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Applied Sciences
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Novel Advances of Image and Signal Processing
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Novel Advances of Image and Signal Processing

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 (10 January 2022) | Viewed by 23288

Special Issue Editors

Prof. Dr. Volodymyr Mosorov

E-Mail Website
Guest Editor
Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology, 90-924 Lodz, Poland
Interests: process tomography; applied radiation; medical measurements; image and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Robert Hanus

E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Rzeszów, Poland
Interests: Signal processing; metrology and measurement systems; analysis of random signals
Prof. Dr. Dariusz Czerwinski

E-Mail Website
Guest Editor
Institute of Computer Science, Lublin University of Technology, Lublin, Poland
Interests: Cloud computing; data security in network systems, data mining systems; artificial neural networks; wireless sensory networks; Internet of Things; numerical modeling of electromagnetic devices

Special Issue Information

Dear Colleagues,

In the last few decades, we have observed rapid advances in technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. This advance is toward using artificial intelligence and machine learning based on computation, e.g., neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Therefore, it is important to explore recent trends in these research areas and applications around the world. Application areas include, but are not limited to:

  • Image and video processing: image filtering, restoration and enhancement, image segmentation, video segmentation and tracking, feature extraction and analysis, motion detection and estimation, computer vision, pattern recognition, and content-based image retrieval;
  • Signal processing: spectral analysis, time-frequency and time-scale representation, statistical signal processing, filtering, detection and estimation, nonlinear signal processing, radar, antennas, telecommunications systems, and acoustics;
  • Biomedical signal processing: EMG, ECG, biometric and biomedical imaging, remote sensing, and other applications.

In this Special Issue, we invite submissions concerning the development of novel algorithms of image and signal processing for the Internet of Things platforms. Survey papers and reviews are also welcomed.

Prof. Dr. Volodymyr Mosorov
Prof. Dr. Robert Hanus
Prof. Dr. Dariusz Czerwiński
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. Applied Sciences 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

  • big data computing
  • intelligent algorithms
  • intelligent and learning control
  • biomedical and biological signal processing
  • embedded systems for signal processing
  • system modeling and simulation, dynamics, and control
  • signal, audio, and speech analysis and processing

Published Papers (6 papers)

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Research

19 pages, 7222 KiB  
Article
Personal Identification Using an Ensemble Approach of 1D-LSTM and 2D-CNN with Electrocardiogram Signals
by Jin-A Lee and Keun-Chang Kwak
Appl. Sci. 2022, 12(5), 2692; https://doi.org/10.3390/app12052692 - 04 Mar 2022
Cited by 20 | Viewed by 2706
Abstract
Conventional personal identification methods (ID, password, authorization certificate, etc.) entail various issues, including forgery or loss. Technological advances and the diffusion across industries have enhanced convenience; however, privacy risks due to security attacks are increasing. Hence, personal identification based on biometrics such as [...] Read more.
Conventional personal identification methods (ID, password, authorization certificate, etc.) entail various issues, including forgery or loss. Technological advances and the diffusion across industries have enhanced convenience; however, privacy risks due to security attacks are increasing. Hence, personal identification based on biometrics such as the face, iris, fingerprints, and veins has been used widely. However, biometric information including faces and fingerprints is difficult to apply in industries requiring high-level security, owing to tampering or forgery risks and recognition errors. This paper proposes a personal identification technique based on an ensemble of long short-term memory (LSTM) and convolutional neural network (CNN) that uses electrocardiograms (ECGs). An ECG uses internal biometric information, representing the heart rate in signals using microcurrents and thereby including noises during measurements. This noise is removed using filters in a preprocessing step, and the signals are divided into cycles with respect to R-peaks for extracting features. LSTM is used to perform personal identification using ECG signals; 1D ECG signals are transformed into the time–frequency domain using STFT, scalogram, FSST, and WSST; and a 2D-CNN is used to perform personal identification. This ensemble of two models is used to attain higher performances than LSTM or 2D-CNN. Results reveal a performance improvement of 1.06–3.75%. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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11 pages, 1119 KiB  
Article
New Orthogonal Transforms for Signal and Image Processing
by Andrzej Dziech
Appl. Sci. 2021, 11(16), 7433; https://doi.org/10.3390/app11167433 - 12 Aug 2021
Cited by 6 | Viewed by 2563
Abstract
In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms. The simplicity of calculating the forward and inverse transforms is one of the important features of the presented approach. The conditions [...] Read more.
In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms. The simplicity of calculating the forward and inverse transforms is one of the important features of the presented approach. The conditions for creating symmetric, orthogonal matrices are defined. It is shown that for the selection of the elements of an orthogonal matrix that meets the given conditions, it is necessary to select only a limited number of elements. The general form of the orthogonal, symmetric matrix having an exponential form is also presented. Orthogonal basis functions based on the created matrices can be used for orthogonal expansion leading to signal approximation. An exponential form of orthogonal, sparse matrices with variable parameters is also created. Various versions of orthogonal transforms related to the created full and sparse matrices are proposed. Fast computation of the presented transforms in comparison to fast algorithms of selected orthogonal transforms is discussed. Possible applications for signal approximation and examples of image spectrum in the considered transform domains are presented. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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19 pages, 11651 KiB  
Article
Underwater Image Mosaic Algorithm Based on Improved Image Registration
by Yinsen Zhao, Farong Gao, Jun Yu, Xing Yu and Zhangyi Yang
Appl. Sci. 2021, 11(13), 5986; https://doi.org/10.3390/app11135986 - 27 Jun 2021
Cited by 9 | Viewed by 1950
Abstract
In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the [...] Read more.
In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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19 pages, 14820 KiB  
Article
Fractional Order Processing of Satellite Images
by Manuel Henriques, Duarte Valério and Rui Melicio
Appl. Sci. 2021, 11(11), 5288; https://doi.org/10.3390/app11115288 - 07 Jun 2021
Viewed by 1758
Abstract
Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using [...] Read more.
Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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15 pages, 4117 KiB  
Article
Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
by Krzysztof Strzecha, Marek Krakós, Bogusław Więcek, Piotr Chudzik, Karol Tatar, Grzegorz Lisowski, Volodymyr Mosorov and Dominik Sankowski
Appl. Sci. 2021, 11(10), 4625; https://doi.org/10.3390/app11104625 - 19 May 2021
Cited by 11 | Viewed by 4480
Abstract
This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of [...] Read more.
This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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13 pages, 4276 KiB  
Article
Non-Contact SpO2 Prediction System Based on a Digital Camera
by Ali Al-Naji, Ghaidaa A. Khalid, Jinan F. Mahdi and Javaan Chahl
Appl. Sci. 2021, 11(9), 4255; https://doi.org/10.3390/app11094255 - 07 May 2021
Cited by 23 | Viewed by 8474
Abstract
Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the [...] Read more.
Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position. Full article
(This article belongs to the Special Issue Novel Advances of Image and Signal Processing)
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