prevention of obesity and metabolic diseases. I adopted a technique that automatically recognizes food intake amount by combining the identification of food types through image recognition and a technique that uses acoustic modality to recognize chewing events. The accuracy of using audio signal to detect eating activity is seriously degraded in a noisy environment. To alleviate this problem, contact sensing methods have conventionally been adopted, wherein sensors are attached to the face or neck region to reduce external noise. Such sensing methods, however, cause dermatological discomfort and a feeling of cosmetic unnaturalness for most users. In this study, a noise-robust and non-contact sensing method was employed, wherein ultrasonic Doppler shifts were used to detect chewing events. The experimental results showed that the mean absolute percentage errors (MAPEs) of an ultrasonic-based method were comparable with those of the audio-based method (15.3 vs. 14.6) when 30 food items were used for experiments. The food intake amounts were estimated for eight subjects in several noisy environments (cafeterias, restaurants, and home dining rooms). For all subjects, the estimation accuracy of the ultrasonic method was not degraded (the average MAPE was 15.02) even under noisy conditions. These results show that the proposed method has the potential to replace the manual logging method. Full article
(This article belongs to the Special Issue Ultrasonic Pattern Recognition by Machine Learning)
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24 pages, 5081 KiB  
Article
A Secure Live Signature Verification with Aho-Corasick Histogram Algorithm for Mobile Smart Pad
by Kuo-Kun Tseng, He Chen, Charles Chen and Charinrat Bansong
Electronics 2021, 10(11), 1337; https://doi.org/10.3390/electronics10111337 - 2 Jun 2021
Viewed by 2955
Abstract
There is a long history of using handwritten signatures to verify or authenticate a “signer” of the signed document. With the development of Internet technology, many tasks can be accomplished through the document management system, such as the applications of digital contracts or [...] Read more.
There is a long history of using handwritten signatures to verify or authenticate a “signer” of the signed document. With the development of Internet technology, many tasks can be accomplished through the document management system, such as the applications of digital contracts or important documents, and more secure signature verification is demanded. Thus, the live handwriting signatures are attracting more interest for biological human identification. In this paper, we propose a handwriting signature verification algorithm by using four live waveform elements as the verification features. A new Aho-Corasick Histogram mechanism is proposed to perform this live signature verification. The benefit of the ACH algorithm is mainly its ability to convert time-series waveforms into time-series short patterns and then perform a statistical counting on the AC machine to measure the similarity. Since AC is a linearly time complexity algorithm, our ACH method can own a deterministic processing time. According to our experiment result, the proposed algorithm has satisfying performance in terms of speed and accuracy with an average of 91% accuracy. Full article
(This article belongs to the Special Issue Ultrasonic Pattern Recognition by Machine Learning)
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Review

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30 pages, 2268 KiB  
Review
Recent Advances in Machine Learning Applied to Ultrasound Imaging
by Monica Micucci and Antonio Iula
Electronics 2022, 11(11), 1800; https://doi.org/10.3390/electronics11111800 - 6 Jun 2022
Cited by 21 | Viewed by 13568
Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest [...] Read more.
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. Full article
(This article belongs to the Special Issue Ultrasonic Pattern Recognition by Machine Learning)
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