Search

The significant brightness contrast between stars and orbiting planets often hinders the detection of exoplanets. This paper presents the development and validation of an experimental setup and digital filtering techniques for a rotational shearing interferometer (RSI) aimed at enhancing exoplanet detection. The method leverages controlled phase shifts and spatial frequency modulation through Risley and Dove prisms to isolate faint planetary signals from dominant starlight. Laboratory experiments use HeNe lasers to simulate a star-planet system, and spatial filters ensure precise wavefront alignment. The interferometer's rotational shearing capabilities enhance the accuracy of phase alignment, allowing for significant suppression of starlight and improved detection of planetary signals. Additionally, applying Fourier-based digital filtering techniques further enhances detection sensitivity by reducing background noise. Experimental results demonstrate an 80% reduction in noise and up to a 20% increase in detection sensitivity compared to traditional interferometric methods. The RSI's performance represents a significant advancement in interferometric techniques, suggesting its potential for real-world astronomical applications. However, further optimization is required to address challenges associated with space-based observations. This work sets the foundation for future research aimed at refining optical configurations and digital filtering techniques for exoplanet detection. [ABSTRACT FROM AUTHOR]

The widespread use of risograph printing in printing houses requires an increase in the speed of equipment while maintaining the printing quality of the original layouts. The problem of risograph printing is not always obtaining high-quality images, since the risographs used in the standard driver (periodic, non-periodic) do not always allow you to get the printed image the same as in the original. This is reflected in the insufficiency of parameters such as image clarity and sharpness. Problems of poor quality of risograph printing can be solved by changing the settings of the risograph driver for the original layout with additional use of digital filtering and image rasterization. This will require the creation of additional image rasterization methods to improve the initial indicators of the original layout in contrast, clarity, and the correctness of the transmission of halftone images and will improve the quality of printed images, as well as ensure the efficiency of the printing process for a wide range of original layouts.

Тhe methods of amperostatic coulometry and the design of existing equipment for their application are analyzed. A new approach to the design of a coulometric system for the National Amount of Substance Electrochemical Standard is proposed. It consists in combining the means for performing the main operations of the primary amperostatic coulometry method in a single hardware and software unit that performs titration and automated control of this process, as well as receiving and processing the measuring signals required for this. The structure and functional diagram of the measuring unit of a precision automatic coulometry titrator using the latest element base are developed. The appearance and brief description of the created compact measuring system are given. The proposed algorithm for inversion of the measured voltage is described, which made it possible to significantly improve the accuracy of titration current measurement. A new version of median filtration of the measuring signal is presented, which made it possible to increase the accuracy of recording changes in the solution potential and determining the moment of end of the electrochemical reaction. The effectiveness of the developed technical solutions is confirmed by the results of international comparisons, to which references are provided. References 18, figures 4. [ABSTRACT FROM AUTHOR]

AbstractThe detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. This paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241Am, 60Co, 137Cs, 192Ir, 239Pu, and 235U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods. [ABSTRACT FROM AUTHOR]

The significant brightness contrast between stars and orbiting planets often hinders the detection of exoplanets. This paper presents the development and validation of an experimental setup and digital filtering techniques for a rotational shearing interferometer (RSI) aimed at enhancing exoplanet detection. The method leverages controlled phase shifts and spatial frequency modulation through Risley and Dove prisms to isolate faint planetary signals from dominant starlight. Laboratory experiments use HeNe lasers to simulate a star-planet system, and spatial filters ensure precise wavefront alignment. The interferometer’s rotational shearing capabilities enhance the accuracy of phase alignment, allowing for significant suppression of starlight and improved detection of planetary signals. Additionally, applying Fourier-based digital filtering techniques further enhances detection sensitivity by reducing background noise. Experimental results demonstrate an 80% reduction in noise and up to a 20% increase in detection sensitivity compared to traditional interferometric methods. The RSI’s performance represents a significant advancement in interferometric techniques, suggesting its potential for real-world astronomical applications. However, further optimization is required to address challenges associated with space-based observations. This work sets the foundation for future research aimed at refining optical configurations and digital filtering techniques for exoplanet detection.

A thorough examination of current developments in adaptive filtering for coherent dual-polarization optical communication systems is provided in this work. The emphasis is on high-capacity networks made possible by dual-polarization, coherent detection, variable bit-rate transceivers. The review explores the effectiveness of different adaptive algorithms in coherent receivers, the importance of dual polarization, and the function of adaptive filters in reducing channel impairments. The research also sheds light on the trade-offs and difficulties related to flexible bitrate optical transceivers. The paper comprises an extensive assessment of dynamic optical networks, categorized by network granularity and generation, in addition to the study of adaptive filtering. A comprehensive understanding of the developments in dynamic optical networking technologies is provided by the discussion of the evolution and traits of each generation. The poll also covers optical access networks, emphasizing the acceptance and advantages of optical access protocols as IEEE EPON and ITU-T GPON. The study also examines how digital filtering might be used to manage transmission constraints, highlighting the significance of segmenting digital filtering into distinct blocks. An overview of the types and applications of optical filters utilized in optical communication systems is included in the discussion. The study wraps up with a review of the literature that summarizes current research on optical communication systems, such as studies on adaptive algorithms' convergence properties, coherent dual-polarization receivers, and machine learning's use in optical fiber communication. The contributions of this paper include a thorough analysis of adaptive algorithm performance, a comparative study of dynamic optical networks, and a comprehensive overview of recent research in optical communication systems. [ABSTRACT FROM AUTHOR]

In this paper, the issues on current optimization methods for identifying modern digital camera photosensors are presented. Methods for improving the reliability of identification of digital cameras are discussed, i.e., determining the digital camera used to take a particular photograph. Homogeneous images were optically recorded to form a noise signature, and sets of amateur images were tested for identification of three types of cameras from the images. Digital image filtering and image identity metric were optimized. An optimal digital filter was selected to evaluate smoothed images to obtain the noise signatures of the identified cameras. An optimal identity criterion was obtained by comparison of camera noise signatures. Using an optimal filter and identity criterion allowed increasing, on average, the reliability of identifying the camera used to take a particular photograph by > 60 times. This work provides reference to solve image problems in image registration and processing, security, forensics, and big data analysis. [ABSTRACT FROM AUTHOR]

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point. [ABSTRACT FROM AUTHOR]

For cardiovascular disease diagnosis, the utility of the Electrocardiogram (ECG) signal is comprehensive. For accurate ECG analysis, early diagnosis and evaluation of cardiac disorders, embedded low and high-frequency noise must be eradicated from it. Baseline wandering is an example of low-frequency noise, whereas muscle artefacts and power line interference are examples of high-frequency noise. This paper presents a novel method for cancellation of power line interference that is based on variational mode decomposition (VMD) and digital filtering techniques. To accomplish an efficacious ECG denoising process, narrow-band variational mode functions (VMFs) are created by decomposing the noisy ECG signal using VMD method. The aim is to eliminate powerline interference noise (PLI) using a variable notch filter designed using these narrow-band VMFs. For cancellation of powerline interference, centre frequency of all VMFs is estimated and depending upon the presence of powerline interference, a notch filter with frequency response adjusted to the estimate obtained from VMFs is designed to remove it. Utilizing the signal-to-noise ratio (SNR) and correlation coefficient, the effectiveness of the suggested denoising method is evaluated. The proposed approach is tested on synthetic ECG signals and standard MIT-BIH arrhythmia database, demonstrating that the proposed approach reduces powerline interference noise better than the current state of art methods. [ABSTRACT FROM AUTHOR]

Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop. Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record. Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification. [ABSTRACT FROM AUTHOR]

It is possible to determine the ionospheric scintillation index with a modified NovAtel GPStation-6 dual-frequency GPS/GLONASS receiver by measuring small-scale fluctuations in the total electron content of the ionosphere and calculating their standard deviation. Based on the analysis of the shortcomings of the GPStation-6 receiver, the algorithm for monitoring of small-scale fluctuations of the total electron content based on digital filtering is substantiated. Experimental results of measurement of small-scale fluctuations of the ionospheric total electron content and results of estimation of their root-mean-square deviation and ionospheric scintillation index are presented. [ABSTRACT FROM AUTHOR]

Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop.Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record.Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification.

Background: Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)—a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body—or photoplethysmography (PPG). Methods: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. Results: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. Conclusions: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process. [ABSTRACT FROM AUTHOR]

The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.

With growing interest in rocking structures, it is important to quantify the effects of ground motion processing schemes on rocking response. To this end, this paper studied the influence of different ground motion correction schemes and parameter values on the rocking displacement spectrum. When the problem is treated on an individual ground motion basis, then it seems that ground motion correction does have an influence on the rocking response, especially for more slender blocks. This influence is larger for causal filtering. However, no specific trend related to the cut off period of the filter can be observed (at least for cut off periods longer than or equal to 10 s) or on the type of the recording device (analog or digital). On the other hand, when treating the problem statistically (by comparing the statistics of the response due to sets of ground motion, not a single ground motion), the effect is significantly reduced: The processing schemes do not induce any significant bias to the rocking response and the motion‐to‐motion variability seems more important than the ground motion correction method. This conclusion applies to both analog and digital records, both causal and acausal filters, and to all near field pulse like, near field no pulse like, and far field records. [ABSTRACT FROM AUTHOR]

The groundwater level observed from a monitoring well is a mixed signal resulting from different stimulations. It is challenging to obtain a specific component from this mixed signal. This is the case for obtaining the pumping component from a monitoring well in an area with many pumping wells. This study developed a signal processing method to extract the average magnitude of pumping (AMP) from the observed groundwater signals. AMP represents the variation over time in the groundwater level caused by pumping surrounding a monitored well. It is obtained from a digital filtering procedure. The use of a Gaussian high-pass filter removes the long-term trends from the groundwater signal and reveals a high-frequency signal that is usually hidden. After applying frequency analysis and verifying it by fast Fourier transformation (FFT), the pumping frequency was identified. The pumping frequency is set as the center frequency for a band-pass filter to obtain AMP. To validate and understand the factor influencing AMP, a three-dimensional (3D) numerical model was constructed to simulate artificial groundwater fluctuations. The synthetic case study showed that the pumping rate and the distance between the pumping wells and the monitoring well are factors that control AMP. We further applied this methodology to evaluate AMP for three monitoring wells in Tuku Township, located in the Chou-Shui Chi alluvial fan in central Taiwan. The results showed a negative correlation between the AMP variation and rainfall events, describing long-term trends for pumping and the influence of pumping on the groundwater level variation with time. Furthermore, the results revealed the scale of the AMP at each monitoring well, and the area with a higher AMP had a higher drawdown magnitude daily. Finally, the average pumping rate of a region can be estimated using AMP. [ABSTRACT FROM AUTHOR]

O b j e c t i v e s . In radar systems for moving objects tracking, there are often gaps in the measurement of coordinates.The problem is mostly fully solved in continuous time in the theory of systems with a random structure within the framework of statistical Bayesian theory of filtration in the presence of complete a priori statistical information. This approach leads to complex algorithms that are difficult to implement in practice. The purpose of investigation was to develop a filtering algorithm in conditions of information interruptions based on the use of extended least squares method.M e t h o d s . Methods of estimation theory are used, in particular, the extended least squares method, which makes it possible to find relatively simple algorithms with a minimum amount of a priori knowledge about the characteristics of the impacts.R e s u l t s . The algorithm for filtering radar signals has been developed, based on measurements of the moments of breaks and extrapolation of the measured coordinates at intervals of information lack. The resulting algorithm is nonlinear and therefore tracking disruptions may occur in the filter. The results of the algorithm are demonstrated using a model example. The estimation of the filtering accuracy and tracking failure conditions is carried out.Co n c l u s i o n . A filtering algorithm has been developed that allows determining the moments of the onset of breaks and extrapolating the estimates of useful information. The comparative simplicity of the algorithm makes it suitable for practical use.

The paper proposes parallel algorithms for solving digital filtering problems of different dimensions using modern universal computers. Theoretical estimates of the complexity and speedup are obtained, which confirm the high efficiency of these algorithms. Some of the proposed parallel algorithms are implemented using computers with a multi-core processor, and real estimates of the speedup are obtained, which agree well with theoretical ones. [ABSTRACT FROM AUTHOR]

Elimination of undesired signals from a mixture of captured signals in body area sensing systems is studied in this paper. A series of filtering techniques including a priori and adaptive approaches are explored in detail and applied involving decomposition of signals along a new system's axis to separate the desired signals from other sources in the original data. Within the context of a case study in body area systems, a motion capture scenario is designed and the introduced signal decomposition techniques are critically evaluated and a new one is proposed. Applying the studied filtering and signal decomposition techniques demonstrates that the functional based approach outperforms the rest in reducing the effect of undesired changes in collected motion data which are due to random changes in sensors positioning. The results showed that the proposed technique reduces variations in the data for average of 94% outperforming the rest of the techniques in the case study although it will add computational complexity. Such technique helps wider adaptation of motion capture systems with less sensitivity to accurate sensor positioning; therefore, more portable body area sensing system. [ABSTRACT FROM AUTHOR]

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR—respiration rate; TI/TE—inhalation/exhalation time; IER—inhalation-to-exhalation time; V—flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland–Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation ( r ¯ = 0.92) and low mean difference ( MD ¯ = −0.27 BrPM (breaths per minute)), limits of agreement ( LoA ¯ = +1.16/−1.75 BrPM), and mean absolute error ( MAE ¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users. [ABSTRACT FROM AUTHOR]

Tension control is very important for the intelligent control system of rapier looms because stable tension guarantees a tight fabric structure, moderate elasticity and good forming. To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals. The median filtering and limiting filtering algorithms are integrated to solve the uncertainty problem caused by the disturbance and fluctuation of the tension signal, and an excellent sample dataset is obtained. The attenuation factor and the number of learning times are introduced to design and adjust the learning rate of the back propagation neural network algorithm. In addition, the overfitting problem of the backpropagation neural network model in the current research process is solved. The experimental simulation results show that the tension detection fluctuation range of this method is 0.8 kg, and the tension detection error is within 0.1%. On the basis of the existing tension control algorithm, the tension detection accuracy is improved, which presents a new research perspective for the wide application of high-precision tension control strategies in rapier looms. It is of great importance to improve the production efficiency and fabric quality of rapier looms.

The article discusses an important issue of railway line construction and maintenance, which fundamentally is the verification of geometric parameters of the railway track. For this purpose, mobile measurements have been performed using a measuring platform with two properly arranged GNSS receivers, which made it possible to determine the base vector of the platform. The measuring functionality of the system was extended by IMU. In this article, the effect of measuring conditions on the accuracy of the results collected from GNSS receivers is analyzed. In particular, the advisability of digital filtering of the recorded coordinates to eliminate disturbances is indicated. The article also presents the possible use of GNSS devices and the IMU unit for determining the direction angle and the longitudinal and lateral inclination angles of the railway track. This makes it possible to verify the track geometry in the horizontal plane by determining the positions of straight sections, circular arcs, and transition curves. It is indicated that the results of measurements are repeatable despite the dynamic interaction between the railway track and the measuring platform. The results confirm the usefulness of the applied GNSS and IMU signal processing method for monitoring the geometrical parameters of the railway track in operating conditions. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A method that combines Lucy-Richardson deconvolution algorithm (LRA) with a multiview projection setup to improve imaging reconstruction of objects embedded in scattering media is presented. In the first step, the medium was illuminated with a laser beam and single-shot multiple images of the object were obtained from different viewpoints. Next, the above procedure was performed under the same conditions but only the medium, without the object, was illuminated by a light point source to obtain the system PSF. Third, each sub-image of the object was digitally cropped, extracted from the array, and deconvolved with the average PSF image following the LRA. Finally, all the sub-deconvolved images were shifted to a common point and superimposed to reveal the shape of the hidden object. Various setup conditions including medium turbidity, thickness, target shape, and illumination state were tested and compared. Results indicate an effective performance of the proposed method. [ABSTRACT FROM AUTHOR]

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient's condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated. [ABSTRACT FROM AUTHOR]

Introduction. To improve the vibrating rollers in order to increase sealing capacity, reliability and vibration safety, the interaction of vibrating roller parts between each other and with compacted soil is simulated mathematically. The developed models are validated, i.e. compared with the results of experimental studies. However, the known results of experimental studies were obtained based on a relatively small list of vibrating roller models and soil types, as well as in a steady vibration mode. The paper presents the results of experimental studies, which used a vibratory drum of a roller to study its vertical vibration accelerations both in the steady vibration, as well as transient mode at turning the vibration generator on (speedup) and off (halt). This expands the range of opportunities to validate the existing and newly developed mathematical models.Materials and methods. Experimental studies of vertical vibration accelerations of a drum were conducted using the DM-617 vibrating roller when compacting natural sand-gravel aggregate. The accelerometer readings show high-frequency harmonics, which makes it significantly more difficult to determine amplitude values of vibration accelerations, therefore a low-frequency filter with a boundary frequency of 200 Hz was used for digital processing.Results. It was determined that when the DM-617 vibrating roller is compacting soil with maximum driving force within the range of variation of dynamic modulus of soil deformation Evd=14…25 MPа, amplitude values of vertical vibration accelerations of the vibratory drum are from 65... 77 to -61... -69 m/s2 . At the start-up (speedup) of vibration generator, acceleration amplitudes are 1.1 times higher than vertical accelerations of steady operation mode of the vibrating drum and practically do not depend on the dynamic modulus of soil deformation Evd. At turning off (stop) of the vibration generator, amplitude of vertical accelerations do not exceed the vertical accelerations of the steady operation mode of the vibratory drum.Discussion and conclusion. The vertical acceleration amplitudes of vibratory drums of DM-617 do not depend on the dynamic soil deformation modulus Evd, and this is consistent with the results of experimental studies of the vibratory drum mounted on DM-614. The obtained vertical vibration accelerations of the drum mounted on DM-617 in the steady vibration mode, as well as at switching the vibration generator on (speedup) and off (halt) make it possible to verify the existing and developed mathematical models of interaction of vibrating rollers with compacted soil.Financial transparency: the authors have no financial interest in the presented materials or methods. There is no conflict of interest.

The report presents results of using the digital filters in numerical algorithms for the problem of restoration of the dynamically distorted signal from the observed one knowing the transfer function of the measuring transducer based on the spline method. Two approaches were used to reconstruct the dynamically distorted incoming signal in the presence of interference of different nature. In the first one, the moving average filter or the Savitsky–Goley filter were used. The second approach presents a new numerical algorithm, according to which the reconstructed signal is a result of averaging of approximate optimal dynamic measurements obtained by combining the spline method and the sampling theorem. The report presents the results of computational experiments of each approach on a data set obtained during bench tests of the pressure sensor. To demonstrate the effectiveness of the proposed methods the numerical results are compared with the data of the control sensor.

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients' information and to store data in disk. Doctors can download, look up and analyze patients' EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals' quality, and are easy to use and carry. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a structure of a portable cardiograph. Contents and functions of all blocks are overviewed. We describe two main structures of portable cardiographs, prepare a comparative table of characteristics for different analog front-ends. All individual features of the chips are listed as well. We register a test signal and try to improve its quality by means of digital filtration, taking the signal gained from certified Biopack equipment as a reference. We calculate device power consumption to compute required battery volume. Finally, energy efficient preprocessing algorithm is proposed.

The synthesis of a digital low-pass quasi-Gaussian filter with a finite impulse response function, a unit gain at zero frequencies, and nonnegative weighting factors is considered. The filter is designed to reduce noise in Poisson observations. A Gaussian function is used for the prototype filter, and an approximation functional is formed. The functional is subjected to stochastic global minimization using a modified algorithm of simulated annealing. The quasi-Gaussian filter is studied on the basis of model and experimental Poisson observations, and its efficiency is confirmed. [ABSTRACT FROM AUTHOR]

In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance. The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset. The obtained results were promising and complied with the initial assumptions, which stated that the "relax"-phase, despite relaxing sounds stimuli, is strongly affected with the "focus"-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data. Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results. [ABSTRACT FROM AUTHOR]

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented. [ABSTRACT FROM AUTHOR]

Copyright of Electronics & Control Systems is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When humans are listening to music they perceive beats, rhythms and melodies. Music stimuli induce motor system activities and it has a powerful emotion trigger effect. Since music is a potential stimulus in electroencephalogram based emotion research we supposed that different kinds of songs are recognizable from electroencephalogram signal. In this study we try to recognize music-induced electroencephalogram responses with the popular Neurosky Mindwave device. This paper describes the test conditions and the efficiency of an artificial neural network in combination with different data pre-processing techniques. The final outcomes show the negative effect of frequency decomposition and that the meditation level has more significant effect on the recognition than a particular song. DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21482

Abstract In this paper, a novel pulse interference filter for fiber Bragg grating (FBG) interrogation based on the tunable Fabry-Perot (F-P) filtering principle is proposed and experimentally demonstrated. The self-developed FBG interrogation system is devised for the aircraft health management of key structures. Nevertheless, the pulse interference is detected in the reflection spectrum of FBG causing interrogation system unstable. To address the problem, the first-order lag pulse broadening filter is proposed in this paper. The first-order lag filter is applied to preprocess and smooth the original signal, meanwhile enhancing the signal-to-noise ratio (SNR). Afterwards, peaks of reflection spectrum are distinguished with pulse interference by pulse broadening. Experimental results indicate that 634 peaks are detected before adopting the first-order lag pulse broadening filter. Comparatively, the number of peaks decreases to 203 after filtering the interference pulse, and the correct rate of peak detection is higher than 98.5%. Through the comparison with the finite impulse response (FIR) filter, the advantage of first-order lag filter is proved. The vibration monitoring experiment demonstrates that this system has high dynamic precision with a dynamic interrogation range of 0 Hz–400 Hz, and the maximum repetition rate of 800 Hz.