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Symmetry
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Multidimensional Signal Processing and Its Applications
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Multidimensional Signal Processing and Its Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 13220

Special Issue Editors

Prof. Dr. Roumen Kountchev
*
E-Mail Website
Guest Editor
Faculty of Telecommunications, Department of Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: 3D image representation; image compression; medical image enhancement; pattern recognition; 3D signal processing; image watermarking; deep learning
*
Dr. Rumen Mironov

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Guest Editor
Faculty of Telecommunications, Department of Radio Communications and Video Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: image processing; multidimensional signal processing; pattern recognition; programming; digital signage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

One of the main tendencies in signal processing is the creation of new approaches for intelligent processing and analysis of multidimensional (MD) signals in various application areas. The advance of the contemporary computer systems opens new abilities for synergic relation between theoretical approaches and their applications.  Symmetry plays an important role in signal processing as it can be used to reduce the complexity of the problems to be solved in various application areas of modern life such as telecommunications, computer vision, healthcare, bioinformatics, remote ecological monitoring, agriculture, forestry, etc.

This Special Issue is devoted to recent advances in MD signal processing related to the analysis and use of symmetries in different multidisciplinary areas. The aim of this Special Issue is to present investigations and achievements in the area of MD signal processing in various multidisciplinary areas: analysis and recognition of MD images, MD image representation, compression and super-resolution; MD images transmission; MD computer vision; learning-based MD image processing and recognition; neural networks for MD image processing; generic and fuzzy MD image object segmentation; MD image retrieval and mining; multi-spectral and multi-view intelligent image processing; web-based MD images search; forensic MD analysis; MD image interpolation; MD visualization, virtual and augmented reality; spatio-temporal filtering, and any other topics related to the concept of symmetry in MD signal processing.

Prof. Dr. Roumen Kountchev
Prof. Dr. Rumen Mironov
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. Symmetry is an international peer-reviewed open access monthly 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

  • symmetry
  • multidimensional signal processing
  • tensor image decomposition
  • medical information systems
  • telecommunications
  • computer vision
  • healthcare
  • bioinformatics
  • remote ecological monitoring
  • agriculture
  • forestry

Published Papers (5 papers)

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Research

14 pages, 1512 KiB  
Article
Third-Order Tensor Decorrelation Based on 3D FO-HKLT with Adaptive Directional Vectorization
by Roumen K. Kountchev, Rumen P. Mironov and Roumiana A. Kountcheva
Symmetry 2022, 14(5), 854; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14050854 - 21 Apr 2022
Cited by 1 | Viewed by 1399
Abstract
In this work, we present a new hierarchical decomposition aimed at the decorrelation of a cubical tensor of size 2n, based on the 3D Frequency-Ordered Hierarchical KLT (3D FO-HKLT). The decomposition is executed in three consecutive stages. In the first stage, [...] Read more.
In this work, we present a new hierarchical decomposition aimed at the decorrelation of a cubical tensor of size 2n, based on the 3D Frequency-Ordered Hierarchical KLT (3D FO-HKLT). The decomposition is executed in three consecutive stages. In the first stage, after adaptive directional vectorization (ADV) of the input tensor, the vectors are processed through one-dimensional FO-Adaptive HKLT (FO-AHKLT), and after folding, the first intermediate tensor is calculated. In the second stage, on the vectors obtained after ADV of the first intermediate tensor, FO-AHKLT is applied, and after folding, the second intermediate tensor is calculated. In the third stage, on the vectors obtained from the second intermediate tensor, ADV is applied, followed by FO-AHKLT, and the output tensor is obtained. The orientation of the vectors, calculated from each tensor, could be horizontal, vertical or lateral. The best orientation is chosen through analysis of their covariance matrix, based on its symmetry properties. The kernel of FO-AHKLT is the optimal decorrelating KLT with a matrix of size 2 × 2. To achieve higher decorrelation of the decomposition components, the direction of the vectors obtained after unfolding of the input tensor in each of the three consecutive stages, is chosen adaptively. The achieved lower computational complexity of FO-AHKLT is compared with that of the Hierarchical Tucker and Tensor Train decompositions. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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16 pages, 1089 KiB  
Article
Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques
by Jacek Łukasz Wilk-Jakubowski
Symmetry 2021, 13(7), 1299; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13071299 - 19 Jul 2021
Cited by 14 | Viewed by 3372
Abstract
Symmetry plays a key role in the processing and analysis of not only visual but also acoustic signals in various multidisciplinary areas. New innovative and environmentally friendly methods for extinguishing flames are still being sought worldwide. One of these techniques appears to be [...] Read more.
Symmetry plays a key role in the processing and analysis of not only visual but also acoustic signals in various multidisciplinary areas. New innovative and environmentally friendly methods for extinguishing flames are still being sought worldwide. One of these techniques appears to be the acoustic method. A laboratory stand was built for this purpose, which was coupled with the tested prototype of a high-power acoustic extinguisher, and then the original experiments and analyses of extinguishing effectiveness were carried out. For extinguishing, waveforms with specified parameters selected symmetrically around the frequency for which the extinguisher was designed were used. The aim of this article is to present and discuss selected measurement results concerning the possibility of flame extinguishing with the use of sinusoidal acoustic waves of low frequency (below 21 Hz), as well as with the use of frequency sweeping techniques with set parameters. Such an extinguisher can be equipped with an intelligent module so that the extinguisher may be activated automatically (without human intervention) when flames are detected. The benefits of this combination as well as the importance of image processing for flame detection are also presented in this paper. This solution, with its good fire detection and fast response, may be applicable for extinguishing firebreaks in particular. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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24 pages, 13970 KiB  
Article
A Postoperative Displacement Measurement Method for Femoral Neck Fracture Internal Fixation Implants Based on Femoral Segmentation and Multi-Resolution Frame Registration
by Kaifeng Liu, Kouki Nagamune, Keisuke Oe, Ryosuke Kuroda and Takahiro Niikura
Symmetry 2021, 13(5), 747; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13050747 - 25 Apr 2021
Viewed by 2149
Abstract
Femoral neck fractures have a high incidence in the geriatric population and are associated with high mortality and disability rates. With the minimally invasive nature, internal fixation is widely used as a treatment option to stabilize femoral neck fractures. The fixation effectiveness and [...] Read more.
Femoral neck fractures have a high incidence in the geriatric population and are associated with high mortality and disability rates. With the minimally invasive nature, internal fixation is widely used as a treatment option to stabilize femoral neck fractures. The fixation effectiveness and stability of the implant is an essential guide for the surgeon. However, there is no long-term reliable evaluation method to quantify the implant’s fixation effect without affecting the patient’s behavior and synthesizing long-term treatment data. For the femur’s symmetrical structure, this study used 3D convolutional networks for biomedical image segmentation (3D-UNet) to segment the injured femur as a mask, aligned computerized tomography (CT) scans of the patient at different times after surgery and quantified the displacement in the specified direction using the generated 3D point cloud. In the experimental part, we used 10 groups containing two CT images scanned at the one-year interval after surgery. By comparing manual segmentation of femur and segmentation of femur as a mask using neural network, the mask obtained by segmentation using the 3D-UNet network with symmetric structure fully meets the requirements of image registration. The data obtained from the 3D point cloud calculation is within the error tolerance, and the calculated displacement of the implant can be visualized in 3D space. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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22 pages, 4998 KiB  
Article
A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool
by Suliman Mohamed Fati, Amgad Muneer, Nur Arifin Akbar and Shakirah Mohd Taib
Symmetry 2021, 13(4), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13040686 - 15 Apr 2021
Cited by 13 | Viewed by 3862
Abstract
High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is [...] Read more.
High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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11 pages, 280 KiB  
Article
The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition
by Hsiuying Wang
Symmetry 2021, 13(1), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13010019 - 24 Dec 2020
Cited by 1 | Viewed by 1686
Abstract
High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the [...] Read more.
High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application. Full article
(This article belongs to the Special Issue Multidimensional Signal Processing and Its Applications)
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