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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 Cellular Nuclei Segmentation Using Transformer Model
- Mateusz Erezman
- Tomasz Dziubich
2024
Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041.
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 Pełny tekst 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 Pełny tekst 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.
Deep Video Multi-task Learning Towards Generalized Visual Scene Enhancement and Understanding
- Efkleidis Katsaros
2024 Pełny tekst
The goal of this thesis was to develop efficient video multi-task convolutional architectures for a range of diverse vision tasks, on RGB scenes, leveraging i) task relationships and ii) motion information to improve multi-task performance. The approach we take starts from the integration of diverse tasks within video multi-task learning networks. We present the first two datasets of their kind in the existing literature, featuring frame-level annotations for both visual scene enhancement and understanding. This thesis proposes novel architectures, capable of accommodating multiple tasks across various hierarchy levels. The second contribution of this thesis extends those findings into the MOST (Multi-Output, -Scale, -Task) model which exploits the inherent multi-scale nature of convolutional networks in a manner that benefits video multi- tasking. Thereafter, we propose a principled pruning approach inspired by NAS (Neural Architecture Search), named NSS (Neural Structure Search). NSS discovers a more effective MOST network, which boosts performance while simultaneously reducing computational requirements and parameter count. Lastly, we introduce ATB (Adaptive Task Balancing), an efficient training method that ensures tasks are trained at consistent rates with almost no additional computational cost, enabling a more balanced multi-task training process.
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 Pełny tekst 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 Pełny tekst 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 Pełny tekst 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 Pełny tekst 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.
Design and Performance Evaluation of the Energy Subsystem of a Hybrid Light andWave Energy Harvester
- Marcin Drzewiecki
- Piotr Kołodziejek
- Jarosław Guziński
2024 Pełny tekst ENERGIES
The paper presents the design and performance of an energy subsystem (ES) dedicated to hybrid energy harvesters (HEHs): wave energy converters (WECs) combined with photovoltaic panels (PVPs). The considered ES is intended for compact HEHs powering autonomous end-node devices in distributed IoT networks. The designed ES was tested experimentally and evaluated in relation to the mobile and wireless distributed communication use case. The numerical evaluation was based on the balance of the harvested energy versus the energy consumed in the considered use case. The evaluation results proved that the ES ensured energy surplus over the considered IoT node consumption. It confirmed the proposed solution as convenient to the compact HEHs applied for sustainable IoT devices to power them with renewable energy harvested from light and sea waves. It was found that the proposed ES can provide the energy autonomy of the IoT end node and increase its reliability through a hybrid energy-harvesting approach.
DESIGN ERRORS CAUSE AN EMERGENCY OF THE REINFORCED CONCRETE TANK FOR COKE
- Tomasz Majewski
- Maciej Niedostatkiewicz
2024
The quality of the developed design documentation and the maintenance of the technological regime during construction works have a decisive impact on the subsequent safety of the structure, as well as the safety of use of the building. The paper describes the defects and damages of an open, rectangular reinforced concrete tank for hot process water, which failed during a short period of operation. The article also presents the proposed solution to remove the existing damage to the tank structure.
Design of a Cellular Dual-Band Sticker Antenna for Thickness-Independent 3D-Printed Substrates
- Adrian Bekasiewicz
- Khadijeh Askaripour
- Marek Wójcikowski
- Tuan-Vu Cao
2024 Pełny tekst
Additive manufacturing technology provides high flexibility in designing custom enclosures for prototype devices such as nodes of distributed sensor networks. Although integration of components is desired from the perspective of sensor mobility, it might negatively affect the performance of radio-connectivity due to couplings between the antenna and system peripherals, as well as other unaccounted effects of the 3D printed enclosure. In this work, a design of a dual-band cellular antenna is considered. The structure is optimized to work on plastic substrates characterized by thicknesses ranging from 1 mm to 5 mm, respectively. The antenna features a –10 dB bandwidth within frequencies from 0.74 GHz to 1.05 GHz and 1.49 GHz to 1.92 GHz. Owing to a simple topology the structure can be implemented in the form of a copper-based sticker and attached on a 3D printed material (e.g., the enclosure of the device). The radiator has been compared against the state-of-the-art antennas in terms of bandwidth and gain.
Design of a Shape-Memory-Alloy-Based Carangiform Robotic Fishtail with Improved Forward Thrust
- Mithilesh Kumar Koiri
- Dubey Vineet
- Kumar Anuj Sharma
- Daniel Chuchała
2024 Pełny tekst SENSORS
Shape memory alloys (SMAs) have become the most common choice for the development of mini- and micro-type soft bio-inspired robots due to their high power-to-weight ratio, ability to be installed and operated in limited space, silent and vibration-free operation, biocompatibility, and corrosion resistance properties. Moreover, SMA spring-type actuators are used for developing different continuum robots, exhibiting high degrees of freedom and flexibility. Spring- or any elasticmaterial- based antagonistic or biasing force is mostly preferred among all other biasing techniques to generate periodic oscillation of SMA actuator-based robotic body parts. In this model-based study, SMA-based spring-type actuators were used to develop a carangiform-type robotic fishtail. Fin size optimization for the maximization of forward thrust was performed for the developed system by varying different parameters, such as caudal fin size, current through actuators, pulse-width modulation signal (PWM), and operating depth. A caudal fin with a mixed fin pattern between the Lunate and Fork “Lunafork” and a fin area of approximately 5000 mm2 was found to be the most effective for the developed system. The maximum forward thrust developed by this fin was recorded as 40 gmf at an operation depth of 12.5 cm in a body of still water.
Design of a Wideband High-Gain Monopulse Antenna for X- and Ku-Bands Applications
- Zhi Xing Chen
- Ali Farahbakhsh
- Jia Xin Lv
- Huafeng Su
- Xiu Yin Zhang
2024 Pełny tekst IEEE Access
The present study provides a wideband high-gain monopulse antenna based on a dielectric lens operating in X- and Ku-bands, in which a wideband dielectric lens is designed and employed to fulfill the radiation pattern and bandwidth necessities of a monopulse antenna. The proposed configuration has four horns allowing for the simultaneous creation of 1 and 6 designs in two perpendicular planes. The main advantages of the proposed dielectric lens are low cost, lightweight, and easy fabrication using 3-D printing technology. The measurement findings show that the peak gain of the sum pattern is 28.9 dBi with a peak aperture efficiency of 60% over the desired frequency bandwidth. The suggested design can produce a simultaneous sum and two distinct difference patterns in orthogonal planes, meeting the rigorous demands for speed and accuracy in tracking applications.
Design of Compact and Wideband Groove Gap Waveguide-Based Directional Couplers
- Mahdieh Rabbanifard
- Davood Zarifi
- Ali Farahbakhsh
- Michał Mrozowski
2024 IEEE Access
This paper proposes a compact cross-shaped groove gap waveguide structure for creating wideband and compact directional couplers with different coupling levels. Groove gap waveguide technology is applied to overcome fabrication challenges of printed and hollow waveguide structures in high frequency bands. The validity of the novel concept is demonstrated through the design and evaluation of several compact broadband directional couplers, featuring 3-, 4.5-, 6-, and 10- dB coupling levels, alongside the fabrication and testing of a compact, wideband 3-dB directional coupler prototype. In addition, an equivalent circuit is proposed to present the behavior of the 3-dB coupler. The comparison of simulation and experimental results for the prototype shows good agreement. The measured transmission coefficients in the output ports are −3±0.5 dB with a phase imbalance of ±2.5∘ over 17.9-24 GHz frequency band. The findings confirm the suitability of the proposed directional coupler structure as a compact and self-packaged solution for high-frequency applications.