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In this paper, the analysis of noise and sensitivity for different BP filter designs is observed. Calculation of the voltage noise spectral density, root mean square (RMS) value of the noise voltage, Schoeffler sensitivity and the multi-parameter sensitivity is done for the simply designed (SD) active RC, noise and sensitivity optimized (NO and SO) active RC, operational transconductance amplifiers (OTA-C) and switched capacitor (SC) filter designs. This is observed for the fourth-order Chebyshev band-pass filter, with central frequency fc = 4 kHz, bandwidth 1 kHz and pass-band ripple αmax = −0.1 dB. Cascade of two second-order sections is used for fulfilment realization of all four observing designs. Programming tools, such as MATLAB and SPICE (LTspice), are also used for the analyses. Highlights • We used three different designs and optimization (noise and sensitivity) procedure for design and optimize the fourth-order Chebyshev band-pass filter • Our results suggest which design is best to use for improving noise and sensitivity of this filter • MATLAB and SPICE software were used for calculations and simulations of different band-pass filter designs and analysis of noise and sensitivity

Electroencephalography (EEG) is well known method of recording electrical brain activity with electrodes placed along the scalp. One of the challenging tasks in this field is the removal of electrical signals that are not related to brain activity.In this paper, an algorithm for the removal of the EEG signals corresponding to the eye blink artifacts is presented. The presented algorithm is based on ADJUST artifact removing tool, which uses independent component analysis (ICA) for signal decomposition. For every signal component returned by the ICA algorithm, temporal-spatial features are calculated, upon which every independent component is classified as artifact or non-artifact, and removed accordingly.

In this paper, generation of oscillatory synthetic signal simulating brain network dynamics is proposed. The methods follows the expectations of brain network connectivity, which include expressed phase angle differences and signal mixture. We have generated thirty-two signals simulating two groups of sixteen electrodes. Each signal is a mixture of two signal components. The first signal component is synchronized within a group of electrodes but delayed to obtain different phase angles, while the second signal component is unique to each electrode. This simulation give us possibility to present that all brain regions or sources are not connected with each other and that the phase difference between connected oscillators (sources or channels) is not necessary close to zero and could be arbitrary. Phase locking value (PLV) and phase lag index (PLI) measures of connectivity have been calculated on the synthetic signals.

Brain Computer Interfaces (BCI) are devices that use brain signals for control or communication. Since they don't require movement of any part of the body, BCI are the natural choice for assisted communication when a person is unable to move. In this article, BCI based communicator for persons in locked-in state is described. It is based on P300 brain response of the user, thus does not require prior training, movement or imagination of movement. Auditory paradigm is selected in order to apply the communicator in cases where visual ability is also impaired. The communicator was designed to prove also whether low cost hardware with reduced electrode set could be used efficiently in everyday environment, without the need for expert personnel. The design of the communicator is described first, followed by detailed analyses of the performance when used by either healthy or disabled subjects. It is shown that auditory paradigm is the primary factor that limits the accuracy of communication. Hardware characteristics and reduced electrode set influence the accuracy in a negative way as well, while different questions and answer types produce no major differences.

INTRODUCTION: As a result of this global health crisis caused by the COVID-19 pandemic, the medical industry is searching for innovations that have the potential to automate the diagnostic process of COVID-19 and serve as an assistive tool for clinicians. OBJECTIVES: X-ray images have shown to be useful in the diagnosis of COVID-19. The goal of this research is to demonstrate an approach for automatic segmentation of lungs in chest X-ray images. METHODS: In this research DeepLabv3+ with Xception_65, MobileNetV2, and ResNet101 as backbones are used in order to perform lung segmentation. RESULTS: The proposed approach was experimented on X-ray images and has achieved an average mIOU of 0.910, F1 of 0.925, accuracy of 0.968, precision of 0.916, sensitivity of 0.935, and specificity of 0.977. CONCLUSION: Based on the obtained results, the proposed approach proved to be successful in terms of lung segmentation in chest X-ray images and has a great potential for clinical use.

Transmission line parameters vary with environmental and operating conditions, thus introducing uncertainties in power flow studies and protection zone settings of relay protection equipment. Variations in line parameters are fundamental for the determination of static and dynamic line power ratings. Usually, nominal values are given without an associated confidence interval, and therefore an important piece of information is missing. With the proliferation of phasor measurement units (PMUs) that allows for synchronized measurements at different geographical positions arouse the possibility for simple and efficient measurements of line parameters. This article analyzes uncertainties associated with the estimation of line parameters, based on the equivalent $\pi $ model, valid for short-and medium-length transmission lines. This problem has been addressed in literature, but only from a theoretical perspective and the obtained results were verified with simulations only. Here, measurements conducted on two 220-kV transmission lines are analyzed. It was found that the correlation between voltage and current measurements must be taken into consideration when analyzing uncertainty propagation law. Only then statistical analysis of estimations matches the predictions obtained by applying the theoretical model. Accuracy limits were analyzed as well, but in this case some results were within the calculated limits, while others did not follow predictions. Improvements in accuracy limits’ determination require the separation of environmental and operating condition variations from deviations due to measurement equipment, thus requiring additional information.

INTRODUCTION: The development of epidemiological curve models is one of the key factors in the combat of epidemiological diseases such as COVID-19. OBJECTIVES: The goal of this paper is to develop a system for automatic training and testing of AI-based regressive models of epidemiological curves using public data, which involves automating the data acquisition and speeding up the training of the models. METHODS: The research applies Multilayer Perceptron (MLP) for the creation of models, implemented within a system for automatic data fetching and training, and evaluated using the coefficient of determination (R2). Training time is lowered through the application of data filtering and simplifying the model selection. RESULTS: The developed system can train high precision models rapidly, allowing for quick model delivery All trained models achieve scores which are higher than 0.95. CONCLUSION: The results show that the development of a quick COVID-19 spread modeling system is possible.

Contingency analysis (CA) is a well-known function in power system planning and operation. In accordance with CA results, the system operator dispenses information regarding static security of the power system (overloads and/or voltage outside tolerable limits). However, classic CA with remedial action schemes cannot distinguish safe operating regimes from potentially dangerous ones in terms of voltage (in)stability. In fact, voltage instability is considered as one of the major threats leading to power system insecurity. Therefore, in this study an enhanced contingency analysis (ECA) is presented where the classical CA is extended with static voltage analysis based on the modal analysis. The article presents a dedicated methodology for the proposed ECA tool, with special emphasis on the analysis of corrective measures provided by the system operator, intended for enhancing power system security (regulation transformer action, distributed generation and energy storage). Also the influence of the load model was analyzed by simulation and the main conclusions are presented. The study demonstrated the advantages that distributed generation resources and energy storage can provide in the context of voltage stability. Also, the simulations acknowledged the importance of correct load modeling, since over or under estimation of a certain load-type component can result in too optimistic or too pessimistic power system operation limits.

Traditional electrical power systems were designed with an assumption of unidirectional power flows through electrical networks. For many years power plants had been the only sources of electrical energy, and the energy had been delivered to the consumers by the means of transmission and distribution networks exclusively.With the emergence of distributed generation units, power flows have become bidirectional, which has made it impossible to estimate the voltages and currents in substation feeder nodes by measuring only the values at the beginning of the feeder. As the aforementioned estimation process is the basis of traditional voltage regulation techniques, it is obvious that under the new circumstances existing methods have to be upgraded.The article demonstrates how the open source tool PandaPower along with the Python programming language was utilized to analyze and evaluate several different scenarios of using photovoltaic distributed sources inverters for voltage regulation in low voltage electrical power networks.

This paper describe the analyses of EEG data during and after infrared (IR) stimulation of a head. IR stimulation is a type of photobiomodulation (PBM) therapy, a non-invasive form of therapy that uses light energy to start non – thermal photochemical processes in the cellular structures. IR LEDs at 850 nm were used to generate stimulation modulated with 10 Hz square wave. Spectrum of EEG signals were analyzed, with focus on Alpha frequency band that includes frequency of IR light modulation. Frontal, central, parietal, and occipital electrode sites were taken in consideration in three healthy adults. Changes in Alpha frequency band were observed during stimulation and immediately after (short–term effect). There were no changes between consecutive measurements (long–term effect). Most pronounced change of spectrum was measured for Alpha band and lower frequencies.

Photovoltaic (PV) system inverters usually operate at unitary power factor, injecting only active power into the system. Recently, many studies have been done analyzing potential benefits of reactive power provisioning, such as voltage regulation, congestion mitigation and loss reduction. This article analyzes possibilities for loss reduction in a typical medium voltage distribution system. Losses in the system are compared to the losses in the PV inverters. Different load conditions and PV penetration levels are considered and for each scenario various active power generation by PV inverters are taken into account, together with allowable levels of reactive power provisioning. As far as loss reduction is considered, there is very small number of PV inverters operating conditions for which positive energy balance exists. For low and medium load levels, there is no practical possibility for loss reduction. For high loading levels and higher PV penetration specific reactive savings, due to reactive power provisioning, increase and become bigger than additional losses in PV inverters, but for a very limited range of power factors.

In distribution networks with normal and high load capacitor banks are used in MV/LV substations for reducing power losses. However, underground MV cable lines and capacitor banks have a similar impact on power loss reduction in the distribution system so in specific conditions such as long underground MV cable lines and low electric energy consumption the question arises about the justification for the use of capacitor banks. This paper presents an approach to the analysis of the justification for the use of capacitor banks in indicated circumstances. The approach is based on monitoring statistics of average measured power consumption during the representative period. The operation of the monitored distribution network is simulated for each short interval of the selected period. For each short interval, simulations are made both with and without the use of capacitor banks and energy savings and losses are calculated. On the basis of calculated data, the cumulative sum of saved or lost energy during the whole selected period is calculated and a conclusion is reached on the justification for the use of capacitor banks in the monitored network.

High impedance faults are rather difficult to detect, due to their inherent characteristics. However, in most cases high-impedance low-current faults evolve to low-impedance high-current faults, thus giving rise to life hazard situations and equipment damage. In this article, a novel back up protection scheme for high impedance fault detection, based on synchrophasor measurement technology, is proposed. In fact, the presented detection method can be thought as the combination of a classic differential and a classic admittance relay. The protection device designated for high impedance fault detection, incorporating the proposed novel protection algorithm, permanently monitors transmission line’s shunt admittance thus being able to detect changes-increase in the resistive part of shunt admittance, namely the shunt conductance. Actually, a sudden rise of transmission line’s shunt conductance is a strong indicator of high impedance fault inception. Conducted computer simulations confirmed a high degree of selectivity and sensitivity of the proposed method. Moreover, the technical feasibility of the approach was tested considering real-world measurement data obtained for two 220 kV transmission lines. From the acquired data, measurement uncertainties of the monitored parameters in normal operating conditions, were calculated. The results demonstrate excellent sensitivity of the proposed method, even for the case of short transmission lines during low-loading conditions.

EEG data signal processing has been performed in order to outline the parameters for the comparison between participants with autism (5 subjects) and typically developing children acting as the control group (14 subjects). Based on the previous studies, the autistic group has a significantly higher delta/alpha ratio than the typical developing children. In this paper, we have tested this hypothesis on new participants. Furthermore, statistical features such as mean value, grand-mean value and their ratios have been calculated. The research was conducted by observing signals on electrodes F3, Fz, F4, C3, Cz, C4, P3, Pz and P4. We observed power delta-alpha, theta-high beta, theta-low beta, theta-alpha ratio, grand mean values of magnitude spectrum and 1st moments of the grand mean values of the spectral distribution. The statistically significant difference between observed groups we found when tested 1st moments of the grand mean values of the spectral distribution.

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

The power system state estimator based on the support vector machine (SVM) and the weighted least squares (WLS) method is presented in the paper. The WLS provides state estimations necessary for creating SVM model which is then used for state estimation. The developed algorithm was tested on the IEEE systems, and the performance indicators were calculated in order to compare the accuracy of estimation and the measurement error filtering. The results indicate that the proposed hybrid model outperforms the classical WLS-based state estimation in terms of accuracy and improves measurement error filtering in comparison to the classical estimator.

In this paper an analysis of noise through four different approaches to filter design is performed. The voltage noise spectral density and root mean square (RMS) value of noise voltage for the simply designed (SD) active RC, noise optimized (NO) active RC, operational transconductance amplifiers (OTA-C) and switched capacitor (SC) filter design are calculated for fourth-order Chebyshev band-pass filter, with central frequency f c =4kHz, bandwidth 1kHz and pass band ripple $\alpha_{max}$=-0.1dB. For the achievement realization of all four designs a cascade of two second order sections are used. The analyses is performed using Matlab and SPICE (LTspice) programming tools.

This paper describes a communication system, designed to help people with disabilities to communicate with others. In this case, it is built on Event Related Potentials (ERP) which occur as a response to stimuli. The stimuli are auditory answers to the asked questions, where one of the stimulus is the correct answer. The stimulus which is the possible correct answer is the target stimulus and other stimuli are considered as non- targets. The only reliable information available is the onset of the stimuli, based on this information the spatio-temporal pattern for the target and non-targets is expected to be uncorrelated to the ongoing brain activity and with each other. This allows us to train the spatial filter coefficients for the know targets, so that it can use this trained data set to correctly classify targets. Results are obtained by optimizing the spatial filter parameters prior to classification, and the classification results are analyzed for several healthy participants.

In this paper, noise propagation through a two-stage nonlinear algorithm for measurement of harmonic distortion in power systems according to IEC 61000-4-7 is analyzed. Noise generated in the instrument’s input circuitry is considered as an aggregated noise source without detailed characterization of its origin. The first stage of the measurement algorithm is the discrete Fourier transform (DFT) of the input signal. Noise propagation through the DFT is analyzed by means of probability theory. The result of this analysis, the probability density function (pdf) of the DFT spectrum amplitudes, is used for characterization of the second stage of the measurement algorithm – the root-sum-square grouping of several spectrum components into a single harmonic distortion index. In this way, pdfs of the most common harmonic indices are derived and analyzed. The study presented reveals that noise generated in the input stage produces, besides the already known variance of the results, an offset in the harmonic distortion indices. Analysis of the resulting pdfs implies that an increase in the number of components in the grouping procedure increases the offset error, whereas the standard deviations are essentially independent of the number of components included in the group. The validity of the model derived has been tested with a prototype of a low-cost portable instrument for three-phase power quality measurements.

Power distribution systems often exhibit different harmonic behaviours at different locations. It is not usually possible to completely characterise the distortion at a particular site, however, due to insufficient data about the measured system and instrumentation inability to evaluate or register complete set of measurement quantities. In this paper existing power quality standards are analysed and new harmonic distortion indices are derived. A new index describing the total harmonic distortion in current measurements, TRD, is suggested in order to simplify and standardise harmonic measurements. Another index representing the increase of harmonic voltage distortion with TRD, equivalent harmonic impedance zeq, is proposed as an efficient way of characterising a particular site. The framework described here can be used to predict future harmonic behaviour at any previously measured point. Finally, extensive harmonic measurements of a real distribution system were performed to prove the usefulness of the proposed indices.

The affective and cognitive states of the user can be effectively monitored by electroencephalography (EEG). The human taste stimulation and perception produces changes in the EEG signals of the brain at several subdivisions of insula, orbitofrontal cortex and anterior cingulate cortex. In this work we propose a method which can be used to monitor these signals elicited by taste stimulation and perception using a simple gaming EEG device. Emotion elicited by taste recall is stronger than stimulus elicited emotion will be re-established but with a gaming EEG device. The results are presented for a number of subjects and obtained using the research edition of the gaming EEG headset.

In this paper noise analysis of three different filter designs is performed. Voltage noise spectral density and RMS noise voltage in simply designed active RC, noise optimized active RC and switched capacitor (SC) filter design are calculated for fifth order Chebyshev low pass filter, with a cut-off frequency f c =3.4kHz, and pass-band ripple α max =−0.5dB. All three designs are realized as a cascade of two second order sections and one first order section. All filter analysis has been performed using MATLAB and SPICE program tools. Through the analysis it is shown that SC based filter has significantly lower noise when compared with active RC based filter.

The discrete time switched capacitor (SC) based filters have number of advantages over the classical continuous time active RC based filters, the one most important being reduced circuit silicon area, allowing SC filters to be integrated to a single monolithic integrated circuit (IC). In this paper we have designed fifth order Chebyshev low pass SC filter, with a cut-off frequency f_x=3.4 kHz, and pass-band ripple a_ma x =-0.5 dB, and we have compared its time and frequency performance with a performance of the active RC based filter. The both SC and active RC filters are realized as a cascade of two second order sections and one first order section. All filter analysis has been performed using MATLAB and SPICE program packages where SC based filter has shown significant noise and sensitivity improvement, when compared with active RC based filter., Vremensko diskretni filtri s preklapanim kapacitetima imaju niz prednosti nad klasičnim vremensko kontinuiranim aktivnim RC filtrima. Jedna od važnijih prednosti jest smanjena površina silicija, omogućavajući integraciju filtra u integriranoj thenologiji (IC). U ovom radu dizajniran je Chebyshev nisko propusni filtar s preklapanim kapacitetima s graničnom frekvencijom f_c = 3.4 kHz i valovitosti alpha_max = -0.5 dB, te su mu vremenska i frekvencijska svojstva uspoređena sa svojstvima aktivnog RC filtra. Oba filtra realizirana su kao kaskada dviju sekcija drugog reda te jedne sekcije prvog reda. Analiza oba filtra izvršena je u MATLAB i SPICE programskim paketima, te je kod SC filtra pokazano znatno smanjenje šuma i osjetljivosti u usporedbi s aktivnim RC filtrom.

The influence of noise on the measurement uncertainty of different harmonic indices calculated according to IEC grouping algorithms is analyzed in this paper. The results of a previously published paper are extended to cases where the input noise is superimposed on to the harmonic signal. Probability density functions (pdf) of the harmonic indices are obtained by means of numerical integration and are used for assessment of the statistical properties of the output noise. The values of the offset errors and standard deviations are compared to uncertainties obtained by applying the uncertainty propagation law, as indicated by The Guide to the Expression of Uncertainty in Measurement (GUM).

When a power transformer is switched to power supply there is a transient inrush of current in each of the transformer phases. Its peak value mainly depends on the resistance of the primary winding, the point on the voltage wave at the instant of switching the transformer on, and the residual magnetic flux of the transformer. The inrush current is predicted by the time stepping 2D finite-element method (FEM) coupled with the external electrical circuit. During the switching on process the electromagnetic forces that appear as a result of inrush currents and theirs leakage magnetic fields are calculated. An experimental transformer is built and the measured results are given., Uključivanjem transformatora na mrežu napajanja pojavljuje se u svakoj od faza tranzijentna struja uklapanja. Njezina vršna vrijednost ovisi o otporu primarnog namota, trenutačnoj vrijednosti napona u trenutku uključivanja i remanentnom magnetskom tijeku. Struja uklapanja računata je primjenom 2D metode konačnih elemenata (MKE) spregnute s vanjskim električnim krugom, u vremenskoj domeni. Elektromagnetske sile na namote za vrijeme procesa uključivanja pojavljuju se uslijed tranzijentnih struja i njihovih rasipnih polja. Mjerenja struje uklapanja provedena su na eksperimentalnom transformatoru.

In this paper, a virtual instrument for bio-impedance measurements in oral cavity is described and characterized. The instrument is realized by using general purpose USB data acquisition device and LabVIEW. The flexibility of the virtual instrument allows the evaluation of different values and indices, in order to determine most suitable measurement parameter. The realized instrument was tested on electrical models with known impedance values and during in-vivo measurements.

A simple measurement is proposed for source identification and characterization. Power transformer is proposed as a point to perform the measurements in a cheap and practical way. Simulation has been undertaken to confirm validity of the proposed method. The procedure for the load characterization, outlined in this paper, has been tested in real power distribution system to establish the contribution of the certain industrial customer to the harmonic distortion. In contrary to previous assumption, it has been found out that the analyzed customer is not dominant source of distortion and is only partially responsible for the harmonic distortion at the PCC.