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A digital phase-locked loop (DPLL) based on sliding window infinite Fourier transform (SWIFT) and αSWIFT is proposed to extract the vibration and velocity parameters from the phase-modulated carrier signal. The phase of a carrier signal gets modulated when it is reflected from a vibration object due to the Doppler effect. Therefore, the phase-modulated carrier signal contains the Doppler signal that has the vibration information. The SWIFT or αSWIFT structure is designed from sliding discrete-time Fourier transform (DTFT) where the exponential window provides higher weightage to recent samples. It acts as a phase detector in the proposed DPLL, and its bandwidth can be varied based on the values of the time-constant. A controller unit, along with a bi-quad oscillator, is introduced in the proposed DPLL structure to remove any leakages in the system and to synchronize the system units in the parameter estimation. The proposed scheme is implemented in MATLAB/SIMULINK environment and validation for real-time applications is done using dSPACE 1202 MicrolabBox. [ABSTRACT FROM AUTHOR]

Doppler tomography (DT) is a method that allows the reconstruction of 2D or 3D images of the inside of the examined object. For this purpose, a two-transducer ultrasound probe is used. In this method, the Doppler phenomenon and the so-called Doppler signal are used to obtain an image. Therefore, the probe is one of the most important components of the measurement system of this method. It should be noted that Doppler tomography differs significantly from the well-known Doppler method of measuring blood flow in blood vessels. In the DT method, stationary cross-sectional images of the object under examination are obtained. In order to produce the Doppler effect in this case, the probe can move around or along the object being imaged. This paper will present a simulation of the effect of the frequency of ultrasonic wave generated by the ultrasound probe on the imaging quality for a single inclusion. [ABSTRACT FROM AUTHOR]

Purpose: Vibration parameter estimation using a non-contact technique for industry applications, biomedical applications, condition monitoring of civil structures, and rotating machines. Methods: An alpha sliding window infinite Fourier transform (α SWIFT) based digital phase-locked loop (DPLL) is proposed to measure the vibration signal. The proposed DPLL is mainly comprised of a quadrature detector, which is derived from the α SWIFT structure, a moving averager unit, a controller unit, and a bi-quad oscillator. The α SWIFT structure provides inherent stability as the system poles always lie inside the unit circle and has high noise rejection capability due to narrow bandwidth at high values of time-constant. Further, the α SWIFT structure works on an exponential window function that gives higher weight to recent samples and also reduces the leakage effects. The Pareto front multi-objective particle swarm optimization (PF-MOPSO) algorithm is introduced in the DPLL for computing the controller gains. Three non-conflicting objective functions, namely amplitude error, phase error, and total harmonic distortion (THD) are considered in the PF-MOPSO algorithm to optimize the controller parameters. The bi-quad oscillator generates the variable pulses based on the extracted signal to synchronize the remaining units. Results: The proposed DPLL is able to extract the multi-frequency components of the vibration parameter using a non-contact technique. The system is capable of measuring low frequencies up to 0.2 Hz and low amplitudes up to 0.06 mm. The proposed DPLL can also extract the vibration parameters up to the noise level of - 2 dB. [ABSTRACT FROM AUTHOR]

The Doppler ultrasound technique is used to detect vascular stenosis as a non-invasive procedure. To look for characteristics of Doppler ultrasound signals that are susceptible to stenosis, an accurate model of the Doppler ultrasound signal is required. This study models Doppler ultrasound waves from blood flow through stenotic arteries of varying degrees. In this model, it was expected that random scattering points will move based on their velocity. The Doppler influence was also included by modifying the form of the scattered signal at any time. Modeling the blood flow pattern through arteries helped identify the scattering point’s velocity profile and Doppler spectrum. The Tu and Devil model was used to consider a cosine stenosis shape because it is sufficiently similar to the natural shape of stenosis in the arteries. According to the Womersley model, the input flow to the stenotic zone was the same as the pulsatile blood flow in the vessel. As a result, the flow amplitude and Reynolds number changes will be near to reality. The fluid was deemed as non-Newtonian in the form of the power law to provide a more precise approximation to blood. When the calculated velocity profile was compared to the applied input value, the error rate was 6% for both normal and stenotic (70%) situations, validating the model’s accuracy in mimicking Doppler signals. This new computer model can be used to evaluate spectral analysis methods on simulated Doppler signals with clinically important underlying flow patterns. [ABSTRACT FROM AUTHOR]

In this study, a method has been proposed to model doppler ultrasound signals from blood flow passing through stenosed vessels using simulation of RF signals obtained from scattering points (Red blood cells) in different times and depths. In this model, it is supposed that several scattering points are randomly distributed in the vessel. The scattering points can be located in new positions based on their velocity in any time. Therefore, doppler Effect can be observed in anytime, with changing of received signals shape obtained from moving scatterers. The velocity profile of the scattering point was determined by modeling the blood flow pattern through arteries to further elucidate the Doppler spectrum of the applied ultrasound signals. A cosine stenosis shape was considered using Tu & Devil model as it is sufficiently similar to the normal shape of stenosis in the arteries. The input flow to the stenosed zone was the same as the pulsatile blood flow in the vessel, based on the Womersley model. As a result, changing of flow intensity and Reynolds number are very similar to reality. For similarity of considered fluid to blood fluid, the intended fluid is used in the form of non-Newtonian fluid. Investigation of the estimated velocity profile compared with the applied input value led to the error rate of 6% for both normal and stenosed (70%) cases, confirming the accuracy of this model and the method for simulating doppler signal.

Subject and Purpose. Comparative analysis of the shapes of phase portraits of Doppler signals (DS) reflected from drone rotating rotors is given with regard to different time delays. The shapes of DS phase portraits are examined for various rotational velocities and numbers of the rotating rotors. Methods and Methodology. A method using phase portraits is suggested for analysis of Doppler signals reflected from drone rotating rotors. The method determines the number of the rotors and estimates their rotational velocities. Results. It has been found that the shape of the phase portrait of the baseline signal is practically independent of the time delay and can be described as an occasional movement of the image point following the phase trajectory in the center of the phase portrait. The appearance of characteristic regions on the periphery of the phase portrait allows separating baseline and sounding signals. It has been shown that the shape of the DS phase portrait of the rotating rotor during the flight movement of the drone depends on the time delay value. With a larger delay, phase portraits similar in shape appear at regular intervals equal to the Doppler signal period. With a larger rotational velocity of the rotor, the rate of similar phase portrait appearance diminishes. Conclusion. During the sounding of drone rotating rotors, the shapes of DS phase portraits depend on the value of time delay. With a larger number of rotors in the drone flight, the periodic change character as to the DS phase portrait shape remains unchanged. In this case, DS phase portrait shapes differ substantially for different numbers of drone rotors

Subject and Purpose. The paper gives analysis to the waveforms of Doppler signals experimentally measured during the sounding of multirotor drones. Various velocities and numbers of the revolving rotors change Doppler signal waveforms. Also, these waveforms are not simple enough to take advantage of general laws of the examined phenomena and write the model equations immediately. In the present work, we seek to check capabilities of fractal nondifferentiable functions as to the empirical modeling of Doppler signals reflected from the revolving rotors of the drone. Methods and Methodology. In accordance with the standard manner of empirical modeling, we begin with the measurement and analysis of experimental temporal rows of Doppler signals obtained during the drone sounding. On this basis, the model structure (type of the functions) is determined. Then a turn comes of the procedure of the model parameter calculation. Finally, the actual and simulated Doppler signals are analyzed in qualitative and quantitative terms. Results. The characteristic features of some fractal nondifferentiable functions have been considered for their adoption in the radar signal modeling. The Doppler signal reflected from the revolving rotors of the drone has been represented as a sum of the fractal signal (modelling the low-frequency component of the Doppler signal) and the Weierstrass-Mandelbrot modified function (modelling the high-frequency component of the Doppler signal). The procedure of the model parameter choice has been examined at various speed values and numbers of the revolving rotors of the drone. Conclusion. The qualitative and quantitative analyses given to the measured and simulated Doppler signals validate the model we have suggested and show that the model provides a good agreement between the waveform features of the measured and simulated signals.

Subject and purpose. The frequency-temporal analysis signals of Doppler radar are in-process conducted multirotor drone. The wide use multirotor drone the results in the necessity of arrangement their flights and control after their use. The basic structural feature of such drone is a presence of rotors which provide their motion, the number of which can change depending on a construction. Methods and methodology. The method experimental researches of the revolved rotors of drone are offered with the use of Doppler radar. Terms and composition of the instrumentation-informative system, in-use during the leadthrough of experiment are described. Results. It is set as a result of the conducted analysis, that with the increase pulsatance of rotation rotor the period of Doppler signal (DS) diminishes (frequency rises), however fixed frequency DS less than pulsatance of rotation, set from a control of drone stand. Thus, than anymore frequency of rotation, the more difference of these frequencies. The features of structure (forms) DS and their spectrums which are complicated at the increase frequency of rotation rotor and their amount are exposed. The features of DS are investigational at different direction of rotation rotors (in one side or in different) at similar their geometrical location. Influence of direction of rotation is thus rotined on the form of temporal realization of DS and their spectrums. Conclusion. The exposed differences in form Doppler signals can be in future utillized for recognition type of drone, and also at the construction models of Doppler signals reflected from drone.

The Doppler signal of mitral valve is a biomedical signals and it is acquired by Doppler ultrasound device from mitral valve of hearth. It contains useful information about mitral valve and it can be used to diagnose mitral valve diseases by doctors. However, biomedical signal might be contaminated with noise or disruptive interference. In literature, different approaches have been introduced to remove the noise or interference from biomedical signal, but adaptive filtering is most popular technique for noise filtering. Therefore, a novel adaptive noise cancellation techniques based FIR digital filter was proposed using artificial bee colony algorithm (ABC) and it is employed for the noise reduction on noisy mitral valve Doppler signal. So, the meaningful data about mitral valve was saved to diagnose mitral valve diseases using denoising approach. Then, this novel approach was compared with the other methods suggested in literature such as recursive least square (RLS) algorithm or discrete wavelet transform (DWT). Also, to demonstrate the efficiency, different digital filter order and noise cases were used suggested in literature. DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21845

The rotation of rotor blades on small unmanned aerial vehicles (UAVs) introduces micro-Doppler (m-D) signatures to discriminate UAVs from other aircrafts or birds. Compared with the Doppler signal induced by the translation of the platform, the m-D signal, however, is rather weak due to the small radar cross section (RCS) of rotor blades. Moreover, the m-D signal always consists of multiple frequency components, most of which are induced by the vibration of the platform. This paper focuses on the extraction of the rotation signal via the empirical mode decomposition (EMD) algorithm, which would decompose an arbitrary signal into a series of intrinsic mode functions (IMFs). The constitution of IMF is investigated by using the partial differential equations (PDEs) to analyze the distribution of the extrema. For separating the rotation signal, the essence is to unify the frequency of extrema in the corresponding IMF exhibiting the rotation features. First, the fitting Doppler signal is constructed from the data with the Doppler property in the IMF. Second, a criterion is proposed to establish the masking signal, after which the rotation components are extracted from the residual m-D signal via another EMD. Simulations are carried out to demonstrate the effectiveness of the proposed method in solving the mode-mixing problem. The results derived from the measured data validate the feasibility of the proposed approach in the identification of small UAVs. [ABSTRACT FROM AUTHOR]

Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human's can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals. To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%., Competing Interests: Declaration of competing interest “None Declared”, (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The Doppler signal of mitral valve is a biomedical signals and it is acquired by Doppler ultrasound device from mitral valve of hearth. It contains useful information about mitral valve and it can be used to diagnose mitral valve diseases by doctors. However, biomedical signal might be contaminated with noise or disruptive interference. In literature, different approaches have been introduced to remove the noise or interference from biomedical signal, but adaptive filtering is most popular technique for noise filtering. Therefore, a novel adaptive noise cancellation techniques based FIR digital filter was proposed using artificial bee colony algorithm (ABC) and it is employed for the noise reduction on noisy mitral valve Doppler signal. So, the meaningful data about mitral valve was saved to diagnose mitral valve diseases using denoising approach. Then, this novel approach was compared with the other methods suggested in literature such as recursive least square (RLS) algorithm or discrete wavelet transform (DWT). Also, to demonstrate the efficiency, different digital filter order and noise cases were used suggested in literature. [ABSTRACT FROM AUTHOR]

Objectives: To describe the ultrasound abnormalities seen in septic arthritis and to assess their associations with clinical, biological, and radiological outcomes.Methods: We prospectively included 34 patients with septic arthritis of a native joint (knee, n=19; shoulder, n=6; hip, n=4; ankle, n=3; or wrist, n=2). Ultrasonography was performed to record synovial-membrane thickness and vascularity, joint effusion, and abnormalities of adjacent soft tissues, at baseline then 4days, 2weeks, and 3months later. Motion-range limitation of the affected joint was evaluated after 3months. Radiography was performed at inclusion and after 3months.Results: Mean age was 63.7±17.6years. After 3months, 20 (58.8%) patients had motion-range limitation with worsening of the total radiological score (P<0.001). The proportion of patients with synovitis was very high initially (96.4% at baseline, 96.3% after 4days, and 100% after 2weeks) then diminished to 77.8% after 3months (P=0.051). Synovial-membrane thickness was significantly higher after 4days and 2weeks compared to baseline (median, +17.3% and +20%, respectively; P=0.015) and was significantly lower after 3months compared to the earlier time points (median, -31.5%, P=0.015). A positive Doppler signal was common at baseline (n=18, 64.3%) then significantly less so after 3months (n=7, 25.9%; P=0.04). An unchanged or higher Doppler grade after 2weeks compared to baseline was associated with motion-range limitation at last follow-up (P=0.033).Conclusion: We report the first study on ultrasound evidence of synovitis, joint effusion, and soft tissue alterations at baseline and over time in patients with septic arthritis. Persistent synovitis and joint effusion 3months after starting antibiotic therapy was not associated with treatment failure. However, Doppler signal changes over the first 2weeks were associated with the 3-month functional outcome. [ABSTRACT FROM AUTHOR]