Frequency Characteristics Of Signals Research Articles

Signal Processing and Waveform Re-Tracking for SAR Altimeters on High Mobility Platforms with Vertical Movement and Antenna Mis-Pointing.

Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have been proposed in previous works. However, these models were mainly proposed for satellite altimeters and are not applicable to high-mobility platforms such as aircraft, unmanned aerial vehicles (UAVs), and missiles, which may have a large antenna mis-pointing angle and significant vertical movement. In this paper, we propose a novel semi-analytical waveform model and signal processing method for SAR altimeters with vertical movement and large antenna mis-pointing angles. A new semi-analytical expression that can be numerically computed for the flat pulse response (FSIR) is proposed. The 2D delay-Doppler map is then obtained by numerical computation of the convolution between the proposed analytical function, the probability density function, and the time/frequency point target response of the radar. A novel delay compensation method based on sinc interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain signal frequency characteristics. In addition, a height estimation method based on least squares (LS) estimation is proposed. The LS estimator does not have an analytical solution, and requires iterative solving through gradient descent. We evaluate the performance of the proposed estimation strategy using simulated data for typical airborne scenarios. When the mis-pointing angles are within 10 degrees, the normalized quadratic error (NQE) of the proposed model is less than 10-10 and the RMSE of τ obtained by the re-tracking method fitted by the proposed model is less than 0.2 m, which indicates the high applicability of the model and accuracy of the re-tracking method.

Electromyography Indices of Handgrip Force with Swinging Motion

Electromyography (EMG) is a powerful tool for studying muscle activity, but there is limited research on EMG signals in the muscles of the human forearm, which poses challenges for prosthetic hand development. This study utilized the maximum voluntary contraction (MVC) normalization method to analyze the flexor carpi radialis muscle during handgrip and swinging motions. The MVC indices revealed a significant proportion of high-amplitude MVC results. We conducted three statistical analyses to validate the indices. One-way ANOVA showed significant differences in mean values among the seven subjects during the percent MVC test. RMS study demonstrated a linear correlation between muscle contraction and movement. Boxplot analysis revealed variations within the interquartile range and median values across the entire MVC range. To achieve these results, we employed an eighth-order Gaussian function for curve fitting and exponential weighted moving average. The median interquartile range showed high discrepancies, while the differences between MVC increments were minimal, providing reliable indices for swinging motion. This suggests that the fat layer thickness may influence the muscle signal's frequency characteristics. In summary, our study highlights the untapped potential of EMG signals in the forearm muscles for prosthetic hand development. By employing MVC normalization and conducting rigorous statistical analyses, we uncovered significant findings that contribute to advancements in this field. Our insights provide hope and inspiration for researchers and practitioners seeking to enhance prosthetic technology.

Characterizing Rockbursts and Analysis on Hilbert-Huang Transform Spectrum of Microseismic Events, Shuangjiangkou Hydropower Station, Based on Microseismic Monitoring

The Shuangjiangkou hydropower station in China has complex geological conditions with high in situ stress. During the tunnel excavation, rockbursts occurred frequently, which seriously affected construction progress. Microseismic (MS) monitoring technology was used to explore rock MS activities to predict rockbursts. The MS monitoring system can capture a large number of MS signals. Based on Hilbert–Huang transform (HHT) instantaneous frequency analysis technology, using MATLAB software (R2022a) to write a program to convert the MS waveform, the frequency and energy characteristics of MS signals at a certain time can be obtained. By analyzing the frequency and energy characteristics of every event, the microseism active areas can be determined, and then rockbursts can be predicted scientifically. This paper selected two different construction sites, which were the main powerhouse and the access tunnel in the main powerhouse, as the research background. Introducing HHT instantaneous time–frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. The results indicated that some parameter anomalies in the event spectrum can predict rockbursts. For complex tunnel construction conditions, the HHT time–frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful.

Temporal convolution network‐based time frequency domain integrated model of multiple arch dam deformation and quantification of the load impact

Deformation is an intuitive reflection of the safety status of a dam. The construction of a dam deformation prediction model can predict the deformation and interpret the effects of environmental loads. The current research mainly focuses on the predictive ability of the model and rarely involves the interpretation of the load impact on deformation. Meanwhile, the selection of the model factors, such as water pressure factors and temperature factors, mostly relies on prior knowledge. In addition, the complex structure of multiple arch dams makes it difficult to capture the relationship between deformation and environmental loads. Consequently, the performance of conventional models based only on time domain information may be insufficient. In this paper, a deformation prediction model is established by integrating time frequency domain information. First, the deformation and load monitoring data are decomposed and regrouped according to the frequency characteristic of signals via kurtosis index-based VMD. Second, the sequence relationship between the dam deformation and loads under different frequency characteristics is automatically captured based on the temporal convolution network (TCN). Finally, a quantitative method of the load impact is proposed based on the network parameters. The case results show that the proposed modeling paradigm has significantly improved the prediction accuracy. The quantification result of the load impact on the horizontal displacement change of the dam conforms to the actual state of the project during the analysis period. The work effectively supplements the research on the prediction of ML-based models and interpretation of the load impact on deformation.

Three-point bending properties of 3D_C/C_TiC_Cu composites based on acoustic emission technology

Ceramic matrix composites with three-dimensional (3D) braided structures have been become one of the research hotspots owing to their excellent properties such as high strength and high-temperature resistance. In this study, acoustic emission (AE) as a non-destructive technique was utilised to monitor the three-point bending process of 3D_C/C_TiC_Cu composites and investigate the damage change of the materials under different bending stress. Firstly, the sentry function is used to divide the damage stages. Then the acoustic feature parameters are used to evaluate the damage condition. Finally, the time–frequency characteristics of damage signals are compared to verify the damage type of the composites at different stages. The results demonstrate that the sentry function can effectively distinguish the four stages of the damage process: the initial damage stage, the damage developing stage, the rapid damage stage and the failure stage. By analysing the acoustic feature parameters and time–frequency characteristics of the signals, the damage mechanism and the damage state of the composites can be obtained qualitatively. Consequently, the AE technique is an effective tool for evaluating damage evolution of 3D_C/C_TiC_Cu composites.

Energy Evolution Mechanism of Air Shock Wave Propagation and Attenuation Based on VMD–HT Energy Spectrum

Energy spectrum is an important tool for reflecting time–frequency characteristics of signals. The energy spectra of air shock wave at different explosion distances ware analyzed to investigate the energy evolution mechanism in the propagation and attenuation process. Waveforms of air shock wave were obtained from explosion tests and numerical simulations. Energy propagation and attenuation mechanisms of air shock wave were discussed on the basis of the wave theory of fluid. Instantaneous and marginal energy spectra of air shock wave were calculated using variational modal decomposition (VMD) and Hilbert transform (HT). Energy evolution laws of air shock wave with time, frequency, and explosion distance were analyzed according to the statistical results of the energy spectra. Results showed that the instantaneous energy peak of air shock wave is directly proportional to the square of its pressure peak while inversely proportional to the third power of propagation distance. Nonlinear attenuation of air shock wave will cause frequency dispersion and decelerate the attenuation rate of the total energy of air shock wave. The energy evolution laws of air shock wave with time and frequency reflected by instantaneous and marginal energy spectra were consistent with the theoretical analysis results.

A Novel Feature Extraction Approach for Mechanical Fault Diagnosis Based on ESAX and BoW Model

Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the symbolic aggregate approximation ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the extremum symbolic aggregate approximation into usable digital feature vectors, the bag-of-words model in natural language processing is employed to perform the counting statistics of the fault-related words, and the laplacian score algorithm is then utilized to re-rank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Statistical n-Best AFD-Based Sparse Representation for ECG Biometric Identification

Electrocardiogram (ECG) biometric recognition as a personal identification method is receiving more and more attention because it can support live verification results. Compared with other biometric-based methods, it can provide higher security performance. The difficulty of the problem lies in how to stably extract ECG signal features and achieve real-time verification. In this study, a new type of sparse representation learning framework called statistical $n$ -best adaptive Fourier decomposition (SAFD) originated by Qian is adopted in ECG biometric identification. Adaptive Fourier decomposition (AFD) is a recently developed combination of transform-based signal decomposition and sparse representation method, which can adaptively select the atoms from a redundant dictionary through orthogonal processing. The advantage of the AFD-type methods is that each atom in the dictionary has a precise mathematical formula with good analytic properties. This characteristic is significantly distinguished it from other existing sparse representations, where the atoms learned are usually matrix data and cannot be described mathematically. The proposed SAFD extends the existing $n$ -best AFD from processing single signal to multi-signals and implements the $n$ -best AFD in the stochastic Hardy space. Therefore, the small number of learned atoms by SAFD is sufficient to capture internal structure and robustness of the signal and generate a discriminative representation that reflects the time–frequency characteristics of signals. It is very suitable for non-stationary signals like ECG. The proof of convergence of the algorithm is presented. Extensive experiments are conducted on five public databases collected in different realistic conditions, and an average identification accuracy of 98.0% is achieved. In addition, less than 1 ms for one matching process makes it possible to be implemented in real time. Experimental results demonstrate that the proposed method can achieve superior performance compared to other state-of-the-art ECG biometric identification methods.

SSVEP-EEG Denoising via Image Filtering Methods.

Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.

Chromatic processing for feature extraction of PD-induced UHF signals in GIS

Partial discharge (PD) detection is an effective means of discovering insulation faults in gas-insulated switchgear (GIS). One of the most extensively used methods in PD detection has historically been the ultrahigh frequency (UHF) method. This study evaluates the chromatic processing methodology and its key factors for feature extraction of UHF signals in GIS. Three types of artificial defects are installed in the GIS tank at 0°, 90°, and 180°, respectively. The features of the UHF signals are extracted in the chromatic space, and PD discrimination of the defects is achieved. The influences of processors are studied before the feature selections are suggested. The time-stepping method is proposed to determine the rules of UHF signal frequency characteristics that vary with time. Finally, the process and options of the chromatics-inspired methodology are summarized.

Проблеми енергоефективності систем інтернету речей

The paper considers the energy efficiency problems of IoT systems when using power supplies of limited capacity. Such power sources include batteries and disposable batteries. The small amount of energy in the power supply of IoT devices requires additional time to design a system that will interact with IoT devices. With proper design, a person will be less likely to interfere with the system to replace the power supply. In the Internet of Things, devices that are sensors or controllers are connected by a single network. This system is used to improve the living conditions of man, and should work without human intervention. In such systems, power supplies should allow IoT devices to operate for several years without replacing the power supply. When designing an IoT system, it is necessary to perform an analysis of data exchange protocols to select the optimal ones to work in the system. Protocols can be divided into two categories: signaling protocols and data structure protocols. Signal transmission protocols determine the data transmission medium, frequency characteristics of signals, data encoding methods, the structure of data packets. Data structure protocols define a method of describing data to quickly separate some data from others during processing. To select protocols, you must define the data that will be transmitted over the network. Next, analyze the data types. Based on the analysis, determine the optimal data structure protocol. Then you need to determine the signal transmission protocol. It must rely on the data structure protocol, as its data will be transmitted in the IoT system. You can view the baud rate and coverage radius. High-speed protocols deplete the power supply faster. If there is no need for high speed, then such protocols can be discarded immediately. There may be situations where several protocols will give the same results. In this case, system developers must decide which one to use. Keywords: Internet, Internet of Things, network, IoT devices, computer system.

Short-term fibre intake estimation in goats using surface electromyography of the masseter muscle

The demand for quality control and environmentally acceptable animal production are important requirements in livestock monitoring and control. Monitoring the ingestive behaviour of grazing ruminants has applications in measuring animal welfare, detection of animal diseases and modelling the animal production system. In this work, a non-invasive method for fibre intake estimation on ruminants is presented using a surface electromyography (sEMG) based sensor system. In order to acquire bite/chewing sEMG signals, superficial disposable electrodes were placed on three uncastrated male goats’ masseter muscle, housed in individual pens, and data was sampled during eating using an analogue-to-digital converter. Feed samples and-left-overs were weighed before and after the experiment, respectively, to estimate the total intake. Electromyographic preprocessed data was sent to a computer, where seven signal features were extracted. Feed intake was modelled by fitting sEMG features as a predictor by means of a linear model. Results indicate that fibre intake could be successfully predicted in goats eating several forages (Tifton 68 and Tifton 85 grass hay, bad quality Tifton hay, and forage oat hay) with coefficients of determination ( R 2 ) higher than 0.867, using the signal feature called Slope Sign Change (SSC) as a predictor, which expresses signal frequency characteristics and indicates physiological aspects of bite/chewing. The present work demonstrates that sEMG based systems can be used as a non-invasive feed fibre intake estimation tool.

Spectral analysis framework for compressed sensing ultrasound signals.

Compressed sensing (CS) is the theory of the recovery of signals that are sampled below the Nyquist sampling rate. We propose a spectral analysis framework for CS data that does not require full reconstruction for extracting frequency characteristics of signals by an appropriate basis matrix. The coefficients of a basis matrix already contain the spectral information for CS data, and the proposed framework directly utilizes them without completely restoring original data. We apply three basis matrices, i.e., DCT, DFT, and DWT, for sampling and reconstructing processes, subsequently estimating the attenuation coefficients to validate the proposed method. The estimation accuracy and precision, as well as the execution time, are compared using the reference phantom method (RPM). The experiment results show the effective extraction of spectral information from CS signals by the proposed framework, and the DCT basis matrix provides the most accurate results while minimizing estimation variances. The execution time is also reduced compared with that of the traditional approach, which completely reconstructs the original data. The proposed method provides accurate spectral analysis without full reconstruction. Since it effectively utilizes the data storage and reduces the processing time, it could be applied to small and portable ultrasound systems using the CS technique.

Remote distributed monitoring system of switched reluctance motor

The reliable operation, dynamic performance analysis and control strategy research of a switched reluctance motor (SRM) require an online monitoring system to display and record its operating status. However, due to the large amount of data, the nonlinear electromagnetic characteristics and the harsh working environment of SRM, it is very difficult to monitor a SRM’s operation status in real time. In order to solve these problems, a new structure of the SRM monitoring system, which uses Digital Signal Processor (DSP) and the hardwired Transmission Control Protocol/Internet Protocol embedded Ethernet controller W5500, is presented in this paper. The W5500 and DSP’s direct memory access modules are employed for data capture and transfer to reduce the digital signal processing workload. The digital signal processing program is implemented by the hybrid programming method, which shortens the filtering time. Consequently, the DSP has sufficient resources to acquire multiple signals with a high sampling frequency and can adopt a more complex filtering algorithm, which enhances the accuracy and real-time performance of the system. Moreover, the amplitude–frequency characteristics of signals are analyzed. Then, the detection circuits and finite impulse response filters are designed to achieve the targeted acquisition and filtering. Besides, the impact of harsh environment on the system is reduced by adjusting the data transmission modes according to different working conditions. As a result, the scope of application of the system has been extended. The proposed system has a novel structure and strong practicability, which exhibits great guiding significance for the development of a SRM.

Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy

To achieve an effective feature extraction for power transformer vibration signals, the authors propose a method for signal feature extraction based on empirical wavelet transform (EWT) and multiscale entropy (MSE). First, transformer vibration signals are decomposed into several empirical wavelet functions (EWFs) with the method of EWT. Then, the frequency characteristics of signals are demonstrated in the time-frequency representation by applying a Hilbert transform to each EWF component. Finally, in order to quantify the extracted features, the MSEs of components being highly correlated with the original signals are calculated to construct the eigenvectors of transformer vibration signals. Several experiments are presented showing the effectiveness of this method compared with the classic empirical mode decomposition method.

Indication of the Destruction Stage of Epoxy PES-Filled Composite Using the Connected Parameter of Acoustic Emission Rate and the Spectral Characteristics of AE Signals

Abstract The methods of non-destructive testing have an important role in the problem of maintenance of transport and transport infrastructure. They make possible the early detection of internal defects in engineering structures and elements of vehicle design. The samples of composite materials used in the construction of transport infrastructure elements and transport vehicles have been tested. This paper shows the features of AE, which may allow more efficient and accurate analysis of the state of composite objects. The relation of the intensity and frequency characteristics of signals has been revealed. This work discusses the possibility of unambiguous identification of the stage of composite object destruction on the basis of these parameters.

EFFECT OF NANO-SiO2 PARTICLES ON PARTIAL DISCHARGE SIGNAL CHARACTERISTICS OF FR3 TRANSFORMER OIL

Liquid insulation for high voltage transformer applications based on natural esters derived from abundantly available vegetable oils are becoming popular in recent times. Since these natural ester based oils have environmental advantages and superior thermal performance, electrical utilities are slowly replacing the conventional mineral oils with natural ester based vegetable oils. FR3 oil, which is a soya based natural ester oil with superior dielectric and thermal characteristics, is becoming popular as an alternate insulating medium for high voltage transformers. With recent developments in nanotechnology field, it is possible to enhance the dielectric performance characteristics of natural ester based oils, which is a major constraint for high voltage transformer applications. However few research reports are only available in the area of nanofluids based on natural esters for high voltage insulation applications. In depth analysis and collection of large data base of insulation performance of natural ester based vegetable oils is important to improve the confidence level over nano-fluids based on natural esters. Considering these facts, in the present work, partial discharge characteristics of nano-SiO2 modified FR3 oil at different electrode configurations are investigated at different %wt filler concentrations. Important parameters such as partial discharge inception voltage, stable PD formation voltage, partial discharge amplitude at different voltage magnitude and PD signal frequency characteristics are evaluated. From the results, it is observed that the partial discharge performance of FR3 oil is significantly improved with the addition of nano-SiO2 filler. Since in recent times FR3 oil is commercially used in many transformers, these results will be useful for enhancing the dielectric strength of high voltage transformers.

Evaluation of dynamic measurement uncertainty in the time domain in the application to high speed rotating machinery

This article proposes a spectral method of assessing the dynamic uncertainty of measurement devices, that allows the investigation of the accuracy of measurement in dynamic operating conditions in the frequency domain and the evaluation of the amplitude values of dynamic uncertainty based on input signal frequency characteristics and spectral function. The results were tested when evaluating the dynamic uncertainty in measurements of the vibration acceleration of an engine. It was established that the maximum value of the dynamic uncertainty of the measurement of the vibration acceleration stands at 0.14 m/s2 for an observation time of 300 s and with a nominal value of the signal of vibration acceleration of 2.34 m/s2 at a frequency of 6 kHz, which in relative units is 6%.

Термодинамический и хаотический подходы в оценке параметров тремора при охлаждении испытуемых

The results of the research performance of the neuromuscular system of boys and girls aged from 20 to 28 years old, living in the North for over 15 years and have a different experience of tempering procedures. Mean age was 22.8 years. Indications tremors were recorded before and after local cold exposure, based on comparing the body´s response in all groups surveyed. It is proved that the local cold exposure affects the parameters of the neuro – muscular man (tremorogrammy) in three groups of subjects (group 1 to 15 people is not quenched, 2nd group of 15 people hardenable less than a year, the third group of 15 people hardenable 2-xs). Were calculated and statistical indicators obtained amplitude – frequency characteristics of signals, calculate the area and the matrix of pairwise comparisons tremorogramm three groups of subjects before and after exposure. Quasi-attractor showed the greatest difference in comparison with the stochastics and the matrix of pairwise comparisons in trained almost do not change before and after cold exposure, which is indicative of a high adaptation to cold exposure.