Spectral Analysis Of Signals Research Articles

Research on Magnetic Memory Detection of Weld Fatigue Damage Based on Two-Dimensional Spectral Entropy

Abstract In light of the intrinsic relationship between weld fatigue damage and the characteristics of magnetic leakage signals, fatigue experiments were conducted on butt welds to analyze the evolution of magnetic leakage signal as fatigue cycles increased. The introduction of two-dimensional spectral entropy allowed for the construction of a fatigue stage division model based on the two-dimensional spectral entropy diagram. The results revealed that distinctions between magnetic leakage signal under loaded and unloaded conditions were solely evident in their magnitude, with consistent curve distribution trends observed across various fatigue cycles. Furthermore, the distribution pattern of magnetic leakage signal displayed significant fluctuations, characterized by a sharp gradient increase following the emergence of fatigue cracks. These distribution characteristics of magnetic leakage signal differed markedly from the magnetic field distribution at other fatigue stages, with a higher growth rate observed as fatigue cycles increased. The two-dimensional spectral entropy spectrum analysis of magnetic leakage signals at different fatigue stages indicated the disorder in the magnetic leakage signal distribution within the high fatigue zone, signifying the expansion of fatigue microcracks. To some extent, two-dimensional spectral entropy can serve as an early warning system for detecting fatigue cracks in welds and achieving early identification of fatigue microcracks.

Fiber-optic spectrum monitoring of wavelength-division-multiplexed telecommunication signals with MHz update rates.

We propose a novel (to our knowledge) and simple real-time optical monitoring (RTOM) system for dynamic spectral analysis of telecommunication signals, involving electro-optic (EO) temporal sampling followed by dispersion-induced frequency-to-time mapping and high-speed photodetection. This system enables tracking of the presence and relative intensity of multiple wavelength-division-multiplexed (WDM) data streams that span over a broad frequency band with high resolution, accuracy, and fast measurement update rates. We derive the design conditions and trade-offs of the proposed scheme and report proof-of-concept experiments and a numerical result that demonstrate successful spectral monitoring of dense-WDM signals with different modulation formats and bit rates, over the full C-band, with the needed resolution to discern channels separated by a few tens of GHz, and with an unprecedented fast measurement update rate in the MHz range.

Secrets on the rock: analysis and discussion of the Dunde Bulaq rock art site

The Altay Prefecture in Xinjiang Uyghur Autonomous Region, China, boasts an abundance source of rock art, with the Dunde Bulaq rock art complex holding paramount significance in the study of local culture. This work employs hyperspectral techniques for data collection and analysis of the Dunde Bulaq I rock art site. Signal decomposition, pigment classification/clustering, and spectral curve analysis were employed to delve deeply into the information of the rock art hyperspectral data. Furthermore, cross-validation was utilized to reveal hidden information within rock art. To comprehend these hidden information, a more profound historical tracing was conducted, employing methods such as literature retrieval and image analysis, uncovering the internal relationship between the site I and the Chemurchek culture. This provides fresh material for studying the Bronze Age cultures within the Altai region.

Detection of Superrefraction at the Top of the Atmospheric Boundary Layer from COSMIC-2 Radio Occultations

Abstract Superrefraction at the top of the atmospheric boundary layer introduces problems for assimilation of radio occultation data in weather models. A method of detection of superrefraction by spectral analysis of deep radio occultation signals introduced earlier has been tested using 2 years of COSMIC-2/FORMOSAT-7 radio occultation data. Our analysis shows a significant dependence of the probability of detection of superrefraction on the signal-to-noise ratio, which results in a certain sampling nonuniformity. Despite this nonuniformity, the results are consistent with the known global distribution of superrefraction (mainly over the subtropical oceans) and show some additional features and seasonal variations. Comparisons to the European Centre for Medium-Range Weather Forecasts analyses and limited set of radiosondes show reasonable agreement. Being an independent measurement, detection of superrefraction from deep radio occultation signals is complementary to its prediction by atmospheric models and thus should be useful for assimilation of radio occultation data in the atmospheric boundary layer.

Study of the relationship between the parameters of electromagnetic-acoustic transformation and the stress-strain state of the metal to solve the problem of remote monitoring of power equipment

The implementation of Russia’s energy strategy provides for the creation of an intelligent control system for active-adaptive electrical networks of the electric power complex, which contains a system for remote diagnostic monitoring of energy equipment, including metal load-bearing structures of electrical energy generation, transmission and consumption facilities. Promising in this regard is the use of a high-performance electromagnetic-acoustic method, which allows non-contact detection of metal defects and monitoring of their stress-strain state. But existing electromagnetic-acoustic diagnostic tools do not have sufficient sensitivity and information content to solve this problem. The article presents the results of experimental studies aimed at identifying and processing informative parameters of electromagnetic-acoustic transformation for the implementation of remote diagnostic monitoring of the stress-strain state and metal damage of power equipment. Based on the research results, it was proposed to use a frequency model formed as a result of spectral analysis of the electromagnetic-acoustic transducer signal as an integral parameter characterizing the state of the metal.

Grain moisture control in the technological process of drying based on the dielcometric method

The paper discusses the problem of grain moisture control in the process of drying, analyzes the dielcometric method based on a high-frequency and ultra-high-frequency meter. Scientific directions of research are aimed at determining the basic requirements for methods of moisture control in non-equilibrium processes of heat and moisture exchange during drying of grain crops, and identifying the accuracy of dielcometric high-frequency and ultra-high-frequency methods of moisture control in the process of drying grain crops, and maintaining the necessary storage conditions in warehouses. Spectral analysis of signals requiring demodulation becomes increasingly complex as frequencies increase, with the greatest challenges occurring in the millimeter wave and at higher frequencies. These application notes discuss the development of a prototype and the design of instruments and describe methods for making accurate measurements with minimal costs in various operating conditions, and give metrological characteristics of a prototype moisture meter obtained during the testing period in laboratory and production conditions.

Sliding spectral correlation analysis of non-contact photoplethysmography signals for assessment of heart rate

Sliding spectral correlation analysis of non-contact photoplethysmography signals for assessment of heart rate

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals.

Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin-film wing interference signals (WISs) can discriminate free-flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave-infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS-based surveillance is therefore a potent tool for remote insect identification and surveillance.

Using Software-Defined radio receivers for determining the coordinates of low-visible aerial objects

The object of this study is the process of determining the coordinates of low-visible aerial objects. The main hypothesis of the research assumed that the signals emitted by airborne systems of airborne objects that are not visible to radar stations have a greater power than the signal reflected from the airborne object. This, in turn, could improve the signal/noise ratio and, accordingly, the accuracy of determining the coordinates of low-visible aerial objects. It is suggested to use Software-Defined Radio receivers to receive such signals emitted by on-board systems of low-visible aerial objects. It was established that the main sources of signals for Software-Defined Radio receivers are signals of command, telemetry, target channels, manual control channels, and satellite navigation. It was established that an additional distinguishing feature when determining the coordinates of low-visible aerial objects is the uniqueness of their spectra and spectrograms. The method of determining the coordinates of low-visible aerial objects when using Software-Defined Radio receivers has been improved, which, unlike the known ones, involves: – the use as signals for Software-Defined Radio of signal receivers of on-board equipment of low-visible aerial objects; – the use of a priori coordinate values of a low-visible aerial object; – conducting additional spectral analysis of signals of on-board systems of low-visible aerial objects. The spectra and spectrograms of signals of on-board systems of aerial objects when using non-directional and directional antennas were experimentally determined. The experimental studies confirm the possibility of using the Software-Defined Radio receiver to receive signals from airborne equipment and improve the signal-to-noise ratio. The accuracy of determining the coordinates of aerial objects when using Software-Defined Radio receivers was evaluated. A decrease in the error of determining plane coordinates by the Software-Defined Radio system of receivers compared to the accuracy of determining coordinates by the P-19 MA radar station was established by an average of 1.88–2.47 times, depending on the distance to the aerial object

Spatial and Spectral EEG Signal Analysis with Case Study of Slogans on Consumer’s Behaviour

Neuromarketing utilizes neuroscientific techniques to investigate consumer behavior, providing valuable insights beyond traditional research methods such as questionnaires and interviews which may not provide a complete understanding of consumer decision-making processes.Electroencephalography (EEG) has emerged as a promising tool for analyzing consumer responses to marketing stimuli. Nevertheless, the neural processing of slogans and their impact on short-term memory recall using EEG signals remains understudied. This research aims to bridge this gap by examining the neural activity associated with the recall of slogans using EEG analysis. By employing a spatial selection and spectral processingmethod, which involves Butterworth BPF filtering and L2-norm normalization to identify optimal channel combinations, active brain areas involved in slogan processing can be identified. Results reveal prominent activation in the frontal and occipital regions, particularly the F4 channel, indicating active recall and visual processing in individuals who correctly respond to slogans. These findings underscore the significance of slogans as visual marketing stimuli and offer insights for effective branding strategies. Leveraging EEG signals and understanding short-term memory processes enables marketers to optimize the impact of slogans on consumer engagement and brand recognition.

Structure of Fluctuations in the Edge Plasma of a Stellarator in Modes with Transport Transitions

The evolution of fluctuating signals from electrostatic and magnetic probes and reflectometry on the L-2M stellarator is presented. The L-2M facility is a quasi-stationary toroidal magnetic trap in which plasma is generated and heated by high-power pulsed microwave radiation. Pulses with transitions to improved confinement modes accompanied by an increase in energy, increase in plasma density, and restructuring of the peripheral electric field, were analyzed. Spectral analysis of signals is carried out using Fourier analysis and various wavelets. The possible influence of magnetohydrodynamic and kinetic instabilities on the development of transient processes is considered.

Structure of Fluctuations in the Edge Plasma of a Stellarator in Modes with Transport Transitions

The evolution of fluctuating signals from electrostatic and magnetic probes and reflectometry on the L-2M stellarator is presented. The L-2M facility is a quasi-stationary toroidal magnetic trap in which plasma is generated and heated by high-power pulsed microwave radiation. Pulses with transitions to improved confinement modes accompanied by an increase in energy, increase in plasma density, and restructuring of the peripheral electric field, were analyzed. Spectral analysis of signals is carried out using Fourier analysis and various wavelets. The possible influence of magnetohydrodynamic and kinetic instabilities on the development of transient processes is considered.

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APIT-MEMD associated with FTT that produce less overlapping between IMF3 and 4 (p = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research.

Identification of natural oscillation frequencies of constructions from low-amplitude seismic signals (on the example of the Sayano-Shushenskaya HPP dam according to the monitoring data of 2001-2021)

Based on the spectral analysis of low-amplitude seismic signals records (continuous monitoring data for 2001-2021) from one of the stations of the seismological network located at a distance of 4.4 km from the Sayano-Shushenskaya HPP, a method for determining the daily values of natural oscillation frequencies of constructions has been developed (the frequencies of the first seven modes are identified with an error no more than 0.01 Hz). The results of processing and analysis of unique data indicate a continuous and non–slowing increase in the values of the natural oscillation frequencies of the dam in the range of 0.02-0.05 Hz during the observation period. This is explained either by silting up the bottom of the reservoir in the area adjacent to the dam, or by adapting the dam and its base with subsequent increase in mechanical rigidity of the construction. At the same time, the intervals are analyzed at which the influence of seasonal environmental influences on the construction is insignificant (summer-autumn period, the water level in the reservoir is close to the maximum). The developed method of identification of natural oscillation frequencies of constructions from low-amplitude seismic signals is intended to monitor their technical condition, in order to prevent the risks of destruction of dams, industrial structures, infrastructure facilities and civil buildings with a high degree of reliability and is economically profitable in comparison with known solutions.

Relaxation spectral analysis in multi-contrast vascular magnetic particle imaging.

Magnetic nanoparticles (MNPs) are used as tracers without ionizing radiation in vascular imaging, molecular imaging, and neuroimaging. The relaxation mechanisms of magnetization in response to excitation magnetic fields are important features of MNPs. The basic relaxation mechanisms include internal rotation (Néel relaxation) and external physical rotation (Brownian relaxation). Accurate measurement of these relaxation times may provide high sensitivity for predicting MNP types and viscosity-based hydrodynamic states. It is challenging to separately measure the Néel and Brownian relaxation components using sinusoidal excitation in conventional MPI. We developed a multi-exponential relaxation spectral analysis method to separately measure the Néel and Brownian relaxation times in the magnetization recovery process in pulsed vascular MPI. Synomag-D samples with different viscosities were excited using pulsed excitation in a trapezoidal-waveform relaxometer. The samples were excited at different field amplitudes ranging from 0.5 to 10 mT at intervals of 0.5 mT. The inverse Laplace transform-based spectral analysis of the relaxation-induced decay signal in the field-flat phase was performed by using PDCO, a primal-dual interior method for convex objectives. Néel and Brownian relaxation peaks were elucidated and measured on samples with various glycerol and gelatin concentrations. The sensitivity of viscosity prediction of the decoupled relaxation times was evaluated. A digital vascular phantom was designed to mimic a plaque with viscous MNPs and a catheter with immobilized MNPs. Spectral imaging of the digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. The relationship between the Brownian relaxation time from different tissues and the number of periods for signal averages was evaluated for a scan time estimation in the simulation. The relaxation spectra of synomag-D samples with different viscosity levels exhibited two relaxation time peaks. The Brownian relaxation time had a positive linear relationship with the viscosity in the range 0.9 to 3.2 mPa · s. When the viscosity was >3.2 mPa · s, the Brownian relaxation time saturated and did not change with increasing viscosity. The Néel relaxation time decreased slightly with an increase in the viscosity. The Néel relaxation time exhibited a similar saturation effect when the viscosity level was >3.2 mPa · s for all field amplitudes. The sensitivity of the Brownian relaxation time increased with the field amplitude and was maximized at approximately 4.5 mT. The plaque and catheter regions were differentiated from the vessel region in the simulated Brownian relaxation time map. The simulation results show that the Néel relaxation time was 8.33±0.09 μs in the plaque region, 8.30±0.08 μs in the catheter region, and 8.46±0.11 μs in the vessel region. The Brownian relaxation time was 36.60±2.31 μs in the plaque region, 30.17±1.24 μs in the catheter region, and 31.21±1.53 μs in the vessel region. If we used 20 excitation periods for image acquisition in the simulation, the total scan time of the digital phantom was approximately 100 s. Quantitative assessment of the Néel and Brownian relaxation times through inverse Laplace transform-based spectral analysis in pulsed excitation, highlighting their potential for use in multi-contrast vascular MPI.

Hybrid Method of Speech Signals Spectral Analysis Based on the Autoregressive Model and Schuster Periodogram

Hybrid Method of Speech Signals Spectral Analysis Based on the Autoregressive Model and Schuster Periodogram

IIR Cascaded-Resonator-Based Complex Filter Banks

The use of a filter bank of IIR filters for the spectral decomposition and analysis of signals has been popular for many years. As such, a new filter-bank resonator-based structure, representing an extremely hardware-efficient structure, has received a good deal of attention. Recently, multiple-resonator (MR)-based and general cascaded-resonator (CR)-based filters have been proposed. In comparison to single-resonator-based analyzers, analyzers with a higher multiplicity of resonators in the cascade provide lower side lobes and a higher attenuation in stopbands. In previous works, it was shown that the CR-based filter bank with infinite impulse response (IIR) filters, which is numerically more efficient than one with finite impulse response (FIR) filters, is suitable for dynamic harmonic analysis. This paper uses the same approach to design complex digital filter banks. In the previous case, the optimization task referred to the frequency responses of harmonic filters. In this work, the harmonic filters of the mother filter bank are reshaped so that the frequency response of the sum (or difference, depending on the parity of the number of resonators in the cascade) of two adjacent harmonic filters is optimized. This way, an online adaptive filter base can be obtained. The bandwidth of the filters in the designed filter bank can be simply changed online by adding or omitting the output signals of the corresponding harmonics of the mother filter.

SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut

In the past few decades, data-driven deep learning analysis has been developed and successfully applied in many domains, such as spectral signal analysis. The convolutional layer was regarded as an efficient feature extractor, and the residual learning framework was used to train the deep neural network. In this paper, by replacing identity blocks in a residual network with proposed short-cut structures, we present a new residual learning framework coupled with multiscale convolutional layers, whose advantages are discussed from the perspective of gradient descent during backpropagation and information theory. This study proved that the proposed short-cut structure can pass the loss gradient from the last layer to minimize the vanishing gradient problem. The experiments also showed that the multiscale design of our network can extract features from spectral signals more efficiently than traditional convolutional layers. The proposed new residual framework was tested on four public datasets to prove the network’s efficiency. Compared to the traditional residual network, the proposed method can decrease RMSE by 36.9% on average and the R2 reached 0.9544 on average.

Compact real-time RF spectrum analyzer with 16 GHz instantaneous bandwidth based on photonic frequency-shifting loops

Spectral analysis of broadband RF signals in real time is of primary importance for numerous applications. So far, the instantaneous bandwidth of real-time spectrum analyzers based on conventional digital techniques is limited to a few GHz. This limitation is set by the clock jitter of the analog-to-digital converters, and by the processing capabilities in real time of digital electronics. On the contrary, analog architectures based on microwave photonics are not constrained by such limitations, and offer potentially a very high instantaneous bandwidth. However, they generally suffer from inherent limitations, such as large footprint and high complexity. Here, we propose a much simpler architecture of RF spectrum analyzer based on frequency-shifting loops. It utilizes only compact commercial telecom components, a single CW laser, and slow electronic resources (10 MSa/s). The probability of intercept is 100%, the instantaneous bandwidth reaches 16 GHz, and the spectral and temporal resolutions are respectively equal to 160 MHz and 50 µs. Our system is expected to open new avenues in embedded applications of microwave photonics.

Functional connectivity and power spectral density analysis of EEG signals in trained practitioners of Bhramari pranayama

Bhramari Pranayama, which belongs to the group of pranayama, is a calming breadth practice. In the present study, we aim to investigate the cortical dynamics associated with Bhramari pranayama. We evaluated the power spectral density and cross-spectral density matrices in thirty trained practitioners before, during, and after they performed Bhramari pranayama. A significant increase (p < 0.05) in overall theta (12.64 µV2/Hz and 17.51 µV2/Hz) and alpha spectral power (19.36 µV2/Hz and 26.11 µV2/Hz) in the right temporal region of the brain was found during and after the pranayama. Additionally, a significant increase (p < 0.05) in gamma spectral power in the right (1.99 µV2/Hz) and left temporal (1.66 µV2/Hz) and parietal (1.62 µV2/Hz) regions, was observed during and after the pranayama. The cross-spectral density significantly increases after performing the Bhramari pranayama compared to before the pranayama in the alpha band (p < 0.05). Subsequently, all-to-all functional connectivity between EEG channels in sensor space is presented using circular graphs. Further graph theory analysis is employed to assess the effects of Bhramari pranayama on functional brain networks. These findings reveal that the alpha band's functional network topology of Bhramari meditators is more integrated during and after the meditation. Also, the power spectral changes are more prominent in the temporal region of the brain due to the generation of humming sound. By measuring these changes, meditators can determine the effectiveness of their pranayama practice inducing relaxation. Therefore, these results are valuable for enhancing pranayama practice and help in understanding the cortical mechanisms underlying pranayama.