Wavelet Spectrum Research Articles

Инструментальные наблюдения и статистический анализ вертикальных профилей температуры в юго-западной части залива Петра Великого Японского моря

The study is based on data from four moorings stretched out in the north-northwest – south-southeast direction across the isobaths in the southwestern Peter the Great Bay, the Sea of Japan, on 3–14 October, 2022. For this purpose, timeseries of vertical profiles of temperature and its gradient were expanded into empirical orthogonal functions; timescales were estimated using wavelet transform. In all cases, the leading mode captures vertical stratification variability related to the thermocline shoaling or deepening, manifesting itself by concerted oscillations of temperature and its vertical gradient, which occurred on the near-inertial, semi-diurnal and two-diurnal timescales. The near-inertial oscillations were the most intense during most of the time, while the semi-diurnal ones were the most intense on the fourth and fifth days. From the joint wavelet spectra it was found that temperature anomalies moved towards the shallower area and the speed was estimated as equal to 0.44–0.55 and 0.95–1.11 m/s for the near-inertial and semi-diurnal timescales, respectively. The speed on the two-diurnal timescale differs by an order of magnitude between the pairs of two deeper and two shallower moorings, equaling to 1.17 and 0.15 m/s, respectively. The results were interpreted as related to the near-inertial and semi-diurnal internal wave propagation from the continental slope towards the inner shelf and two-diurnal timescale was speculated to be a difference between them caused by non-linearity.

Causal sources of the significant high-amplitude magnetic anomaly zone in the northern south China Sea continental margin

The magnetic anomaly map shows an obvious NE-SW trending high-amplitude magnetic anomaly zone (NSCSMA) in the northern South China Sea (SCS) continental margin that extends from southwest Taiwan to the area at approximately 114°E and 20°N. The origin of this high-amplitude magnetic anomaly zone is important for understanding the magmatism, margin rifting, and tectonic evolution of the SCS. To gain insight into this issue, in this study, wavelet spectrum analysis, 2D magnetic modeling, and compact inversion were used to identify the causal source of the NSCSMA. Combining wavelet spectrum analysis with magnetic modeling results, we find that the depths of the major magnetic sources are not consistent along the strike of the NSCSMA (∼15 km in the east and ∼25 km in the west). The compact inversion results along five profiles across the NSCSMA also revealed two kinds of geometries (quasi-dike in the east and dome-like in the west) of the causal sources of the NSCSMA with different depths. Based on our findings and previous paleo-subduction model, we propose that the major source of the NSCSMA is serpentinized upper mantle materials associated with paleo-Pacific plate subduction.

Wet and dry spell induced changes in the soil CO2 effluxes of Pine and Oak ecosystems of Central Himalaya: a comparative assessment for monsoon and winter seasons.

Soil efflux of CO2( ) is known to be dependent on natural drying and rewetting of the soil. Although the central Indian Himalayan region is predominantly occupied with two ecosystems, i. e. Pine (Pinus roxburghii) and Oak (Quercus leucotrichophora), differences in their dynamics and responses of to varying wet and dry spells were hardly known. To address this knowledge gap, this study provides a comparative assessment of variability from Pine and Oakecosystems of central Himalaya as a response to rainfall induced wet and dry spells of monsoon and winter seasons. The data presented in this study are collected for242 days of 2021-22 that include monsoon and winter seasons from a Pine and an Oak sites. The mean sof Pine and Oak sites are found to be 3.95(± 0.02) and 3.61(± 0.01) μmol.m-2.s-1, respectively. We find that the winter reduction in the in comparison to monsoon at the Pine site (78%) is more substantial than at Oak site (64.6%). The cross wavelet spectra of and monsoon rainfall amount at the Oak site, unlike the Pine site, indicate a negative relationship. The rainfall spell duration and amount of monsoon wet spells are noted to have an inverse relationship with at both sites, although, increasing rainfall spell duration in winter is noted to increase at Pine and Oak sites. Similarly, increasing is observed with increasingdry spells of monsoon at both sites. Results of this study indicate that in comparison to Oak, variability at Pine ecosystem is primarily driven by abiotic factors wherein wet spell is a major determinant.

Ultrasound wavelet spectra enable direct tissue recognition and full-color visualization

Traditional brightness-mode ultrasound imaging is primarily constrained by the low specificity among tissues and the inconsistency among sonographers. The major cause is the imaging method that represents the amplitude of echoes as brightness and ignores other detailed information, leaving sonographers to interpret based on organ contours that depend highly on specific imaging planes. Other ultrasound imaging modalities, color Doppler imaging or shear wave elastography, overlay motion or stiffness information to brightness-mode images. However, tissue-specific scattering properties and spectral patterns remain unknown in ultrasound imaging. Here we demonstrate that the distribution (size and average distance) of scattering particles leads to characteristic wavelet spectral patterns, which enables tissue recognition and high-contrast ultrasound imaging. Ultrasonic wavelet spectra from similar particle distributions tend to cluster in the eigenspace according to principal component analysis, whereas those with different distributions tend to be distinguishable from one another. For each distribution, a few wavelet spectra are unique and act as a fingerprint to recognize the corresponding tissue. Illumination of specific tissues and organs with designated colors according to the recognition results yields high-contrast ultrasound imaging. The fully-colorized tissue-specific ultrasound imaging potentially simplifies the interpretation and promotes consistency among sonographers, or even enables the applicability for non-professionals.

The ionospheric response during the 2013 stratospheric sudden warming over the East Asia region

During the 2013 SSW, ionospheric disturbances were observed at eighteen stations on three meridional chains of 100°E, 110°E and 120°E ranging from 19.03°N to 49.21°N at latitudes over the East Asia region. The total electron content (TEC) response showed evident semidiurnal disturbances with TEC enhancement in the morning and depletion in the afternoon. The maximum TEC increased by more than 220 % along the 120°E longitude. In addition, the semidiurnal disturbance of the TEC was enhanced and indicated evident latitudinal and longitudinal dependence. The wavelet spectra of semidiurnal disturbance in TEC presented quasi-16-day planetary wave-like oscillations at middle latitudes and quasi-10-day planetary wave-like oscillations near Sanya at low latitudes. Moreover, the zonal winds and meridional winds at altitudes of 92 km and 96 km showed quasi-16-day planetary waves at the Mohe station located at middle latitudes and quasi-10-day planetary waves at the Sanya station located at low latitudes, which maybe relate to the semidiurnal disturbance of the TEC. In particular, the 12hrs oscillation of the TEC showed a quasi-16-day periodic component at middle latitudes and a quasi-10-day periodic component at low latitudes, indicating that the modulated semidiurnal tides may transmit these 10-day and 16-day planetary wave-like oscillations to the ionosphere. The coupling between the atmosphere and ionosphere may be strengthened by quasi-16-day waves at middle latitudes and quasi-10-day waves at low latitudes (near Sanya). This finding can further confirm the PW-tide interaction theory during the 2013 SSW event, and it is of significance in the middle and low latitude ionospheric response to SSW.

Predictable patterns within the kelp forest can indirectly create temporary refugia from ocean acidification

Kelps are recognized for providing many ecosystem services in coastal areas and considered in ocean acidification (OA) mitigation. However, assessing OA modification requires an understanding of the multiple parameters involved in carbonate chemistry, especially in highly dynamic systems. We studied the effects of sugar kelp (Saccharina latissima) on an experimental farm at the north end of Hood Canal, Washington—a low retentive coastal system. In this field mesocosm study, two oyster species (Magallana gigas, Ostrea lurida) were exposed at locations in the mid, edge, and outside the kelp array. The Hood Head Sugar Kelp Farm Model outputs were used to identify dominating factors in spatial and temporal kelp dynamics, while wavelet spectrum analyses helped in understanding predictability patterns. This was linked to the measured biological responses (dissolution, growth, isotopes) of the exposed organisms. Positioned in an area of high (sub)-diel tidal fluxes with low retention potential, there were no measurable alterations of the seawater pH at the study site, demonstrating that the kelp array could not induce a direct mitigating effect against OA. However, beneficial responses in calcifiers were still observed, which are linked to two causes: increased pH predictability and improved provisioning through kelp-derived particulate organic resource utilization and as such, kelp improved habitat suitability and indirectly created refugia against OA. This study can serve as an analogue for many coastal bay habitats where prevailing physical forcing drives chemical changes. Future macrophyte studies that investigate OA mitigating effects should focus also on the importance of predictability patterns, which can additionally improve the conditions for marine calcifiers and ecosystem services vulnerable to or compromised by OA, including aquaculture sustainability.

Processing and analysis of friction vibration signal of diamond-like carbon film microtextured dry gas seal rings

PurposeThis paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.Design/methodology/approachThe friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.FindingsThe end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.Originality/valueThe results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

Fault Distance Measurement Method Based on Wavelet Energy Spectrum and BWO Algorithm Optimized CNN-GRU Hybrid Neural Network

Aiming at the problems such as noise interference in the current fault ranging methods for flexible DC transmission lines, a single-ended intelligent fault ranging method is proposed as a hybrid neural network based on wavelet energy spectrum and BWO algorithm to optimize the multi-head attention mechanism of CNN combined with gated recurrent unit GRU. First, the correlation between wavelet spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the model input of the neural network. Second, the hybrid neural network model is constructed and trained to mine the deep fault information in the time series, and the parameters of the hybrid neural network model with added multi-head attention are optimized using the iterative optimization search of the beluga algorithm to achieve fast and accurate positioning of the fault distance. Finally, the network is trained using the constructed four-terminal Zhangbei flexible DC transmission system model to collect data, and the experimental results prove that the method has high distance measurement accuracy, strong anti-interference ability and generalization ability, and a certain degree of resistance to transition resistance.

Upper Ocean variations at IODP Hole U1505C in the northern South China Sea and their response to the East Asian Monsoon during the middle Miocene

We used the samples from International Ocean Discovery Program (IODP) Hole U1505C in the northern South China Sea (SCS) to reconstruct the upper ocean profiles and discussed the East Asian Monsoon (EAM) variations during 15.0–12.5 Ma. 15 genera and 41 species were identified, and 7 planktonic foraminifer datums were recognized, then a reliable chronostratigraphic framework was updated. A principal component analysis (PCA) was used to identify the suitable assemblages for reconstructing upper water profiles, based on planktonic foraminifera relative abundance and isotope records. The sea surface temperature (SST), paleo-productivity, and the depth of thermocline (DOT) were reconstructed by the content of warm water species, high productivity species, and the ratio of mixed layer to thermocline species, respectively. Three main phases were identified: (1) during 15.0–14.7 Ma, the SST and paleo-productivity were high, while the thermocline was shallow, indicating a warm and rainy climate influenced by East Asian Summer Monsoon (EASM); (2) during 14.7–13.8 Ma, the SST was lower, the paleo-productivity increased significantly, and the thermocline deepened, indicating an enhanced East Asian Winter Monsoon (EAWM); (3) during 13.8–12.5 Ma, the SST increased slightly, the paleo-productivity deceased, while the DOT showed neglectable change, indicating a stable EAWM and a waning EASM. The results of the Morlet wavelet spectrum revealed that EAM and the upper ocean profiles in the northern SCS were predominantly regulated by the eccentricity cycles, emphasizing the significant impact of low-latitude processes on climate variations.

An unsupervised machine learning approach for real-time damage detection in bridges

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

Effects of Sampling Length and Overlap Ratio on EEG Mental Arithmetic Task Performance: A Comparative Study

Cognitive tasks have become quite popular in recent years. Understanding this sort of neurological research, its real-world applications, and how it may be improved in future studies are crucial. For this purpose, our study compares the classification accuracies for various segment lengths and overlap ratios for EEG recordings collected from 36 healthy volunteers during mental arithmetic tasks. EEG features are extracted from brain signals using the wavelet spectrum and the sample length and the overlap ratio of the sliding Windows are used as parameters. Feature selection was conducted using Correlation-Based and ReliefF feature selections. Subsequently, for classification results, Support Vector Machine, Random Forest, C4.5 Algorithm and k-Nearest Neighbor algorithms were employed, with the outcomes supported by the F1-score and Matthew's correlation coefficient. Therefore, the reliability of the obtained results has been ensured. In the comparisons obtained, the best average results for Accuracy, F1-score, and Matthew's correlation coefficient were found to be 0.990, 0.987, and 0.975 respectively, when applying the ReliefF feature selection method with the Support Vector Machine classifier.

Wide-band fluctuations of solar active regions probed with SHARP magnetograms

Context. The power spectra of the fluctuation noise of the solar active region (AR) areas and magnetic fluxes sequentially observed in time contain information about their geometrical features and the related fundamental physical processes. These spectra are analysed for five different ARs with various magnetic field structures. Aims. The goal of this work is to detect the characteristic properties of the Fourier and wavelet spectra evaluated for the time series of the fluctuating areas and radial magnetic fluxes of the active regions. Accordingly, this work gathers information on the properties of noise in the different cases considered. Methods. The AR area and radial magnetic flux time series were built using SHARP magnetogram datasets that cover nearly the entire time of the ARs’ transits over the solar disk. Then we applied Fourier and wavelet analyses to these time series using apodization and detrendization methods for the cross-comparison of the results. These methods allow for the detection and removal of the artefact data edge effects. Finally, we used a linear least-squares fitting method for the obtained spectra on a logarithmic scale to evaluate the power-law slopes of the fluctuation spectral power versus frequency (if any). Results. According to our results, the fluctuation spectra of the areas and radial magnetic fluxes of the considered ARs differ from each other to a certain extent, both in terms of the values of the spectral power-law exponents and their frequency bands. Conclusions. The characteristic properties of the fluctuation spectra for the compact, dispersed, and mixed-type ARs exhibit noticeable discrepancies amongst each other. It is plausible to conclude that this difference might be related to distinct physical mechanisms responsible for the vibrations of the AR areas and/or radial magnetic fluxes.

Investigation of inlet flow influenced on the first rotor flow instability at blade non-synchronous vibration state

Multistage axial compressor first stage rotor blades have occurred non-synchronous vibration (NSV). An experiment, including fluid and structure measurements, is adopted at the NSV occurred conditions to conduct a detailed investigation about NSV and rotating instability. The blade vibration stress was obtained from the strain gauges. High-frequency response two-hole pressure probes captured the flow characteristics at the inlet and outlet of the first stage rotor. NSV has a complex aerodynamic disturbance source. Wavelet and frequency spectrum analyses of pressure and blade stress results were utilized to determine the relationship between the blade vibration and the flow field. The inlet flow angle has been adjusted through the inlet guide vane (IGV) to reduce the vibration intensity. This paper revealed the aerodynamic origin behind this adjustment of IGV angle operation. The yaw angle and the axial direction March number at the first stage rotor inlet at different inlet guide vane (IGV) angles revealed the aerodynamic effect. Three operation points, including the maximum stress point and near surge point, are analyzed to broaden the cognition boundary of the NSV. The time and space characteristics of the rotating instabilities have been studied with the azimuthal mode analysis method. This research confirmed that the enlarged inlet incidence angle and the tip flow worsened caused the first stage rotor non-synchronous vibration in this multistage axial compressor. These experiments help find a straight relationship between the NSV and rotating instability.

Discrimination of Internal Solitary Waves from Coarse Time Resolution Field Observational Data by Using Wavelet Analysis

Internal solitary waves (ISWs) are large-amplitude internal waves which would destroy underwater engineering. Finding an easy way to discriminate ISWs from field observational data is crucial. Two time--series datasets, one contained ISWs and another only containing internal tides, were obtained from filed observations. Based on single-layer velocity data, wavelet spectrum shows significant high value in short time-scale domain when ISWs pass, whilst having no signal in that domain when only internal tides exist, indicating the capability of wavelet analysis on ISWs detection. Wavelet variances of the dataset with ISWs has a bimodal distribution versus periods with two peaks around 40 min and 110 min, which can also be reproduced by a numerical model, indicating that the energy within period band of 10–120 min is caused by ISWs. By using the conceived signal processing techne, data reconstruction can precisely obtain the arrival time of ISWs and retain about 91.4% of the original signal. It is found that, based on a field observational dataset with even a coarse sampling interval for up to 20 min, the existence of ISWs can be easily discriminated by using wavelet analysis, which provides us an economic method for the early warning of ISWs in ocean engineering.

Implication of kilometre-scale fluctuations on the MGS radio-occultation-derived electron density profiles: artefacts or ionospheric wave-like structures

ABSTRACT This study investigates the vertical kilometre-scale fluctuations (KSFs) seen on the Mars Global Surveyor (MGS) radio-occultation (RO)-derived electron density profiles (EDPs) by wavelet spectrum analysis. A delicate five-peak signature is revealed: in the northern polar region near the dawn–dusk terminator, the amplitudes of the KSF peak at fixed altitudes with a constant separation of approximately 28 km. The feature could not be noticed on a single EDP; averaging with at least tens of continuously collected EDPs is needed, suggesting that the phenomenon is not instantaneous (time duration of each RO event is ∼7 min), but on a time-scale of days (observation time for tens of continuous RO events). Applying similar background criteria, Mars Atmosphere and Volatile EvolutioN (MAVEN) Radio Occultation Science Experiment derived EDPs could also occasionally capture similar multipeak features, indicating either that it is a natural ionospheric structure or that both MGS and MAVEN suffer the same artefact. The two indications are discussed, and a preliminary hypothesis is raised. A linear superposition with a 28 km standing wave might modulate the amplitude of KSF, and the 28 km standing wave might be associated with the ionospheric buoyancy waves disturbed by the upstream solar wind, while the plasma instabilities might trigger the KSF in the backgrounds of the draped magnetic field and day-to-night neutral winds.

Sensitivity analysis in the wavelet domain: a comparison study

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

Method of Extracting the Instantaneous Phases and Frequencies of Respiration from the Signal of a Photoplethysmogram

We propose for the first time a method for extracting the instantaneous phases of respiration from the signal of a photoplethysmogram (PPG). In addition to the instantaneous phases of respiration, this method allows for more accurately extracting the instantaneous frequencies of respiration from a PPG than other methods. The proposed method is based on a calculation of the element-wise product of the wavelet spectrum of a PPG and the sequence of intervals between the heartbeats extracted from a PPG, and a calculation of the skeleton of the resulting spectrum in the respiratory frequency range. It is shown that such an element-wise product makes it possible to extract the instantaneous phases and instantaneous frequencies of respiration more accurately than using the wavelet transform of a PPG signal or the sequence of the heartbeat intervals. The proposed method was verified by analyzing the signals from healthy subjects recorded during stress-inducing cognitive tasks. This method can be used in wearable devices for signal processing.

Wavelets in the analysis of local time series of the Earth's surface air

The practical application of local smoothing and wavelet analysis methods for studying the spectral composition and coherent relationships of local average annual surface air temperatures with solar activity and the displacement of the Earth's North Pole is presented. A preliminary analysis of local time series of surface temperatures revealed the presence of emissions and their localization. It is shown that to eliminate the influence of outliers (short-term events) on the reliability of identifying a long-term nonlinear trend, the wavelet decomposition method, which filters high frequencies, is most suitable. Functional approximation models are constructed and compared at different levels of wavelet decomposition of the data. Time or scale smoothing is used to improve the reliability of the wavelet spectrum. Based on data on average annual surface air temperatures in Yalta (44.48⁰, 34.17⁰, = 72.0 m) for the time interval from 1869 to 2022, functional models of long-term trends were built and used to obtain short-term forecasts. Information about the linear relationship of events in the compared time series is obtained and discussed in the analysis of wavelet cross-correlation, wavelet coherence and phase coherence. Local similarities were discovered between data on surface air temperature and solar activity data (Wolf numbers) for a period of ∼(30–70) years, as well as oscillations with period of 11 years, manifested in the constancy of the phase difference and an increase in the modulus of wavelet coherence power over time. Localized similarities were also found in data on surface air temperature in Yalta and in data on displacements of the Earth's mean pole relative to the conventional beginning of EOP (IERS) CO1 in the interval of periods ∼ (30–70) years.

Analysis of Axial Acceleration for the Detection of Rail Squats in High-Speed Railways

A squat is a type of fatigue defect caused by short-wavelength rotational contact; if squats are detected early, the maintenance cost of the track can be effectively reduced. In this paper, a method for the early detection of squats is presented based on ABA (axle box acceleration) and frequency signal processing techniques. To increase the measurement sensitivity for the squat, ABA was used to measure the longitudinal vibration. Compared to vertical ABA, longitudinal ABA does not include vibrations from rail fasteners and sleepers, so it is possible to effectively measure the vibration signal in relation to the impact of the rail. In this paper, vibration data were measured and analyzed by installing a 3-axis accelerometer on the wheel axle of the KTX; squat signals were more effectively extracted using the longitudinal vibration measurement presented above. The algorithm to detect the position of squats was developed based on wavelet spectrum analysis. This study was verified for the section of a domestic high-speed line, and as a result of conducting field verification for this section, squats were detected with a hit rate of about 88.2%. The main locations where the squats occurred were the rail welds and the joint section, and it was confirmed that unsupported sleepers occurred at locations where the squats occurred in some sections.

Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes.

This paper presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.