sensor signal processing
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Analytical devices that combine sensitive biological component with a physicochemical detector hold a great potential for various applications, e.g., environmental monitoring, food analysis or medical diagnostics. Continuous efforts to develop inexpensive sensitive biodevices for detecting target substances typically focus on the design of biorecognition elements and their physical implementation, while the methods for processing signals generated by such devices have received far less attention. Here, we present fundamental considerations related to signal processing in biosensor design and investigate how undemanding signal treatment facilitates calibration and operation of enzyme-based biodevices. Our signal treatment approach was thoroughly validated with two model systems: (i) a biodevice for detecting chemical warfare agents and environmental pollutants based on the activity of haloalkane dehalogenase, with the sensitive range for bis(2-chloroethyl) ether of 0.01-0.8 mM and (ii) a biodevice for detecting hazardous pesticides based on the activity of γ-hexachlorocyclohexane dehydrochlorinase with the sensitive range for γ-hexachlorocyclohexane of 0.01-0.3 mM. We demonstrate that the advanced signal processing based on curve fitting enables precise quantification of parameters important for sensitive operation of enzyme-based biodevices, including: (i) automated exclusion of signal regions with substantial noise, (ii) derivation of calibration curves with significantly reduced error, (iii) shortening of the detection time, and (iv) reliable extrapolation of the signal to the initial conditions. The presented simple signal curve fitting supports rational design of optimal system setup by explicit and flexible quantification of its properties and will find a broad use in the development of sensitive and robust biodevices.
- MeSH
- biosenzitivní techniky metody MeSH
- chemické bojové látky analýza MeSH
- chlorované uhlovodíky analýza MeSH
- enzymy metabolismus MeSH
- ether analogy a deriváty analýza MeSH
- hexany analýza MeSH
- hydrolasy metabolismus MeSH
- kalibrace MeSH
- látky znečišťující životní prostředí analýza MeSH
- lyasy metabolismus MeSH
- počítačové zpracování signálu * MeSH
- senzitivita a specificita MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- 1-chlorohexane MeSH Prohlížeč
- bis(2-chloroethyl)ether MeSH Prohlížeč
- chemické bojové látky MeSH
- chlorované uhlovodíky MeSH
- dehydrochlorinases MeSH Prohlížeč
- enzymy MeSH
- ether MeSH
- haloalkane dehalogenase MeSH Prohlížeč
- hexany MeSH
- hydrolasy MeSH
- látky znečišťující životní prostředí MeSH
- lyasy MeSH
The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically.
- Klíčová slova
- algorithm, diagnostics, impeller blade, monitoring, signal processing, steam turbine, vibration,
- MeSH
- algoritmy MeSH
- chirurgické nástroje MeSH
- pára * MeSH
- počítačové zpracování signálu MeSH
- vibrace * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- pára * MeSH
This paper presents a novel analog front-end for EMG sensor signal processing powered by 1 V. Such a low supply voltage requires specific design steps enabled using the 28 nm fully depleted silicon on insulator (FDSOI) technology from STMicroelectronics. An active ground circuit is implemented to keep the input common-mode voltage close to the analog ground and to minimize external interference. The amplifier circuit comprises an input instrumentation amplifier (INA) and a programmable-gain amplifier (PGA). Both are implemented in a fully differential topology. The actual performance of the circuit is analyzed using the corner and Monte Carlo analyses that comprise fifth-hundred samples for the global and local process variations. The proposed circuit achieves a high common-mode rejection ratio (CMRR) of 105.5 dB and a high input impedance of 11 GΩ with a chip area of 0.09 mm2.
This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.
- Klíčová slova
- EMI-free, Least Mean Squares (LMS) algorithm, Normalized Least Mean Square (NLMS) algorithm, adaptive system, fetal heart rate (fHR), fetal heart sounds (fHS), fetal phonocardiography (fPCG), interferometer, maternal heart rate (mHR), maternal heart sounds (mHS),
- MeSH
- algoritmy MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- poměr signál - šum MeSH
- srdeční frekvence plodu * MeSH
- srdeční ozvy MeSH
- těhotenství MeSH
- Check Tag
- lidé MeSH
- těhotenství MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Ship acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To address these challenges, we propose an automated pipeline that integrates Empirical Mode Decomposition (EMD), adaptive wavelet filtering, feature selection, and a Bayesian-optimized Random Forest classifier. The framework begins with EMD-based decomposition, where the most informative Intrinsic Mode Functions (IMFs) are selected using Signal-to-Noise Ratio (SNR) analysis. Wavelet filtering is applied to reduce noise, with optimal wavelet parameters determined via SNR and Stein's Unbiased Risk Estimate (SURE) criteria. Features extracted from statistical, frequency domain (FFT), and time-frequency (wavelet) metrics are ranked, and the top 11 most important features are selected for classification. A Bayesian-optimized Random Forest classifier is trained using the extracted features, ensuring optimal hyperparameter selection and reducing computational complexity. The classification results are further enhanced using a majority voting strategy, improving the accuracy of the final object identification. The proposed approach demonstrates high accuracy, improved noise suppression, and robust classification performance. The methodology is scalable, computationally efficient, and suitable for real-time maritime applications.
This paper introduces a new current-controlled current-amplifier suitable for precise measurement applications. This amplifier was developed with strong emphasis on linearity leading to low total harmonic distortion (THD) of the output signal, and on linearity of the gain control. The presented circuit is characterized by low input and high output impedances. Current consumption is significantly smaller than with conventional quadratic current multipliers and is comparable in order to the maximum processed input current, which is ±200 µA. This circuit is supposed to be used in many sensor applications, as well as a precise current multiplier for general analog current signal processing. The presented amplifier (current multiplier) was designed by an uncommon topology based on linear sub-blocks using MOS transistors working in their linear region. The described circuit was designed and fabricated in a C035 I3T25 0.35-µm ON Semiconductor process because of the demand of the intended application for higher supply voltage. Nevertheless, the topology is suitable also for modern smaller CMOS technologies and lower supply voltages. The performance of the circuit was verified by laboratory measurement with parameters comparable to the Cadence simulation results and presented here.
- Klíčová slova
- CMOS, accuracy, current amplifier, current controlled gain, electronic control, linearity, sensor signal processing,
- Publikační typ
- časopisecké články MeSH
The monitoring of data from global positioning system (GPS) receivers and remote sensors of physiological and environmental data allow forming an information database for observed data processing. In this paper, we propose the use of such a database for the analysis of physical activities during cycling. The main idea of the proposed algorithm is to use cross-correlations between the heart rate and the altitude gradient to evaluate the delay between these variables and to study its time evolution. The data acquired during 22 identical cycling routes, each about 130 km long, include more than 6,700 segments of length 60 s recorded with varying sampling periods. General statistical and digital signal processing methods used include mathematical tools to reject gross errors, resampling using selected interpolation methods, digital filtering of noise signal components, and estimating cross-correlations between the position data and the physiological signals. The results of a regression between GPS and physiological data include the estimate of the time delay between the heart rate change and gradient altitude of about 7.5 s and its decrease during each training route.
- MeSH
- algoritmy MeSH
- cyklistika fyziologie MeSH
- geografické informační systémy * MeSH
- lidé MeSH
- počítačové zpracování signálu * MeSH
- regresní analýza MeSH
- srdeční frekvence fyziologie MeSH
- telemetrie metody MeSH
- zeměpis MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
- Klíčová slova
- biosignals, digital signal processing, sensors, vital sign monitoring,
- MeSH
- lidé MeSH
- lůžka * MeSH
- monitorování fyziologických funkcí přístrojové vybavení metody MeSH
- počítačové zpracování signálu MeSH
- spánek fyziologie MeSH
- vitální znaky * fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Analyte flow influences the performance of every gas sensor; thus, most of these sensors usually contain a diffusion barrier (layer, cover, inlet) that can prevent the negative impact of a sudden change of direction and/or the rate of analyte flow, as well as various unwanted impacts from the surrounding environment. However, several measurement techniques use the modulation of the flow rate to enhance sensor properties or to extract more information about the chemical processes that occur on a sensitive layer or a working electrode. The paper deals with the experimental study on how the analyte flow rate and the orientation of the electrochemical sensor towards the analyte flow direction influence sensor performance and current fluctuations. Experiments were carried out on a semi-planar, three-electrode topology that enabled a direct exposure of the working (sensing) electrode to the analyte without any artificial diffusion barrier. The sensor was tested within the flow rate range of 0.1-1 L/min and the orientation of the sensor towards the analyte flow direction was gradually set to the four angles 0°, 45°, 90° and 270° in the middle of the test chamber, while the sensor was also investigated in the standard position at the bottom of the chamber.
- Klíčová slova
- amperometric sensor, analyte flow, current fluctuations, signal-to-noise ratio,
- Publikační typ
- časopisecké články MeSH
This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.
- Klíčová slova
- breathing analysis, computational intelligence, depth sensors, human-machine interaction, image processing, signal processing,
- MeSH
- dechová frekvence fyziologie MeSH
- dospělí MeSH
- dýchání MeSH
- lidé středního věku MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- polysomnografie metody MeSH
- senzitivita a specificita MeSH
- spánek fyziologie MeSH
- syndromy spánkové apnoe patofyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
