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Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (8 October 2021) | Viewed by 46572

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Special Issue Editors

Prof. Dr. Michal Choras

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Guest Editor

Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; machine learning; AI; security; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Robert Burduk

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Guest Editor

Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: machine learning; pattern recognition; AI; business intelligence

Dr. Agata Giełczyk

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Guest Editor

Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: image processing; biometrics; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rafal Kozik

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Guest Editor

Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; cybersecurity; AI
Special Issues, Collections and Topics in MDPI journals

Dr. Tomasz Marciniak

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Guest Editor

Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: telecommunication; networks; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 12th International Conference on Computer Recognition Systems (CORES) and the 12th International Conference on Image Processing and Communications (IP&C) will be held on June 28–30, 2021, in Bydgoszcz, Poland. The websites of the event are listed below:

http://ipc-conference.utp.edu.pl/

http://cores.pwr.edu.pl

The goal of both CORES 2021 and IP&C 2021 is to gather researchers and high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing, machine learning (shallow and deep), including papers in the areas of modern telecommunications and cybersecurity.

Authors of selected papers from the conference will be invited to submit extended versions of their original papers and contributions under the conference topics (new papers closely related with the conference themes are also welcome).

The scope includes but is not limited to the following:

Classification and interpretation of text, video, voice
Statistical, soft and structural methods of pattern recognition
Image processing, analysis and interpretation
Features extraction and selection
Machine learning
Trends and relations recognition and analysis
Data and Web mining
Machine-oriented knowledge representation and inference methods
Knowledge-based decision support systems
Advanced signal processing methods
Special hardware architecture
Applications
Biometrics
Cyber Security
Next Generation Networks
Optical Backbone and Access Networks
Network Reliability
New Services in IP Networks
QoS in IP Networks
Regular Structures of Communications Networks
Web Applications
IoT
Cloud/Edge and Fog Networks

Prof. Dr. Michal Choras
Prof. Dr. Robert Burduk
Dr. Agata Giełczyk
Prof. Dr. Rafal Kozik
Dr. Tomasz Marciniak

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. Entropy 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 2600 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

computer recognition
pattern recognition
image processing
telecommunications
machine learning

Published Papers (14 papers)

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Editorial

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3 pages, 162 KiB

Open AccessEditorial

Advances in Computer Recognition, Image Processing and Communications

by Michał Choraś, Robert Burduk, Agata Giełczyk, Rafał Kozik and Tomasz Marciniak

Entropy 2022, 24(1), 108; https://0-doi-org.brum.beds.ac.uk/10.3390/e24010108 - 10 Jan 2022

Cited by 1 | Viewed by 1383

Abstract

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...] [...] Read more.

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

Research

Jump to: Editorial

35 pages, 16121 KiB

Open AccessArticle

Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface

by Zbigniew Zakrzewski

Entropy 2021, 23(11), 1554; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111554 - 22 Nov 2021

Cited by 5 | Viewed by 1894

Abstract

The paper presents a method of selecting an optical channel for transporting the double-sideband radio-frequency-over-fiber (DSB-RFoF) radio signal over the optical fronthaul path, avoiding the dispersion-induced power penalty (DIPP) phenomenon. The presented method complements the possibilities of a short-range optical network working in the flexible dense wavelength division multiplexing (DWDM) format, where chromatic dispersion compensation is not applied. As part of the study, calculations were made that indicate the limitations of the proposed method and allow for the development of an algorithm for effective optical channel selection in the presence of the DIPP phenomenon experienced in the optical link working in the intensity modulation–direct detection (IM-DD) technique. Calculations were made for three types of single-mode optical fibers and for selected microwave radio carriers that are used in current systems or will be used in next-generation wireless communication systems. In order to verify the calculations and theoretical considerations, a computer simulation was performed for two types of optical fibers and for two selected radio carriers. In the modulated radio signal, the cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) format and the 5G numerology were used. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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25 pages, 1324 KiB

Open AccessArticle

How to Effectively Collect and Process Network Data for Intrusion Detection?

by Mikołaj Komisarek, Marek Pawlicki, Rafał Kozik, Witold Hołubowicz and Michał Choraś

Entropy 2021, 23(11), 1532; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111532 - 18 Nov 2021

Cited by 11 | Viewed by 2369

Abstract

The number of security breaches in the cyberspace is on the rise. This threat is met with intensive work in the intrusion detection research community. To keep the defensive mechanisms up to date and relevant, realistic network traffic datasets are needed. The use of flow-based data for machine-learning-based network intrusion detection is a promising direction for intrusion detection systems. However, many contemporary benchmark datasets do not contain features that are usable in the wild. The main contribution of this work is to cover the research gap related to identifying and investigating valuable features in the NetFlow schema that allow for effective, machine-learning-based network intrusion detection in the real world. To achieve this goal, several feature selection techniques have been applied on five flow-based network intrusion detection datasets, establishing an informative flow-based feature set. The authors’ experience with the deployment of this kind of system shows that to close the research-to-market gap, and to perform actual real-world application of machine-learning-based intrusion detection, a set of labeled data from the end-user has to be collected. This research aims at establishing the appropriate, minimal amount of data that is sufficient to effectively train machine learning algorithms in intrusion detection. The results show that a set of 10 features and a small amount of data is enough for the final model to perform very well. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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13 pages, 15264 KiB

Open AccessArticle

Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

by Krzysztof Okarma, Wojciech Chlewicki, Mateusz Kopytek, Beata Marciniak and Vladimir Lukin

Entropy 2021, 23(11), 1525; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111525 - 17 Nov 2021

Cited by 7 | Viewed by 1665

Abstract

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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14 pages, 2987 KiB

Open AccessArticle

Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks

by Marek Pawlicki and Ryszard S. Choraś

Entropy 2021, 23(10), 1304; https://0-doi-org.brum.beds.ac.uk/10.3390/e23101304 - 03 Oct 2021

Cited by 3 | Viewed by 1583

Abstract

Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting—and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including block-matching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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20 pages, 1264 KiB

Open AccessArticle

ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

by Sandra Śmigiel, Krzysztof Pałczyński and Damian Ledziński

Entropy 2021, 23(9), 1121; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091121 - 28 Aug 2021

Cited by 48 | Viewed by 11651

Abstract

The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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21 pages, 335 KiB

Open AccessArticle

Towards an Efficient and Exact Algorithm for Dynamic Dedicated Path Protection

by Ireneusz Szcześniak, Ireneusz Olszewski and Bożena Woźna-Szcześniak

Entropy 2021, 23(9), 1116; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091116 - 27 Aug 2021

Cited by 4 | Viewed by 1727

Abstract

We present a novel algorithm for dynamic routing with dedicated path protection which, as the presented simulation results suggest, can be efficient and exact. We present the algorithm in the setting of optical networks, but it should be applicable to other networks, where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or networks capable of advance resource reservation. To the best of our knowledge, we are the first to propose an algorithm for this long-standing fundamental problem, which can be efficient and exact, as suggested by simulation results. The algorithm can be efficient because it can solve large problems, and it can be exact because its results are optimal, as demonstrated and corroborated by simulations. We offer a worst-case analysis to argue that the search space is polynomially upper bounded. Network operations, management, and control require efficient and exact algorithms, especially now, when greater emphasis is placed on network performance, reliability, softwarization, agility, and return on investment. The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated “on-the-fly” based on the input graph. We corroborated the optimality of the results of the proposed algorithm with brute-force enumeration for networks up to 15 nodes large. We present the extensive simulation results of dedicated-path protection with signal modulation constraints for elastic optical networks of 25, 50, and 100 nodes, and with 160, 320, and 640 spectrum units. We also compare the bandwidth blocking probability with the commonly-used edge-exclusion algorithm. We had 48,600 simulation runs with about 41 million searches. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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14 pages, 563 KiB

Open AccessArticle

Representation of a Monotone Curve by a Contour with Regular Change in Curvature

by Yevhen Havrylenko, Yuliia Kholodniak, Serhii Halko, Oleksandr Vershkov, Oleksandr Miroshnyk, Olena Suprun, Olena Dereza, Taras Shchur and Mścisław Śrutek

Entropy 2021, 23(7), 923; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070923 - 20 Jul 2021

Cited by 19 | Viewed by 2222

Abstract

The problem of modelling a smooth contour with a regular change in curvature representing a monotone curve with specified accuracy is solved in this article. The contour was formed within the area of the possible location of a convex curve, which can interpolate a point series. The assumption that if a sequence of points can be interpolated by a monotone curve, then the reference curve on which these points have been assigned is monotone, provides the opportunity to implement the proposed approach to estimate the interpolation error of a point series of arbitrary configuration. The proposed methods for forming a convex regular contour by arcs of ellipses and B-spline ensure the interpolation of any point series in parts that can be interpolated by a monotone curve. At the same time, the deflection of the contour from the boundaries of the area of the possible location of the monotone curve can be controlled. The possibilities of the developed methods are tested while solving problems of the interpolation of a point series belonging to monotone curves. The problems are solved in the CAD system of SolidWorks with the use of software application created based on the methods developed in the research work. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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15 pages, 2313 KiB

Open AccessArticle

Selection of the Optimal Smart Meter to Act as a Data Concentrator with the Use of Graph Theory

by Piotr Kiedrowski

Entropy 2021, 23(6), 658; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060658 - 24 May 2021

Cited by 5 | Viewed by 1603

Abstract

Changing the construction of mart Meter (SM) devices, more specifically equipping them with more than one communication module, facilitates the elimination of a Transformer Station Data Concentrator (TSC) module, moving its function to one of the SMs. The opportunity to equip a chosen device in an additional communication module makes it possible to use it as an acquisition node. The introduction of this solution creates a problem with the optimum selection of the above-mentioned node out of all the nodes of the analyzed network. This paper suggests the criterion of its location and, as per the criterion, the way of conduct using the elements of the graph theory. The discussion is illustrated with the examples of the possibility to use the method for the optimization of the architecture of the network. The described method makes it possible to choose the location of a backup acquisition node as well as locate intermediary nodes (signal repeaters) in case of a failure (removal) of some SM devices. In the era of the common introduction of dispersed telemetric systems requiring an adequate level of performance and reliability of information transmission, the offered method can be used for the optimization of the structures of Smart Grids. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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25 pages, 1715 KiB

Open AccessArticle

Hfinger: Malware HTTP Request Fingerprinting

by Piotr Białczak and Wojciech Mazurczyk

Entropy 2021, 23(5), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050507 - 23 Apr 2021

Cited by 5 | Viewed by 3044

Abstract

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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13 pages, 797 KiB

Open AccessArticle

Interpolation with Specified Error of a Point Series Belonging to a Monotone Curve

by Yevhen Havrylenko, Yuliia Kholodniak, Serhii Halko, Oleksandr Vershkov, Larysa Bondarenko, Olena Suprun, Oleksandr Miroshnyk, Taras Shchur, Mścisław Śrutek and Marta Gackowska

Entropy 2021, 23(5), 493; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050493 - 21 Apr 2021

Cited by 21 | Viewed by 1523

Abstract

The paper addresses the problem of modeling a smooth contour interpolating a point series belonging to a curve containing no special points, which represents the original curve with specified accuracy. The contour is formed within the area of possible location of the parts of the interpolated curve along which the curvature values are monotonously increased or decreased. The absolute interpolation error of the point series is estimated by the width of the area of possible location of the curve. As a result of assigning each intermediate point, the location of two new sections of the curve that lie within the area of the corresponding output section is obtained. When the interpolation error becomes less than the given value, the area of location of the curve is considered to be formed, and the resulting point series is interpolated by a contour that lies within the area. The possibility to shape the contours with arcs of circles specified by characteristics is investigated. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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18 pages, 3968 KiB

Open AccessArticle

An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

by Xixin Zhang, Yuhang Yang, Zhiyong Li, Xin Ning, Yilang Qin and Weiwei Cai

Entropy 2021, 23(4), 435; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040435 - 08 Apr 2021

Cited by 56 | Viewed by 8071

Abstract

In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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13 pages, 5378 KiB

Open AccessArticle

A Novel Method of Vein Detection with the Use of Digital Image Correlation

by Zbigniew Lutowski, Sławomir Bujnowski, Beata Marciniak, Sylwester Kloska, Anna Marciniak and Piotr Lech

Entropy 2021, 23(4), 401; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040401 - 28 Mar 2021

Cited by 3 | Viewed by 2589

Abstract

Digital image correlation may be useful in many different fields of science, one of which is medicine. In this paper, the authors present the results of research aimed at detecting skin micro-shifts caused by pulsation of the veins. A novel technique using digital image correlation (DIC) and filtering the resulting shifts map to detect pulsating veins was proposed. After applying the proposed method, the veins in the forearm were visualized. The proposed technique may be used in the diagnosis of venous stenosis and may also contribute to reducing the number of adverse events during blood collection. The great advantage of the proposed method is the lack of the need to have specialized equipment, only a typical mobile phone camera is needed to perform the test. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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21 pages, 7771 KiB

Open AccessArticle

Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading

by Lu Wang, Peng Wang, Linhai Wu, Lijia Xu, Peng Huang and Zhiliang Kang

Entropy 2021, 23(3), 272; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030272 - 25 Feb 2021

Cited by 19 | Viewed by 2841

Abstract

With the promotion of intelligent substations, more and more robots have been used in industrial sites. However, most of the meter reading methods are interfered with by the complex background environment, which makes it difficult to extract the meter area and pointer centerline, which is difficult to meet the actual needs of the substation. To solve the current problems of pointer meter reading for industrial use, this paper studies the automatic reading method of pointer instruments by putting forward the Faster Region-based Convolutional Network (Faster-RCNN) based object detection integrating with traditional computer vision. Firstly, the Faster-RCNN is used to detect the target instrument panel region. At the same time, the Poisson fusion method is proposed to expand the data set. The K-fold verification algorithm is used to optimize the quality of the data set, which solves the lack of quantity and low quality of the data set, and the accuracy of target detection is improved. Then, through some image processing methods, the image is preprocessed. Finally, the position of the centerline of the pointer is detected by the Hough transform, and the reading can be obtained. The evaluation of the algorithm performance shows that the method proposed in this paper is suitable for automatic reading of pointer meters in the substation environment, and provides a feasible idea for the target detection and reading of pointer meters. Full article

(This article belongs to the Special Issue Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021)

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