High Frequency Components Research Articles

Hybrid Preprocessing for Neural Network-Based Stock Price Prediction

In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.

Gravitational bremsstrahlung from Yukawa and nucleon collisions

We obtain the gravitational emission from particles scattering via the Yukawa interaction, presenting both classical and approximate quantum results. We also estimate the contribution from the tensor part of the internucleon interaction. This emission is the main source of a very high frequency component to the stochastic background in the Solar System and in neutron stars. The emission from the Sun (allowing for Debye screening) and from a typical neutron star are obtained. The gravitational wave luminosity of the Sun is 41±10 MW. Published by the American Physical Society 2024

Acupuncture improves the symptoms, serum ghrelin, and autonomic nervous system of patients with postprandial distress syndrome: a randomized controlled trial

BackgroundWhether gastrointestinal hormones in patients with postprandial distress syndrome (PDS) are altered by acupuncture, and whether such alterations are related to the autonomic nervous system (ANS), remains an open question.ObjectiveThis study aims to investigate the effects of acupuncture on symptoms, serum hormones, and ANS in PDS patients.MethodsThis randomized controlled clinical trial was conducted at Beijing Hospital of Traditional Chinese Medicine affiliated with Capital Medical University. Sixty-two PDS patients were randomly assigned equally to acupuncture or sham acupuncture arm (3 sessions per week for 4-week). The main outcome measures which were evaluated at baseline and 4-week included cardinal symptoms, serum hormones including ghrelin, vasoactive intestinal peptide (VIP), substance P (SP), and ANS.ResultsAmong the 62 randomly assigned participants, 51 (82%) were included in the baseline characteristics and outcome analysis. Gastrointestinal symptoms including response rate (p = 0.001) and dyspepsia symptom severity (p = 0.002) were significantly improved after acupuncture treatment. Serum ghrelin concentration was significantly higher in acupuncture group than in sham acupuncture group (8.34 ± 3.00 ng/ml versus 6.52 ± 2.00 ng/ml, p = 0.022) after 4-week treatment, instead of VIP and SP (p > 0.05). The acupuncture group had significantly higher vagal activity, showing with increasing of high-frequency component (HF, p ≤ 0.001) and decreasing of the ratio of low-frequency and HF (p ≤ 0.001). In relationship analysis, the HF component exhibited a significant inverse correlation with symptom severity (R = − 0.501, p ≤ 0.001), but not with ghrelin level (R = 0.026, p = 0.865).ConclusionAcupuncture may improve the symptoms and increase the ghrelin level of PDS patients, the therapeutic effect of acupuncture was associated with the alteration of vagal activity.Trial registration: The trial is registered with the ISRCTN registry, ISRCTN12511434. Registered 31 March 2017, https://www.isrctn.com/.

Sex-differences in neuromuscular control of hip abductors during isometric submaximal tasks

BackgroundOne key focus in the literature is the need to better understand how males and females perform neuromuscular control, which requires consideration of muscle morphology, as it may change neural drive during force production. Although previous studies focused on muscles around the knee and ankle, it is crucial to explore the behavior of other muscles, such as the hip abductors, since many lower limb conditions more common in females have been associated with alterations in hip muscles. Research questionsAre electromyography (EMG) variables (amplitude and low- and high frequency bands) of hip abductors during submaximal isometric tasks different between males and females? How is muscle size associated with EMG responses during these tasks? MethodsThirty-six participants (males, n = 18; females, n = 18) had muscle and subcutaneous thickness of gluteus medius (GMed) and tensor fascia latae (TFL) measured. They then performed an estimated one-repetition maximum (1RM) test in a side-lying hip abduction exercise, followed by two 10-s submaximal tasks: (i) side-lying hip abduction with 50 % 1RM and (ii) with 20 % 1RM. GMed and TFL EMG amplitude and frequency bands (low and high frequency components) were analyzed. ResultsFemales had higher GMed EMG amplitude, lower low-frequency, and higher high-frequency components than males in both tasks (p = 0.001–0.040). No differences were observed in TFL EMG variables. Greater GMed thickness was associated with lower amplitudes in the 50 % 1RM task (r = − 0.35; p = 0.03), while greater TFL thickness was associated with lower low-frequency [20 % 1RM: r = − 0.70; p = 0.002; 50 %-1RM: r = − 0.65; p = 0.005] and higher high-frequency components [20 % 1RM: r = 0.74; p = 0.001; 50 %-1RM: r = 0.76; p < 0.001] only in females. SignificanceMales and females employ different neuromuscular control strategies, which vary among the synergists for hip abduction.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software

In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the contrast and noise characteristics of LFMRI scans, addressing the limitation of available in-vivo LFMRI datasets for training deep learning models. In the simulation data, the Relative Contrast Ratio (RCR) increased, and similar improvements were observed in the in-vivo data across different imaging conditions. Comparative evaluations demonstrate that our model performs better than the widely used non-deep learning method, BM4D, in enhancing RCR and maintaining high spatial frequency components in in-vivo data.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Super Resolution (SR) images contain more useful information than Low Resolution (LR) images. Generally, SR images are highly preferred over LR images in the medical field due to their high quality. Generally, SR images are affected by many factors, like blur, noise, and decimation. Therefore, the present study proposes Deep Feature Blend Attention Mechanism for generating SR images with optimal outcomes. In this study, a Deep Learning (DL) based encoder-decoder is used to extract the detailed information from the LR image, assist in noise removal, and improve image quality. Using Gaussian blur, an LR image is produced, which is given as input to the encoder-decoder. The Attention mechanism with feature blending is performed to produce the reconstructed SR image. The low and high frequency component issues from feature map are improved using the attention mechanism. Also, feature blend attention mechanism is implied to reconstruct SR images from LR images. The present study utilizes the feature blend technique, which helps select the optimal features and blending to attain the optimal feature set. The study is performed with and without the feature blend technique in attention mechanism to expose the impact of the feature blend technique, and it is used to avoid overfitting issues and produce optimal results. The performance of the proposed system is assessed by using PSNR and SSIM to show the system's efficiency. The proposed system is compared with other state-of-the-art studies to expose the efficacy of the present system.

Integration of polyoxometalate into defective UiO-66-NH2(Zr/Hf) for visible-light-driven hydrogen photogeneration

UiO-66 plays a crucial role in photocatalytic systems due to its superior stability and tunability. However, the charge transfer from organic ligands to metals is rarely involved in the photo-excitation of the pristine material. Herein, several Keggin-type Cs2.5H0.5PW12O40 (CsPW) were successfully incorporated into a bimetallic metal-organic framework (MOF) using a one-pot, one-step method, resulting in a series of defective CsPW@UNH(Hf-Zr) as optimized photocatalysts for visible-light-driven hydrogen evolution. Further investigations demonstrate that a Z heterojunction mechanism proceeds in the CsPW@UNH(0.65Hf) heterojunction to boost the charge separation via the chemical interaction between the CsPW and UNH(0.65Hf) components. The hydrogen evolution rate for 5 %CsPW@UNH(0.65Hf) is nearly two orders of magnitude greater than that of the bare UNH(Zr). The coexistence of Hf3+ and Zr3+ was observed in defective polyoxometalate-encapsulated MOF (POM@MOF) for the first time during photocatalysis, confirming the successful conjugated π electrons transition from the amine-containing ligand to bimetallic cluster. This study offers a novel guideline for exploring efficient MOF-based photocatalysts through defect engineering.

Transient flow and noise characteristics of vortex-turbulence-noise interaction in centrifugal pump

Centrifugal pump operation noise is an avoidable common phenomenon in engineering practices, and its characteristics are closely influenced by the flow characteristics of the fluid. In this paper, a numerical simulation of centrifugal pump operation with different flow rates is carried out using the detached eddy simulation turbulence model and compared with the experimental results. Then the noise source and far-field radiated noise characteristics of the centrifugal pump are investigated using Lighthill’s analogy. The results show that the noise of centrifugal pump is closely related to the flow characteristics of impeller, especially the generation and development of vortex. The relationship between turbulent kinetic energy (TKE) and vortex is complex, involving the generation, development and shedding of vortex. The influence of flow rate variation on vortex structure in impeller is studied by vortex transport equation. It is found that the relative vortex stretching (RVS) term is the main driving force of vortex distribution. The distribution of the Coriolis force (CORF) term is mainly concentrated in the inlet and outlet areas of the flow channel, which is related to the rotational motion of the impeller and the dynamic characteristic of the fluid. The power spectral density inside the impeller is mainly concentrated in the low frequency region, indicating that the low frequency noise component is dominant in energy. The directivity curve of far-field noise pressure level is influenced by hydrodynamic effect, impeller rotation direction and pump design structure, and will have a certain deviation. The spectral curve analysis of velocity, vorticity, pressure and sound pressure level shows that these physical quantities have strong correlation on the spectrum, in which the low frequency component is large, and the high frequency component is complex and volatile.

Relationship Between High Frequency Component of Heart Rate Variability and Delta EEG Power During Sleep in Women With Irritable Bowel Syndrome Compared to Healthy Women.

To explore the relationship between the high frequency (HF) heart rate variability (HRV) and electroencephalogram (EEG) delta band power in women with irritable bowel syndrome (IBS) versus healthy control women. Twenty women with IBS and twenty healthy controls were studied over three consecutive nights using polysomnography in a sleep laboratory. To avoid the first night effect, only second-night data were analyzed. Power spectral analysis was applied to HRV and EEG recordings. The linear system coherence/phase analysis assessed the relationship between normalized HF power of HRV and normalized delta band power of EEG during the first four NREM-REM sleep cycles. Women with IBS exhibited a significantly higher percentage of NREM sleep, higher normalized HF, lower normalized low frequency (LF) and decreased LF/HF ratio of HRV in the first four NREM-REM sleep cycles compared to controls. Additionally, their normalized delta band power was significantly lower in these sleep cycles and over the whole night. The phase shift between HF and delta band power was significantly longer in the IBS group. While the coherence between normalized HF and normalized delta band power was lower in the IBS group, the difference was not statistically significant. The coherence/phase analysis showed a dysregulated interaction between autonomic and central nervous systems in women with IBS, manifested by increased lag time between cardiac and EEG delta band power compared to healthy controls. Whether this dysregulation contributes to the pathophysiology of IBS remains to be determined.

Forecasting Soil Moisture in Caragana Shrubland Using Wavelet Analysis and NARX Neural Network

It is important for sustainable use of soil water resource and high-quality development to forecast the soil moisture in forestland of water-limited regions. There are some soil water models. However, there is not the best model to forecast the change of soil moisture in the caragana shrubland. In this paper, the plant water relationship has been investigated at the same time for a long term in the caragana shrubland of semiarid region of the Loess Plateau of China. The data of soil moisture was divided and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted. The results showed the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in 100 cm layer using model II is 0.8%, then soil water content in the 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9% and 0.4%. The results showed that using model II to predict soil water is well Predicting soil water using model II will be important for sustainable use of soil water resource and high-quality development.

The contribution of dynamic cues to externalization of virtual source in virtual auditory display

It is well known that static virtual auditory display with headphone is inclined to create the auditory perception of in-head-localization, especially for target virtual sources in the median plane. By introducing the dynamic variation of binaural pressures caused by head turning, dynamic virtual auditory display eliminates the in-head-localization effectively. The present work investigates the contributions of dynamic interaural time difference (ITD) and dynamic interaural level difference (ILD) in binaural pressures to the externalization of auditory perception. By modifying the head-related transfer functions used in a dynamic virtual auditory display, binaural signals with dynamic ITD or dynamic ILD alone are created and presented by headphone. A psychoacoustic experiment is conducted to evaluate the extent of perceived externalization. The experiment results prove that, for the target virtual source in the median plane, dynamic ITD and dynamic ILD contribute to the externalization of low and high frequency components of the stimuli respectively. For wideband stimulus with low and high frequency components, both dynamic ITD and dynamic ILD contribute externalization but dynamic ITD contributes more than that of dynamic ILD.

Analysis of nonminimum phase components of noise control filters in active noise control

In the active noise control (ANC), it is known that the optimal filter becomes a noncausal filter due to the effect of the nonminimum phase components of the secondary path. We have investigated how to make the causal optimal filter by replacing the nonminimum phase components with the frequency components of the primary path. Through the computer simulations, we have confirmed that this filter maintained some noise reduction performance and can be used as a quasi-optimal filter. In this paper, as another approach, we attempt to identify noncausal components using the pre-trained adaptive noise control filter. The nonminimum phase components of the secondary path exist in the frequency band below the lowest resonance frequency of the secondary loudspeaker. Based on this fact, we design a noise control filter by using band-limited white noise only with the low frequency components below the lowest resonance frequency in advance. Then, we design another adaptive noise control filter by using the band-limited white noise with high frequency components above the lowest resonance frequency. Finally, we combine these filters in the frequency domain. Through the computer simulations, we show the difficulty to handle the nonminimum phase components in the ANC system.

Simulation of pulse width modulation strategies: validation based on sound quality evaluations

Though the noise emitted by an electrical machine is less loud than the one from a combustion engine, the existing literature shows it is not always more pleasant. This is because of its strong tonal content, due to deterministic excitations (e.g. electromagnetic slotting and switching effects, mechanical gear mesh effects). The main source of magnetic noise is the radiation of the stator submitted to electromagnetic forces, which are harmonic by nature. Their highest frequency components are generated by the Pulse Width Modulation (PWM) technique designed to vary the supply frequency and operating speed of the electric machine. Several PWM strategies exist, leading to specific harmonic spectra. A temporal simulation model has been developed to auralize the corresponding electromagnetic noise emitted by the machine, considering its mechanical and electrical parameters. A testbench has been adapted to test the relevance of the model, both from an electrical and acoustic point of view. The simulation model and the testbench can be used to generate virtual and real sound stimuli to be assessed in a jury testing. The subjective evaluations allow to assess the relevance of the auralization model, but also to identify the least annoying strategies.

A portable test system for the radar

Abstract In order to adapt to the increasingly fierce battlefield confrontation scenarios, a portable, fast deployment and folding test system for the radar is proposed. The corresponding key test items of radar such as spectrum, waveform, lobe, target tracking and data analysis can be completed on the test system. After the Built-in-Test (BIT) information and test system results are integrated, the corresponding evaluation report and implementation recommendations can be provided to the maintenance personnel. This paper expounds the design of each subsystem step by step. The Main Control Unit (MCU) is the task scheduling and information integration center for the system. The corresponding high frequency components, processing components, storage components have been implemented systematic and miniaturized design. In the end, the improvement direction and prospect of the test system are given.

From Gaze Jitter to Domain Adaptation: Generalizing Gaze Estimation by Manipulating High-Frequency Components

From Gaze Jitter to Domain Adaptation: Generalizing Gaze Estimation by Manipulating High-Frequency Components

Analytical and numerical models to predict the shape of incident pulse in split-Hopkinson bar experiments

In typical split-Hopkinson pressure bar experiments (SHPB), the striker bar impacts the incident bar via discs made from soft materials such as copper. These discs, also called pulse shapers, are used (i) to eliminate the high frequency components of the incident pulse, (ii) to obtain a finite rise time of the incident pulse and (iii) to obtain a constant strain rate. Although these pulse shapers have been used for over decades in SHPB experiments, no analytical solutions or simple models are available that can predict the incident pulse as a function of the striker velocity, pulse shaper geometry and material parameters. Assuming that the pulse shaper is a rigid-linearly hardening material, we derive the analytical solution for the incident pulse when the rise time of the incident pulse is less than twice the time taken for a longitudinal wave to travel along the length of the striker. For larger rise times, we additionally assume that the striker is rigid to obtain a simple numerical model to predict the incident pulse in the presence of a pulse shaper. Both these models are validated against numerical simulations and experiments to demonstrate their accuracy.

Illumination-aware divide-and-conquer network for improperly-exposed image enhancement

Improperly-exposed images often have unsatisfactory visual characteristics like inadequate illumination, low contrast, and the loss of small structures and details. The mapping relationship from an improperly-exposed condition to a well-exposed one may vary significantly due to the presence of multiple exposure conditions. Consequently, the enhancement methods that do not pay specific attention to this issue tend to yield inconsistent results when applied to the same scene under different exposure conditions. In order to obtain consistent enhancement results for various exposures while restoring rich details, we propose an illumination-aware divide-and-conquer network (IDNet). Specifically, to address the challenge of directly learning a sophisticated nonlinear mapping from an improperly-exposed condition to a well-exposed one, we utilize the discrete wavelet transform (DWT) to decompose the image into the low-frequency (LF) component, which primarily captures brightness and contrast, and the high-frequency (HF) components that depict fine-scale structures. To mitigate the inconsistency in correction across various exposures, we extract a conditional feature from the input that represents illumination-related global information. This feature is then utilized to modulate the dynamic convolution weights, enabling precise correction of the LF component. Furthermore, as the co-located positions of LF and HF components are highly correlated, we create a mask to distill useful knowledge from the corrected LF component, and integrate it into the HF component to support the restoration of fine-scale details. Extensive experimental results demonstrate that the proposed IDNet is superior to several state-of-the-art enhancement methods on two datasets with multiple exposures.

The Impact of Stimulation Parameters on Cardiovascular Outcomes in Chronic Stroke Patients Following Transcranial Direct Current Stimulation-A Pilot Controlled, Randomized, Double-Blind Crossover Trial.

Stroke survivors often experience autonomic nervous system (ANS) dysfunction. While Transcranial Direct Current Stimulation (tDCS) has been shown to modulate the ANS when applied to the left hemisphere, its effects on the right hemisphere remain unexplored. We aimed to compare the effects of tDCS applied to both the injured and the contralateral hemispheres on heart rate variability (HRV) and functional capacity in individuals post-stroke. Twenty individuals with cerebral hemisphere lesions (ten with right-hemisphere lesions and ten with left-hemisphere lesions) were randomized into four groups: anodal and sham tDCS on the left temporal cortex (T3) and anodal and sham tDCS on the right temporal cortex (T4). HRV was assessed before the intervention, after the six-minute walk test (6MWT), and following tDCS. HRV data were categorized into frequency ranges: low frequency (LF), high frequency (HF), and sympathovagal balance. The 6MWT (meters) was conducted both pre- and post-tDCS. In individuals with right-hemisphere lesions, a higher global LF value was observed (right side: 71.4 ± 16.8 nu vs. left side: 65.7 ± 17.3 nu; p = 0.008), as well as lower values of the HF component (right side: 29.5 ± 18.9 nu vs. left side: 34.0 ± 17.4 nu; p = 0.047), consequently exhibiting higher global values of the low/high-frequency ratio (right side: 3.9 ± 2.8 vs. left side: 2.9 ± 2.4). Regarding the stimulation site, tDCS over T3 led to a lower overall value of the low/high-frequency ratio (left hemisphere: 3.0 ± 2.2 vs. right hemisphere: 3.7 ± 2.9; p = 0.040) regardless of the lesion location. A significant increase in the distance covered in the 6MWT was observed for individuals with lesions in both hemispheres after tDCS at T3. Participants with right-hemisphere lesions exhibited superior global sympathetic autonomic nervous system activity. When the tDCS was applied on the left hemisphere, it maintained lower sympathovagal balance values and improved functional capacity regardless of the hemisphere affected by the stroke.

Photoacoustic Signal Propagation Modes in Bone Tissue: a Simulation Study

Abstract Photoacoustic (PA) imaging has great potential in brain imaging. However, the strong acoustic attenuation and reverberation caused by the skull bone layers present challenges for PA brain imaging. In this work, we developed a three-dimensional (3D) photoacoustic numerical simulation platform using the finite difference time domain (FDTD) method to study PA signal generation and propagation in the human brain. This platform can simulate not only the propagation of longitudinal waves in soft tissues but also shear and longitudinal waves in hard tissues. Using this numerical simulation platform, we extensively simulated the propagation of PA waves in skull models with varying porosities, The results demonstrate that our 3D PA simulation platform accurately captures the propagation details of PA signals in the skull, including the refraction, reflection, and mode conversion of photoacoustic waves at tissue boundaries. Furthermore, an increase in skull porosity prolongs the duration of the photoacoustic signals and leads to high attenuation of PA signals for high frequency components. We expect that this study will contribute to the understanding of the impact on acoustic propagation of photoacoustic signals in cranial bone photoacoustic imaging.