Publikační činnost Katedry kybernetiky a biomedicínského inženýrství / Publications of Department of Cybernetics and Biomedical Engineering (450)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64788

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry kybernetiky a biomedicínského inženýrství (450), dříve nazvané Katedra měřicí a řídicí techniky, v časopisech a v Lecture Notes in Computer Science registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
(IEEE, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Zebar, Dilovan Asaad; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
Edge-cloud remote sensing data-based plant disease detection using deep neural networks with transfer learning
(IEEE, 2024) Mohammed, Mazin Abed; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Almujally, Nouf Abdullah; Al-Attar, Bourair Bourair Sadiq Mohammed Taqi; Memon, Sajida; Marhoon, Haydar Abdulameer; Martinek, Radek
These days, the disease among different plants has been increasing day by day. It is a very hard task for government institutions and farmers to collect data on plant diseases from different distributed lands among regions. Therefore, data collection, disease detection, and processing are the key issues for plants when they are suffering from healthy and unhealthy issues in different lands. This article presents edge-cloud remote sensing data-based plant disease detection by exploiting deep neural networks with transfer learning. The objective is to solve the aforementioned issues, such as data collection at a wide range, disease detection, and processing them with higher accuracy and time on different machines. We suggest transfer learning commutative fuzzy deep convolutional neural network (FCDCNN) schemes based on combinatorial optimization problems. The convex function optimizes the processing time and learning rate of data training on different edge and cloud nodes to collect more and more data from different plants from distributed lands. In the concave function, we predict the diseases among different plants, such as sugarcane, blueberry, cotton, and cherry with images, videos, and numeric values. The plant disease detection app uses edge nodes and remote satellite point cloud nodes to gather and train data using transfer learning and make predictions using fuzzy DCNN schemes that are more accurate and take less time to process. Simulation results show that FCDCNN obtained higher accuracy by 98% with less processing time 25% and trained with a higher ratio of data than existing schemes.
Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes
(Elsevier, 2024) Lakhan, Abdullah; Rashid, Ahmed N.; Mohammed, Mazin Abed; Zebari, Dilovan Asaad; Deveci, Muhammet; Wang, Limin, limin; Abdulkareem, Karrar Hameed; Nedoma, Jan; Martinek, Radek
In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology -based mobile ticket application -based system. We formulate the problem as a multi -agent reinforcement learning framework for biometric ticketing in multi -transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi -agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems.
Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set
(Elsevier, 2024) Saeed, Muhammad Haris; Saeed, Muhammad; Rahman, Atiqe Ur; Ahsan, Muhammad; Mohammed, Mazin Abed; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing longlasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi -criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set -based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub -parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Retinal image dataset of infants and retinopathy of prematurity
(Springer Nature, 2024) Timkovič, Juraj; Nowaková, Jana; Kubíček, Jan; Hasal, Martin; Varyšová, Alice; Kolarčík, Lukáš; Maršolková, Kristýna; Augustynek, Martin; Snášel, Václav
Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.
Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review
(Frontiers Media S.A., 2024) Oczka, David; Augustynek, Martin; Penhaker, Marek; Kubíček, Jan
Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this study, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach. As a result, 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed, and Web of Science databases were used to search for articles in English language, according to the specific query and using the PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago, and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with two++ active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace, and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis.
Construction of a high-temperature sensor for industry based on optical fibers and ruby crystal
(MDPI, 2024) Hercík, Radim; Mikolajek, Martin; Byrtus, Radek; Hejduk, Stanislav; Látal, Jan; Vanderka, Aleš; Macháček, Zdeněk; Koziorek, Jiří
This paper presents the construction of an innovative high-temperature sensor based on the optical principle. The sensor is designed especially for the measurement of exhaust gases with a temperature range of up to +850 degrees C. The methodology is based on two principles-luminescence and dark body radiation. The core of this study is the description of sensing element construction together with electronics and the system of photodiode dark current compensation. An advantage of this optical-based system is its immunity to strong magnetic fields. This study also discusses results achieved and further steps. The solution is covered by a European Patent.
Highly stabilized fiber Bragg grating accelerometer based on cross-type diaphragm
(Optica Publishing Group, 2024) Wei, Heming; Zhuang, Changquan; Che, Jiawei; Zhang, Dengwei; Zhu, Mengshi; Pang, Fufei; Caucheteur, Christophe; Hu, Xuehao; Nedoma, Jan; Martinek, Radek; Marques, Carlos
A fiber Bragg grating (FBG) accelerometer based on cross-type diaphragm was proposed and designed, in which the cross-beam acts as a spring element. To balance the sensitivity and stability, the accelerometer structure was optimized. The experimental results show that the designed device has a resonant frequency of 556 Hz with a considerable wide frequency bandwidth of up to 200 Hz, which is consistent with the simulation. The sensitivity of the device is 12.35 pm/g@100 Hz with a linear correlation coefficient of 0.99936. The proposed FBG accelerometer has simple structure and strong anti-interference capability with a maximal cross-error less than 3.26%, which can be used for mechanical structural health monitoring.
Regular running is related to the knee joint cartilage structure in healthy adults
(Wolters Kluwer Health, Inc., 2024) Jandačka, Daniel; Casula, Victor; Hamill, Joseph; Vilímek, Dominik; Jandačková, Vera Kristýna; Elavsky, Steriani; Uchytil, Jaroslav; Plešek, Jan; Skýpala, Jiří; Golian, Miloš; Burda, Michal; Nieminen, Miika T.
Purpose The purpose of this study was to determine whether regular running distance and biomechanics are related to medial central femur cartilage (MCFC) structure. Methods The cross-sectional study sample consisted of 1164 runners and nonrunners aged 18–65 yr. Participants completed questionnaires on physical activity and their running history. We performed quantitative magnetic resonance imaging of knee cartilage—T2 relaxation time (T2) mapping (high T2 indicates cartilage degeneration)—and a running biomechanical analysis using a three-dimensional motion capture system. A 14-d monitoring of the physical activity was conducted. Results Those aged 35–49 yr were at 84% higher odds of having MCFC T2 in the highest level (85th percentile, P < 0.05) compared with youngest adults indicating that MCFC structures may be altered with aging. Being male was associated with 34% lower odds of having T2 at the highest level (P < 0.05) compared with females. Nonrunners and runners with the highest weekly running distance were more likely to have a high T2 compared with runners with running distance of 6–20 km·wk−1 (P < 0.05). In addition, the maximal knee internal adduction moment was associated with a 19% lower odds of having T2 at the highest level (P < 0.05). Conclusions Females compared with males and a middle-aged cohort compared with the younger cohort seemed to be associated with the degeneration of MCFC structures. Runners who ran 6–20 km·wk−1 were associated with a higher quality of their MCFC compared with highly active individuals and nonrunners. Knee frontal plane biomechanics was related to MCFC structure indicating a possibility of modifying the medial knee collagen fibril network through regular running.
The role of smart optical biosensors and devices on predictive analytics for the future of aquaculture systems
(Elsevier, 2024) Soares, Maria Simone; Singh, Ragini; Kumar, Santosh; Jha, Rajan; Nedoma, Jan; Martinek, Radek; Marques, Carlos
Recirculating aquaculture systems (RAS) have been rising quickly in the last decade, representing a new way to farm fish with sustainable aquaculture practices. This system is an environmentally and economically sustainable technology for farming aquatic organisms by reusing the water in production. RAS present some benefits compared with other aquaculture methods, for instance, allows the minimization of water usage and disease occurrence, the absence of antibiotics in these systems, shortens the production cycle, functions as a water treatment system, allows the improvement of the feed conversion, and a reduction in the alteration of coastal habitat, among others. However, this is a complex system with complex interactions between the number of fish and water quality parameters, which can compromise the fish welfare. Currently, there is a huge gap in the global aquaculture sector in terms of smart sensors for cortisol (stress hormone), bacteria, water pollutants, volatile organic compounds and micro/nano-plastics assessment. This sector does not measure such critical parameters which brings a weak understanding of the wellbeing of fish. Therefore, it is crucial to implement point of care (POC) sensors for those critical parameters' assessment via multiparameter solution and predictive analytic capabilities for data supply. This work presents an overall introduction about the impact of the RAS on fish production and its necessity as protein as well as the actual solutions for those problems. Additionally, it reviews the actual state of the art in terms of potential multiparameter POC sensors and predictive analytical approaches that have been investigated in recent years for future application in aquaculture with the aim to guide the researchers on the sector's needs. Additionally, future perspectives are also described in order to digitize the aquaculture sector with novel optical systems and biosensing elements.
Analysis on fetal phonocardiography segmentation problem by hybridized classifier
(Elsevier, 2024) Kong, Lingping; Barnová, Kateřina; Jaroš, René; Mirjalili, Seyedali; Snášel, Václav; Pan, Jeng-Shyang; Martinek, Radek
Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision -making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..
Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules
(Elsevier, 2024) Prauzek, Michal; Krömer, Pavel; Mikuš, Miroslav; Konečný, Jaromír
This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy-based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology’s potential for diverse geographical applications and compatibility with low-performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally-trained control strategies and can be successfully re-applied in other locations.
FBG sensor for heart rate monitoring using 3D printing technology
(IEEE, 2024) Fajkus, Marcel; Kostelanský, Michal; Fridrich, Michael; Čubík, Jakub; Kepák, Stanislav; Križan, Daniel; Martinek, Radek; Mohammed, Mazin Abed; Nedoma, Jan
Currently, the use of fiber-optic Bragg gratings in biomedical applications, especially in the field of magnetic resonance imaging (MRI), is becoming popular. In these applications, the fiber Bragg grating (FBG) encapsulation plays a crucial role in terms of the accuracy and reproducibility of the measurements. This paper describes in detail the fabrication method of a prototype FBG sensor, which is realized by encapsulating a Bragg grating between two layers of the MR-compatible material Acrylonitrile Butadiene Styrene (ABS) by 3D printing. The sensor thus created, implemented, for example, on the chest of a human body, enables monitoring of the vital functions of the human body. The paper describes the complete procedure for the creation of the prototype sensor, including strain and temperature dependence, as well as results of long-term experimental measurements against the conventional electrocardiography (ECG) standard. Results based on the objective Bland-Altman (B-A) method confirm that the implemented sensor can be used for reliable monitoring of cardiac activity (>95% based on B-A). Taking into account the single fiber optic cable, its simple implementation, its small size and weight < 5g, the presented sensor represents an interesting alternative to conventional ECG.
Reduce power energy cost using Hybrid Six Sigma based on fuzzy MADM: A case study in mechanical factory
(IEEE, 2024) Duc, Minh Ly; Bilík, Petr; Martinek, Radek
Production costs are always the top concern of company managers in improving production and business efficiency. The cost of energy is one of the major costs that manufacturing companies must pay. This research paper proposes a Hybrid Six Sigma method based on fuzzy Multi-Attribute Decision Making (MADM), Industry 4.0, and digital numerical control (DNC). A fuzzy MADM method to select problems to improve and build an Industry 4.0 system with Internet of Things (IoT) devices, calling for automatic machining programs using Radio Frequency Identification (RFID) systems and management. Manage production equipment maintenance system using a digital numerical control (DNC) system. Measuring industry 4.0 system user satisfaction in manufacturing using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of research on applying industry 4.0 techniques to the induction heat treatment process eliminate the dependence on worker skills and simplify the operation of the induction heat treatment process. Improve employee satisfaction with process operating conditions. Reduce the cost of electrical energy arising due to the coil maintenance system by applying the Industry 4.0 system. The result after the improvement is that the defect rate decreased from 47.2% to 4.9%. In terms of money, the reduction in losses due to defects is reduced from 6,593 USD per year to 549 USD per year. This research paper builds a sample continuous improvement model to apply to other production processes at other manufacturing companies in terms of applying industry 4.0 systems with IoT devices such as RFID and barcode readers in operations. automatically call the machining program of the machining machine and build an autonomous and preventive maintenance system using the industry 4.0 system to make improvements in process automation, smart data management, and analytics, using Internet of Things (IoT) to connect devices in the production process create a flexible production process.
Enhancing power quality in Industry 4.0 manufacturing using the multi-criteria selection method
(IEEE, 2024) Duc, Minh Ly; Bilík, Petr; Martinek, Radek
Industry 4.0 technology is growing rapidly in the manufacturing industry and other businesses. Devices used in Industry 4.0 are manufactured with high-frequency switching functions and generate harmonics that negatively affect power quality. Choosing the direct or parallel connection method of the harmonic current absorber depends on the specific requirements of the system and the goal of improving power quality. Shunt Active Power Filter (SAPF) is the best device currently used to improve power quality. This study proposes to use the fuzzy-rough MARCOS method to make decisions on SAPF selection based on experts' opinions to improve the quality of power sources at the source of smart manufacturing plants using Industry 4.0 devices. This study implements two decision-making methods in Multi-Criteria Decision-Making (MCDM). The first is the SWARA method (Stepwise Weight Assessment Ratio Analysis), and the second is the MARCOS method (Measurement Alternatives and Ranking According to Compromise Solution). The fuzzy-rough method is used to incorporate uncertain information into the results of decision-making and to use linguistic values. The analysis results of the fuzzy-rough SWARA method show that the price factor and power filter range has the greatest influence on the choice of SAPF for harmonic mitigation. Analysis results from the fuzzy-rough MARCOS method show that manufacturer Schneider Electric has the best features according to the evaluation results from decision makers. Sensitivity analysis methods were used to confirm the findings. The harmonic value THDi displayed in the field after installing the harmonic filter is, respectively, THDi $1=5$ %, THDi $2=6$ %, and THDi $3=5$ %, it meets the regulations of Circular 30/2019/TT-BCT. According to this circular, the requirement for total harmonic value (THDi) is below 12%. With THDi1, THDi2, and THDi3 values all below 12%. In operating electrical systems in production and business environments, using SAPF filters for harmonic mitigation helps improve power quality. The fuzzy-rough method is applied, and the decision maker's decisions are used to adjust the intention to use the SAPF set to suit the conditions.
An SDN-enabled fog computing framework for wban applications in the healthcare sector
(Elsevier, 2024) Tripathy, Subhranshu Sekhar; Bebortta, Sujit; Mohammed, Mazin Abed; Nedoma, Jan; Martinek, Radek; Marhoon, Haydar Abdulameer
For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing.
A novel approach to utilization vehicle to grid technology in microgrid environment
(Elsevier, 2024) Blažek, Vojtěch; Vantuch, Tomáš; Slanina, Zdeněk; Vysocký, Jan; Prokop, Lukáš; Mišák, Stanislav; Piecha, Marian; Walendziuk, Wojciech
This article presents a novel approach to the Vehicle To Grid (V2G) technology in a microgrid with a Demand Side Response (DSR) algorithm. The research describes the microgrid control system used on a physical testing platform. The platform simulates a small-scale microgrid with a photovoltaic plant (PV) as its primary stochastic energy source. The local control system is based on a Demand Side Response algorithm called Active Demand Side Management (ADSM). The ADSM algorithm is implemented with a non-dominated sorting genetic algorithm II (NSGA-2). The article presents the study of the microgrid operation using the results of two experiments. The first experiment includes three scenarios representing electricity consumption in three ordinary households, exploiting a small-scale microgrid during four seasons. Every scenario compares the microgrid’s insufficient energy with and without optimization, with an EV and without EV, and with the tariff mode (energy supply from the distribution network in a chosen time). The second experiment deals with the effect of the size of the static battery in the microgrid on insufficient energy and the efficiency of the optimization itself. The results reveal a fundamentally positive impact of optimizing the control system, which uses an EV, on the potential insufficient energy in the microgrid platform.
A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption
(Elsevier, 2024) Mohammed, Zainab Khalid; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Zebari, Dilovan Asaad; Lakhan, Abdullah; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.
A multi-objectives framework for secure blockchain in fog–cloud network of vehicle-to-infrastructure applications
(Elsevier, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short -term memory (LSTM) schemes is the best way to meet the problem's limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.
Advancements and challenges in non-invasive electrocardiography for prenatal, intrapartum, and postnatal care: A comprehensive review
(IEEE, 2024) Marnani, Roya Aghadavoud; Jaroš, René; Pavlíček, Jan; Martinek, Radek; Vilímková Kahánková, Radana
Non-invasive electrocardiography (NI-ECG) has become an indispensable tool for monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and postpartum care. This review emphasizes the distinct advantages of NI-ECG, including extended monitoring capabilities and valuable insights into fetal and neonatal health. The exploration of textile electrodes is highlighted as a promising alternative, offering improved comfort and reduced skin irritation compared to traditional adhesive electrodes. However, challenges in NI-ECG persist, with electrode placement, quantity, and noise removal being key considerations. The review underscores the significance of addressing interference sources, such as maternal and fetal body signals, motion artifacts, and electrode-skin contact. Additionally, the discussion extends to computer-aided diagnostics, presenting novel approaches for classifying fetal and neonatal health during pregnancy and delivery. Ongoing research aims to optimize electrode placement, develop advanced noise reduction algorithms, and explore sophisticated classification methodologies. These advancements hold the potential to enhance electronic monitoring, enabling early detection of abnormalities and promoting improved outcomes in prenatal and neonatal care.

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