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Advanced Information Processing Methods and Their Applications
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Advanced Information Processing Methods and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 18537

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Pavel Lyakhov

E-Mail Website
Guest Editor
Department of Applied Mathematics and Mathematical Modeling, North-Caucasus Federal University, 355009 Stavropol, Russia
Interests: high performance computing; residue number system arithmetic; digital signal processing; digital image processing; machine learning; artificial intelligence; medical imaging; custom hardware development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of information technology opens up new opportunities for quality improvement in many areas of human activity. Modernity is characterized by a significant increase in the volume of extracted and processed information, which leads to the problem of developing new approaches to organizing computations, including neurocomputing and quantum computing. Digital circuits are also under active development, especially in improving performance and reducing power consumption for use in mobile and embedded devices. New problem-oriented solutions based on FPGA and ASIC are constantly being developed for a variety of applications. New digital signal, image, and video processing devices must meet the growing practical needs for high speed and quality of work. The widespread use of machine learning methods and new methods for big data processing will help humans in many areas. One of the most promising areas for the application of modern IT technologies is biomedical data processing. An interesting issue in modern science is the development of new brain–computer interfaces. Medicine is another major application of computer science. Another important application of computer science is medicine, especially in the context of the development of new tools for the diagnosis and support of patients with COVID.

The latest technological developments in the areas listed above will be shared through this Special Issue. We invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Prof. Dr. Pavel Lyakhov
Guest Editor

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • Neurocomputing 
  • Quantum computing 
  • Digital circuits 
  • Digital signal processing 
  • Machine learning 
  • Deep neural networks 
  • Big data 
  • Biomedical data processing 
  • Brain–computer interfaces 
  • Medical imaging

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Published Papers (6 papers)

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Editorial

Jump to: Research

2 pages, 154 KiB  
Editorial
Special Issue on Advanced Information Processing Methods and Their Applications
by Pavel Lyakhov
Appl. Sci. 2022, 12(18), 9090; https://doi.org/10.3390/app12189090 - 9 Sep 2022
Viewed by 1027
Abstract
The rapid development of information technology opens up new opportunities in many areas of human activity [...] Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)

Research

Jump to: Editorial

27 pages, 8620 KiB  
Article
Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments
by Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, Olukayode Karunwi, Yongsung Kim, Cheng-Chi Lee and Chun-Ta Li
Appl. Sci. 2022, 12(11), 5713; https://doi.org/10.3390/app12115713 - 3 Jun 2022
Cited by 40 | Viewed by 5199
Abstract
Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths [...] Read more.
Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models. Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)
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12 pages, 6545 KiB  
Article
Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
by Syeda Minahil, Jun-Hyung Kim and Youngbae Hwang
Appl. Sci. 2021, 11(19), 9255; https://doi.org/10.3390/app11199255 - 5 Oct 2021
Cited by 4 | Viewed by 2447
Abstract
In infrared (IR) and visible image fusion, the significant information is extracted from each source image and integrated into a single image with comprehensive data. We observe that the salient regions in the infrared image contain targets of interests. Therefore, we enforce spatial [...] Read more.
In infrared (IR) and visible image fusion, the significant information is extracted from each source image and integrated into a single image with comprehensive data. We observe that the salient regions in the infrared image contain targets of interests. Therefore, we enforce spatial adaptive weights derived from the infrared images. In this paper, a Generative Adversarial Network (GAN)-based fusion method is proposed for infrared and visible image fusion. Based on the end-to-end network structure with dual discriminators, a patch-wise discrimination is applied to reduce blurry artifact from the previous image-level approaches. A new loss function is also proposed to use constructed weight maps which direct the adversarial training of GAN in a manner such that the informative regions of the infrared images are preserved. Experiments are performed on the two datasets and ablation studies are also conducted. The qualitative and quantitative analysis shows that we achieve competitive results compared to the existing fusion methods. Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)
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14 pages, 7005 KiB  
Article
Design and Implementation of Novel Efficient Full Adder/Subtractor Circuits Based on Quantum-Dot Cellular Automata Technology
by Mohsen Vahabi, Pavel Lyakhov and Ali Newaz Bahar
Appl. Sci. 2021, 11(18), 8717; https://doi.org/10.3390/app11188717 - 18 Sep 2021
Cited by 15 | Viewed by 3022
Abstract
One of the emerging technologies at the nanoscale level is the Quantum-Dot Cellular Automata (QCA) technology, which is a potential alternative to conventional CMOS technology due to its high speed, low power consumption, low latency, and possible implementation at the atomic and molecular [...] Read more.
One of the emerging technologies at the nanoscale level is the Quantum-Dot Cellular Automata (QCA) technology, which is a potential alternative to conventional CMOS technology due to its high speed, low power consumption, low latency, and possible implementation at the atomic and molecular levels. Adders are one of the most basic digital computing circuits and one of the main building blocks of VLSI systems, such as various microprocessors and processors. Many research studies have been focusing on computable digital computing circuits. The design of a Full Adder/Subtractor (FA/S), a composite and computing circuit, performing both the addition and the subtraction processes, is of particular importance. This paper implements three new Full Adder/Subtractor circuits with the lowest number of cells, lowest area, lowest latency, and a coplanar (single-layer) circuit design, as was shown by comparing the results obtained with those of the best previous works on this topic. Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)
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21 pages, 401 KiB  
Article
An Algorithm for Fast Multiplication of Kaluza Numbers
by Aleksandr Cariow, Galina Cariowa and Janusz P. Paplinski
Appl. Sci. 2021, 11(17), 8203; https://doi.org/10.3390/app11178203 - 3 Sep 2021
Cited by 2 | Viewed by 1494
Abstract
This paper presents a new algorithm for multiplying two Kaluza numbers. Performing this operation directly requires 1024 real multiplications and 992 real additions. We presented in a previous paper an effective algorithm that can compute the same result with only 512 real multiplications [...] Read more.
This paper presents a new algorithm for multiplying two Kaluza numbers. Performing this operation directly requires 1024 real multiplications and 992 real additions. We presented in a previous paper an effective algorithm that can compute the same result with only 512 real multiplications and 576 real additions. More effective solutions have not yet been proposed. Nevertheless, it turned out that an even more interesting solution could be found that would further reduce the computational complexity of this operation. In this article, we propose a new algorithm that allows one to calculate the product of two Kaluza numbers using only 192 multiplications and 384 additions of real numbers. Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)
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15 pages, 2754 KiB  
Article
System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing
by Pavel Lyakhov, Mariya Kiladze and Ulyana Lyakhova
Appl. Sci. 2021, 11(16), 7213; https://doi.org/10.3390/app11167213 - 5 Aug 2021
Cited by 14 | Viewed by 4007
Abstract
Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the [...] Read more.
Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals. Full article
(This article belongs to the Special Issue Advanced Information Processing Methods and Their Applications)
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