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Gdańsk University of Technology

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Publications from the year 2024

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  • Data on LEGO sets release dates and worldwide retail prices combined with aftermarket transaction prices in Poland between June 2018 and June 2023
    • Wiktor Oczkoś
    • Bartosz Podgórski
    • Wiktoria Szczepańska
    • Tomasz Maria Boiński
    2024 Full text Data in Brief

    The dataset contains LEGO bricks sets item count and pricing history for AI-based set pricing prediction. The data spans the timeframe from June 2018 to June 2023. The data was obtained from three sources: Brickset.com (LEGO sets retail prices, release dates, and IDs), Lego.com official web page (ID number of each set that was released by Lego, its retail prices, the current status of the set) and promoklocki.pl web page (the retail prices for Poland, prices from aftermarket transactions). The data was merged based on the official LEGO set ID. With high granularity of the data (averaged monthly prices per LEGO set) the dataset permits the computation of variables at the set level and could support both aggregate and time-series analyses whereas the sparseness of the data permits the analysis of collector behavior allowing pinpointing of expected qualities from the purchased products and their resale potential. This may be useful to a broad range of researchers and data scientists using statistical methods and machine-learning techniques for price prediction.

  • Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
    • Farzin Kazemi
    • Torkan Shafighfard
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    Fiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required for design criteria, has been a challenge because of their high uncertainties. Statistical and empirical models have been extensively utilized. However, such models require extensive experimental work and can produce incorrect outcomes when there are complicated interactions among the qualities of concrete, the makeup of the blend, and the curing environment. To address this issue, machine learning (ML) methods have been increasingly applied in recent years to solve complex structural engineering problems. Predictive models can provide a strong solution for time-consuming numerical simulations and expensive experiments. This study explores the ML techniques applied in this context and provides a comprehensive analysis of artificial intelligence methods used for predicting the mechanical properties of FRCs. It also highlights the key observations, challenges, and future trends in this field. This study serves as a valuable resource for researchers in selecting accurate models that match their applications. It also encourages material engineers to become familiar with and employ ML methods to design FRC mixtures appropriately.

  • Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
    • Piotr Januszewski
    • Dominik Grzegorzek
    • Paweł Czarnul
    2024 Full text

    The Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results demonstrate that NeuraLCB can learn from various datasets, while Neural Greedy necessitates extensive coverage of the action-space for effective learning. Moreover, the way data is collected significantly affects offline methods’ efficiency. This underscores the critical role of dataset quality in offline policy learning.

  • Dc Leakage Current in Isolated Grid-Connected dc Nanogrid - Origins and Elimination Methods
    • Mohammadreza Azizi
    • Oleksandr Husev
    • Oleksandr Veligorskyi
    • Marek Turzyński
    • Ryszard Strzelecki
    2024

    The LV dc system is a relatively new trend in the distribution sector, which seems to grow widely in the near future due to its promising advantages. In this context, LV dc protection and grounding are challenging issues. Although the galvanically isolated connection mode of dc nanogrid to the ac grid has high reliability, the leakage current can still be injected into the ac grid through the interwinding capacitors and the insulation resistance between the primary and secondary windings of the transformer. The way of grounding the dc nanogrid can also be a determining factor in the leakage current and its dc components. This study deals with the leakage current in the galvanically isolated dc nanogrid. Then, it examines the dc leakage current and its relationship with the dc nanogrid grounding and finally provides solutions to remove the dc components in the leakage current.

  • Dead time effects compensation strategy by third harmonic injection for a five-phase inverter
    • Krzysztof Łuksza
    • Dmytro Kondratenko
    • Arkadiusz Lewicki
    2024 Full text Archives of Electrical Engineering

    This paper proposes a method for compensation of dead-time effects for a fivephase inverter. In the proposed method an additional control subsystem was added to the field-oriented control (FOC) scheme in the coordinate system mapped to the third harmonic. The additional control loop operates in the fixed, orthogonal reference frame ( α - β coordinates) without the need for additional Park transformations. The purpose of this method is to minimize the dead-time effects by third harmonic injection in two modes of operation of the FOC control system: with sinusoidal supply and with trapezoidal supply. The effectiveness of the proposed control method was verified experimentally on a laboratory setup with a prototype five-phase interior permanent magnet synchronous machine (IPMSM). All experimental results were presented and discussed in the following paper.

  • Decisional-DNA-Based Digital Twin Implementation Architecture for Virtual Engineering Objects
    • Syed Imran Shafiq
    • Cesar Sanin
    • Edward Szczerbicki
    2024 Full text CYBERNETICS AND SYSTEMS

    Digital twin (DT) is an enabling technology that integrates cyber and physical spaces. It is well-fitted for manufacturing setup since it can support digitalized assets and data analytics for product and process control. Conventional manufacturing setups are still widely used all around the world for the fabrication of large-scale production. This article proposes a general DT implementation architecture for engineering objects/artifacts used in conventional manufacturing. It will empower manufacturers to leverage DT for real-time decision-making, control, and prediction for efficient production. An application scenario of Decisional-DNA based anomaly detection for conventional manufacturing tools is demonstrated as a case study to explain the architecture.

  • Deep eutectic solvent-based shaking-assisted extraction for determination of bioactive compounds from Norway spruce roots
    • Alina Kalyniukova
    • Alica Varfalvyová
    • Justyna Płotka-Wasylka
    • Tomasz Majchrzak
    • Patrycja Makoś-Chełstowska
    • Ivana Tomášková
    • Vítězslava Pešková
    • Filip Pastierovič
    • Anna Jirošová
    • Vasil Andruch
    2024 Full text Frontiers in Chemistry

    Polyphenolic compounds play an essential role in plant growth, reproduction, and defense mechanisms against pathogens and environmental stresses. Extracting these compounds is the initial step in assessing phytochemical changes, where the choice of extraction method significantly influences the extracted analytes. However, due to environmental factors, analyzing numerous samples is necessary for statistically significant results, often leading to the use of harmful organic solvents for extraction. Therefore, in this study, a novel DESbased shaking-assisted extraction procedure for the separation of polyphenolic compounds from plant samples followed by LC-ESI-QTOF-MS analysis was developed. The DES was prepared from choline chloride (ChCl) as the hydrogen bond acceptor (HBA) and fructose (Fru) as the hydrogen bond donor (HBD) at various molar ratios with the addition of 30% water to reduce viscosity. Several experimental variables affecting extraction efficiency were studied and optimized using one-variable-at-a-time (OVAT) and confirmed by response surface design (RS). Nearly the same experimental conditions were obtained using both optimization methods and were set as follows: 30 mg of sample, 300 mg of ChCl:Fru 1:2 DES containing 30% w/w of water, 500 rpm shaking speed, 30 min extraction time, 10°C extraction temperature. The results were compared with those obtained using conventional solvents, such as ethanol, methanol and water, whereby the DES-based shaking-assisted extraction method showed a higher efficiency than the classical procedures. The greenness of the developed method was compared with the greenness of existing procedures for the extraction of polyphenolic substances from solid plant samples using the complementary green analytical procedure index (ComplexGAPI) approach, while the results for the developed method were better or comparable to the existing ones. In addition, the practicability of the developed procedure was evaluated by application of the blue applicability grade index (BAGI) metric. The developed procedure was applied to the determination of spruce root samples with satisfactory results and has the potential for use in the analysis of similar plant samples.

  • Deep eutectic solvents with solid supports used in microextraction processes applied for endocrine-disrupting chemicals
    • Jose Grau
    • Aneta Chabowska
    • Justyna Werner
    • Agnieszka Zgoła-Grześkowiak
    • Magdalena Fabjanowicz
    • Natalia Jatkowska
    • Alberto Chisvert
    • Justyna Płotka-Wasylka
    2024 TALANTA.The International Journal of Pure and Applied Analytical Chemistry

    The determination of endocrine-disrupting chemicals (EDCs) has become one of the biggest challenges in Analytical Chemistry. Due to the low concentration of these compounds in different kinds of samples, it becomes necessary to employ efficient sample preparation methods and sensitive measurement techniques to achieve low limits of detection. This issue becomes even more struggling when the principles of the Green Analytical Chemistry are added to the equation, since finding an efficient sample preparation method with low damaging properties for health and environment may become laborious. Recently, deep eutectic solvents (DESs) have been proposed as the most promising green kind of solvents, but also with excellent analytical properties due to the possibility of custom preparation with different components to modify their polarity, viscosity or aromaticity among others. However, conventional extraction techniques using DESs as extraction solvents may not be enough to overcome challenges in analysing trace levels of EDCs. In this sense, combination of DESs with solid supports could be seen as a potential solution to this issue allowing, in different ways, to determine lower concentrations of EDCs. In that aim, the main purpose of this review is the study of the different strategies with solid supports used along with DESs to perform the determination of EDCs, comparing their advantages and drawbacks against conventional DES-based extraction methods.

  • Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
    • Kashif Shaheed
    • Piotr Szczuko
    • Munish Kumar
    • Imran Qureshi
    • Qaisar Abbas
    • Ihsan Ullah
    2024 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    Biometric 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 pre- sentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associ- ated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.

  • Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
    • Maksym Albin Jopek
    • Krzysztof Pastuszak
    • Sebastian Cygert
    • Myron G Best
    • Thomas Würdinger
    • Jacek Jassem
    • Anna Żaczek
    • Anna Supernat
    2024 Full text IEEE Journal of Translational Engineering in Health and Medicine-JTEHM

    The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient’s type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) - SHAP. They returned a set of 60 genes with the highest impact on the models’ decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians’ decision-making process in finding the solution for the black-box problem. Clinical and Translational Impact Statement— Utilizing TEPs-based liquid biopsies and deep learning, our study offers a novel approach to early cancer detection, highlighting cancer origin. The integration of Explainable AI reinforces trust in predictive outcomes. Category: Early/Pre-Clinical Research.

  • Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
    • Munusamy Arun
    • Thanh Tuan Le
    • Debabrata Barik
    • Prabhakar Sharma
    • Sameh M. Osman
    • Van Kiet Huynh
    • Jerzy Kowalski
    • Van Huong Dong
    • Viet Vinh Le
    2024 Case Studies in Thermal Engineering

    Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

  • Defected Ag/Cu-MOF as a modifier of polyethersulfone membranes for enhancing permeability, antifouling properties and heavy metal and dye pollutant removal
    • Vahid Vatanpour
    • Rabia Ardic
    • Berk Esenli
    • Bahriye Eryildiz-Yesir
    • Parisa Yaqubnezhad Pazoki
    • Atefeh Jarahiyan
    • Firouz Matloubi Moghaddam
    • Roberto Castro Munoz
    • Ismail Koyuncu
    2024 SEPARATION AND PURIFICATION TECHNOLOGY

    In this study, a novel bimetallic metal-organic framework (MOF) i.e. Ag/Cu-MOF was synthesized using a solvothermal method and later incorporated at different concentrations (0.1–2 wt%) using a phase inversion method for modification and antifouling property improvement of polyethersulfone (PES) membranes. The resulting Ag/Cu-MOF characteristics were investigated using different techniques, such as FTIR, XRD, FE-SEM and EDX. The membranes were characterized by FE-SEM, contact angle, porosity, mean pore size, surface roughness and zeta potential. Furthermore, membrane performance was examined using pure water flux, BSA, Pb(II), dye removal and fouling properties. In particular, the results showed that the addition of 1.0 wt% of the Ag/Cu-MOF decreased the water contact angle from 68.5° to 59.6° while enhancing overall porosity from 45.1 % to 56.0 %. The maximum water permeability was obtained with 1.0 wt% Ag/Cu-MOF (ca. 100 L/m2.h.bar) representing 1.9 times higher flux than that of the bare PES membrane due to the hydrophilic nature of the bimetallic MOF. As for the rejection performance, high Pb(II), BSA, reactive black 5 and reactive red 120 rejections values were observed as 92.6 %, 99.5 %, 96.4 % and 98.4 %, respectively. The Ag/Cu-MOF embedded membrane showed antibacterial behavior against Escherichia coli and antifouling properties, causing a considerable decrease in fouling resistance parameters and significant improvement in the antifouling properties of the PES membrane. The results of this study demonstrated that the Ag/Cu-MOF could be a promising material for boosting the polymeric membrane properties.

  • Deformation mitigation and twisting moment control in space frames
    • Ahmed Manguri
    • Najmadeen Saeed
    • Robert Jankowski
    2024 Full text Structures

    Over the last five decades, space frames have centered on the modernization of touristic zones in view of architectural attractions. Although attempts to control joint movement and minimize axial force and bending moment in such structures were made sufficiently, twisting moments in space frames have been underestimated so far. In space frames, external load or restoring the misshapen shape may cause twisting in members. We herein developed a robust computational algorithm to reduce the induced torsional moment through shape restoration within a desired limit by changing the length of active bars that are placed in space frames. Applying optimization algorithms like interior-point and Sequential quadratic programming (SQP), a direct correlation was pursued between bar length alteration and twisting in structural members. A numerical model of a single-layer space frame resembling an egg captures the twisting moment in all members, achieving a specified limit. The overall length change of the active members using an iterative process based on a heuristic that considers a threshold on the minimum length change of the active members.

  • Degradacja i uszkodzenia podbudowy jako przyczyny awarii betonowych posadzek przemysłowych
    • Sylwia Świątek-Żołyńska
    • Maciej Niedostatkiewicz
    • Władysław Ryżyński
    2024

    Posadzki i nawierzchnie betonowe doznają podczas użytkowania zróżnicowanych w swojej skali i czasie degradacji takich jak spękania, zarysowania i klawiszowanie płyt. Część z tych uszkodzeń jest spowodowana wadami wykonania, niską jakością betonu lub błędami projektowymi płyty konstrukcyjnej, ale w znacznej części jest to związane z wadliwą podbudową. Podbudowa jest bowiem istotnym elementem składowym posadzki, zapewniającym wymaganą nośność i sztywność całego układu

  • DEM modelling of concrete fracture based on its structure micro-CT images
    • Michał Nitka
    • Andrzej Tejchman-Konarzewski
    2024

    W rozdziale książki zawarto numeryczne wyniki mezoskopowe dotyczące postępującego pękania betonu na poziomie kruszywa. Do badania procesu pękania belki betonowej z karbem w trzech (3D) i dwóch (2D) wymiarach zastosowano metodę elementów dyskretnych (DEM). Niejednorodność betonu uwzględniono stosując czterofazowy opis: kruszywo, zaprawa, makropustki i międzyfazowe strefy przejściowe. W obliczeniach DEM na podstawie zdjęć rentgenowskich mikro-CT przyjęto rzeczywistą postać i rozmieszczenie kruszywa w betonie. Osiągnięto dobry poziom zgodności w odniesieniu do siły pionowej wpływającej na ewolucję przemieszczenia otworu wylotowego pęknięcia i kształtu pęknięcia pomiędzy analizą DEM a pomiarami laboratoryjnymi. Ewolucja zerwanych styków, sił normalnych kontaktu, rotacji cząstek, energii wewnętrznych, kształtu kruszywa oraz porowatości i szerokości ITZ były szeroko zbadane numerycznie na poziomie agregatów. Wyniki 3D również mocno kontrastowały z wynikami 2D. Pokazano, że model 3D DEM jest potencjalnym narzędziem do modelowania umożliwiającym przewidywanie i zrozumienie pękania betonu na poziomie mezoskopowym i makroskopowym.

  • DESIGN AND EXECUTION ERRORS AS A CAUSE OF DAMAGE TO ANTI- ELECTROSTATIC FLOORING
    • Sylwia Świątek-Żołyńska
    • Maciej Niedostatkiewicz
    • Władysław Ryżyński
    2024

    Apart from technological lines, industrial floors are a key element in the scope of maintaining the continuity of work of both production plants and logistics centers. The constantly developing industry of industrial flooring includes both classic design and technological flooring solutions, as well as specialist solutions used in facilities where technological processes or storage require system protection against static electricity. The basic design and implementation activity, apart from the use of earthing systems for the elements of the supporting structure of the facility, is the execution of anti-electrostatic floors with parameters and functional features adapted to the function of the facility.

  • Design and Experimental Validation of a Metamaterial-Based Sensor for Microwave Imaging in Breast, Lung, and Brain Cancer Detection
    • Musa Hamza
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024 Full text Scientific Reports

    This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC’s permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.

  • Design and experimental verification of multi-layer waveguide using pin/hole structure
    • Hasan Raza
    • Sławomir Kozieł
    • Leifur Leifsson
    2024 IEEE Antennas and Wireless Propagation Letters

    This study presents a novel technique for minimizing RF leakage in metallic hollow waveguides fabricated using the multilayer split-block method. By integrating a pin/hole wall into the split-block multilayers, a substantial reduction of RF leakage can be achieved while reducing the circuit size and mitigating the performance variations. To validate the proposed approach, a slot antenna fed by single ridge waveguide has been prototyped and experimentally validated. The simulated and measured results demonstrate that the slot antenna is well matched (|S11|  –10 dB) within the frequency range 29 GHz to 34 GHz, whereas its gain is about 8 dBi.

  • Design and Implementation of Multi-Band Reflectarray Metasurface for 5G Millimeter Wave Coverage Enhancement
    • Bilal Malik
    • Shahid Khan
    • Sławomir Kozieł
    2024 Full text Scientific Reports

    A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60o at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3 × 3.3 mm2) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32 × x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20-25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages.

  • Design and Optimization of Metamaterial-based Highly-isolated MIMO Antenna with High Gain and Beam Tilting Ability for 5G Millimeter Wave Applications
    • Bashar Esmail
    • Sławomir Kozieł
    2024 Full text Scientific Reports

    This paper presents a wideband multiple-input multiple-output (MIMO) antenna with high gain and isolation, as well as beam tilting capability, for 5G millimeter wave (MMW) applications. A single bow-tie antenna fed by a substrate-integrated waveguide (SIW) is proposed to cover the 28 GHz band (26.5–29.5 GHz) with a maximum gain of 6.35 dB. To enhance the gain, H-shaped metamaterial (MM)-based components are incorporated into the antenna substrate. The trust-region (TR) gradient-based search algorithm is employed to optimize the H-shape dimensions and to achieve a maximum gain of 11.2 dB at 29.2 GHz. The MM structure offers zero index refraction at the desired range. Subsequently, the MIMO system is constructed with two vertically arranged radiators. Another MM, a modified square resonator (MSR), is embedded between the two radiators to reduce the mutual coupling and to tilt the antenna main beam. Herein, the TR algorithm is again used to optimize the MSR dimensions, and to enhance the isolation to a maximum of 75 dB at 28.6 GHz. Further, the MSR can tilt the E-plane radiation by ±20˚ with respect to the end-fire direction when alternating between the two ports' excitation. The developed system is validated experimentally with a good matching between the simulated and measured data.