A semi-automated spectral approach to analyzing cyclical growth patterns using fish scales

We introduce a new semi-automated approach to analyzing growth patterns recorded on fish scales. After manually specifying the center of the scale, the algorithm radially unwraps the scale patterns along a series of transects from the center to the edge of the scale. A sliding window Fourier transform is used to produce a spectrogram for each sampled transect of the scale image. The maximum frequency over all sampled transects of the average spectrogram yields a well-discriminated peak frequency trace that can then serve as a growth template for that fish. The spectrogram patterns of individual fish scales can be adjusted to a common period accounting for differences in date of return or size of fish at return without biasing the growth profile of the scale. We apply the method to 147 Atlantic salmon scale images sampled from 3 years and contrast the information derived with this automated approach to what is obtained using classical human operator measurements. The spectrogram analysis quantifies growth patterns using the entire scale image rather than just a single transect and provides the possibility of more robustly analyzing individual scale growth patterns. This semi-automated approach that removes essentially all the human operator interventions provides an opportunity to process large datasets of fish scale images and combined with advanced analyses such as deep learning methods could lead to a greater understanding of salmon marine migration patterns and responses to variations in ecosystem conditions.

MMV method: a new approach to compare protein sequences under binary representation

In the present work, a new form of descriptor using minimal moment vector (MMV) is introduced to compare protein sequences in the frequency domain under their component wise binary representations. From every sequence, 20 different binary component sequences are formed, each corresponding to 20 amino acids. Each such vector is now shifted from the time domain to the frequency domain by applying the Fast Fourier Transform (FFT). Next, the power spectrum calculated from the FFT values for each component sequence is so normalized that the sum of the components equals 1. The descriptor is defined as a 20-component vector composed of the 20 second-order minimal moments calculated from the normalized spectrum of the 20 component sequences. Once the descriptor is known, the distance matrix is created by applying the Euclidean Distance measure. The phylogenetic tree is generated by applying the unweighted pair group method with the arithmetic mean (UPGMA) algorithm using Molecular Evolutionary Genetics Analysis11 (MEGA11) software. In this work, the datasets used for similarity studies are 9 NADH dehydrogenase 5 (ND5), 12 Baculoviruses, 24 Transferrins (TF) proteins, and 50 Spike Protein of coronavirus. A qualitative measure using rationalized perception is used to compare the effectiveness of the proposed method. Quantitative measure based on symmetric distance (SD) is used to compare the phylogenetic trees of the present method with those obtained by other methods. It is observed that the phylogenetic trees generated by the proposed technique are at par with their known biological references, and they produce results better than those of the earlier methods.Communicated by Ramaswamy H. Sarma.

FFT-Based Simultaneous Calculations of Very Long Signal Multi-Resolution Spectra for Ultra-Wideband Digital Radio Frequency Receiver and Other Digital Sensor Applications

The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor's bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2P-j for j=0, 1, 2, …, J, where j is defined as the spectrum level with frequency resolution 2j·Δf. Here Δf is the frequency resolution at j=0. However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P; e.g., P≥20. The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths 2P-j for all J+1 levels, concurrently, while one long 2P-length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2P-length FFT, so there is no need to perform additional calculations in order to obtain them.

A Short-Term Time-Series Data Analysis Algorithm for Flavor Release during the Start of Eating

"Retronasal aroma" refers to the aroma released from food during consumption and traveling through the nose after leaving the mouth. It is closely related to the behavior of odor compounds released from food into the mouth and plays a crucial role in our overall perception of flavor. As a result, research focusing on measuring the behavior of retronasal aroma has gained attention for exploring the relationship between sensory perception and flavor. We attempted to develop a data analysis method that specifically targets a time span of a few seconds to tens of seconds, starting from when food is placed in the mouth during eating and extending to just after swallowing. In this study, we observed a strong correlation between the periodic waveform data derived from performing the third derivative (jerk) on the detection intensity data obtained using a mass spectrometer and the behavior of the detection intensity. Furthermore, by performing a frequency analysis using a fast Fourier transform on the jerk data, it was possible to extract the frequencies that contribute to sensory perception during eating. Furthermore, the reconstructed jerk data derived from the extracted data using the inverse fast Fourier transform provided a clearer explanation of sensory perception during eating. Our algorithm suggests new short-term time-series data applications.

Quantification of Slip Band Distribution in Polycrystals: An Automated Fast Fourier Transform Decomposition Approach

Plastic deformation is accumulated in slip bands in a wide variety of engineering alloys. Multiple material and loading conditions impact their distribution and degree of slip localization, but these effects are rarely quantified. To tackle this, the current work introduces a fast Fourier transform (FFT) decomposition method and applies it to a tensile-loaded polycrystalline nickel-based superalloy imaged via high-resolution digital image correlation and electron backscatter diffraction. This approach identifies active slip planes over the FFT images of individual grains and performs inverse transforms such that slip band traces with shared orientations are isolated. This technique enabled the largest quantification of slip band spacings and in-plane strains to date, with a total of 6,557 slip bands detected. The results show that the slip band spacings increase with grain size, with no evident dependence on grain orientation and Schmid factor. Slip bands are found to develop similar spacings along different octahedral planes and continue to spread over larger regions of the grain as the resolved shear stress of the active slip system increases. The FFT decomposition technique, which could be employed with multiple microscopy techniques, will allow for much-needed large-scale quantitative studies of slip localization.

Searching for Alternatives to the Savitzky-Golay Filter in the Spectral Processing Domain

An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist" in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.

Intermediate Deformable Image Registration via Windowed Cross-Correlation

In population and longitudinal imaging studies that employ deformable image registration, more accurate results can be achieved by initializing deformable registration with the results of affine registration where global misalignments have been considerably reduced. Such affine registration, however, is limited to linear transformations and it cannot account for large nonlinear anatomical variations, such as those between pre- and post-operative images or across different subject anatomies. In this work, we introduce a new intermediate deformable image registration (IDIR) technique that recovers large deformations via windowed cross-correlation, and provide an efficient implementation based on the fast Fourier transform. We evaluate our method on 2D X-ray and 3D magnetic resonance images, demonstrating its ability to align substantial nonlinear anatomical variations within a few iterations.

Voltage-Modulated van der Waals Interaction in Single-Molecule Junctions

Understanding how molecular geometry affects the electronic properties of single-molecule junctions experimentally has been challenging. Typically, metal-molecule-metal junctions are measured using a break-junction method where electrode separation is mechanically evolving during measurement. Here, to probe the impact of the junction geometry on conductance, we apply a sinusoidal modulation to the molecular junction electrode position. Simultaneously, we probe the nonlinearity of the current-voltage characteristics of each junction through a modulation in the applied bias at a different frequency. In turn, we show that junctions formed with molecules that have different molecule-electrode interfaces exhibit statistically distinguishable Fourier-transformed conductances. In particular, we find a marked bias dependence for the modulation of junctions where transmission is mediated thorough the van der Waals (vdW) interaction. We attribute our findings to voltage-modulated vdW interactions at the single-molecule level.

Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study

We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.

Changing features of the Northern Hemisphere 500-hPa circumpolar vortex

The tropospheric circumpolar vortex (CPV), an important signature of processes steering the general atmospheric circulation, surrounds each pole and is linked to the surface weather conditions. The CPV can be characterized by its area and circularity ratio (R _c ), which both vary temporally. This research advances previous work identifying the daily 500-hPa Northern Hemispheric CPV (NHCPV) area, R _c , and temporal trends in its centroid by examining linear trends and periodic cycles in NHCPV area and R _c (1979-2017). Results suggest that NHCPV area has increased linearly over time. However, a more representative signal of the planetary warming may be the temporally weakening gradient which has blurred NHCPV distinctiveness-perhaps a new indicator of Arctic amplification. R _c displays opposing trends in subperiods and an insignificant overall trend. Distinct annual and semiannual cycles exist for area and R _c over all subperiods. These features of NHCPV change over time may impact surface weather/climate.

Higher reliability and validity of Wavelet-ALFF of resting-state fMRI: From multicenter database and application to rTMS modulation

Amplitude of low-frequency fluctuation (ALFF) has been widely used for localization of abnormal activity at the single-voxel level in resting-state fMRI (RS-fMRI) studies. However, previous ALFF studies were based on fast Fourier transform (FFT-ALFF). Our recent study found that ALFF based on wavelet transform (Wavelet-ALFF) showed better sensitivity and reproducibility than FFT-ALFF. The current study aimed to test the reliability and validity of Wavelet-ALFF, and apply Wavelet-ALFF to investigate the modulation effect of repetitive transcranial magnetic stimulation (rTMS). The reliability and validity were assessed on multicenter RS-fMRI datasets under eyes closed (EC) and eyes open (EO) conditions (248 healthy participants in total). We then detected the sensitivity of Wavelet-ALFF using a rTMS modulation dataset (24 healthy participants). For each dataset, Wavelet-ALFF based on five mother wavelets (i.e., db2, bior4.4, morl, meyr and sym3) and FFT-ALFF were calculated in the conventional band and five frequency sub-bands. The results showed that the reliability of both inter-scanner and intra-scanner was higher with Wavelet-ALFF than with FFT-ALFF across multiple frequency bands, especially db2-ALFF in the higher frequency band slow-2 (0.1992-0.25 Hz). In terms of validity, the multicenter ECEO datasets showed that the effect sizes of Wavelet-ALFF with all mother wavelets (especially for db2-ALFF) were larger than those of FFT-ALFF across multiple frequency bands. Furthermore, Wavelet-ALFF detected a larger modulation effect than FFT-ALFF. Collectively, Wavelet db2-ALFF showed the best reliability and validity, suggesting that db2-ALFF may offer a powerful metric for inspecting regional spontaneous brain activities in future studies.

Acoustic Emission Source Characterisation during Fatigue Crack Growth in Al 2024-T3 Specimens

While acoustic emission (AE) testing can be used as a valuable technique in structural health monitoring and non-destructive testing, little research has been conducted to establish its sources, particularly in 2024-T3 aluminium alloys. The major contribution of this work is that it provides a method to obtain a better linear relationship of count rate with crack growth rate based on waveform. This paper aims to characterise AE sources by synchronising the AE waveforms with load levels and then to propose possible dominant frequency ranges. The AE waveforms during fatigue crack growth in edge-notched 2024-T3 aluminium specimens, from an initial crack length of 10 mm to 70 mm, were collected at two different load ratios R = 0.125 and 0.5. At the same time, the crack growth rate was determined using thermal imaging and associated control software. The AE waveforms obtained were processed using the fast Fourier transform. It was shown that a significantly higher AE count rate was recorded at R = 0.125 compared to R = 0.5 when the maximum load was kept the same. This means that the R-ratio would affect the total amount of AE activities collected. It was also found that the dominant frequency range of the AE waveforms directly related to crack growth was 152-487 kHz, and the ranges due to crack closure were likely to be 310 kHz-316 kHz and 500-700 kHz. Based on the proposed frequency ranges, waveform selection was conducted and a better linear relationship between count rate and crack growth rate was observed. This study provides a better understanding of the AE sources and waveforms for future structural health monitoring applications.

Analysis and visualization methods for detecting functional activation using laser speckle contrast imaging

BACKGROUND

Previous studies have used regional cerebral blood flow (CBF) hemodynamic response to measure brain activities. In this work, we use a laser speckle contrast imaging (LSCI) apparatus to sample the CBF activation in somatosensory cortex (S1BF) with repetitive whisker stimulation. Traditionally, the CBF activations were processed by depicting the change percentage above baseline; however, it is not clear how different methods influence the detection of activations.

AIMS

Thus, in this work we investigate the influence of different methods to detect activations in LSCI.

MATERIALS & METHODS

First, principal component analysis (PCA) was performed to denoise the CBF signal. As the signal of the first principal component (PC1) showed the highest correlation with the S1BF CBF response curve, PC1 was used in the subsequent analyses. Then, we used fast Fourier transform (FFT) to evaluate the frequency properties of the LSCI images and the activation map was generated based on the amplitude of the central frequency. Furthermore, Pearson's correlation coefficient (C-C) analysis and a general linear model (GLM) were performed to estimate the S1BF activation based on the time series of PC1.

RESULTS

We found that GLM performed better in identifying activation than C-C. Additionally, the activation maps generated by FFT were similar to those obtained by GLM. Particularly, the superficial vein and arterial vessels separated the activation region as segmented activated areas, and the regions with unresolved vessels showed a common activation for whisker stimulation.

DISCUSSION AND CONCLUSION

Our research analyzed the extent to which PCA can extract meaningful information from the signal and we compared the performance for detecting brain functional activation between different methods that rely on LSCI. This can be used as a reference for LSCI researchers on choosing the best method to estimate brain activation.

An Interleaved Segmented Spectrum Analysis: A Measurement Technique for System Frequency Response and Fault Detection

A frequency spectrum segmentation methodology is proposed to extract the frequency response of circuits and systems with high resolution and low distortion over a wide frequency range. A high resolution is achieved by implementing a modified Dirichlet function (MDF) configured for multi-tone excitation signals. Low distortion is attained by limiting or avoiding spectral leakage and interference into the frequency spectrum of interest. The use of a window function allowed for further reduction in distortion by suppressing system-induced oscillations that can cause severe interference while acquiring signals. This proposed segmentation methodology with the MDF generates an interleaved frequency spectrum segment that can be used to measure the frequency response of the system and can be represented in a Bode and Nyquist plot. The ability to simulate and measure the frequency response of the circuit and system without expensive network analyzers provides good stability coverage for reliable fault detection and failure avoidance. The proposed methodology is validated with both simulation and hardware.

Magnetic Field-Modulated Plasmonic Scattering of Hybrid Nanorods for FFT-Weighted OCT Imaging in NIR-II

We report a method for fast Fourier transform (FFT)-weighted optical coherence tomography (OCT) in the second biological tissue transparency window by actively modulating the plasmonic scattering of Fe₃O₄@Au hybrid nanorods using magnetic fields. Instead of tracking the nanoparticles' lateral displacement in conventional magnetomotive OCT imaging, we monitor the nanorod rotation and optical signal changes under an alternating magnetic field in real time. The coherent rotation of the nanorods with the field produces periodic OCT signals, and the FFT is then used to convert the periodic OCT signals in the time domain to a single peak in the frequency domain. This allows automatic screening of nanorod signals from the random biological noises and reconstruction of FFT-weighted images using a computer program based on a time-sequence image set. Compared with conventional magnetomotive OCT, the FFT-weighted imaging technique creates enhanced OCT images with dB-scale contrast over an order of magnitude higher than the original images.

Dynamic development of the extracellular matrix in chick cornea

Understanding the mechanisms of structural organization of corneal stroma is scientifically interesting and may lead to advances in corneal tissue engineering. In the past, we used the combination of second harmonic generation and fast Fourier transform to study a number of topics including the structural alteration of human keratoconus. Recently, we applied this approach to studying changes of chick embryonic cornea during development. As numerous studies were performed with ex vivo specimens, a thorough understanding of the parameters associated with the development process cannot be achieved until experiments are performed in living chick embryos.

Nonparametric spectral methdods for multivariate spatial and spatial-temporal data

We propose computationally efficient methods for estimating stationary multivariate spatial and spatial-temporal spectra from incomplete gridded data. The methods are iterative and rely on successive imputation of data and updating of model estimates. Imputations are done according to a periodic model on an expanded domain. The periodicity of the imputations is a key feature that reduces edge effects in the periodogram and is facilitated by efficient circulant embedding techniques. In addition, we describe efficient methods for decomposing the estimated cross spectral density function into a linear model of coregionalization plus a residual process. The methods are applied to two storm datasets, one of which is from Hurricane Florence, which struck the souteastern United States in September 2018. The application demonstrates how fitted models from different datasets can be compared, and how the methods are computationally feasible on datasets with more than 200,000 total observations.

Classification algorithm using halftone features of counterfeit bills and CNN

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non-destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment.

The Influence of Hydrophobic Blocks of PEO-Containing Copolymers on Glyceryl Monooleate Lyotropic Liquid Crystalline Nanoparticles for Drug Delivery

The investigation of properties of amphiphilic block copolymers as stabilizers for non-lamellar lyotropic liquid crystalline nanoparticles represents a fundamental issue for the formation, stability and upgraded functionality of these nanosystems. The aim of this work is to use amphiphilic block copolymers, not studied before, as stabilizers of glyceryl monooleate 1-(cis-9-octadecenoyl)-rac-glycerol (GMO) colloidal dispersions. Nanosystems were prepared with the use of poly(ethylene oxide)-b-poly(lactic acid) (PEO-b-PLA) and poly(ethylene oxide)-b-poly(5-methyl-5-ethyloxycarbonyl-1,3-dioxan-2-one) (PEO-b-PMEC) block copolymers. Different GMO:polymer molar ratios lead to formulation of nanoparticles with different size and internal organization, depending on the type of hydrophobic block. Resveratrol was loaded into the nanosystems as a model hydrophobic drug. The physicochemical and morphological characteristics of the prepared nanosystems were investigated by dynamic light scattering (DLS), cryogenic transmission electron microscopy (cryo-TEM), fast Fourier transform (FFT) analysis and X-ray diffraction (XRD). The studies allowed the description of the lyotropic liquid crystalline nanoparticles and evaluation of impact of copolymer composition on these nanosystems. The structures formed in GMO:block copolymer colloidal dispersions were compared with those discussed previously. The investigations broaden the toolbox of polymeric stabilizers for the development of this type of hybrid polymer/lipid nanostructures.

Fibromyalgia Detection Based on EEG Connectivity Patterns

Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease.

Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.

Frequency domain analysis and clinical outcomes of pulsatile and non-pulsatile blood flow energy during cardiopulmonary bypass

INTRODUCTION

The superiority of pulsatile perfusion during cardiopulmonary bypass remains controversial. We analyzed the frequency-domain characteristics and organ protection of pulsatile and nonpulsatile flow in adult patients with valvular disease.

METHODS

EEP and SHE were used to calculate blood flow energy in 60 patients. The Fast Fourier Transform was employed to analyze the power spectral density and power density ratio (Rvpd) of flow energy. Changes in endothelin-1, nitric oxide, interleukin-6,10, tumor necrotic factor, S100β, NSE, blood and urinary β2-microglobulin levels were investigated to assess the endothelial function, inflammatory reaction, kidney and brain injury during CPB.

RESULTS

EEP and SHE in PP group at each time point were 1.52-1.62 times and 2.03-2.22 times higher respectively compared with NP group. Power spectral density analysis demonstrated that the blood flow energy frequencies in each group were all within 40 Hz and the low frequency energy (0-5 Hz) was dominant in physiological perfusion (>90%). The energy ratio of 0-5 Hz at radial artery was significantly decreased compared with that of post arterial filter in PP (81% vs 64%) and NP (63% vs 37%) group. The power density ratio (Rvpd) was higher than that of NP in all frequency ranges at the radial artery (9.51 vs 4.68 vs 3.59) and arterial filter (3.87 vs 2.69 vs 2.38). The S100β, NSE Urinary and plasma β2-microglobulin level were significantly increased at 6 and 24 hours after surgery in two group, and significantly higher in group NP.

CONCLUSION

PP provided more energy than NP. The proportion of low frequency energy in the pulsatile or nonpulsatile flow is significantly reduced. The low-frequency energy is significantly attenuated during conduction to peripheral tissues in nonpulsatile flow. The surplus pulsatile energy influences the secretion of endothelial and inflammatory factors, and demonstrate better cerebral and kidney protective effect at the biological marker level.

Postural and clinical outcomes of sustained natural apophyseal glides treatment in cervicogenic dizziness patients: A randomised controlled trial

OBJECTIVE

To evaluate how self-report and posturographic measures could be affected in patients with cervicogenic dizziness undergoing sustained natural apophyseal glides.

DESIGN

Randomised controlled single-blind study.

SETTING

Tertiary rehabilitation centre.

SUBJECTS

Patients affected by cervicogenic dizziness, diagnosed by applying accepted criteria. Forty-one patients (19 male, 22 female: mean age 44.3 ± 14.8 years) receiving treatment, and 39 patients (18 male, 21 female: mean age 43.8 ± 13.9 years) receiving placebo were included in the study.

INTERVENTIONS

The treatment group underwent sustained natural apophyseal glides, while the placebo was constituted by a detuned laser. Both groups received their interventions six times over 4 weeks.

MAIN MEASURES

Outcomes were tested by means of self-report measures such as perceived dizziness, neck disability, anxiety and depression. Also, cervical range of motion and posturography testing with power spectra frequency were analysed.

RESULTS

When compared to placebo, treated patients demonstrated a significant decrease in perceived dizziness (post-treatment total Dizziness Handicap Inventory score 20.5 ± 5.3 as compared to 26.2 ± 6 baseline), neck disability and pain (Neck Disability Index and Neck Pain Index post-treatment scores 12.5 ± 4.3 and 45.6 ± 15.1, respectively, as compared to baseline scores of 15.1 ± 4.8 and 62.5 ± 14.3), as well as significant improvement in cervical range of motion and some posturographic parameters.

CONCLUSION

Sustained natural apophyseal glides may represent a useful intervention in reaching short-term beneficial effects in patients with cervicogenic dizziness, with respect to self-perceived symptoms, proprioceptive integration and cervical range of motion improvement.

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait—the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel’s brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland–Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r² = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r² = 0.98). Bland–Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r² = 0.00038; 0.21 ± 0.96, p = 0.87, r² = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.

Frequency Spectra Analysis of Drawbar Pulls Generated by Special Driving Wheels Improving Tractive Performance

Driving wheel operation is characterized by force interactions with the ground, manifested in the form of vibrations. Signals generated by driving wheels can be analyzed in the frequency spectrum of tractor drawbar pull. The paper presents the analysis of a drawbar pull signal generated by a tractor equipped with two types of special driving wheels and standard tires. Beside the evaluation of special driving wheels’ properties according to drawbar power, the frequency spectra of measured signals were analyzed using a fast Fourier transformation. The model spectrum intervals for the standard tires, spike tires, and blade wheels were calculated according to the number of rubber lugs, blades, or spikes and compared with the experimental results. The results showed that the specific frequencies typical for blades and spikes were identified in model spectrum intervals. In the case of standard tires, the spectrum components typical for rubber lugs of the tire tread pattern were not identified. The highest amplitude of the typical frequency component was detected in the case of blades wheels, which showed the highest difference in drawbar power in comparison with the standard tires. Smaller dimensions of spikes resulted in lower amplitude and lower difference in drawbar power in comparison with the standard tires.

Numerical homogenization of thermal conductivity of particle-filled thermal interface material by fast Fourier transform method

Thermal interface material (TIM) is pivotal for the heat dissipation between layers of high-density electronic packaging. The most widely used TIMs are particle-filled composite materials, in which highly conductive particulate fillers are added into the polymer matrix to promote heat conduction. The numerical simulation of heat transfer in the composites is essential for the design of TIMs; however, the widely used finite element method (FEM) requires large memory and presents limited computational time for the composites with dense particles. In this work, a numerical homogenization algorithm based on fast Fourier transform was adopted to estimate the thermal conductivity of composites with randomly dispersed particles in 3D space. The unit cell problem is solved by means of a polarization-based iterative scheme, which can accelerate the convergence procedure regardless of the contrast between various components. The algorithm shows good precision and requires dramatically reduced computation time and cost compared with FEM. Moreover, the effect of the particle volume fraction, interface thermal resistance between particles (R-PP), interface thermal resistance between particle and matrix (R-PM), and particle size have been estimated. It turns out that the effective conductivity of the particulate composites increases sharply at a critical filler volume fraction, after which it is sensitive to the variation of filler loading. We can observe that the effective thermal conductivity of the composites with low filler volume fraction is sensitive to R-PM, whereas the it is governed by R-PP for the composites with high filler content. The algorithm presents excellent efficiency and accuracy, showing potential for the future design of highly thermally conductive TIMs.

Analysis of Acoustic Emission Signals Recorded during Freeze-Thaw Cycling of Concrete

This manuscript deals with a complex analysis of acoustic emission signals that were recorded during freeze-thaw cycles in test specimens produced from air-entrained concrete. An assessment of the resistance of concrete to the effects of freezing and thawing was conducted on the basis of a signal analysis. Since the experiment simulated testing of concrete in a structure, a concrete block with the height of 2.4 m and width of 1.8 m was produced to represent a real structure. When the age of the concrete was two months, samples were obtained from the block by core drilling and were subsequently used to produce test specimens. Testing of freeze-thaw resistance of concrete employed both destructive and non-destructive methods including the measurement of acoustic emission, which took place directly during the freeze-thaw cycles. The recorded acoustic emission signals were then meticulously analysed. The aim of the conducted experiments was to verify whether measurement using the acoustic emission method during Freeze-thaw (F-T) cycles are more sensitive to the degree of damage of concrete than the more commonly employed construction testing methods. The results clearly demonstrate that the acoustic emission method can reveal changes (e.g., minor cracks) in the internal structure of concrete, unlike other commonly used methods. The analysis of the acoustic emission signals using a fast Fourier transform revealed a significant shift of the dominant frequency towards lower values when the concrete was subjected to freeze-thaw cycling.

New parallel computing algorithm of molecular dynamics for extremely huge scale biological systems

In this paper, we address high performance extreme-scale molecular dynamics (MD) algorithm in the GENESIS software to perform cellular-scale molecular dynamics (MD) simulations with more than 100,000 CPU cores. It includes (1) the new algorithm of real-space nonbonded interactions maximizing the performance on ARM CPU architecture, (2) reciprocal-space nonbonded interactions minimizing communicational cost, (3) accurate temperature/pressure evaluations that allows a large time step, and (4) effective parallel file inputs/outputs (I/O) for MD simulations of extremely huge systems. The largest system that contains 1.6 billion atoms was simulated using MD with a performance of 8.30 ns/day on Fugaku supercomputer. It extends the available size and time of MD simulations to answer unresolved questions of biomacromolecules in a living cell.

Effect of Hammer Type on Generated Mechanical Signals in Impact-Echo Testing

The impact-echo diagnostic method is a well-known nondestructive pulse compression test method, which can be relatively easily used for the testing of concrete and reinforced concrete elements. The evaluation of the measurement with this method is based on the analysis of the signal itself in the time and frequency domains. This allows acquisition of information on the velocity of the mechanical wave, the resonant frequency of the specimen or on the presence of internal defects. The ability to interpret these measurements depends on the experience of the diagnostic technician. The advent of classification algorithms in the field of machine learning has brought an increasing number of applications where the entire interpretation phase can be considerably simplified with the help of classification models. However, this automated evaluation procedure must be provided with the information of whether the signal acquired by the test equipment has actually been measured under optimally set conditions. This paper proposes a procedure for the mutual comparison of different measuring setups with a variable tip type, hammer handle and impact force. These three variables were used for a series of measurements which were subsequently compared with each other using multi-criteria evaluation. This offers a tool for the evaluation of measured data and their filtering. As an output of the designed method, each measurement is marked by a score value, which represents how well the acquired signal fit the weight demands for each observed feature of the signal. The method allows the adjustment of selected demands for a specific application by means of set thresholds. This approach enables the understanding of characteristics of the signal in the automated pre-processing of measured data, where computing power is limited. Thus, this solution is potentially suitable for remote long-term observations with sensor arrays or for acoustic emission signals pre-processing.

Performance and Its Limits in Rigid Body Protein-Protein Docking

The development of fast Fourier transform (FFT) algorithms enabled the sampling of billions of complex conformations and thus revolutionized protein-protein docking. FFT-based methods are now widely available and have been used in hundreds of thousands of docking calculations. Although the methods perform "soft" docking, which allows for some overlap of component proteins, the rigid body assumption clearly introduces limitations on accuracy and reliability. In addition, the method can work only with energy expressions represented by sums of correlation functions. In this paper we use a well-established protein-protein docking benchmark set to evaluate the results of these limitations by focusing on the performance of the docking server ClusPro, which implements one of the best rigid body methods. Furthermore, we explore the theoretical limits of accuracy when using established energy terms for scoring, provide comparison with flexible docking algorithms, and review the historical performance of servers in the CAPRI docking experiment.

Characteristics of Harmonic Indexes of the Arterial Blood Pressure Waveform in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is an important public health issue worldwide. T2DM correlates with cardiovascular disease. Arterial stiffness is also a key factor that can be thought of as a surrogate marker. Nevertheless, it was unclear which harmonic indexes of blood pressure waveforms (BPWs) from subjects' radial artery pulses would be affected by T2DM. Therefore, the objective of this study was to investigate whether and how harmonic indexes can be used to discriminate hemodynamic differences between patients with T2DM and non-T2DM. This helps us to build objective results no matter who conducts the examination instead of pulse diagnosis in traditional way. We enrolled T2DM and non-T2DM patients as experimental and control groups, respectively, from the Department of Family Medicine in the National Taiwan University Hospital and the Department of Internal Medicine in Taipei's Veterans General Hospital from December 2017 to January 2019. ANSWatch® Model TS-0411 was used to capture the BPWs. Amplitude proportions (C_n values) were calculated from harmonics 1-10 of the BPW using fast Fourier transform. Thirty-two T2DM and 15 non-T2DM patients were enrolled. T2DM patients had significant differences in C₁ (p = 0.031) and C₅ (p = 0.041). The study suggests that analyzing the harmonic characteristics of non-invasively measured BPW of radial artery may be a potential and easy-to-perform approach to discriminate T2DM-induced hemodynamic changes.

Grain Boundary Induced Ultralow Threshold Random Laser in a Single GaTe Flake

Random lasing is a lasing phenomenon realized in random media, and it has attracted a great deal of attention in recent years. An essential requirement for strong random lasing is to achieve strong and recurrent scattering among grain boundaries of a disordered structure. Herein, we report a random laser (RL) based on individual polycrystalline GaTe microflakes (MFs) with a lasing threshold of 4.15 kW cm^-2, about 1-2 orders of magnitude lower than that of the reported single GaN microwire random laser. The strongly enhanced light scattering and trapping benefit from the reduced grain size in the polycrystalline GaTe MF, resulting in a ultralow threshold. We also investigate the dependence of spatially localized cavities' dimension on the pumping intensity profile and temperature. The findings provide a feasible route to realize RL with a low threshold and small size, opening up a new avenue in fulfilling many potential optoelectronic applications of RL.

Specific EEG Encephalopathy Pattern in SARS-CoV-2 Patients

We used quantified electroencephalography (qEEG) to define the features of encephalopathy in patients released from the intensive care unit after severe illness from COVID-19. Artifact-free 120-300 s epoch lengths were visually identified and divided into 1 s windows with 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel and window, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands. Furthermore, Shannon's spectral entropy (SSE) and synchronization by Pearson's correlation coefficient () were computed; cases of patients diagnosed with either infectious toxic encephalopathy (ENC) or post-cardiorespiratory arrest (CRA) encephalopathy were used for comparison. Visual inspection of EEGs of COVID patients showed a near-physiological pattern with scarce anomalies. The distribution of EEG bands was different for the three groups, with COVID midway between distributions of ENC and CRA; specifically, temporal lobes showed different distribution for EEG bands in COVID patients. Besides, SSE was higher and hemispheric connectivity lower for COVID. We objectively identified some numerical EEG features in severely ill COVID patients that can allow positive diagnosis of this encephalopathy.

Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data

In this study, we propose a methodology for the identification of potential fault occurrences of railway point-operating machines, using unlabeled signal sensor data. Data supplied by Network Rail, UK, is processed using a fast Fourier transform signal processing approach, coupled with the mean and max current levels to identify potential faults in point-operating machines. The method developed can dynamically adapt to the behavioral characteristics of individual point-operating machines, thereby providing bespoke condition monitoring capabilities in situ and in real time. The work described in this paper is not unique to railway point-operating machines, rather the data pre-processing and methodology is readily applicable to any motorized device fitted with current sensing capabilities. The novelty of our approach is that it does not require pre-labelled data with historical fault occurrences and therefore closely resembles problems of the real world, with application for smart city infrastructure. Lastly, we demonstrate the problems faced with handling such data and the capability of our methodology to dynamically adapt to diverse data presentations.

Stochastic excitation for high-resolution atomic force acoustic microscopy imaging: a system theory approach

In this work, a high-resolution atomic force acoustic microscopy imaging technique is developed in order to obtain the local indentation modulus at the nanoscale level. The technique uses a model that gives a qualitative relationship between a set of contact resonance frequencies and the indentation modulus. It is based on white-noise excitation of the tip-sample interaction and uses system theory for the extraction of the resonance modes. During conventional scanning, for each pixel, the tip-sample interaction is excited with a white-noise signal. Then, a fast Fourier transform is applied to the deflection signal that comes from the photodiodes of the atomic force microscopy (AFM) equipment. This approach allows for the measurement of several vibrational modes in a single step with high frequency resolution, with less computational cost and at a faster speed than other similar techniques. This technique is referred to as stochastic atomic force acoustic microscopy (S-AFAM), and the frequency shifts of the free resonance frequencies of an AFM cantilever are used to determine the mechanical properties of a material. S-AFAM is implemented and compared with a conventional technique (resonance tracking-atomic force acoustic microscopy, RT-AFAM). A sample of a graphite film on a glass substrate is analyzed. S-AFAM can be implemented in any AFM system due to its reduced instrumentation requirements compared to conventional techniques.

Elliptic Curve Cryptography for Wireless Sensor Networks Using the Number Theoretic Transform

We implement elliptic curve cryptography on the MSP430 which is a commonly used microcontroller in wireless sensor network nodes. We use the number theoretic transform to perform finite field multiplication and squaring as required in elliptic curve scalar point multiplication. We take advantage of the fast Fourier transform for the first time in the literature to speed up the number theoretic transform for an efficient realization of elliptic curve cryptography. Our implementation achieves elliptic curve scalar point multiplication in only 0.65 s and 1.31 s for multiplication of fixed and random points, respectively, and has similar or better timing performance compared to previous works in the literature.

An Ultra-Area-Efficient 1024-Point In-Memory FFT Processor

Current computation architectures rely on more processor-centric design principles. On the other hand, the inevitable increase in the amount of data that applications need forces researchers to design novel processor architectures that are more data-centric. By following this principle, this study proposes an area-efficient Fast Fourier Transform (FFT) processor through in-memory computing. The proposed architecture occupies the smallest footprint of around 0.1 mm 2 inside its class together with acceptable power efficiency. According to the results, the processor exhibits the highest area efficiency ( FFT / s / area ) among the existing FFT processors in the current literature.

Age-related Assessment of Postural Control Development: A Cross-sectional Study in Children and Adolescents

The aim of this study was to outline an age-dependent range of posturography measures obtained from healthy children. One hundred and fifty healthy 4-18-year-old children underwent video Head Impulse Test and static posturography testing. Surface, length and power spectra values were compared between each group of pupils and 32 healthy adults. As well as a significant (p < 0.025) increase in surface and length, when compared to healthy adults, increased values within the low/middle frequency domain and within the high frequency domain were also observed in 4-13 and 4-7-year-old children, respectively. In conclusion, although the nature of postural control development remains largely undetermined, this study represents a first attempt to outline an age-dependent normal range of the relative contribution of inputs in typically developing children.

SWSPM: A Novel Alignment-Free DNA Comparison Method Based on Signal Processing Approaches

Computing similarity between 2 nucleotide sequences is one of the fundamental problems in bioinformatics. Current methods are based mainly on 2 major approaches: (1) sequence alignment, which is computationally expensive, and (2) faster, but less accurate, alignment-free methods based on various statistical summaries, for example, short word counts. We propose a new distance measure based on mathematical transforms from the domain of signal processing. To tolerate large-scale rearrangements in the sequences, the transform is computed across sliding windows. We compare our method on several data sets with current state-of-art alignment-free methods. Our method compares favorably in terms of accuracy and outperforms other methods in running time and memory requirements. In addition, it is massively scalable up to dozens of processing units without the loss of performance due to communication overhead. Source files and sample data are available at https://bitbucket.org/fiitstubioinfo/swspm/src.

Analysis of Phase-Shift Pulse Brillouin Optical Time-Domain Reflectometry

Distributed strain and temperature can be measured by using local Brillouin backscatter in optical fibers based on the strain and temperature dependence of the Brillouin frequency shift. The technique of analyzing the local Brillion backscatter in the time domain is called Brillouin optical time domain reflectometry (BOTDR). Although the best spatial resolution of classic BOTDR remains at around 1 m, some recent BOTDR techniques have attained as high as cm-scale spatial resolution. Our laboratory has proposed and demonstrated a high-spatial-resolution BOTDR called phase-shift pulse BOTDR (PSP-BOTDR), using a pair of probe pulses modulated with binary phase-shift keying. PSP-BOTDR is based on the cross-correlation of Brillouin backscatter and on the subtraction of cross-correlations obtained from the Brillouin scatterings evoked by each phase-modulated probe pulse. Although PSP-BOTDR has attained 20-cm spatial resolution, the spectral analysis method of PSP-BOTDR has not been discussed in detail. This article gives in-depth analysis of the Brillouin backscatter and the correlations of the backscatters of the PSP-BOTDR. Based on the analysis, we propose new spectral analysis methods for PSP-BOTDR. The analysis and experiments show that the proposed methods give better frequency resolution than before.

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.

Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.

Partial Inductance Model of Induction Machines for Fault Diagnosis

The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.

[Rhythm analysis of body surface potential mapping recordings from atrial fibrillation patients based on autocorrelation function]

The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients' preoperative and postoperative BSPM data (each patient's data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient's proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.

Using the fast fourier transform in binding free energy calculations

According to implicit ligand theory, the standard binding free energy is an exponential average of the binding potential of mean force (BPMF), an exponential average of the interaction energy between the unbound ligand ensemble and a rigid receptor. Here, we use the fast Fourier transform (FFT) to efficiently evaluate BPMFs by calculating interaction energies when rigid ligand configurations from the unbound ensemble are discretely translated across rigid receptor conformations. Results for standard binding free energies between T4 lysozyme and 141 small organic molecules are in good agreement with previous alchemical calculations based on (1) a flexible complex ( R≈0.9 for 24 systems) and (2) flexible ligand with multiple rigid receptor configurations ( R≈0.8 for 141 systems). While the FFT is routinely used for molecular docking, to our knowledge this is the first time that the algorithm has been used for rigorous binding free energy calculations. © 2017 Wiley Periodicals, Inc.

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

OBJECTIVE

This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.

METHOD

Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual-manual task) and distracted (poststarting of visual-manual task) driving periods. Average relative spectral power in a low frequency range (0-0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.

RESULTS

Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual-manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.

CONCLUSIONS

This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.

A novel method for the detection of VEP signals from frontal region

PURPOSE

This paper studies the feasibility of frontal positioning of electrode to detect electroencephalographic signals.

METHODS

The experiments were conducted using a board with light-emitting diodes (LEDs) designed to stimulate SSVEP and FVEP. The flashing frequencies were conducted at 15, 23, and 31 Hz. We used the Quick Amp brain wave amplifier to collect brain wave signals at a sampling rate of 1 kHz using a frequency filter band of 10-100 Hz.

RESULTS

We found that the energy power of VEP will gradually increase from Oz position to Fp2 position. We analyze the data, proving that the Fp2 position can also be used to collect VEP data.

CONCLUSIONS

Traditional measurements at the Oz location are limited because of the interference from human hair, and an additional electrode is required to detect eye movement and filter this electro-oculogram signal. Our proposed method can effectively acquire critical visually evoked potential and electro-oculogram signals without the electrode at the Oz location, which require extensive preparation of removing hair strands. We also reduced the number of electrodes used in receiving electroencephalographic signals. The frontal electrode positioning could be a remarkable breakthrough to design brain computer interface.

Arrhythmia Evaluation in Wearable ECG Devices

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

The DisVis and PowerFit Web Servers: Explorative and Integrative Modeling of Biomolecular Complexes

Structure determination of complex molecular machines requires a combination of an increasing number of experimental methods with highly specialized software geared toward each data source to properly handle the gathered data. Recently, we introduced the two software packages PowerFit and DisVis. These combine high-resolution structures of atomic subunits with density maps from cryo-electron microscopy or distance restraints, typically acquired by chemical cross-linking coupled with mass spectrometry, respectively. Here, we report on recent advances in both GPGPU-accelerated software packages: PowerFit is a tool for rigid body fitting of atomic structures in cryo-electron density maps and has been updated to also output reliability indicators for the success of fitting, through the use of the Fisher z-transformation and associated confidence intervals; DisVis aims at quantifying the information content of distance restraints and identifying false-positive restraints. We extended its analysis capabilities to include an analysis of putative interface residues and to output an average shape representing the putative location of the ligand. To facilitate their use by a broad community, they have been implemented as web portals harvesting both local CPU resources and GPGPU-accelerated EGI grid resources. They offer user-friendly interfaces, while minimizing computational requirements, and provide a first interactive view of the results. The portals can be accessed freely after registration via http://milou.science.uu.nl/services/DISVIS and http://milou.science.uu.nl/services/POWERFIT.

Nonlinear Conte-Zbilut-Federici (CZF) Method of Computing LF/HF Ratio: A More Reliable Index of Changes in Heart Rate Variability

Objectives:

Acupuncture treatments are safe and effective for a wide variety of diseases involving autonomic dysregulation. Heart rate variability (HRV) is a noninvasive method for assessing sympathovagal balance. The low frequency/high frequency (LF/HF) spectral power ratio is an index of sympathovagal influence on heart rate and of cardiovascular health. This study tests the hypothesis that from rest to 30% to 50% of peak oxygen consumption, the nonlinear Conte-Zbilut-Federici (CZF) method of computing the LF/HF ratio is a more reliable index of changes in the HRV than linear methods are.

Methods:

The subjects of this study were 10 healthy young adults. Electrocardiogram RR intervals were measured during 6-minute periods of rest and aerobic exercise on a cycle ergometer at 30% and 50% of peak oxygen consumption (VO_2peak).

Results:

The frequency domain CZF computations of the LF/HF ratio and the time domain computations of the standard deviation of normal-to-normal intervals (SDNN) decreased sequentially from rest to 30% VO_2peak (P < 0.001) to 50% VO_2peak (P < 0.05). The SDNN and the CZF computations of the LF/HF ratio were positively correlated (Pearson’s r = 0.75, P < 0.001). fast Fourier transform (FFT), autoregressive (AR) and Lomb periodogram computations of the LF/HF ratio increased only from rest to 50% VO_2peak.

Conclusion:

Computations of the LF/HF ratio by using the nonlinear CZF method appear to be more sensitive to changes in physical activity than computations of the LF/HF ratio by using linear methods. Future studies should determine whether the CZF computation of the LF/HF ratio improves evaluations of pharmacopuncture and other treatment modalities.