Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Intelligent human-centric lighting for mental wellbeing improvement

In recent years, the main area of interest in the issue of influencing mental states of people is the impact of lighting on human beings, their wellbeing but also workplace productivity. This work discusses in detail the problem of positively influencing people using intelligent technologies, especially the role of the colors. We describe techniques and technologies needed to implement the case study of an intelligent lighting system. The system proposed can detect humans from an IP camera, find faces, and detect emotion. The main aim is to adjust the lights accordingly to the emotional result to improve the mood of people while taking into consideration the principles of color psychology and daytime. We have evaluated our case study solution in a real-world environment and collected the feedback from participants in the form of a questionnaire. Evaluation of participants' wellbeing was based on their subjective statements. There were several ideas on further functionality extension which needs to be explored. Among them is including wearable devices to the proposed system, validate the emotional results according to them, but also determine the impact of an increasing number of users interacting with the system at the same time.Web of Science159art. no. 155014771987587

Testing of the voice communication in smart home care

This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.Web of Science5art. no. 1

Federated learning for edge computing: A survey

New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed.Web of Science1218art. no. 912

Application of a new CO2 prediction method within family house occupancy monitoring

The article describes the application of Python for verification of a newly designed method of CO2 prediction from measurements of indoor parameters of temperature and relative humidity within occupancy monitoring in real conditions of a family home. The article describes the implementation of non-electric quantities (indoor CO2 concentration, indoor temperature, indoor relative humidity) measurement in five rooms of a family home (living room, kitchen, children's room, bathroom, bedroom) using Loxone technology sensors. The IBM IoT (Internet Of Things) was used for storing and subsequent processing of the measured values within the time interval of December 22, 2018, to December 31, 2018. The devised method used radial basis function (artificial neural networks (ANN)) mathematical method (implementation in Python environment) to perform accurate predictions. For further increase of the accuracy and reduction of prediction noise from the obtained course of the predicted signal, multiple variations of the LMS adaptive filter algorithm (Sign, Sign-Sign, Sign-Regressor) were used (implemented in the MATLAB SW tool). The accuracy of the newly proposed CO2 concentration prediction method exceeds 95% in the selected experiments.Web of Science915877215876

An optical-based sensor for automotive exhaust gas temperature measurement

The article introduces the design of an optical-based sensor that measures automotive exhaust gas temperatures (EGTs) over a wide temperature range. To measure temperature, we combined the luminescence method and the blackbody radiation (BBR) principle. We also developed our own measurement hardware that includes the means to process and evaluate the signals obtained for temperature conversion using optical methods for application in the target temperature range (-40 degrees C to 820 degrees C). This temperature range is specified by the automotive industry according to current combustion engine designs and emission requirements, which stipulate accurate measurement of operating temperature for optimal functioning. Current measurement solutions are based on the thermocouple principle. This approach is problematic, especially with regard to electromagnetic interference and self-diagnostics, and problems also exist with the gradual penetration of moisture into the temperature probe under extreme thermal stress. The case study confirmed the full functionality of the new optical sensor concept. The benefit of the proposed concept is full compatibility with existing conceptual solutions while maintaining the advantages of optical-based sensors. The results indicated that a combination of the BBR and luminescence methods with a ruby crystal in the proposed solution produced an average absolute error of 2.32 degrees C in the temperature range -40 degrees C to 820 degrees C over a measurement cycle time of 0.25 s.Web of Science71art. no. 700571

Noise reduction in industry based on virtual instrumentation

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction. In the Industry 4.0 era, the mass development of voice control (speech recognition) in various industrial applications is possible, especially as related to augmented reality (such as hands-free control via voice commands). As Industry 4.0 relies heavily on radiofrequency technologies, some brief insight into this problem is provided, including the Internet of things (IoT) and 5G deployment. This study was carried out in cooperation with the industrial partner Brose CZ spol. s.r.o., where sound recordings were made to produce a dataset. The experimental environment comprised three workplaces with background noise above 100 dB, consisting of a laser/magnetic welder and a press. A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft. We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency. Using virtual devices, the study was carried out on large speakers with 20 participants (10 men and 10 women). The experiments included a large number of repetitions (100 times for each command under different noise conditions). Statistical results confirmed the efficiency of the tested algorithms. Laser welding environment efficiency was 27% before applied filtering, 76% using the least mean square (LMS) algorithm, and 79% using LMS + independent component analysis (ICA). Magnetic welding environment efficiency was 24% before applied filtering, 70% with LMS, and 75% with LMS + ICA. Press workplace environment efficiency showed no success before applied filtering, was 52% with LMS, and was 54% with LMS + ICA.Web of Science6911096107

Advanced signal processing methods for condition monitoring

Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

An investigation of thermoelectric generators used as energy harvesters in a water consumption meter application

In this study, we present the results of measuring the performance of selected Peltier cells such as thermoelectric Peltier cooler modules (TEC), thermoelectric micro-Peltier cooler modules (TES), and thermoelectric Seebeck generator modules (TEG). The achieved results are presented in the form of graphs of powering system output voltage or power efficiency functions of the load impedance. Moreover, a technical solution is also presented that consists of designing a water consumption power supply system, using a renewable energy source in the form of a Peltier cell. The developed measuring system does not require additional batteries or an external power source. The energy needed to power the system was obtained from the temperature difference between two sides of a thermoelectric cell, caused by the measured medium which was flowing in a copper water pipe. All achieved results were investigated for the temperature difference from 1 to 10 K in relation to the ambient temperature.Web of Science1413art. no. 376

Self tuning techniques on PLC background and control systems with self tuning methods desing

Advanced Process Control techniques have become standard functions of distributed control systems. Self tuning methods belong to Advanced Process Control (APC) techniques. APC techniques contain software packages for advanced control based on mathematical methods. APC tools are designed to increase the process capacity, yield and quality of products. Most of nowadays digital industry regulators and PLCs are provided with some kind of the self tuning constant algorithm. Practical part of the paper deals with design of the control systems which contain self tuning regulator. A control system with PID Self Tuner by Siemens and with visualization in WinCC is designed. There is a description of an implementation of the PID regulator as a function block which can be also used for extension control functions. Control systems for relay and moment self tuner with visualizations in WinCC are also designed