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Workshop on Approximation and Randomization Algorithms in Communication Networks
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IEEE International Joint Conference on Neural Networks
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International Symposium on the Mathematical Theory of Networks and Systems
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Conference on Artificial Neural Networks and Expert systems
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International Symposium on Parallel Architectures, Algorithms and Networks
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International Conference on Embedded Wireless Systems and Networks
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International Conference on Broadband Communications, Networks and Systems
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International Conference on Security and Privacy for Communication Networks
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International Conference on Wireless Networks and Mobile Communications
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International Conference on Engineering Applications of Neural Networks
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International Conference on AD-HOC Networks and Wireless
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Testbeds and Research Infrastructures for the Development of Networks and Communities
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Modulacja 3D-SVPWM dla przekształtników wielopoziomowych czterogałęziowych na bazie funkcji kształtu
PublicationPrzedstawiono realizację algorytmu modulacji napięcia 3D-SVPWM (ang. 3-Dimensional Space Vector Pulse Width Modulation) dla trójpoziomowego czterogałęziowego przekształtnika 3L-NPC (ang. 3-Level Neutral-Point-diode-Clamped Converter). Do wyznaczania współczynników wypełnień w modulacji szerokości impulsów zastosowano liniowe funkcje kształtu czworościennego elementu skończonego. Proponowany algorytm modulacji nie jest ograniczony...
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Modified SPWM Technique with Zero-Sequence Voltage Injection for a Five-Phase, Three-Level NPC Inverter
PublicationThis article presents a modified sinusoidal pulse-width modulation (SPWM) scheme for a five-phase, three-level neutral-point-clamped inverter. The modulation scheme deploys a modified min–max function to inject the zero-sequence components into the reference modulating signals; hence enabling the effective utilization of the DC-link voltage. Balanced split-input DC-link voltages were achieved through further incorporation of adjustable...
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The Optical Transport Network Control Based on SDN Architecture
PublicationThe aim of this publication is to present research results on the usability of the Software-Defined Networking concept to control transport networks. For this purpose, an easy-to-use connection scheduler was developed capable of controlling connections in optical transport networks. The authors would like to present this solution and details of constructed SDN architecture implemented for modern optical transport solutions based...
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Taxonomy of Schemes for Resilient Routing
PublicationThis chapter provides a taxonomy of schemes for resilient routing followed by a discussion of their application to contemporary architectures of communication networks. In particular, a general classification of schemes for resilient routing is first presented followed by a description of the reference schemes for IP networks. The chapter in its later part focuses on the representative techniques of resilient routing for a multi-domain...
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Aleksandra Parteka dr hab. inż.
PeopleAbout me: I am an associate professor and head of doctoral studies at the Faculty of Management and Economics, Gdansk University of Technology (GdanskTech, Poland). I got my MSc degree in Economics from Gdansk University of Technology (2003) and Universita’ Politecnica delle Marche (2005), as well as MA degree in Contemporary European Studies from Sussex University (2006, with distinction). I received my PhD in Economics...
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Energy-Efficient Self-Supervised Technique to Identify Abnormal User Over 5G Network for E-Commerce
PublicationWithin the realm of e-commerce networks, it is frequently observed that certain users exhibit behavior patterns that differ substantially from the normative behaviors exhibited by the majority of users. The identification of these atypical individuals and the understanding of their behavioral patterns are of significant practical significance in maintaining order on e-commerce platforms. One such method for accomplishing this...
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Energy-Efficient Self-Supervised Technique to Identify Abnormal User Over 5G Network for E-Commerce
PublicationWithin the realm of e-commerce networks, it is frequently observed that certain users exhibit behavior patterns that differ substantially from the normative behaviors exhibited by the majority of users. The identification of these atypical individuals and the understanding of their behavioral patterns are of significant practical significance in maintaining order on e-commerce platforms. One such method for accomplishing this objective...
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Electron attachment to representative cations composing ionic liquids
PublicationUsing ab initio electronic structure methods with flexible atomic orbital basis sets, we investigated the electronic structure and stability of reduction products of selected representative cations (C+) constituting ionic liquids. We found that an electron attachment to such cations leads to the neutral radicals, whereas a subsequent attachment of another (i.e., excess) electron leads to adiabatically stable anions only in two...
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Anionic states of C6Cl6 probed in electron transfer experiments
PublicationThis is the first comprehensive investigation on the anionic species formed in collisions of fast neutral potassium (K) atoms with neutral hexachlorobenzene (C6Cl6) molecules in the laboratory frame range from 10 up to 100 eV. In such ion-pair formation experiments, we also report a novel K+ energy loss spectrum obtained in the forward scattering giving evidence of the most accessible electronic states. The vertical electron affinity...
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Controlling the europium oxidation state in diopside through flux concentration
PublicationThis paper explores the connection between the H3BO3 flux concentration and the co-existence of Eu2+ and Eu3+ dopants within CaMgSi2O6 crystals (diopside). The samples were synthesised using a solid-state synthesis method under varying atmospheric conditions, including oxidative (air), neutral (N2), and reductive (H2/N2 mixture) environments. Additionally, some materials underwent chemical modification by partially substituting...
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Properties of Oxygen Vacancy and Hydrogen Interstitial Defects in Strontium Titanate: DFT + Ud,p Calculations
PublicationThis work presents extensive theoretical studies focused on the mixed ion-electron transport in cubic strontium titanate (STO). A new approach to the description of this difficult system was developed within the framework of linear-scaling Kohn–Sham density functional theory, as realized in the ONETEP program. The description we present is free of any empirical parameters and relies on the Hubbard U and Hund’s J corrections applied...
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Vehicle detector training with minimal supervision
PublicationRecently many efficient object detectors based on convolutional neural networks (CNN) have been developed and they achieved impressive performance on many computer vision tasks. However, in order to achieve practical results, CNNs require really large annotated datasets for training. While many such databases are available, many of them can only be used for research purposes. Also some problems exist where such datasets are not...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublicationBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublicationThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
PublicationThe economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of insitu groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an...
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TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA
PublicationThe paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results...
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Path Loss Modelling for Location Service Applications
PublicationThe aim of this paper is the path loss modeling for the radiolocation services in radiocommunication networks, particularly in cellular networks. The main results of the measurements obtained in the physical layer of the UMTS are introduced. A new method for the utilization of the multipath propagation phenomenon to improve the estimation of the distance between the mobile station (MS) and the base station (BS) is outlined. This...
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New type T-Source inverter
PublicationThis paper presents different topologies of voltage inverters with alternative input LC networks. The basic topology is known in the literature as a Z-source inverter (ZSI). Alternative passive networks were named by the authors as T-sources. T-source inverter has fewer reactive components in comparison to conventional Z-source inverter. The most significant advantage of the T-source inverter (TSI) is its use of a common voltage...
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Dissociative electron attachment and anion-induced dimerization in pyruvic acid
PublicationWe report partial cross sections for the dissociative electron attachment to pyruvic acid. A rich fragmentation dynamics is observed. Electronic structure calculations facilitate the identification of complex rearrangement reactions that occur during the dissociation. Furthermore, a number of fragment anions produced at electron energies close to 0 eV are observed, that cannot originate from single electron-molecule collisions....
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On thio‐substituted N‐heterocyclic arsines
PublicationMetathesis of N-heterocyclic chloroarsines with sulfur-based nucleophiles furnished thio-substituted 1,3,2-diazarsolidines and 1,3,2-diazarsolenes. Crystallographic and NMR spectroscopic studies revealed that a thiocyanato-diazarsolene exhibits a salt-like structure composed of weakly interacting thiocyanate and arsenium ions, while the remaining products formed neutral molecules. The structural data indicate that the heterocyclic...
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N-doped carbon materials as electrodes for highly stable supercapacitors
PublicationThis article reports a strategy to use nitrogen-doped carbon materials as electrodes for supercapacitors. Depending on the carbon precursor, the porous structure is changed with specific surface area reached up to 2270 m2 g−1. The capacitance of carbon materials used as electrodes is related strictly to pore size. The microstructure and nitrogen functionalities enable a high capacitance (327 F g−1) and cycle durability. The nanoporous...
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A repeated game formulation of network embedded coding for multicast resilience in extreme conditions
PublicationComputer networks and data sharing applications are vital for our current society and fundamental for any available ICT solution, so that networking is considered as one of the key critical infrastructures and its correct behavior should be always enforced, even in case of disasters or severe execution conditions. Resilience is a strongly demanding nonfunctional requirement for current computer networks, and one of the key factors...
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A Simple Neural Network for Collision Detection of Collaborative Robots
PublicationDue to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents...
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Evolutionary Algorithms in MPLS network designing
PublicationMPLS technology become more and more popular especially in core networks giving great flexibility and compatibility with existing Internet protocols. There is a need to optimal design such networks and optimal bandwidth allocation. Linear Programming is not time efficient and does not solve nonlinear problems. Heuristic algorithms are believed to deal with these disadvantages and the most promising of them are Evolutionary Algorithms....
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Surface EMG-based signal acquisition for decoding hand movements
Open Research DataBiosignal processing plays a crucial role in modern hand prosthetics. The challenge is to restore functionality of a lost limb based on the signals acquired from the surface of the stump. The number of sensors (emg channels) used for signal acquisition influence the quality of a prosthetic hand. Modern algorithms (including neural networks) can significantly...
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Pathological brain network activity: memory impairment in epilepsy
PublicationOur thinking, memory and cognition in general, relies upon precisely timed interactions among neurons forming brain networks that support cognitive processes. The surgical evaluation of drug-resistant epilepsy using intracranial electrodes provides a unique opportunity to record directly from human brain and to investigate the coordinated activity of cognitive networks. In this issue of Neurology®, Kleen and colleagues1 implicate...
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Radio Link Measurement Methodology for Location Service Applications
PublicationThe aim of this paper is the methodology of measurements executed in a radio link for the realization of radiolocation services in radiocommunication networks, particularly in cellular networks. The main results of the measurements obtained in the physical layer of the universal mobile telecommunications system (UMTS) are introduced. A new method for the utilization of the multipath propagation phenomenon to improve the estimation...
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Intelligent Autonomous Robot Supporting Small Pets in Domestic Environment
PublicationIn this contribution, we present preliminary results of the student project aimed at the development of an intelligent autonomous robot supporting small pets in a domestic environment. The main task of this robot is to protect a freely moving small pets against accidental stepping on them by home residents. For this purpose, we have developed the mobile robot which follows a pet and makes an alarm signal when a human is approaching....
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Architektury klasyfikatorów obrazów
PublicationKlasyfikacja obrazów jest zagadnieniem z dziedziny widzenia komputerowego. Polega na całościowej analizie obrazu i przypisaniu go do jednej lub wielu kategorii (klas). Współczesne rozwiązania tego problemu są w znacznej części realizowane z wykorzystaniem konwolucyjnych głębokich sieci neuronowych (convolutional neural network, CNN). W tym rozdziale opisano przełomowe architektury CNN oraz ewolucję state-of-the-art w klasyfikacji...
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Utilization of a Non-Linear Error Function in a Positioning Algorithm for Distance Measurement Systems Designed for Indoor Environments
PublicationA new positioning algorithm for distance measurement systems is outlined herein. This algorithm utilizes a non-linear error function which allows us to improve the positioning accuracy in highly difficult indoor environments. The non-linear error function also allows us to adjust the performance of the algorithm to the particular environmental conditions. The well-known positioning algorithms have limitations, mentioned by their...
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OCENA WŁAŚCIWOŚCI ROZLEGŁYCH SYSTEMÓW UZIEMIEŃ PRACUJĄCYCH W UKŁADACH OCHRONY ODGROMOWEJ
PublicationW wyniku połączenia wszystkich elementów uziemionych w obiekcie budowlanym do szyny ekwipotencjalnej powstaje często bardzo rozległy system uziemień. Ocena tak rozległego systemu za pomocą metod wykorzystujących przebiegi wolnozmienne prowadzi do zbyt optymistycznych wniosków odnośnie do ich właściwości podczas odprowadzania prądów wyładowań atmosferycznych. Pomiary z zastosowaniem metody wykorzystującej przebiegi impulsowe pomiaru...
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Ocena systemów uziemień z wykorzystaniem pomiarów metodą udarową
PublicationW artykule zostały omówione procedury i wymagania stawiane systemom uziemień przez przepisy normalizacyjne z punktu widzenia ich skuteczności w systemie ochrony odgromowej. Podstawą tak sformułowanej oceny są pomiary z zastosowaniem metody udarowej. Metoda taka okazuje się bardzo przydatna zwłaszcza w przypadku uziemień rozległych, jakie pojawiają się we współczesnych obiektach wskutek połączenia różnych elementów uziemiających...
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ASPEKTY BEZPIECZEŃSTWA W OCENIE WYNIKÓW POMIARÓW UZIEMIEŃ ODGROMOWYCH
PublicationOcena rozległego systemu za pomocą metod wykorzystujących przebiegi wolnozmienne prowadzi do zbyt optymistycznych wniosków odnośnie ich właściwości podczas odprowadzania prądów wyładowań atmosferycznych. Bardziej bezpieczne wnioski powstają w wyniku pomiarów z zastosowaniem metody wykorzystującej przebiegi impulsowe, ponieważ wtedy jest uwzględniana tylko ta część nawet bardzo rozległego systemu uziemień, która bierze udział w...
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S’attaquer à la suprématie du masculin sur le féminin : le français inclusif dans les publications des universités françaises dans les réseaux sociaux
PublicationThis paper aims to examine the use of inclusive French in the Internet publications of Paris universities on their social media. Three higher education institutions were selected: Paris Dauphine-PSL University, Gustave Eiffel University, and Sorbonne Paris North University. The publications were obtained from Facebook, Instagram, and LinkedIn. Firstly, the groups of people to whom the use of inclusive French referred...
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Process zone in the Single Cantilever Beam under transverse loading. - Part I: Theoretical analysis
PublicationSingle Cantilever Beam (SCB) specimen loaded with a transverse force parallel to the crack front is proposed for the analysis of crack propagation phenomena under mixed mode conditions. The stress redistribution in the adhesive layer in the vicinity of the crack front so as the beam deformation are estimated using a Timoshenko beam on elastic foundation model. This model emphasizes the Mode II contribution due to flexural beam...
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Application possibilities of LBN for civil engineering issues
PublicationBayesian Networks (BN) are efficient to represent knowledge and for the reasoning in uncertainty. However the classic BN requires manual definition of the network structure by an expert, who also defines the values entered into the conditional probability tables. In practice, it can be time-consuming, hence the article proposes the use of Learning Bayesian Networks (LBN). The aim of the study is not only to present LBN, which can...
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublicationThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...