Focus variation technology as a tool for tissue surface characterization

Objective. This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. Approach. Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used. Both analyses were applied to computer simulations of surface EMG baseline with the presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. Main results. Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. Significance. The findings implied the presence of a hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.

The variation of device characteristics is a challenge to present and future large-scale integrations. One of the origins of the variation is line width roughness (LWR). To facilitate the efforts to cope with LWR, we developed a method to accurately characterize LWR basing on the analysis of power spectral densities (PSDs). Because experimental PSDs are intrinsically discrete, we derive simple analytic formulas of the discrete PSDs by assuming that the autocorrelation function (ACF) exponentially decays with distance. The PSDs calculated by using the formulas agree excellently with experimentally obtained PSDs of photoresist LWR. From the result we find that the photoresist LWR of this study has a standard deviation of 2.5 nm and an exponentially-decaying autocorrelation function with a correlation length of 35 nm. Although the experimental PSDs inevitably contain a component produced by scanning-electron-microscope (SEM)-image noise, the two components of LWR and noise are separately determined by the method of this study. Due to this feature, the resultant variance of LWR is independent of the noise intensity. However, it is still important to reduce the noise component in order to maintain the accuracy of analysis especially in the case of LWR that has an unknown functional form of PSD. This is because even the PSDs that have different functional forms sometimes look alike in the presence of large noise. To reduce the noise effect, it is effective to average the SEM images perpendicularly to fine lines before edge detections. The procedure does not reduce the variance unlike averaging along the lines. The method of this study is applicable not only to LWR but also to other cases as far as the ACF exponentially decays with distance, or equivalently the spectral line shape is Lorentzian. Accordingly, it forms the basis for spectral analysis of most experimental results.

In the first half of the 90s, computers of adequate speed of operation and processing softwares became increasingly available, made it possible to realize 3D surface characterisation. In the literature different directives can be found none of them has become widely used. Beside the extension of 2D parameter based technique to 3D many other methods have been developed. One is the power spectral density (PSD) technique when “global” surface characterisation is carried out using complex mathematical tools. Nowadays one of the most important tasks in tribology to design the surfaces optimised to the operation. In the present study topographical measurements and PSD characterizations were performed to study the engineering surfaces produced by turning and to analyse the effectiveness of 2D and 3D PSD.

We investigate a new metric power spectral density (PSD),for characterizing the performance of seeing-limited large telescope such as thirty meter telescope(TMT ). As the PSD is directly related to the performance of the atmosphere which plays an important role in ground based facilities, it represents the efficiency lose due to mid and high-spatial frequency components in observing time. The metric also properly counts for the optic error of the mirror itself such as the deviations from a perfect surface, and metrology measurement errors .The metric can multiply all the errors which differentiates from the traditional ones, such as RMS. We also numerically confirm this feature for Karman model atmosphere error multiplied with the sample of our vendor and the TMT M3.Additonaly, we discuss other pertinent feature of the PSD, including its relationship to Zernike aberration ,and RMS of wave front errors.

With development of advanced lithography processes, simultaneous reduction of line width and edge roughness along with the shrinkage of critical dimension of main feature is a continuous challenge. Recent years, unbiased roughness characterization was introduced in pattern roughness analysis by applying power spectral density method. Through power spectral density analysis, the data of all measurement points from scanning electron microscope are processed by Fourier transform, and the roughness behaviors of inspected pattern are converted from spatial domain to frequency domain. Autocorrelation analysis is an effective means to identify the periodic behavior of line width or edge roughness. In our research, roughness of dense lines under different lithography conditions, including reflectivity of bottom anti-reflection coating materials, photoresist, illumination and post exposure bake temperature, was characterized using power spectral density, autocorrelation methods as well as with standard deviation.

In order to reveal the unsteady features of gas–solid flow, the pressure fluctuations were measured at different locations along the length of the pipeline while conveying powders through the pipeline. Power spectral density (PSD) functions were obtained for the analysis of the pressure fluctuation. Two types of powders (fly ash and alumina) were used in this analysis. The PSD analysis was conducted by taking into account different aspects such as flow conditions (dilute or dense), location of transmitter (top and bottom transmitters), location of transmitter along the length of the pipeline (three different locations), material property (fly ash or alumina), etc. Analysis of signals from top and bottom transmitters shows that it is not possible to identify the flow mode at upper and lower portions of pipeline. The magnitude of power is found to be higher for alumina as compared to fly ash. PSD parametric analysis reveals that frequency bandwidth and average power decreases exponentially with increase in solid loading ratio.

In this study, we show that the correct determination of surface morphology using scanning force microscopy (SFM) imaging and power spectral density (PSD) analysis of the surface roughness is an extremely demanding task that is easily affected by experimental parameters such as scan speed and feedback parameters. We present examples were the measured topography data is significantly influenced by the feedback response of the SFM system and the PSD curves calculated from this experimental data do not correspond to that of the true topography. Instead, either features are "lost" due to low pass filtering or features are "created" due to oscillation of the feedback loop. In order to overcome these serious problems we show that the interaction signal (error signal) can be used not only to quantitatively control but also to significantly improve the quality of the topography raw data used for the PSD analysis. In particular, the calibrated error signal image can be used in combination with the topography image in order to obtain a correct representation of surface morphology ("true" topographic image). From this "true" topographic image a faithful determination of the PSD of surface morphology is possible. The corresponding PSD curve is not affected by the fine-tuning of feedback parameters, and allows for much faster image acquisition speeds without loss of information in the PSD curve.

This paper mainly focuses on the comparative analysis of Power Spectral Density (PSD) and harmonics between Boost Two-Level Inverter (BTLI) and Multi String Hybrid Capacitor Clamped Multilevel Converter (MSHCCMLC) configurations. Compared with conventional BTLI, the proposed system employs the reduction of harmonics and PSD. Maximum utilization of solar power is achieved through Maximum Power Point Tracking (MPPT) control mechanism. Modified Sinusoidal Pulse Width Modulation (MSPWM) scheme is adopted to attain better quality of output power. The proposed MSHCCMLC requires small LC filter component at the inverter end which makes the system more effective. Simulation and hardware models of 5-level Hybrid Multilevel Converter (HMLC) are developed and the system performances are validated.

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

This paper studies the use of power spectral density (PSD) and related random data analysis techniques in the test laboratory to characterize low frequency loading spectra output response, measured from a specimen, for the purpose of comparing test machine responses. In recent years digital techniques have been developed that simulate aircraft maneuver spectrum loading in the time domain. In this study one of these techniques is used to generate the input load spectrum signal to an electrohydraulic closed-loop test machine for fatigue testing of graphite/epoxy to titanium step lap bonded joints at various frequencies and PSD shapes. The output signal was measured directly from the specimen by recording on tape the dynamic strain gage signal. This signal then was processed to determine the PSD response and root mean square (RMS) time history. These data were compared with the recorded strain amplitude time history and the input load amplitude, frequency, and PSD information. A qualitative correlation was found between variations in specimen fatigue life, output signal waveform and amplitude, and variations in PSD. PSD is a measure of loading energy in the time domain and can be influenced both by load amplitude and waveform variations. Thus, the way a fatigue test machine processes an input load and frequency signal can affect the measured fatigue life of certain materials.

Nowadays power spectral density (PSD) analysis is one of the leading characterisation techniques of surface topography. Fractal dimension obtained from PSD plays a significant role in recent friction, adhesion and wear models.

ABSTRACTThis paper presents results from an investigation into the changes in flow mechanism of pneumatic conveying of fine powders from dilute to fluidized dense-phase. Pressure fluctuations have been analyzed using power spectral density (PSD), Hilbert–Huang transformation (HHT), and wavelet transformation. Pressure fluctuations were obtained from conveying trials performed for two different types of powders, i.e., fly ash (median particle diameter: 30 µm; particle density: 2300 kg m−3; loose-poured bulk density: 700 kg m−3) and white powder (median particle diameter: 55 µm; particle density: 1600 kg m−3; loose-poured bulk density: 620 kg m−3). Power spectra obtained from PSD analysis, energy distribution of intrinsic mode functions obtained from HHT transformation, and wavelet scalogram show unique features related to the changing flow mechanism along the length of the pipeline. Results indicate variation from low frequency to higher frequency components in the signal, along the flow direction. Higher frequency components and wide range of frequencies in the signal obtained at pipeline exit might have resulted from increased level of interactions among solid particles, carrier gas, and pipe wall as compared to the power spectra with single dominant frequency (which corresponds to probable periodic dune formation) at pipeline entry region.

Influence of coherent structures on mass transfer near stagnation region in agitated small tanks

Distributed acoustic sensing (DAS) systems utilize optical fibers as the sensing medium, offering long-distance and wide-range real-time monitoring capabilities. They are widely applied in fields such as seismic monitoring, pipeline leak detection, and railway safety monitoring. To address the challenge of traditional Power Spectral Density (PSD) analysis methods in accurately identifying effective signal frequencies under high-frequency noise interference, this paper proposes a novel spectral feature extraction method that combines variational mode decomposition (VMD) and modal power spectral density (PSD). This approach first employs VMD to decompose vibration signals, effectively filtering out noise and extracting valid signals. Subsequently, PSD is used to extract the spectral characteristics of the sub-modes, and Gaussian functions are applied to fit these spectral curves, forming a feature descriptor matrix that characterizes the vibration source. By compressing the original vibration data into this compact feature matrix, the method aims to replace the original data with the feature matrix for deep learning, enabling intelligent recognition and diagnosis of vibration signal states through training and classification. Using vibration data from coal conveyor rollers at Qinhuangdao Port as an example, the effectiveness of this method in extracting bearing spectral features has been validated. The research findings indicate that this method is suitable for vibration load response evaluation and provides a novel technical tool for intelligent monitoring in DAS systems.

Scale-dependent surface roughness strongly affects critical surface properties of materials, including adhesion, wettability, and optical/thermal properties. As a particular example, tuning the ratio of the true to nominal area—a parameter that depends on the root mean square (RMS) local slope of the finest scales of topography—is an effective approach to tailor the wetting characteristics of solid surfaces. While power spectral density (PSD) analysis of atomic force microscopy (AFM) topographic images allows for directly assessing the scale-dependence of surface roughness, this approach to analyze AFM height maps requires power-law modeling and extrapolation of a PSD with inherently non-normal error distributions. Here, we use a Monte Carlo approach based on synthetic AFM images of known input power-law parameters to (1) evaluate the accuracy of fitting techniques based on the expected distribution of the PSD; (2) evaluate the error propagation from the standard errors of the fitted power-law parameters to the computed RMS slope and area ratio; and (3) evaluate the statistical power of various PSD regression techniques when differentiating surfaces. The results indicated that standard error for ordinary least squares on a log-log PSD (log OLS) underpredicts the observed variance by ∼50%. This underprediction can be eliminated by implementing a log-link gamma regression. Moreover, when propagating the standard error to derived parameters (e.g., the RMS slope), the propagated error is generally conservative relative to the observed variance and closely predicts the observed variance when extrapolating to the finest scale. This result demonstrates the possibility of accurately estimating roughness parameters that are critical for evaluating surface phenomena on the basis of fitting and extrapolating AFM data using self-affine models. Finally, our results provided evidence for the possibility of statistically differentiating surfaces with similar power-law parameters when using weighted gamma regression with a mean of 10 images, as opposed to unweighted log-OLS that requires as many as 10 000 images to differentiate images.