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Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Optical biosensors that consist of a light source, optical elements, and a photodetector are used to detect chemical and biological species and pollutants. This Tutorial discusses the fundamental details of optical biosensing techniques that include materials, working principle, components, sensor configurations, parameters, and future prospects. Optical biosensing techniques include plasmonic [surface plasmon resonance (SPR) and localized SPR], fluorescence, luminescence, Raman scattering, colorimetric, and interferometric methods. Bioreceptor elements play a significant role in detecting the specific analyte that can be synthetic or natural. Surface functionalization techniques to bind the bioreceptor elements on the surface, to control the bioreceptor orientation, have been discussed in detail. The possibility of integration of techniques on a chip, to develop wearable, implantable sensors, and the associated challenges have been fully demonstrated. This Tutorial provides valuable insights into the present state and future directions of optical biosensors for various applications.

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.

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 long-lasting 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.

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.

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.

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.

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.

Abstract Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS $$_{\textrm{SVD}}$$ SVD ), TS using linear prediction (TS $$_{\textrm{LP}}$$ LP ), TS using scaling factor (TS $$_{\textrm{SF}}$$ SF ), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS $$_{\textrm{SVD}}$$ SVD , F1 = 95.30% with TS $$_{\textrm{LP}}$$ LP , F1 = 95.82% with TS $$_{\textrm{SF}}$$ SF , and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.

Abstract Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

Abstract The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject’s chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Abstract In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

The paper presents a validation of novel multichannel ballistocardiography (BCG) measuring system, enabling heartbeat detection from information about movements during myocardial contraction and dilatation of arteries due to blood expulsion. The proposed methology includes novel sensory system and signal processing procedure based on Wavelet transform and Hilbert transform. Because there are no existing recommendations for BCG sensor placement, the study focuses on investigation of BCG signal quality measured from eight different locations within the subject's body. The analysis of BCG signals is primarily based on heart rate (HR) calculation, for which a J-wave detection based on decision-making processes was used. Evaluation of the proposed system was made by comparing with electrocardiography (ECG) as a gold standard, when the averaged signal from all sensors reached HR detection sensitivity higher than 95% and two sensors showed a significant difference from ECG measurement.

Aim of this paper is to evaluate short and long-term changes in T2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T2 relaxation times were calculated for quantitative analysis. The T2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T2 values between the baseline and subsequent measurements, particularly between pre-therapy (M0) and two weeks post-therapy (M1), as well as during the monthly interval checks (M2 - M6). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.

Abstract Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

The mechanical components manufacturing industry is carried out through turning processes using CNC machines. Improving productivity and improving product quality in the CNC process is necessary. Automating operations at the CNC process with Industry 4.0 technology at low cost is a direction in the right direction with the development trend of Industry 4.0 technology. Making decisions to improve CNC processes at operations and implementing continuous improvement is an urgent activity. Industry 4.0 technology helps connect data between customer companies, business sectors, and supply chain partners in a more specific and simple way. Commonly connected data between companies helps decision-makers at each company have a common source of analytical data to make more effective production and business development decisions. Industry 4.0 technologies are the subject of much research, but it is important to consider how to integrate them into the manufacturing world in the most efficient and effective way possible. Manufacturers will benefit from this article’s additional insights to solve their problems more effectively. This paper proposes a Hybrid Six Sigma methodology based on Fuzzy TOPSIS and PLS-SEM methods. First, the Fuzzy TOPSIS method helps decision-makers to select problem points for improvement in an unknown environment to implement continuous improvement. Second, the PLS-SEM method evaluates the impact factors of the results of continuous improvement at the mechanical component factory. The Industry 4.0 technology is proposed to be designed, tested, and installed in the CNC manufacturing process. As a result of this study, the product length dimensional error rate decreased from 54.90% per month to zero defects, saving ${\$}9593$ in annual production costs. Research machines are semi-automatic and cannot increase or digitize their performance because of studies on the application of low-cost Industry 4.0 technology systems to increase the capacity of industrial processes. This study contributes by using Industry 4.0 technology’s low-cost way to improve manufacturing processes using the Hybrid DMAIC method in Six Sigma methodology. This hybrid approach is adaptable and can be used with different business process improvement models.

Customers’ requirements for product quality are increasing and more and more manufacturing companies are improving the quality of processing conditions at each production process by applying advances in Internet Technologies, Internet of Things, Big Data, Industry 4.0, Industrial Engineering Tools, Multi-objective optimization algorithms, Digital Numerical Control (DNC) method, and Six Sigma tools to improve productivity and quality of product. Continuous improvement of the manufacturing process presents a huge opportunity for the transformation of the normal production model to smart manufacturing. Manufacturing condition data, production data, quality of product data, and power quality data are measured and collected in real-time forming big data fed into operation analysis of the development strategy of the manufacturing company. In this study, the implementation of the Hybrid DMAIC (Define-Measure-Analyze-Improve-Control) method in six sigma is presented in detail based on the statistical hypothesis testing techniques for analyzing production data applied at the measurement phase, Multi-objective decision Fuzzy TOPSIS techniques applied to choose the best improvement solution applied at the analysis phase, Multi-objective optimization technique optimizes production conditions, and the DNC method performs automatically program call by RFID system applied at improvement phase, measurement technique, real-time measurement result collection and analysis by Industry 4.0 system and the method of phase shift is used to keep Total Harmonic Distortion (THD) and Total Demand Distortion (TDD) less than 5% within the international standard limits defined by IEEE 519:2022 and IEC 61000 applied at control phase. A common data system linking all activities at a particular manufacturing process and hybrid DMAIC in Six Sigma continuous improvement model is proposed in this study. This research’s results, the rate of occurrence of smaller-than-standard outer diameter defects decreased from 31.20% to 4.5% per month, the grinding process productivity achieved 610 pcs per day while the number of operators decreased from 4 people to 2 people per day, and productivity per person per hour increased from 15 PCS to 30 PCS. The profit is achieved at 11,184 USD per year. The Hybrid DMAIC method in Six Sigma is considered a model of continuous improvement that can be applied to production processes in manufacturing companies. The Hybrid DMAIC method in Six Sigma is simple to implement, easy to deploy into the real environment at production processes, and uses commercial software such as Minitab, LABVIEW, and SPSS. Manufacturing companies apply this research model to help improve competitiveness, improve business performance, and improve customer satisfaction.

The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ.

Abstract On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.

Abstract Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things—the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.

Abstract This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1–14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.

Abstract This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models.

Abstract Monitoring and assessing cardiac activity in animals, especially heart rate variability, has been gaining importance in the last few years as an indicator of animal health, well-being and physical condition. This pilot study tested the sensors based on ballistocardiography sensing the mechanical vibrations caused by the animal’s cardiovascular system, which have proved useful in measuring cardiac activity in humans. To verify the accuracy of these measurement systems, the conventional measurements based on electrocardiography were carried out and the outcomes were compared. The main objectives were to verify the suitability of these sensors in measuring cardiac activity in animals, to determine the advantages and disadvantages of these sensors, and to identify future challenges. Measurements were performed on various animals, specifically a goat, a cow, a horse, and a sheep. Electrocardiographic measurement, which has demonstrated high accuracy in procedures for animals, was used as the study’s gold standard. A disadvantage of this method, however, is the long time required to prepare animals and shear spots to attach electrodes. The accuracy of a ballistocardiographic sensor was compared to reference electrocardiographic signals based on Bland–Altman plots which analysed the current heart rate values. Unfortunately, the ballistocardiographic sensor was highly prone to poor adhesion to the animal’s body, sensor movement when the animal was restless, and motion artefacts. Ballistocardiographic sensors were shown only to be effective with larger animals, i.e., the horse and the cow, the size of these animals allowing sufficient contact of the sensor with the animal’s body. However, this method’s most significant advantage over the conventional method based on electrocardiography is lower preparation time, since there is no need for precise and time-demanding fixation of the sensor itself and the necessity of shaving the animal’s body.

This article presents a comprehensive system for testing and verifying shunt active power filter control methods. The aim of this experimental platform is to provide tools to a user to objectively compare the individual control methods. The functionality of the system was verified on a hardware platform using least mean squares and recursive least squares algorithms. In the experiments, an average relative suppression of the total harmonic distortion of 22% was achieved. This article describes the principle of the shunt active power filter, the used experimental platform of the controlled current injection source, its control system based on virtual instrumentation and control software and ends with experimental verification. The discussion of the paper outlines the extension of the experimental platform with the cRIO RTOS control system to reduce the latency of reference current generation and further planned research including motivation.

Blockchain, a vital technology in today’s era, changed the way we share, manage and exchange our data in a centralized way to decentralized architecture. With the increasing demand for Blockchain, various platforms are available to implement public, private, consortium, or permissioned, permissionless Blockchain. Hyperledger, an open-source, permissioned, distributed ledger-based Blockchain, was hosted by Linux. This paper explores Hyperledger Fabric Private Blockchain Network (HFPBN). The architecture of HFPBN with its components and transaction flow is explored in detail. The Blockchain in HFPBN comprises multiple blocks that are linked to each other. The block elements are discussed in detail with their type and size, and after that, the total size of the block depending upon various parameters is calculated. Further, one application of Blockchain, i.e., Vehicular Ad-hoc Networks (VANETs), is discussed in this paper as a case study. The VANET application is implemented on the Hyperledger Fabric platform. Formulas showing the dependency of various parameters like endorsement policy, number of transactions, and number of reads and writes on block size are derived and shown in their relationship through the graph for the VANET system. The impact of block size on various performance parameters like throughput, latency, memory, and CPU utilization for the VANET system is then analyzed using Hyperledger Caliper. An optimal required value of throughput and latency is achieved for Blockchain-based VANET. Also, the Hyperledger Fabric platform seems suitable for many applications as it creates separate Blockchain for different applications.

The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother's chest.

The article presents a novel strategy for reducing the geometric error of a vehicle headlamp equipped with a set of calibration screws, which represents a product assembly. Using a general method for designing and implementing a digital twin, we determined the optimal configuration for a compensatory element that minimizes the total geometric error. Formulated as a problem of constrained minimization, we solved the error using the gradient method and the Broyden-Fletcher-Goldfarb-Shanno method. Products are automatically adjusted according to this optimal setting during the manufacturing process. The results of this novel method indicate that all points can be aligned when the non-individual calibration satifies a geometrical specification of 92%. The digital twin approach was compared to the manufacturing process on 84,055 samples. Overall, 98.19% of the samples were perfectly aligned.

Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the Kp, Ki parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the Kp, Ki parameter to reach the optimal value by different methods is a promising and popular research direction at present. The Kp, Ki parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used Kp, Ki parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting the Kp, Ki parameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the Kp, Ki parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the Kp, Ki parameter tuning of PI controllers.

Harmonic estimation is essential for mitigating or suppressing harmonic distortions in power systems. The most important idea is that spectrum analysis, waveform estimation, harmonic source classification, source location, the determination of harmonic source contributions, data clustering, and filter-based harmonic elimination capacity are also considered. The feature extraction method is a fundamental component of the optimization that improves the effectiveness of the Harmonic Mitigation method. In this study, techniques to extract fundamental frequencies and harmonics in the frequency domain, the time domain, and the spatial domain include 67 literature reviews and an overall assessment. The combinations of signal processing with artificial intelligence (AI) techniques are also reviewed and evaluated in this study. The benefit of the feature extraction methods is that the analysis extracts the powerful basic information of the feedback signals from the sensors with the most redundancy, ensuring the highest efficiency for the next sampling process of algorithms. This study provides an overview of the fundamental frequency and harmonic extraction methods of recent years, an analysis, and a presentation of their advantages and limitations.

This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditional - gold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method.

The publication focus on a pilot study (design, implementation and verification) of a dynamic weighing system designed for weighing of municipal waste during the dumping of garbage containers. The presented solution is based on fiber Bragg gratings (FBG) and can be additionally implemented into the lifting equipment of commonly employed garbage trucks. The weighing principle is based on the measurement of deformation effect and vibration response of the lifting equipment during the dumping of garbage bins. The measuring system leverages the advantages of power measurement, which use the conversion of the spectral shift of modulated light signal during the measurement to the change in optical power of a pair of spectral overlapping Bragg gratings. Two different methods are presented and discussed: the amplitude method, which analyze the maximum amplitude change of the signal and/or the method, which analyze the time period of the dampened oscillation of the lifting device. Due to the small dimensions, the complete system can be installed together with FBG sensors directly onto the lifting device. The pilot tests of the presented system were carried out for over 4 months, showing an accuracy of up to ±4.04 kg in the range of 10–100 kg for standardized containers used for municipal waste. The system worked in completely standalone mode and the garbage trucks were not modified in any inconvenient way.

This paper presents a novelty approach to usage of the fiber-optic phase-based sensor in railway transportation. We designed and tested the real deployment of this sensor working on the principle of light interferences within optical fibers. The proposed construction of the sensor allowed to increase the sensitivity and thanks to this can be detected and calculated individual axles and wheels of tram vehicles. We performed long-time period measurements (April to September 2019) in diverse climatic conditions, including measurements of 642 tram passages (several different construction types) in real urban traffic. The detection accuracy level was slightly above 99.4 %.

This pilot pre-clinical study demonstrates the applicability of a new type of pneumatic cardiac triggering (PCT) for cardiac imaging. The pilot research compares the novel FPGA-based pad monitoring system for cardiac triggering using ballistocardiography (BCG) with conventional systems based on electrocardiography (ECG) and photoplethysmography (PPG). The implemented system enables cardiac triggering without the need to fix the sensors to the patient's body. This unique approach has the potential to reduce the preparation time for examination and the examination itself and to increase patient's comfort. The pilot pre-clinical study was conducted on 10 subjects at the Siemens Prisma 3T MRI Scanner within the CEITEC Multimodal and Functional Imaging Laboratory - Central European Institute of Technology, Masaryk University, upon the approval of the Ethics Committee. In total, 748 peaks (heart beats) were detected, with 7.347 correctly identified as true positive peaks, 140 incorrectly detected as false positive peaks, and 106 missed peaks (false negative). For all subjects, the total accuracy reached 96.31% and F1 score reached 98.18%. The applicability of the proposed BCG system was also analyzed in terms of objective (BRISQUE, NIQE, PIQE) and subjective evaluation of the images by 10 experts. The study compares images from two basic cardiac sequences - TRUE FISP (Free Induction Decay Steady-State Precession) and PSIR (Phase Sensitive Inversion Recovery) sequences. The BCG system achieves comparable results with the most frequently used and most accurate clinical ECG system used as gold standard. The results prove that the BCG signal captured by our new sensor can be used as a substitute for ECG signal during MRI exam with reliability of 97%.

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy (ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.

This article introduces fiber Bragg grating (FBG) system for monitoring selected traffic flow parameters in urban areas. The system is able to monitor traffic density or speed of cars driving in single lane. The proposed system consists of five Bragg gratings encapsulated in Polydimethylsiloxane polymer and is characterized by easy and inexpensive installation in the milled upper layer of the road covered with an asphalt mixture. The results of first operational tests presented in this study include 3978 passing vehicles of various specifications. The system's vehicle detection success rate is 99.62%. And further, a mean absolute error 1.35 kph and a relative error 2.62% when measuring vehicle speed.

This first-ever study demonstrates the applicability of a fiber Bragg grating (FBG) system for MR cardiac triggering of cardiovascular magnetic resonance at 3 Tesla. The unique patented system senses body movements caused by cardiac activity using a non-invasive ballistocardiography (BCG) sensor. The pilot research compares a novel FBG-based system with clinically used triggering systems based on electrocardiography (ECG) and pulse oximetry (POX). The pilot pre-clinical study was conducted on 8 subjects at a Siemens Prisma 3T MR Scanner. The study compares images from two basic cardiac sequences, TRUE FISP (Free Induction Decay Steady-State Precession) and PSIR (Phase Sensitive Inversion Recovery), using objective methods and subjective evaluation by clinical experts. The study presents original results that confirm the applicability of optical sensors in the field of cardiac triggering having a number of advantages in comparison to conventional solutions, such as no eddy current interference, ease of placement of the sensor on the patient's body, and senor reusability. The proposed FBG-based system achieves comparable results with the most frequently used and most accurate ECG-based and POX-based clinical systems. In terms of subjective evaluation by experts, the FBG system outperformed the POX-based system used in clinical practice.

Autobalancing bridge method is widely used to measure the impedance of various objects in many fields of science and technology. The article presents an innovative approach to address auto-balancing bridge dynamic errors to extend the range of operating frequencies. The mathematical model of auto-balancing bridge, implemented using operational amplifier, was analysed, obtaining its simplified version taking into account the most important sources of errors. The concept was developed and the algorithms for compensation of the impact of dynamic errors on the measurement of admittance components were synthesized. The presented algorithms allow the correction of admittance measurements only based on “raw” results obtained with the use of an auto-balancing bridge. Methodology was described and numerical simulation was carried out on a selected example. As shown in the paper, the developed correction algorithms allow extending significantly (400 times) the range of admittance measurement frequencies. These algorithms do not require interfering the auto-balancing bridge structure, but only engage the computing power of modern measurement channels.

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔTi| ), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95$ % was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73 %, SE = 91.57 %, PPV = 94.80 % and $\text {F1} = 93.12$ %. Using the EEMD method, ACC > 95$ % was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49 %, SE = 97.89 %, PV = 99.53 % and F1 = 98.69 %. In this study, the best results were achieved using the AWT method, which provided ACC > 95 % in all 12 types and levels of interference with average values of ACC = 99.34 %, SE = 99.49 %, PPV = 99.85 % a F1 = 99.67 %.

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study’s goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

This study describes a pilot clinical validation of a new low-cost system for the continuous monitoring of the human body's cardiorespiratory activities within the magnetic resonance examination area. This study primarily focuses on monitoring cardiac activity and the related cardiac triggering. The patented system tested by the authors is based on seismocardiography (SCG). The study was conducted on 18 subjects on a Siemens Prisma 3T MR scanner. Standard anatomical and diffusion sequences were used to test cardiac activity monitoring. A wide range of commonly used diagnostic sequences were used to test imaging of the heart by means of cardiac triggering. System functionality was verified against a commercially available electrocardiography (ECG) system. Monitoring of cardiac activity (detection of the R-R interval in ECG and the AO-AO interval in SCG) was objectively evaluated by determining the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV, i.e. F1. Imaging quality control using Cardiac Triggering was performed by subjective evaluation of images by the physicians. The study conducted clearly confirmed the functionality of the system in terms of continuous cardiac activity monitoring. In all 18 subjects, a mean PPV > 99 % was achieved; F1 > 99 %; SE > 99 %; ACC > 98 %; 1.96σ

This publication describes an original simple low-cost MR fully-compatible and safe fiber-optic breathing sensor (FOBS), which can be used for respiratory triggering and for monitoring the development of respiratory rate within the MR environment and can, thus, serve as prevention from the hyperventilation syndrome. The sensor is created by encapsulation of the Bragg grating into conventional nasal oxygen cannulas. The sensor is immune to minor patient movements, thus limiting movement artifacts to a minimum. Thanks to this fact it can be used for the retrospective/prospective respiratory gating. The sensor is immune to electromagnetic interference (EMI) and can thus be used in any magnetic field (1.5T, 3T, and 7T). The sensor prototype has been tested in both laboratory and real magnetic resonance (3T) environments relative to conventional pneumatic respiration references (PRR). The data measured were statistically evaluated using the objective Bland-Altman method (BAM) and the functionality of the proposed solution was confirmed. Respiratory Triggering functionality was confirmed by the radiologic doctors on the basis of analyzing images using the most used respiratory triggered T2 TSE 3D sequences and by objective method using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE).