Power Spectral Density Analysis Research Articles

A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

Investigating the impact of mindful breathing meditation on brain waves: a study on young adults

Extensive evidence affirms meditation’s impact on both physical and mental well-being, consistently highlighting increased alpha power during meditation. This study investigates how mindful breathing meditation affects brain waves in young adults, using EEG data from 15 participants. Focusing on boosting alpha wave power, the study shows a significant postmeditation increase. Data was processed with Finite Impulse Filters, Independent Component Analysis, Short-term Fourier analysis, and power spectral density analysis. The results highlight substantial alpha wave changes pre- and post-meditation. Additionally, data was preprocessed for machine learning-based classifiers (Support Vector Machine, Logistic Regression, Decision Forest, and Naïve Bayes) to categorize brain waves into Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta. Comparative analysis reveals the strong impact of mindful breathing meditation and binaural beats on brain wave patterns. This research enhances our understanding of meditation’s physiological effects on brain function, emphasizing its potential for improving mental well-being.

Application of modulated vibration to restore driver alertness

Since driving while drowsy is a significant cause of vehicle accidents, road safety could be improved if more effective methods were available for improving driver alertness. The present paper investigated whether it is possible to improve alertness via a wearable device that applied somatosensory vibration to the driver's wrist. The vibration used modulation frequencies ranging from 12 Hz to 50 Hz and a carrier frequency of 250 Hz. Random on-and-off intervals and variable vibration amplitudes were used to minimise sensory adaptation. Fifteen participants undertook a sixty-minute simulated driving task that caused drowsiness. Karolinska Sleepiness Scale (KSS), Power Spectral Density (PSD) and Higuchi's Fractal Dimension (HFD) analyses of brainwave signals were used to identify variations in driver alertness. The vibration stimulus was found to significantly improve alertness when compared to a no-vibration condition. Participants experienced an immediate improvement in alertness that reached significance within 9 min and was then sustained at the level seen at the beginning of the experiment, indicating a full restoration of alertness. This result demonstrates that wearable vibration devices have the potential to improve alertness in drivers.

Effects of high carbon dioxide concentration on emotional processing: Based on multimodal evidence

The effects of CO2 on emotional processing are still incompletely understood, although its effects on cognitive performance are widely recognized. This study hypothesized that elevated CO2 concentration would affect the processing of different valence emotions, which leads to more negative emotions and fewer positive emotions. A within-subject experiment was conducted to compare subjective emotional scores, emotional processing behavioral performance, EEG patterns, and other physiological changes of 30 participants exposed to CO2 concentration at 600 ppm and 5000 ppm. The results suggested that CO2 concentration could affect neural arousal patterns during emotional processing, changes in blood pressure, and salivary cortisol secretion in healthy participants. Emotional experience task performance of participants was also affected by CO2, which was not statistically significant. At the high CO2 concentration, the systolic blood pressure (SBP) level was significantly lower (p = 0.005), while the salivary cortisol level was significantly higher (p = 0.037). Power spectral density analysis showed that there was a significant interaction between CO2 concentration and emotional valence for theta waves in frontal regions (p = 0.029), beta waves in temporal regions (p = 0.017), and gamma waves in frontal-parietal occipital regions (p = 0.013). The present study found that CO2 has a negative effect on emotional processing, such as reducing the speed and accuracy of emotional processing. To some extent, there are also some positive effects, like reducing emotionally-induced increases in blood pressure. This finding warrants further research.

Structural EEG signal analysis for sleep apnea classification.

Diagnosing the sleep apnea can be critical in preventing the person having sleep disorder from unhealthy results. The aim of this study is to obtain a sleep apnea scoring approach by comparing parametric and non-parametric power spectral density (PSD) estimation methods from EEGsignals recorded from different brain regions (C4-M1 and O2-M1) for transient signal analysis of sleep apnea patients. Power Spectral Density (PSD) methods (Burg, Yule-Walker, periodogram, Welch and multi-taper) are examined for the detection of apnea transition states including pre-apnea, intra-apnea and post-apnea together with statistical methods. In the experimental studies, EEG recordings available in the database were analyzed with PSD methods. Results showed that there are statistically significant differences between parametric and non-parametric methods applied for PSD analysis of apnea transition states in delta, theta, alpha and beta bands. Moreover, it was also revealed that PSD of EEG signals obtained from C4-M1 and O2-M1 channels were also found statistically different as proved by classification using the K-nearest neighbour (KNN) method. It was concluded that not only applying different PSD methods, but also EEG signals from different brain regions provided different statistical results in terms of apnea transition states as obtained from KNN classification.

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

During the expansion of a wind farm, the strategic placement of wind turbines can significantly improve wind energy utilization. This study investigates the evolution of wake turbulence in a wind farm after introducing smaller wind turbines within the gaps between larger ones, focusing on aspects such as wind speed, turbulence intensity, and turbulence integral length scale. The flow field conditions are described using parameters like turbulence critical length and power spectral density, as determined through wind tunnel experiments. In these experiments, a single large wind turbine model and nine smaller wind turbine models were used to create a small wind farm unit, and pressure distribution behind the wind turbines was measured under various operating conditions. The results indicate that downstream wind speed deficits intensify as the number of small wind turbines in operation increases. The impact of these smaller turbines varies with height, with a relatively minor effect on the upper blade tip and increasingly adverse effects as you move from the upper blade tip to the lower blade tip. Through an analysis of power spectral density, the contribution of vortex motion to wake turbulence kinetic energy is further quantified. In the far wake region, the number of small wind turbines has a relatively small impact on wind speed fluctuations.

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62±02.06%, specificity of 76.50±23.50%, and accuracy of 75.59±10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

Global vibration control of offshore wind turbines with a flexible monopile foundation using a pendulum-tuned mass damper: Risk mitigation and performance incrementation

This study investigates an optimized design approach for a passive-adaptive pendulum-tuned mass damper (PTMD). The PTMD is used to mitigate structural vibrations in offshore wind turbines (OWTs) with flexible monopile foundations considering Pile-Soil Interaction (PSI). The model OWT is based on the 5-MW design proposed by the National Renewable Energy Laboratory (NREL). The PSI incorporates a pile that is modeled as beam–column elements supported by nonlinear springs and accounts for lateral loads (p-y curves) and axial loads (t-z and Q-z curves) at nodal points. Wind and wave spectra estimation, as well as hydrodynamic and aerodynamic load analysis, are performed using a bespoke MATLAB® program operated in conjunction with an ANSYS® 3-D finite element global model. The resultant peak response at the OWT hub was evaluated through power spectral density (PSD) analysis. Optimal design choices for PTMD parameters are obtained via parametric analysis, which assesses the dynamic response in the along and across directions at the hub and stress at the tower base. A systematic representation considers the uncertainties stemming from the geometric and mechanical properties of the tower, environmental loads, and rotating blade dynamics. To address these uncertainties, a Monte Carlo simulation is employed to assess the structural risk via the Performance-Based Wind Engineering (PBWE) framework. Consequently, the probabilistic evaluation of structural response, foundation stresses, and power production assists in the optimal design process of PTMDs that mitigate the global structural vibrations of OWTs.

Daily lake-level time series spectral analysis using EMD, VMD, EWT, and EFD

ABSTRACT This study investigates the dynamics of daily Urmia Lake level (ULL) changes using spectral analysis tools to discover fluctuating patterns in the ULL series. Therefore, in the present research, the empirical mode decomposition (EMD), variational mode decomposition (VMD), empirical wavelet transform (EWT), and empirical Fourier decomposition (EFD) were used to analyze the ULL signal. ULL series were decomposed into subseries, and the optimized outcome was used. All methods concluded that the ULL series has a steep downward trend. Signal reconstruction was performed, and it was inferred that EFD could not estimate the ULL series appropriately and had root-mean-square error (RMSE) = 12.26. Different from EFD, other methods performed better signal construction according to RMSE and error analysis. The mode-mixing issue was the last step in verifying the capabilities of signal-analyzing methods. Based on the power spectral density (PSD), it was seen that EMDs had mode-mixing problems and limitations in signal decomposition, whereas VMD and EWT did not have these issues. Results demonstrated that the present study has some limitations. Overall, it was concluded that VMD performed better in terms of RMSE, error analysis, reconstruction, mode-mixing problems, and PSD analysis while decomposing and extracting features from the ULL signal.

Examining the interfacial behavior of non-Newtonian gas-liquid two-phase flow in horizontal square microchannels

This study investigates the flow dynamics of a non-Newtonian two-phase flow within a horizontal square microchannel. The experimental apparatus employed an acrylic channel with a side length of 8 × 10−4 m. Nitrogen served as the test gas, and the test liquids consisted of water and CMC (Carboxymethylcellulose) aqueous solutions at concentrations of 0.2 and 0.4 %wt. The superficial velocity of working gas and liquids spanned ranges of 0.26–20 m/s and 0.05–1.0 m/s, respectively. A differential pressure transducer was used to measure pressure gradients, while a high-speed video camera captured the flow behavior for analysis with a custom image processing technique. Nonlinear regression statistical analysis was applied to establish the criteria for flow pattern transition. Furthermore, signal processing techniques such as PSD (Power Spectral Density), DWT (Discrete Wavelet Transform), ANN (Artificial Neural Network), and Kolmogorov entropy were employed to analyze the flow behavior based on the pressure gradient signals from the differential pressure transducers. Our findings reveal the occurrence of bubbly, slug, slug-annular, and churn flow patterns. The nonlinear regression statistical analysis offered ambiguous results for determining flow pattern transitions. However, an empirical coefficient was obtained for the nondimensional liquid height. Moreover, the empirical nondimensional liquid height aligned well with the experimental values. The PSD and DWT analysis corroborated each other in clearly characterizing the flow pattern based on pressure gradient fluctuation and wavelet energy. The Kolmogorov entropy effectively captured the chaotic flow pattern. Furthermore, the ANN analysis demonstrated a solid agreement between the predicted and actual flow patterns.

Effects of anodal transcranial direct current stimulation over motor cortex on resting-state brain activity in the early subacute stroke phase: A power spectral density analysis

IntroductionDespite promising results, the effects of transcranial direct current stimulation (tDCS) in the early stages of stroke and its impact on brain activity have been poorly studied. Therefore, this study aimed to investigate the effect of tDCS applied over the ipsilesional motor cortex on resting-state brain activity in the early subacute phase of stroke. MethodsThis is a pilot, randomized, double-blind, proof-of-concept study. The patients with stroke were randomly assigned into two groups: anodal tDCS (A-tDCS) or sham tDCS (S-tDCS). For A-tDCS, the anode was placed over the ipsilesional motor cortex, while the cathode was placed over the left or right supraorbital area (Fp2 for left stroke or Fp1 for right stroke). For the real stimulation, a constant current of 1.0 mA was delivered for 20 min and then ramped down linearly for 30 s, maintaining a resistance below 10 kΩ. For the sham stimulation, the stimulator was turned on, and the current intensity was gradually increased for 30 s, tapered off over 30 s, and maintained for 30 min without stimulation. Each stimulation was performed for three consecutive sessions with an interval of 1 h between them. The primary outcome was spectral electroencephalography (EEG) analysis based on the Power Spectral Density (PSD) determined by EEG records of areas F3, F4, C3, C4, P3, and P4. Brain Vision Analyzer software processed the signals, EEG power spectral density (PSD) was calculated before and after stimulation, and alpha, beta, delta, and theta power were analyzed. The secondary outcomes included hemodynamic variables based on the difference between baseline (D0) and post-intervention session (D1) values of systolic (SBP) and diastolic (DBP) blood pressure, heart rate (HR), respiratory rate (RR) and peripheral oxygen saturation (SPO2). Mann-Whitney test was used to compare position measurements of two independent samples; Fisher's exact test was used to compare two proportions; paired Wilcoxon signed-rank test was used to compare the median differences in the within-group comparison, and Spearman correlations matrix among spectral power analysis between EEG bands was performed to verify consistency of occurrence of oscillations. Statistical significance was set at P < 0.05. ResultsAn increase in PSD in the alpha frequency in the P4 region was observed after the intervention in the A-tDCS group, as compared to the placebo group (before = 6.13; after = 10.45; p < 0.05). In the beta frequency, an increase in PSD was observed in P4 (before = 4.40; after = 6.79; p < 0.05) and C4 (before = 4.43; after = 6.94; p < 0.05) after intervention in the A-tDCS group. There was a reduction in PSD at delta frequency in C3 (before = 293.8; after = 58.6; p < 0.05) after intervention in the A-tDCS group. In addition, it was observed a strong relationship between alpha and theta power in the A-tDCS group before and after intervention. However, the sham group showed correlations between more power bands (alpha and theta, alpha and delta, and delta and theta) after intervention. There was no difference in hemodynamic variables between the intra- (before and after stimulation) and inter-groups (mean difference). ConclusionAnodal tDCS over the ipsilesional motor cortex had significant effects on the brain electrical activity in the early subacute stroke phase, increasing alpha and beta wave activities in sensorimotor regions while reducing slow delta wave activity in motor regions. These findings highlight the potential of anodal tDCS as a therapeutic intervention in the early stroke phase.

SAR Image Generation Method Using DH-GAN for Automatic Target Recognition.

In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise. To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images. DH-GAN produces images that emulate the high-frequency characteristics of real SAR images. Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach. The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced.

Alpha-2a adrenergic receptor activation in genetic absence epilepsy: An absence status model?

The objective of the study was to propose a candidate animal model of absence status epilepticus induced by specific alpha-2a adrenergic receptor (α2AR) activation. We also aim to investigate the responsiveness of this model to classical anti-status or anti-absence medications. An α2AR agonist, dexmedetomidine (DEX), was injected intracerebroventricularly into adult rats with genetic absence epilepsy, and their electroencephalography (EEG) was recorded. The total duration, number, and mean duration of each spike-and-wave discharges (SWDs) were evaluated. The blocks of absence status events were classified as the initial and second sets of absence statuses. Ethosuximide (ETX) was administered as a pretreatment to another group of rats and later injected with 2.5 μg DEX. In addition, ETX, valproic acid (VPA), diazepam (DIAZ), and atipamezole (ATI) were administered after induced status-like events following DEX administration. Power spectral characteristics and coherence analysis were performed on the EEG to assess the absence status events and sleep. The 2.5 μg dose of DEX increased the total SWD duration and induced continuous SWDs up to 26 min. Following the initial absence status event, sleep was induced; then, the second period of absence status-like activities were initiated. ETX pretreatment blocked the occurrence of absence status-like activities. Power spectral density analyses revealed that DEX-induced post-sleep activities had higher power in delta frequency band (1-4 Hz) and attenuated power of 7 Hz harmonics (14 and 21 Hz) than the pre-injection seizure. The mean duration of SWDs were decreased in all the groups, but occasional prolonged activities were seen in ETX or VPA-injected rats but not with DIAZ or ATI. This study presents an absence status epilepticus animal model that is activated by α2AR activation to investigate the pathophysiological role of absence status. Unlike other agents ATI switched off the second set of absence statuses to normal SWDs, without sedation or lethargy, can show it may preferentially block absence status-like activity. This study proposes a rat model for prolonged seizures, resembling absence status epilepticus. Activating the brain's alpha-2a adrenergic receptor with dexmedetomidine induced seizures lasting up to 26 minutes. Ethosuximide pretreatment and post-treatment with valproic acid, diazepam, and atipamezole decreased induced seizures. The findings suggest this model is valuable for studying absence status epilepticus. In addition, atipamezole normalized abnormal seizures without sedation, hinting at its potential for targeted treatment and further research.

Power spectral density analysis to investigate morphology of Ni films

Ni thin films grown by thermal evaporation and sputtering under different deposition conditions are characterised by structural and morphological properties using X-ray diffraction (XRD) and atomic force microscopy (AFM) techniques. XRD results suggested the growth of polycrystalline face-centred cubic Ni phase for all the samples. Morphological characteristics of the films were compared by analysing AFM data for root mean square roughness, height–height correlation function and power spectral density (PSD) measurements. Applying fractal and k-correlation fitting models to the PSD data, different morphological parameters are quantified. The study suggested that Ni films grown at higher substrate temperature (∼150 °C) by thermal evaporation and at low Ar pressure (∼0.2 Pa) by sputtering techniques yielded films of small surface roughness with a Brownian fractal self-affine surface.

Edge roughness analysis in nanoscale for single-molecule localization microscopy images

Abstract The recent advances in super-resolution fluorescence microscopy, including single-molecule localization microscopy (SMLM), has enabled the study of previously inaccessible details, such as the organization of proteins within cellular compartments and even nanostructures in nonbiological nanomaterials, such as the polymers and semiconductors. With such developments, the need for the development of various computational nanostructure analysis methods for SMLM images is also increasing; however, this has been limited to protein cluster analysis. In this study, we developed an edge structure analysis method for pointillistic SMLM images based on the line edge roughness and power spectral density analyses. By investigating the effect of point properties in SMLM images, such as the size, density, and localization precision on the roughness measurement, we successfully demonstrated this analysis method for experimental SMLM images of actual samples, including the semiconductor line patterns, cytoskeletal elements, and cell membranes. This systematic investigation of the effect of each localization rendering parameter on edge roughness measurement provides a range for the optimal rendering parameters that preserve the relevant nanoscale structure of interest. These new methods are expected to expand our understanding of the targets by providing valuable insights into edge nanoscale structures that have not been previously obtained quantitatively.

Статистика времен возвращения при анализе хаоса в динамических системах

We propose a method of delimiting the existence domains of periodic, quasi-periodic and chaotic solutions of dynamical systems in the parameter space. The method is based on an analysis of the sampling of the times of passing a phase point from the previous intersection of the Poincaré plane to the next one (or from one local maximum to the next). An algorithm for generating a sample of recurrence times with subsequent analysis of the histogram of the obtained sample is constructed. A simple measure of histogram filling allows us to separate periodic and chaotic modes, as well as to estimate the degree of chaoticity of intermediate modes. On simple model signals it is shown that the distribution of recurrence times gives information not contained in the spectral densities of the signal. Then, on the example of the classical Lorentz system, it is shown how a simple measure of filling the histogram of recurrence times allows us to obtain a visual map of modes. The paper presents the results of a comparative analysis of power spectral density and histograms of recurrence times for different modes realized in the Lorentz system at different values of the control parameter (Rayleigh number).

ВИБІР НЕЙРОМЕРЕЖЕВОГО КЛАСИФІКАТОРА ДЛЯ ВИРІШЕННЯ ЗАВДАННЯ АНАЛІЗУ СИГНАЛІВ РЕБ ТА ШТУЧНИХ ЗАВАД

At present, the task of ensuring high-quality control of unmanned aerial vehicles and the transmission of reliable information in a complex interference environment is relevant. This task becomes especially important when intentional interference is used in the form of countermeasures. Various methods are used to detect and localize powerful sources of parasitic signals, including: time domain methods: signal spectrum analysis, correlation function analysis, phase shift analysis; frequency domain methods: signal amplitude response analysis, signal phase response analysis, power spectral density analysis; spatial domain methods: antenna directivity analysis, signal strength analysis at different points in space. Analyzing signals affected by EW and artificial interference is a challenging task. Traditional signal analysis methods are often ineffective for this task because they cannot take into account the complex characteristics of EW and artificial interference. Neural network classifiers are a promising tool for analyzing signals with EW and artificial interference. Neural networks can learn from large datasets and detect complex patterns that cannot be detected by traditional methods. This article describes the process of selecting a neural network classifier for analyzing EW and artificial interference signals. Various factors that should be considered when choosing a neural network architecture, training algorithm, and network parameters are discussed). The paper presents an analytical review of neural network classifiers, their varieties and features, and selects the optimal one for solving the task.

Transmission of slow waves in Masimo O3 near infrared spectroscopy measures

IntroductionCerebral autoregulation (CA) dysfunction is a key complication following brain injury. CA assessment using near-infrared spectroscopy (NIRS) offers a promising alternative to the current non-invasive standard, cerebral blood flow velocity (CBFV) measured with transcranial Doppler. Research questionCan autoregulatory slow waves (frequency range 0.005–0.05 Hz) associated with spontaneous and induced changes in ABP in healthy volunteers be detected by parameters measured with the Masimo O3 NIRS device? MethodsABP, CBFV and Masimo O3 parameters were measured in 10 healthy volunteers at baseline and during ABP oscillations induced by squat/stand manoeuvres. Transmission of slow waves was assessed with power spectral density and coherence analysis in NIRS signals and compared to that of CBFV. ResultsAt baseline, slow waves were detected with sufficient power that substantially exceeded the signals’ measurement resolution in all parameters except cerebral oxygen saturation. During ABP oscillations in the 0.033 Hz range (induced by squat/stand), the power of slow waves increased in all parameters in a similar pattern, with total (cHb) and oxygenated (O2Hb) haemoglobin concentrations most closely mirroring CBFV (median standardised power [first-third quartile], baseline vs squat/stand: CBFV 0.35 [0.28–0.42] vs 0.50 [0.45–0.62], O2Hb 0.47 [0.33–0.68] vs 0.61 [0.59–0.69]). Coherence with ABP increased for both CBFV and NIRS measures from low at baseline (<0.4) to high during induced changes (>0.8). ConclusionSpontaneous fluctuations in ABP can be observed in analysed Masimo O3 metrics to a varying degree. The clinical utility of Masimo O3 signals in CA assessment requires further investigation in brain injury patients.

On-machine frequency analysis of diamond turned surfaces with surface intrinsic mode decomposition

Evaluating surface frequency components in the fabrication process is critical for controlling the machined surface quality. The presence of anisotropic ripples on diamond-turned surfaces makes this challenging. A multiscale frequency evaluation method, referred to as Surface Intrinsic Mode Decomposition (SIMD), is proposed for evaluating on-machine surface measurement (OMSM) data. It decomposes continuous surface probing profiles, incorporating both temporal and spatial frequency information. In comparison to the conventional power spectral density (PSD) analysis method, the approach enriches frequency details over a wider range, which contributes to a more comprehensive understanding of surface quality and helps to identify mid-spatial frequency (MSF) errors.

A Versatile Calibration Method for Rotary-Wing UAS as Wind Measurement Systems

Abstract The use of small uncrewed aircraft systems (UAS) can effectively capture the wind profile in the lower atmospheric boundary layer. This study presents a calibration process to estimate the horizontal wind vector using a rotary-wing UAS in hovering conditions. This procedure does not require wind tunnels or meteorological masts, only the data from the flight control unit and a specific set of calibration flights. A model based on the UAS drag coefficient was proposed and compared to a traditional approach. Validation flights at the German Weather Service MOL-RAO observatory showed that the system can accurately predict wind speed and direction. A modified DJI S900 hexacopter with a Styrofoam sphere casing was used for the study and calibrated for wind speeds between 1 and 14 m s−1. Power spectral density analysis showed the system’s ability to resolve atmospheric eddies up to 0.1 Hz. The overall root-mean-square error was less than 0.7 m s−1 for wind speed and less than 8° for wind direction.