Journal of the University of Zielona Góra

International Journal of Applied Mathematics and Computer Science

Int.Jnl Appl.Math.Comp.Sci.ONL

<abstract><title style='display:none'>Abstract</title>Satellite image classification is essential for many socio-economic and environmental applications of geographic information systems, including urban and regional planning, conservation and management of natural resources, etc. In this paper, we propose a deep learning architecture to perform the pixel-level understanding of high spatial resolution satellite images and apply it to image classification tasks. Specifically, we augment the spatial pyramid pooling module with image-level features encoding the global context, and integrate it into the U-Net structure. The proposed model solves the problem consisting in the fact that U-Net tends to lose object boundaries after multiple pooling operations. In our experiments, two public datasets are used to assess the performance of the proposed model. Comparison with the results from the published algorithms demonstrates the effectiveness of our approach.</abstract>

Abstract

Keywords

<article-title>Classification of High Resolution Satellite Images Using Improved U–Net</article-title>

Classification of High Resolution Satellite Images Using Improved U–Net

{"article-title":"Classification of High Resolution Satellite Images Using Improved U\u2013Net"}

<abstract><title style='display:none'>Abstract</title>Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10−5.</abstract>

Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.)

<article-title>Implementation and Evaluation of Medical Imaging Techniques Based on Conformal Geometric Algebra</article-title>

Implementation and Evaluation of Medical Imaging Techniques Based on Conformal Geometric Algebra

<abstract><title style='display:none'>Abstract</title>Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.</abstract>

National Engineering Laboratory for Technology of Big Data Application in Education

<article-title>An Intelligent Multimodal Framework for Identifying Children with Autism Spectrum Disorder</article-title>

An Intelligent Multimodal Framework for Identifying Children with Autism Spectrum Disorder

<abstract><title style='display:none'>Abstract</title>Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.</abstract>

Bioinformatics and Machine Recognition Department

<article-title>Mathematical Methods of Signal Analysis Applied in Medical Diagnostic</article-title>

Mathematical Methods of Signal Analysis Applied in Medical Diagnostic

{"article-title":"Mathematical Methods of Signal Analysis Applied in Medical Diagnostic"}

<abstract><title style='display:none'>Abstract</title>In microbiology, computer methods are applied in the analysis and recognition of laboratory-acquired microscopic images concerning, for example, bacterial cells or other microorganisms. Proper recognition of the species and genera of bacteria is a key stage in the microbiological diagnostics process, because it allows a quick start of the appropriate therapy. The original method proposed in the paper concerns the automatic recognition of selected species and genera of bacteria presented in digital images. The classification was made on the basis of the analysis of the physical characteristics of bacterial cells using the product of classifier confidence weights. The end result of the classification process is the classification list, sorted in descending order according to the weights of the classifiers. In addition to the correct classification, a list of other possible results of the analysis is obtained. The method thus allows not only the classification, but also an analysis of the confidence level of the selection made. The proposed method can be used to recognize not only bacterial cells, but also other microorganisms, for example, fungi that exhibit similar morphological characteristics. In addition, the use of the method does not require the application of specialized computer equipment, which widens the scope of applications regardless of the laboratory IT infrastructure, not only in microbiological diagnostics, but also in other diagnostic laboratories.</abstract>

<article-title>Recognition of Species and Genera of Bacteria By Means of the Product of Weights of the Classifiers</article-title>

Recognition of Species and Genera of Bacteria By Means of the Product of Weights of the Classifiers

<abstract><title style='display:none'>Abstract</title>Two approximate representations are proposed for distributed parameter systems described by two linear hyperbolic PDEs with two time- and space-dependent state variables and two collocated boundary inputs. Using the method of lines with the backward difference scheme, the original PDEs are transformed into a set of ODEs and expressed in the form of a finite number of dynamical subsystems (sections). Each section of the approximation model is described by state-space equations with matrix-valued state, input and output operators, or, equivalently, by a rational transfer function matrix. The cascade interconnection of a number of sections results in the overall approximation model expressed in finite-dimensional state-space or rational transfer function domains, respectively. The discussion is illustrated with a practical example of a parallel-flow double-pipe heat exchanger. Its steady-state, frequency and impulse responses obtained from the original infinite-dimensional representation are compared with those resulting from its approximate models of different orders. The results show better approximation quality for the “crossover” input–output channels where the in-domain effects prevail as compared with the “straightforward” channels, where the time-delay phenomena are dominating.</abstract>

<article-title>Approximate State–Space and Transfer Function Models for 2×2 Linear Hyperbolic Systems with Collocated Boundary Inputs</article-title>

Approximate State–Space and Transfer Function Models for 2×2 Linear Hyperbolic Systems with Collocated Boundary Inputs

<abstract><title style='display:none'>Abstract</title>The global (absolute) stability of nonlinear systems with fractional positive and not necessarily asymptotically stable linear parts and feedbacks is addressed. The characteristics <italic>u</italic> = <italic>f</italic>(<italic>e</italic>) of the nonlinear parts satisfy the condition <italic>k</italic>1<italic>e</italic> ≤ <italic>f</italic>(<italic>e</italic>) ≤ <italic>k</italic>2<italic>e</italic> for some positive <italic>k</italic>1 and <italic>k</italic>2. It is shown that the fractional nonlinear systems are globally asymptotically stable if the Nyquist plots of the fractional positive linear parts are located on the right-hand side of the circles (−1<italic>/k</italic>1, −1<italic>/k</italic>2).</abstract>

<article-title>Global Stability of Nonlinear Feedback Systems with Fractional Positive Linear Parts</article-title>

Global Stability of Nonlinear Feedback Systems with Fractional Positive Linear Parts

<abstract><title style='display:none'>Abstract</title>We introduce a novel fractional order adaptive control design based on the tube model reference adaptive control (TMRAC) scheme for a class of fractional order linear systems. By considering an adaptive state feedback control configuration, the main idea is to replace the classical reference model with a single predetermined trajectory by a fractional order performance tube guidance model allowing a set of admissible trajectories. Besides, an optimization problem is formulated to compute an on-line correction control signal within specified bounds in order to update the system performance while minimizing a control cost criterion. The asymptotic stability of the closed loop fractional order control system is demonstrated using an extension of the Lyapunov direct method. The dynamical performance of the fractional order tube model reference adaptive control (FOTMRAC) is compared with the standard fractional order model reference adaptive control (FOMRAC) strategy, and the simulation results show the effectiveness of the proposed control method.</abstract>

LGEA Laboratory, Department of Electrical Engineering

Department of Electronics, Electrical Engineering and Automation, National Polytechnic School of Constantine

University of Applied Sciences (HTW) Berlin

<article-title>Fractional Order Tube Model Reference Adaptive Control for a Class of Fractional Order Linear Systems</article-title>

Fractional Order Tube Model Reference Adaptive Control for a Class of Fractional Order Linear Systems

<abstract><title style='display:none'>Abstract</title>The paper proposes a discrete-time sliding mode controller for single input linear dynamical systems, under requirements of the fast response without overshoot and strong robustness to matched disturbances. The system input saturation is imposed during the design due to inevitable limitations of most actuators. The system disturbances are compensated by employing nonlinear estimation by integrating the signum of the sliding variable. Hence, the proposed control structure may be regarded as a super-twisting-like algorithm. The designed system stability is analyzed as well as the sliding manifold convergence conditions are derived using a discrete-time model of the system in the <italic>δ</italic>-domain. The results obtained theoretically have been verified by computer simulations.</abstract>

<article-title>Discrete–Time Sliding Mode Control of Linear Systems with Input Saturation</article-title>

Discrete–Time Sliding Mode Control of Linear Systems with Input Saturation

{"article-title":"Discrete\u2013Time Sliding Mode Control of Linear Systems with Input Saturation"}

<abstract><title style='display:none'>Abstract</title>This paper develops an innovative approach for designing non-parallel distributed fuzzy controllers for continuous-time non-linear systems under persistent perturbations. Non-linear systems are represented using Takagi–Sugeno fuzzy models. These non-PDC controllers guarantee bounded input bounded output stabilisation in closed-loop throughout the computation of generalised inescapable ellipsoids. These controllers are computed with linear matrix inequalities using fuzzy Lyapunov functions and integral delayed Lyapunov functions. LMI conditions developed in this paper provide non-PDC controllers with a minimum *-norm (upper bound of the 1-norm) for the T–S fuzzy system under persistent perturbations. The results presented in this paper can be classified into two categories: local methods based on fuzzy Lyapunov functions with guaranteed bounds on the first derivatives of membership functions and global methods based on integral-delayed Lyapunov functions which are independent of the first derivatives of membership functions. The benefits of the proposed results are shown through some illustrative examples.</abstract>

<article-title>T–S Fuzzy Bibo Stabilisation of Non–Linear Systems Under Persistent Perturbations Using Fuzzy Lyapunov Functions and Non–PDC Control Laws</article-title>

T–S Fuzzy Bibo Stabilisation of Non–Linear Systems Under Persistent Perturbations Using Fuzzy Lyapunov Functions and Non–PDC Control Laws

<abstract><title style='display:none'>Abstract</title>This paper proposes a methodology for observer-based fault estimation of leader-following linear multi-agent systems subject to actuator faults. First, a proportional-integral distributed fault estimation observer is developed to estimate both actuator faults and states of each follower agent by considering directed and undirected graph topologies. Second, based on the proposed quadratic Lyapunov equation, sufficient conditions for the asymptotic convergence of the observer are obtained as a set of linear matrix inequalities. Finally, a numerical example is provided to illustrate the proposed approach.</abstract>

University of Evry—Paris-Saclay University

IT Hermosillo, National Technological Institute of Mexico

<article-title>Distributed Fault Estimation of Multi–Agent Systems Using a Proportional–Integral Observer: A Leader–Following Application</article-title>

Distributed Fault Estimation of Multi–Agent Systems Using a Proportional–Integral Observer: A Leader–Following Application

<abstract><title style='display:none'>Abstract</title>The paper is devoted to the problem of mining graph data. The goal of this process is to discover possibly certain sequences appearing in data. Both rough set flow graphs and fuzzy flow graphs are used to represent sequences of items originally arranged in tables representing information systems. Information systems are considered in the Pawlak sense, as knowledge representation systems. In the paper, an approach involving ant based clustering is proposed. We show that ant based clustering can be used not only for building possible large groups of similar objects, but also to build larger structures (in our case, sequences) of objects to obtain or preserve the desired properties.</abstract>

University of Information Technology and Management in Rzeszów

<article-title>Ant–Based Clustering for Flow Graph Mining</article-title>

Ant–Based Clustering for Flow Graph Mining

{"article-title":"Ant\u2013Based Clustering for Flow Graph Mining"}

<abstract><title style='display:none'>Abstract</title>A scheduling problem in considered on unrelated machines with the goal of total late work minimization, in which the late work of a job means the late units executed after its due date. Due to the NP-hardness of the problem, we propose two meta-heuristic algorithms to solve it, namely, a tabu search (TS) and a genetic algorithm (GA), both of which are equipped with the techniques of initialization, iteration, as well as termination. The performances of the designed algorithms are verified through computational experiments, where we show that the GA can produce better solutions but with a higher time consumption. Moreover, we also analyze the influence of problem parameters on the performances of these meta-heuristics.</abstract>

<article-title>Two Meta–Heuristic Algorithms for Scheduling on Unrelated Machines with the Late Work Criterion</article-title>

Two Meta–Heuristic Algorithms for Scheduling on Unrelated Machines with the Late Work Criterion

<abstract><title style='display:none'>Abstract</title>There are a large number of historical documents in libraries and other archives throughout the world. Most of them are written by hand. In many cases they exist in only one specimen and are hard to reach. Digitization of such artifacts can make them available to the community. But even digitized, they remain unsearchable, and an important task is to draw the contents in the computer readable form. One of the first steps in this direction is to recognize where the lines of the text are. Computational intelligence algorithms can be used to solve this problem. In the present paper, two groups of algorithms, namely, projection-based and tensor voting-based, are compared. The performance is evaluated on a data set and with the procedure proposed by the organizers of the <italic>ICDAR 2009</italic> competition.</abstract>

Wrocław University of Science and Technology

<article-title>Line Segmentation of Handwritten Text Using Histograms and Tensor Voting</article-title>

Line Segmentation of Handwritten Text Using Histograms and Tensor Voting

{"article-title":"Line Segmentation of Handwritten Text Using Histograms and Tensor Voting"}

<abstract><title style='display:none'>Abstract</title>We present a real-time hierarchical approach to an on-line risk assessment at the national level taking into account both local risk analyses performed by key service operators and relevant interdependencies between those services. For this purpose we define mutually agreed predicted service disruption profiles and then propose a coordination mechanism to align those profiles. A simple, four-entity example is provided to illustrate the coordination.</abstract>

<article-title>Real–Time Hierarchical Predictive Risk Assessment at the National Level: Mutually Agreed Predicted Service Disruption Profiles</article-title>

Real–Time Hierarchical Predictive Risk Assessment at the National Level: Mutually Agreed Predicted Service Disruption Profiles

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Editor-in-Chief <A href="http://staff.uz.zgora.pl/jkorbicz/">Józef Korbicz</A>, University of Zielona Góra, Poland Deputy Editor <A href="http://staff.uz.zgora.pl/ducinski/viewpage.php?page_id=12">Dariusz Uciński</A>, University of Zielona Góra, Poland Associate Editors Harald ASCHEMANN, University of Rostock, Germany Jérôme CIESLAK, University of Bordeaux, France Martin GUGAT, Friedrich-Alexander University of Erlangen-Nuremberg, Germany Marios M. POLYCARPOU, University of Cyprus, Nicosia, Cyprus Silvio SIMANI, University of Ferrara, Italy Jerzy STEFANOWSKI, Poznań University of Technology, Poland Guisheng ZHAI, Shibaura Institute of Technology, Tokyo, Japan Board Members Cherukuri ASWANI KUMAR, VIT University, Vellore, India Czesław BAJER, Polish Academy of Sciences, Warsaw, Poland Andrzej BARTOSZEWICZ, Technical University of Łódź, Poland Miguel BERNAL, Sonora Institute of Technology (ITSON), Obregón, Mexico Kishore BINGI, Vellore Institute of Technology, India Paolo CASTALDI, University of Bologna, Italy Zhaohui CEN, Qatar Enviroment and Energy Research Institute, Ar Rayyan, Qatar Bogusław CYGANEK, AGH University of Science and Technology, Cracow, Poland Stefan DOMEK, West Pomeranian University of Technology in Szczecin, Poland Andrzej DZIELIŃSKI, Warsaw University of Technology, Poland Anna FABIJAŃSKA, Lodz University of Technology, Poland Marcin GORAWSKI, Silesian University of Technology, Gliwice, Poland Michał GROCHOWSKI, Gdańsk University of Technology, Poland Xiao HE, Tsinghua University, Beijing, China Janusz KACPRZYK, Polish Academy of Sciences, Warsaw, Poland Jerzy KLAMKA, Polish Academy of Sciences, Gliwice, Poland Jacek KLUSKA, Rzeszów University of Technology, Poland Joanna KOŁODZIEJ, Cracow University of Technology, Poland Jan M. KOŚCIELNY, Warsaw University of Technology, Poland Zdzisław KOWALCZUK, Gdańsk University of Technology, Poland Adam KRZYZAK, Concordia University, Montreal, Canada Piotr KULCZYCKI, AGH University of Science and Technology, Cracow, Poland Maciej KUSY, Rzeszów University of Technology, Poland Francisco-Ronay LÓPEZ-ESTRADA, Technological Institute of Tuxtla Gutiérrez, Mexico Maciej ŁAWRYŃCZUK, Warsaw University of Technology, Poland Vyacheslav MAKSIMOV, Russian Academy of Sciences, Ekaterinburg, Russia Krzysztof MALINOWSKI, Warsaw University of Technology, Poland Wojciech MITKOWSKI, AGH University of Science and Technology, Cracow, Poland Gang NIU, Tongji University, Shanghai, China Ronald J. PATTON, University of Hull, UK Jimoh O. PEDRO, University of the Witwatersrand, Johannesburg, South Africa Witold PEDRYCZ, University of Alberta, Edmonton, Canada Piotr PORWIK, University of Silesia in Katowice, Poland Vicenç PUIG, Technical University of Catalonia, Barcelona, Spain Jianbin QIU, Harbin Institute of Technology, China Ewaryst RAFAJŁOWICZ, Wrocław University of Technology, Poland Rotislav RAZUMCHIK, Russian Academy of Sciences, Moscow, Russia Leszek RUTKOWSKI, Technical University of Częstochowa, Poland Andrey V. SAVCHENKO, National Research University HSE, Nizhny Novgorod, Russia Piotr SKRZYPCZYŃSKI, Poznań University of Technology, Poland Roman SŁOWIŃSKI, Poznań University of Technology, Poland Florin STOICAN, University POLITEHNICA of Bucharest, Romania Andrzej ŚWIERNIAK, Silesian University of Technology, Gliwice, Poland Zoltán SZABÓ, Hungarian Academy of Sciences, Budapest, Hungary Ryszard TADEUSIEWICZ, AGH University of Science and Technology, Cracow, Poland Didier THEILLIOL, University of Lorraine, Nancy, France Haoping WANG, Nanjing University of Science and Technology, China Marcin WITCZAK, University of Zielona Góra, Poland Baozhen YAO, Dalian University of Technology, China Shen YIN, Harbin Institute of Technology, China Alexey ZHIRABOK, Far Eastern Federal University, Vladivostok, Russia Teresa ZIELIŃSKA, Warsaw University of Technology, Poland Jacek M. ZURADA, University of Louisville, USA Editorial Office Agnieszka Rożewska, University of Zielona Góra, Poland – Manager Agata Wiśniewska-Kubicka, University of Zielona Góra, Poland – Technical Editor Contact AMCS Editorial Office University of Zielona Góra, Institute of Control and Computation Engineering ul. prof. Z. Szafrana 2 65-516 Zielona Góra phone: +48683282506 e-mail: <A href="mailto:amcs@uz.zgora.pl">amcs@uz.zgora.pl</A> website: <A href="http://www.amcs.uz.zgora.pl/">www.amcs.uz.zgora.pl</A> Publisher De Gruyter Poland Bogumiła Zuga 32A Str. 01-811 Warsaw, Poland Acknowledgement Electronic publication of Volume 26 (2016, Nos. 1-4) has been financed with the funds of the Polish Ministry of Science and Higher Education for science promoting activities within the agreement no. 904/P-DUN/2016 <A href="http://www.nauka.gov.pl/en/"><IMG alt="" src="https://www.degruyter.com/staticfiles/images/mnisw_opt.jpg"></A>

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<DIV align=left> The International Journal of Applied Mathematics and Computer Science is a quarterly published in Poland since 1991 by the University of Zielona Góra in partnership with De Gruyter Poland (Sciendo) and historically with Lubuskie Scientific Society, under the auspices of the Committee on Automatic Control and Robotics of the Polish Academy of Sciences. The journal strives to meet the demand for the presentation of interdisciplinary research in various fields related to control theory, applied mathematics, scientific computing and computer science. In particular, it publishes high quality original research results in the following areas: <UL> <LI>modern control theory and practice, </LI> <LI>artificial intelligence methods and their applications, </LI> <LI>applied mathematics and mathematical optimisation techniques, </LI> <LI>mathematical methods in engineering, computer science, and biology. </LI> </UL> We are primarily interested in presenting theoretical and application-orientated full-length research papers dealing with the following topics: <UL> <LI>control theory, including optimal control, system identification, adaptive and robust control, multivariable control, and non-linear systems, </LI> <LI>dynamical systems, including spatiotemporal processes, control problems, state and parameter estimation, and sensor networks, </LI> <LI>fault detection and diagnosis, including model-based approaches, observers, and classifiers, </LI> <LI>fault-tolerant control, including the control of continuous-variable and quantised systems, </LI> <LI>robotics, including modelling and simulation, mobile robots, and optimal trajectory planning, </LI> <LI>mathematical modelling and simulation, including numerical algorithms, </LI> <LI>optimisation, including mathematical optimisation techniques, global optimisation, and evolutionary algorithms, </LI> <LI>artificial intelligence, including machine and deep learning, neural networks, knowledge engineering, fuzzy systems, and search methods, </LI> <LI>data mining, data and image processing, and big data, </LI> <LI>classification and pattern recognition, </LI> <LI>biomedical engineering and biomathematics, </LI> <LI>applications in engineering and medicine. </LI> </UL> The editors welcome proposals for exchange between similar journals. Also, all persons interested in bringing out special issues or sections of AMCS are encouraged to contact the Editor-in-Chief. Specials may be published on any important and timely subject within the scope of the journal. All papers proposed for special issues or sections will be refereed and have to meet the same criteria for scientific quality as articles presented in regular issues. Also, all journal rules and guidelines refer to papers published in specials without any exceptions. ISSN: 1641-876X (print version), 2083-8492 (online version) Why subscribe and read <UL> <LI>Premier source of high quality research from all over the world, with special emphasis on papers from Central and Eastern Europe, </LI> <LI>over twenty years of experience in publishing original articles dealing with various aspects of applying mathematical methods to computer science and engineering, </LI> <LI>extensive scope covering, among other things, control systems, optimization, robotics, mathematical and numerical modeling, artificial intelligence, knowledge processing, fault detection and diagnostics, </LI> <LI>one of the fastest growing journals published in Central and Eastern Europe. </LI></UL> Why submit <UL> <LI>Fast, fair, competent and comprehensive peer review (two or three referees are designated for the assessment of each manuscript), </LI> <LI>fast and efficient publication process, </LI> <LI>thorough language correction for authors who are not native English speakers, </LI> <LI>polishing the structure and technical format of each submission, </LI> <LI>promotion of published papers in the vast scientific community of researchers working in the field. </LI></UL> Archiving Sciendo archives the contents of this journal in <A href="https://www.portico.org/">Portico</A> - digital long-term preservation service of scholarly books, journals and collections. Plagiarism policy The editorial board is participating in a growing community of <A href="https://www.crossref.org/services/similarity-check/">Similarity Check System's</A> users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. </DIV>

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Volume 30 (2020): Issue 3 (September 2020) Big Data and Signal Processing (Special section, pp. 399-473), Joanna Kołodziej, Sabri Pllana, Salvatore Vitabile (Eds.)

Volume 30 (2020): Issue 3 (September 2020)

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Volumen 30 (2020): Heft 3 (September 2020)Big Data and Signal Processing (Special section, pp. 399-473), Joanna Kołodziej, Sabri Pllana, Salvatore Vitabile (Eds.)

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Volumen 30 (2020): Heft 3 (September 2020)
Big Data and Signal Processing (Special section, pp. 399-473), Joanna Kołodziej, Sabri Pllana, Salvatore Vitabile (Eds.)