Multidimensional Signal Processing and Deep Learning—Symmetry Approach

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2169

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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
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TK Engineering, 1712 Sofia, Bulgaria
Interests: image processing; image compression and watermarking; CNCs; programmable controllers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multidimensional (MD) signal processing covers various signals (video, audio, X-ray, multispectral, multi-view, and many others), transferred via contemporary communication systems and networks, and analyzed in medical institutions, traffic control systems, forensic investigations, etc. The objective of this SI is to collect and present contemporary research and achievements in the area of the MD signal processing, aimed at multidisciplinary fields of study: analysis and recognition of MD images, compression, and super-resolution; efficient transfer of MD images; MD computer vision; learning of deep neural networks for MD image processing; generic and fuzzy segmentation of objects in MD images, extraction of MD images from databases; intelligent processing of multispectral and multi-view images; web-based search of MD images; forensic and medical analysis; MD image interpolation; visualization; virtual and augmented reality, based on the concept for processing MD signals, using the symmetrical properties of their contents and structure. All these analyses and research topics will be the basis for various applications in the related scientific areas.

Dr. Rumen Mironov
Dr. Roumiana Kountcheva
Guest Editors

Manuscript Submission Information

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Keywords

  • symmetry
  • multidimensional signal processing
  • deep learning
  • deep neural tensor network
  • tensor image decomposition
  • medical information systems
  • telecommunications
  • 3D computer vision
  • bioinformatics
  • remote ecological monitoring

Published Papers (3 papers)

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Research

13 pages, 1987 KiB  
Article
Direction of Arrival Estimation of Coherent Sources via a Signal Space Deep Convolution Network
by Jun Zhao, Renzhou Gui, Xudong Dong and Yufei Zhao
Symmetry 2024, 16(4), 433; https://doi.org/10.3390/sym16040433 - 4 Apr 2024
Viewed by 519
Abstract
In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, [...] Read more.
In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets. Full article
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14 pages, 2337 KiB  
Article
An Improved Sorting Algorithm for Periodic PRI Signals Based on Congruence Transform
by Huixu Dong, Yuanzheng Ge, Rui Zhou and Hongyan Wang
Symmetry 2024, 16(4), 398; https://doi.org/10.3390/sym16040398 - 28 Mar 2024
Viewed by 519
Abstract
Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, [...] Read more.
Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and it is less affected by interfered pulses and pulse loss. Nevertheless, we find that the algorithm causes pseudo-peaks in the remainder histogram when sorting signals such as sliding PRI, sinusoidal PRI, etc. (collectively referred to as periodic PRI signal in this paper) and pseudo-peaks will cause errors in signal sorting. To solve the issue of pseudo-peaks when sorting periodic PRI signals, an improved sorting algorithm based on congruence transform is proposed. According to the analysis of the congruence characteristics of the periodic PRI signal, a novel method is proposed to identify pseudo-peaks based on the histogram peak amplitude and symmetric difference set. The signal sorting algorithm based on congruence transform is improved to achieve a good sorting effect on periodic PRI signals. Simulation experiments demonstrate that the novel algorithm can effectively sort periodic PRI signals and improve Precall, Pd, and Pf by 6.9%, 5.1%, and 3.2%, respectively, compared to the typical similar algorithms. Full article
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18 pages, 1524 KiB  
Article
Analysis of the Recursive Locally-Adaptive Filtration of 3D Tensor Images
by Roumen Kountchev, Rumen Mironov and Roumiana Kountcheva
Symmetry 2023, 15(8), 1493; https://doi.org/10.3390/sym15081493 - 27 Jul 2023
Viewed by 553
Abstract
This work is focused on the computational complexity (CC) reduction of the locally-adaptive processing of 3D tensor images, based on recursive approaches. As a basis, a local averaging operation is used, implemented as a sliding mean 3D filter (SM3DF) with a central symmetric [...] Read more.
This work is focused on the computational complexity (CC) reduction of the locally-adaptive processing of 3D tensor images, based on recursive approaches. As a basis, a local averaging operation is used, implemented as a sliding mean 3D filter (SM3DF) with a central symmetric 3D kernel. Symmetry plays a very important role in constructing the working window. The presented theoretical approach could be adopted in various algorithms for locally-adaptive processing, such as additive noise reduction, sharpness enhancement, texture segmentation, etc. The basic characteristics of the recursive SM3DF are analyzed, together with the main features of the adaptive algorithms for filtration of Gaussian noises and unsharp masking where the filter is aimed at. In the paper, the ability of SM3DF implementation through recursive sliding mean 1D filters, sequentially bonded together, is also introduced. The computational complexity of the algorithms is evaluated for the recursive and non-recursive mode. The recursive SM3DF also suits the 3D convolutional neural networks which comprise sliding locally-adaptive 3D filtration in their layers. As a result of the lower CC, a promising opportunity is opened for higher efficiency of the 3D image processing through tensor neural networks. Full article
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