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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A double resistive-capacitive approach for the analysis of a hybrid battery-ultracapacitor integration study
(MDPI, 2025) Chmielewski, Adrian; Piórkowski, Piotr; Bogdziński, Krzysztof; Krawczyk, Paweł; Lorencki, Jakub; Kopczyński, Artur; Możaryn, Jakub; Costa-Castelló, Ramon; Ožana, Štěpán
The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive-capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg-Marquardt algorithm. The model's accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.
A double resistive-capacitive approach for the analysis of a hybrid battery-ultracapacitor integration study
(MDPI, 2025) Chmielewski, Adrian; Piórkowski, Piotr; Bogdziński, Krzysztof; Krawczyk, Paweł; Lorencki, Jakub; Kopczyński, Artur; Możaryn, Jakub; Costa-Castelló, Ramon ; Ožana, Štěpán
The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive–capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg–Marquardt algorithm. The model’s accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.
Advancements in noninvasive blood pressure measurement: a comprehensive review of cuffed, cuffless, and contactless methods
(IEEE, 2026) Kauzlaričová, Terezie; Augustynek, Martin; Kubíček, Jan
Recent advances in noninvasive blood pressure (BP) monitoring are transforming the field, offering alternatives to traditional cuff-based methods that are often limited by discomfort and intermittent measurement. Following PRISMA guidelines, this review systematically analyses studies published between 2020 and 2024, categorizing current BP measurement techniques into cuff-based, cuffless, and contactless methods. Special attention is given to emerging technologies such as photoplethysmography (PPG), electrocardiography (ECG), bioimpedance (BIOZ), radar sensing, and remote PPG (rPPG), as well as the integration of artificial intelligence (AI) for signal processing and BP estimation. This review evaluates the comparative performance, clinical relevance, and readiness for real-world application across modalities. While cuffless and contactless approaches show significant promise for continuous and user-friendly monitoring, their accuracy often depends on calibration, multimodal data fusion, and robust algorithm design. Ethical considerations related to unobtrusive monitoring are also discussed. This work highlights current limitations, identifies future research opportunities, and supports the development of next-generation, AI-driven BP monitoring systems suitable for both clinical and consumer use.
Automated recognition of worker activity using machine learning and multi-sensor fusion
(Elsevier, 2026) Jaroš, René; Majidzadeh Gorjani, Ojan; Barnová, Kateřina; Lajdolf, Martin; Martinek, Radek; Bilík, Petr; Danys, Lukáš
Activity recognition and worker safety are critical parts of future industrial environments. Consecutive transitions to massive automatization and reductions in workforce could lead to more focused monitoring of the remaining workers. Effective recognition often relies on a number of sensors, such as electromyography leads, accelerome ters, magnetometers, or gyroscopes, with each method potentially impacting system cost. This study focuses on automatic worker activity recognition using multilayer perceptron and multi-sensor fusion. We present a testing methodology and analyze the influence of individual sensors on system accuracy. The experiment therefore aims to systematically quantify the trade-off between the type and number of sensors used and the resulting accuracy, with the goal of providing a quantified basis for designing future systems with an optimal price/performance ratio. The presented results demonstrate that different sensor fusion strategies lead to vastly different accuracies, ranging from 71 % to over 99 %. These early findings highlight the crucial role of optimal sensor selection in the design of cost-effective worker monitoring systems. The study demonstrated that using a combination of the accelerometer, magnetometer, gyroscope, and electromyography resulted in the highest test accuracy of 98.04 %. Among these, the gyroscope and electromyography contributed less significantly. When only the accelerometer, magnetometer, and gyroscope were used, the accuracy dropped by approximately 6 %.
Methods for magnetic signature comparison evaluation in vehicle re-identification context
(MDPI, 2024) Balamutas, Juozas; Navikas, Dangirutis; Markevičius, Vytautas; Čepėnas, Mindaugas; Valinevičius, Algimantas; Žilys, Mindaugas; Prauzek, Michal; Konečný, Jaromír; Frivaldský, Michal; Li, Zhixiong; Andriukaitis, Darius
Intelligent transportation systems represent innovative solutions for traffic congestion minimization, mobility improvements and safety enhancement. These systems require various inputs about vehicles and traffic state. Vehicle re-identification systems based on video cameras are most popular; however, more strict privacy policy necessitates depersonalized vehicle re-identification systems. Promising research for depersonalized vehicle re-identification systems involves leveraging the captured unique distortions induced in the Earth's magnetic field by passing vehicles. Employing anisotropic magneto-resistive sensors embedded in the road surface system captures vehicle magnetic signatures for similarity evaluation. A novel vehicle re-identification algorithm utilizing Euclidean distances and Pearson correlation coefficients is analyzed, and performance is evaluated. Initial processing is applied on registered magnetic signatures, useful features for decision making are extracted, different classification algorithms are applied and prediction accuracy is checked. The results demonstrate the effectiveness of our approach, achieving 97% accuracy in vehicle re-identification for a subset of 300 different vehicles passing the sensor a few times.
Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods
(Frontiers Media S.A., 2026) Vondrák, Jaroslav; Penhaker, Marek
Background: Early detection and diagnosis of myocardial infarction (MI) help physicians save lives through timely treatment. Vectorcardiography (VCG) is an alternative to the 12-lead electrocardiography, providing not only characteristic changes in cardiac electrical activity in MI patients but also unique spatial information often overlooked by traditional methods. Despite its potential, comprehensive comparative studies applying machine learning (ML) techniques specifically to VCG data remain limited. Methods: This study proposes a novel VCG processing methodology using a comparative analysis of machine learning-based algorithms for the automated detection of MI patients from VCG recordings, utilizing extracted domain knowledge VCG features that monitor morphological changes in cardiac activity. For this purpose, records from the PTB Diagnostic dataset were used. The extracted domain knowledge dataset of morphological features was then fed into a diverse set of 210 machine learning configurations, including K-nearest neighbor, Support Vector Machine, Discriminant Analysis, Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes, Logistic Regression, and Ensemble Methods. To further improve classification performance, we combined analyzed high-performing models using a stacking ensemble strategy, which integrates multiple base classifiers into a meta-classifier. Results: The stacking-based decision-level fusion achieved high accuracy of 95.55%, sensitivity of 97.70%, specificity of 86.25%, positive predictive value of 96.86%, negative predictive value of 89.61% and f1-score of 97.27%. Conclusion: The results demonstrate that decision-level fusion via stacking improves classification performance and enhances the reliability of MI detection from VCG recordings, supporting cardiologists in decision-making.
Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography
(Elsevier, 2024) Abburi, Radha; Hatai, Indranil; Jaroš, René; Martinek, Radek; Babu, Thirunavukkarasu Arun; Babu, Sharmila Arun; Samanta, Sibendu
Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1- score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.
Hybrid six sigma based on recursive kalman filter and weibull distribution to estimate the lifespan of Bulb LEDs
(Elsevier, 2024) Duc, Minh Ly; Bilík, Petr; Martinek, Radek
The durability of LED bulbs ensures that the lights can operate for a long time without needing to be replaced or maintained too often. The brightness of high-power LED bulbs provides a strong and efficient light source, saves energy, and has a long lifespan. The traditional method of testing LED bulb life span has limitations such as long testing time, limited accuracy, high cost, inability to predict accurately, and failure to respond quickly. Therefore, the development of new and improved testing methods is necessary to effectively and accurately evaluate and ensure the durability of LED bulbs. This research paper proposes a hybrid Six Sigma method based on the recursive Kalman filter and Weibull distribution to estimate the lifespan of Bulb LEDs. The goal of the new method for evaluating LED bulb life is to provide accurate, reliable, and multi-dimensional information about the life of LED lamps, meeting the requirements of accuracy, diversity, time-saving, resources, credibility, and the ability to evaluate from a variety of perspectives. As a result of the research, the recursive Kalman filter method has strengths such as high accuracy, flexibility, saving computational resources, the ability to handle inaccurate data, and scalability. When applied to LED bulb life assessment, it provides an accurate and reliable estimate of LED lamp life. Research results on longevity testing were reduced from 1650 h to 710 h. The end-of-life test time is 710 h, and the aging time per life test cycle is 135 h. The cost of life testing is reduced from 2100.17 USD to 410.12 USD. This LED bulb lifespan testing method can be applied to similar types of LED products to improve the company's productivity and business efficiency.
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.

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