Low-frequency Characteristics Research Articles

Low-frequency background estimation and noise separation from high-frequency for background and noise subtraction.

In fluorescence microscopy, background blur and noise are two main factors preventing the achievement of high-signal-to-noise ratio (SNR) imaging. Background blur primarily emanates from inherent factors including the spontaneous fluorescence of biological samples and out-of-focus backgrounds, while noise encompasses Gaussian and Poisson noise components. To achieve background blur subtraction and denoising simultaneously, a pioneering algorithm based on low-frequency background estimation and noise separation from high-frequency (LBNH-BNS) is presented, which effectively disentangles noise from the desired signal. Furthermore, it seamlessly integrates low-frequency features derived from background blur estimation, leading to the effective elimination of noise and background blur in wide-field fluorescence images. In comparisons with other state-of-the-art background removal algorithms, LBNH-BNS demonstrates significant advantages in key quantitative metrics such as peak signal-to-noise ratio (PSNR) and manifests substantial visual enhancements. LBNH-BNS holds immense potential for advancing the overall performance and quality of wide-field fluorescence imaging techniques.

NCGLF[formula omitted]: Network combining global and local features for fusion of multisource remote sensing data

The fusion of multisource remote sensing (RS) data has demonstrated significant potential in target recognition and classification tasks. However, there is limited emphasis on capturing both high- and low-frequency information from these data sources. Additionally, effectively integrating multisource data remains a challenging task, as the absence of redundancy and discriminant information hampers the applications of RS data. In this paper, we propose a fusion network called network combining global and local features (NCGLF2) that integrates global and local features (GLF) extracted from multisource RS data. This approach effectively leverages the capabilities of convolutional neural networks (CNNs) to extract high frequency features while utilizing transformer architecture to replicate low frequency information and remote correlations. Firstly, a scale information aggregation (SIA) module extracts multiscale shallow layer features from the input data sources. Secondly, a structural information learning transformer (SIL-Trans) module captures low frequency features, while an invertible neural network (INN) module learns high frequency information. Finally, a GLF fusion module maximizes the complementary characteristics of multisource RS data and GLF to effectively fuse high- and low-frequency information. Our experimental results with three benchmark datasets indicate that NCGLF2 outperforms existing state-of-the-art approaches in terms of feature representation and compatibility with diverse data types. The code is available at https://github.com/renqi1998/NCGLF2.

Low-frequency variability in theta activity modulates the attention-fluctuation across task and resting states

Sustained attention is not constant but fluctuates influencing our task performance. Albeit intensive investigations, it remains unclear whether the attention-fluctuation during tasks is derived from its spontaneous fluctuation in the resting state. Here, we addressed this issue by investigating the attention-fluctuation in both task and resting states, through the EEG measurement of theta-variability. We found significant rest-task modulation of theta-variability, i.e., reduced theta-variability in the task state compared to the resting state. This task and rest modulation was manifested in the low-frequency of theta-variability (<0.1 Hz). Furthermore, the low-frequency theta-variability exhibited a significant rest-task correlation, however, only the low-frequency theta-variability in the task state but not in the resting state was correlated with the behavioral performance. These findings shed light on the low-frequency feature of attention-fluctuation, and advanced our understanding of sustained attention by suggesting that the theta-variability in low-frequencies was relevant to attention level in task state.

Earthworm‐Inspired Triboelectric Nanogenerator with O‐Shaped Multilayer Structure for Marine Ranching

In view of the characteristics of small wave amplitude, stable direction, and low frequency near marine ranching, an energy‐harvesting device is designed and produced through an O‐shaped multilayer earthworm‐like triboelectric nanogenerator (OME–TENG) to provide a self‐sufficient energy supply for sensors without relying on batteries. Under the same conditions, O‐shaped multilayer TENG structure can carry out a fuller contact‐separation movement due to its unique O‐shaped structure, thus the peak power of the O‐shaped multilayer TENG structure can reach 10.4 mW, which is more than twice that of the Z‐shaped multilayer TENG structure. When the water wave frequency is 0.5 Hz and the wave amplitude is about 7.5 cm, the open‐circuit voltage, short‐circuit current, and transferred charge of the left units of the OME–TENG device can reach 300 V, 25 μA, and 900 nC, respectively, and can directly light up 107 light emitting diode. After rectification and parallel connection, the energy collected by the OME–TENG can easily power a digital hygrograph to monitor marine temperature and humidity. Therefore, the OME–TENG shows great application prospects in the energy supply of marine ranching.

Pemanfaatan Gelombang Elektromagnet Extremely Low Frequency (Elf) Dalam Ketahanan Pangan

Physics is considered an important subject that is taught as a subject in itself with various considerations. Physics is seen as both a process and a product, so successful physics learning requires attention to effective and efficient learning. The utilization of Extremely Low Frequency (ELF) electromagnetic waves has great potential in the field of food security. ELF waves have low-frequency characteristics that create a unique potential to influence microbial activity and nutrient availability in improving food security productivity. ELF waves can be used to increase the productivity and quality of food crops, as well as extend the shelf life of food products. Thus, the utilization of ELF waves can help in achieving sustainable food security. In an effort to improve food security, it is necessary to innovate and develop technology that can support agricultural productivity. More research is still needed to fully understand the mechanism of action of ELF waves and how best to use them in agricultural and food processing practices.

A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests.

Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters.

Time domain response analysis of multi-flow channel hydraulic mount

In order to analyze the effect of the combination of long and short inertia channels and orifice flow channels on the time domain response of hydraulic mounts. Firstly, six hydraulic mounts with different combinations of inertia channels and orifice flow channels are proposed. And then, the transfer functions of dynamic stiffness and upper chamber pressure for six structures of hydraulic mounts are derived using the lumped parameter method. Next, the time domain analytic formulas for the transfer force and upper chamber pressure for six structural hydraulic mounts under steady-state excitation and step excitation are obtained using the convolution method. Finally, the analytical formula is compared with the hydraulic mount’s model built by AMEsim; Meanwhile, the effects of inertia terms of inertia channels, damping, and damping of orifice flow channels on hydraulic mounts transfer forces are analyzed; Analyze the effect of transfer force variation and excitation amplitude on hydraulic mounts damping for different configurations of structures. Research shows that inertia channels and orifice flow channels directly affect the low-frequency dynamic characteristics of hydraulic mounts. At the same time, the effective damping height of the hydraulic mounts depends on the excitation amplitude.

Low-Frequency Discontinuous Noise Diagnosis and Reduction of Air Conditioner Outdoor Unit

Abnormal discontinuous low-frequency noise occurs in the outdoor unit when the air conditioner is operating at 86~96 Hz. This noise has the characteristics of low frequency, strong penetration, long transmission distance, and poor sound quality, which can cause serious noise pollution. In order to reduce the low-frequency noise of the outdoor unit of the air conditioner, the discontinuous noise frequency is first located by testing the time-domain and frequency-domain maps of the noise. It is then compared with the frequency of fan rotation noise and structural radiation noise. It is found that the discontinuous noise is caused by the coupling of fan rotation noise and structural radiation noise. It is found that low-frequency noise improvement is significant when the frequency difference between two noise peaks is greater than 8 Hz or when a single noise peak is less than 25 dB(A) by studying the methods of frequency modulation and amplitude modulation of noise peaks. By adjusting the fan speed from 930 rpm to 940 rpm, the problem of 94 Hz noise is solved. Then, through testing the natural frequencies of the pipe and shell, it is found that the natural frequencies of the pipe are close to the excitation frequencies. The structural radiation noise is mainly caused by the pipe. Modal analysis is conducted on the pipe. When the normal boundary with the condenser and the normal boundary with the valve of the pipe is constrained by springs, the simulated values are closer to the measured values. Finally, structural optimization design is carried out on the pipe. When the U-tube of the exhaust pipe is shortened by 30 mm, a 30 g mass block is added to the cold inlet pipe, and a 97 g mass block is added to the lower part of the valve pipe of the suction pipe. The discontinuous noise of the optimized prototype is significantly improved.

Multi-Class Wound Classification via High and Low-Frequency Guidance Network.

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

Wavelet Approximation-Aware Residual Network for Single Image Deraining.

It has been made great progress on single image deraining based on deep convolutional neural networks (CNN). In most existing deep deraining methods, CNNs aim to learn a direct mapping from rainy images to clean rain-less images, and their architectures are becoming more and more complex. However, due to the limitation of mixing rain with object edges and background, it is difficult to separate rain and object/background, and the edge details of the image cannot be effectively recovered in the reconstruction process. To address this problem, we propose a novel wavelet approximation-aware residual network (WAAR), wherein rain is effectively removed from both low-frequency structures and high-frequency details at each level separately, especially in low-frequency sub-images at each level. After wavelet transform, we propose novel approximation aware (AAM) and approximation level blending (ALB) mechanisms to further aid the low-frequency networks at each level recover the structure and texture of low-frequency sub-images recursively, while the high-frequency network can effectively eliminate rain streaks through block connection and achieve different degrees of edge detail enhancement by adjusting hyperparameters. In addition, we also introduce block connection to enrich the high-frequency details in the high-frequency network, which is favorable for obtaining potential interdependencies between high- and low-frequency features. Experimental results indicate that the proposed WAAR exhibits strong performance in reconstructing clean and rain-free images, recovering real and undistorted texture structures, and enhancing image edges in comparison with the state-of-the-art approaches on synthetic and real image datasets. It shows the effectiveness of our method, especially on image edges and texture details.

Sliding behaviors of the trapezoidal roof rock block under a lateral dynamic disturbance

The surrounding rock of underground space is always affected by external dynamic disturbance from the side position, such as blasting vibration from a stope at the same level or seismic waves from adjacent strata. A series of laboratory tests, numerical simulations and theoretical analyses were carried out in this study to disclose the sliding mechanism of roof rock blocks under lateral disturbance. Firstly, the experiments on trapezoidal key block under various clamping loads and disturbance were conducted, followed by numerical simulations using the fast Lagrangian analysis of continua (FLAC3D). Then, based on the conventional wave propagation model and the classical shear-slip constitutive model, a theoretical model was proposed to capture the relative displacement between blocks and the sliding displacement of the key block. The results indicate that the sliding displacement of the key block increased linearly with the disturbance energy and decreased exponentially with the clamping load when the key block was disturbed to slide (without instability). Meanwhile, when the key block was disturbed to fall, two types of instability process may appear as immediate type or delayed type. In addition, the propagation of stress waves in the block system exhibited obvious low-velocity and low-frequency characteristics, resulting in the friction reduction effect appearing at the contact interface, which is the essential reason for the sliding of rock blocks. The results can be applied to practical underground engineering and provide valuable guidance for the early detection and prevention of rock-falling disasters.

Signal Feature Extraction Method of Three-Band Infrared Flame Sensor

A three-band infrared flame sensor is designed and experimentally investigated for the analysis of infrared signals from three radiation sources (Flame, man and ambient). The Fourier transform and empirical mode decomposition (EMD) is applied to the analysis of the spectral characteristics, intrinsic modes and energy spectra. The results show that the received flame infrared signal exhibits significant low-frequency and energy characteristics. Near the zero frequency, the received infrared signal energy of the flame, man and ambient is 3.4 × 104 J, 2.8 × 104 J and 2.1 × 104 J, respectively. In this way the flame signal features can be accurately identified from different radiation sources. It can be believed that the results of the three-band infrared sensor presented here are useful guiding significance and practical application value for fire detection.

Periodical Ultra-Modulation of Broadened Laser Spectra in Dielectrics at Variable Ultrashort Laser Pulsewidths: Ultrafast Plasma, Plasmonic and Nanoscale Structural Effects

Self-phase modulation (SPM) broadening of prompt laser spectra was studied in a transmission mode in natural and synthetic diamonds at variable laser wavelengths (515 and 1030 nm), pulse energies and widths (0.3–12 ps, positively chirped pulses), providing their filamentary propagation. Besides the monotonous SPM broadening of the laser spectra versus pulse energy, which was more pronounced for the (sub)picosecond pulsewidths and more nitrogen-doped natural diamond with its intra-gap impurity states, periodical low-frequency modulation was observed in the spectra at the shorter laser pulsewidths, indicating dynamic Bragg filtering of the supercontinuum due to ultrafast plasma and nanoplasmonic effects. Damping of broadening and ultra-modulation for the longer picosecond pulsewidths was related to the thermalized electron-hole plasma regime established for the laser pulsewidths longer, than 2 ps. Unexpectedly, at higher pulse energies and corresponding longer, well-developed microfilaments, the number of low-intensity, low-frequency sideband spectral modulation features counterintuitively increases, thus indicating dynamic variation of the periods in the longitudinal plasma Bragg gratings along the filaments due to prompt secondary laser–plasmon interactions. The underlying sub- and/or near-wavelength longitudinal nanoscale Bragg gratings produced by femtosecond laser pulses in this regime could be visualized in less hard lithium niobate by atomic force microscopy cross-sectional analysis in the correlation with the corresponding sideband spectral components, supporting the anticipated Bragg filtering mechanism and envisioning the corresponding grating periods.

Research on key risk chain mining method for urban rail transit operations: A new approach to risk management

To ensure the safety of urban rail transit operations and uncover the transmission dynamics of risk sources, a key risk chain mining method for urban rail transit operation is proposed. Firstly, the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation, which adds a new hazard degree evaluation index to the traditional Apriori algorithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources. Secondly, we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges, and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network. Finally, using the actual urban rail transit operation data of a city in China as an example, a total of 17 key risk chains are mined, and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and frequency analysis of key risk chains, and control plans are proposed. The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations, shedding light on the propagation mechanisms, triggering probabilities, and degrees of unsafety associated with risk sources. The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.

ECG Signal Denoising Using an Improved Hybrid DWT-ADTF Approach.

The electrocardiogram signal (ECG) presents a fundamental source of information to consider for the diagnosis of a heart condition. Given its low-frequency features, this signal is quite susceptible to various noise and interference sources. This paper presents an improved hybrid approach to ECG signal denoising based on the DWT and the ADTF methods. The proposed improvements consist of integrating an adaptive [Formula: see text] parameter into the ADTF approach, combining a soft thresholding ADTF-based process with the DWT details, along with employing the mean filter to handle the baseline wandering noise. Furthermore, the proposed approach incorporates several denoising measures based on various proposed noise features, which have also been introduced in this approach. Several real noises collected from the Noise Stress Test Database (NSTDB), as well as several synthetic noises at different SNR levels, are proposed to ensure a thorough assessment of the proposed method's performance. The evaluation focuses on the SN Rimp, PRD, and MSE parameters, as well as the SINAD parameter as a diagnostic distortion measurement. Furthermore, a time complexity evaluation is proposed. The proposed approach demonstrated promising results compared to a recent hybridization of the DWT and ADTF methods, as well as recently published ECG signal denoising-based approaches in various real and synthetic noise cases using different statistical evaluation metrics. In the vast majority of the study cases, the proposed approach outperforms the compared methods in terms of statistical results for real and synthetic noises. Furthermore, compared to these methods, it provides a fairly low time complexity. This is consistent with the ambition of embedding this approach in low-cost hardware architectures.

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework.

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.

MFF-Net: A multitask feature fusion network in dual-frequency domains for detecting smoke from one single picture

The industrial smoke detection is extreme significant for sustainable development and human health, but up to date, its related technologies have not met the real-time and accurate requirements, particularly in the memory-constrained conditions. Towards detecting smoke from one single picture more accurate and efficient, in this paper we present a multitask feature fusion network, dubbed MFF-Net. To specify, according to the characteristics of imbalance found in the frequency distribution of numerous smoke pictures, our MFF-Net is set up with a dual-channel framework, which is composed of the high-frequency and low-frequency channels that process different frequency components respectively. The high-frequency channel is built based on a stream of cross-level fusion inverted residual (CFIR) blocks, each of which encompasses the expansion layer, filtering layer, combination layer, and fusion layer, while the low-frequency channel is formed by embedding the attention mechanism into CFIR blocks. We concatenate the dual channels mentioned above to establish the MFF-Net, in order to systematically fuse high- and low-frequency features of a single smoke picture. As compared with the existing relevant detection models, the depthwise and pointwise convolutions are utilized to make networks more efficient with respect to size and speed, while the dense skip connection is used behind to fuse cross-level features (i.e., semantic and visual features) for keeping high accuracy. Experimental results show that our proposed MFF-Net can trade off multiple tasks in accuracy, memory and computational efficiency.

MFAGNet: multi-scale frequency attention gating network for land cover classification

ABSTRACT As a classic problem of high-resolution remote sensing image tasks, land cover classification has many challenges, for instance, variable scales and low-edge discrimination. Traditional spatial semantic segmentation methods based on single-scale feature extraction are challenging to model multi-scale features. They cannot effectively fit high-resolution remote sensing images with variable scales and are prone to misclassification issues. On the other hand, spatial multi-scale semantic segmentation methods lack edge constraints, which can easily lead to edge segmentation ambiguity issues. We propose a segmentation network based on a multi-scale frequency-domain attention gating mechanism (MFAGNet) to solve the above problems. Specifically, we obtain multi-scale features through multi-scale input and design the discrete cosine transform channel attention module (DCTCAM). In DCTCAM, we extract global low-frequency features and effectively alleviate misclassification caused by scale changes. The edge features of the image are obtained through the high-frequency feature extraction module (HFEM) by using edge detection operators to enhance the model’s ability to recognize edges. Additionally, no further pooling operations are employed except for a single max pooling layer in the backbone network. In this paper, we design a new gating mechanism to dynamically control the extraction ratio of various frequency features so that it can adapt to input images of different scales. This paper conducts comparative experiments on three high-resolution land cover datasets: GID, Vaihingen, and Potsdam. The new algorithm proposed compares with classic semantic segmentation models such as DANet, PSPNet, SegNet, UNet, DeepLabv3+, FcaNet, BiSeNet, AFN, and FastFCN. The experimental results show that MFAGNet is superior to the comparison models. With no significant increase in computing overhead, MFAGNet obtains MIoU of 69.05%, 73.40%, and 79.42% on the standard data sets GID, Vaihingen, and Potsdam, respectively.

Spectral Dual-Channel Encoding for Image Dehazing

In recent years, deep learning-based dehazing models have presented a momentum of dramatic growth. Unfortunately, most deep learning-based approaches heavily rely on synthetically hazed images for model training, which makes these methods brittle to restore hazy images taken from real-world scenes, due to the sample distribution discrepancy between synthetic and realistic images. Although some attempts have been made to overcome this difficulty by augmenting image spatial features with spectral features, the power of the spectral features still remains underutilized. In this paper, we propose the Spectral Dual-Channel Encoding (SDCE) framework for high-quality image dehazing, by unleashing the power of spectral feature encoding. We argue that hazes impose more adverse impacts on high-frequency image features (e.g., outlines and textures) than low-frequency features (e.g., colors), with theoretical and empirical justifications. To better restore hazed high- and low-frequency features, we decompose the hazed images into high- and low-frequency feature components with spectral dual-channel encoding and respectively design effective neural network architectures to recover hazed images on the two feature components. To be specific, we recover the low-frequency feature components with an encoder-decoder, while we specially design a high-frequency aggregation component (HFAC) to recover hazed images on high-frequency feature components, by referring to neighboring feature distributions. We conduct extensive experiments on four real-world image dehazing benchmarks. The experimental results show that our proposed SDCE framework outperforms the state-of-the-art baselines significantly, with an average 4.4% improvement in PSNR and an average 7.7% gain in SSIM.

2D CNN‐based multi‐feature fusion detection method for the magnetic anomaly generated by submarine wake

A Two-Dimensional Convolutional Neural Network (2D-CNN)-based multi-feature fusion detection method is proposed to improve the detection performance of the submarine wake magnetic anomaly in view of its characteristics of low frequency and low signal-to-noise ratio (SNR). The method involves pre-processing the original signal by using the Savitzky-Golay (S–G) filter, followed by Residual Structure processing to extract the time-domain information, FFT to extract the frequency domain information and Minimum-Entropy Filter (MEF) for noise analysis. The 2D-CNN model with three processing branches is utilised for further feature extraction and signal judgement. To train the method, Simulated target signal dataset is obtained through the submarine wake magnetic anomaly simulation model, and the simulated original signal is acquired by stacking measured noise. The proposed method exhibits great detection performance for signals with different Signal-to-Noise Ratios (SNRs) and various types of noise, achieving a recognition accuracy of 90% for signals with SNRs above -10dB. The theoretical detection range of the submarine has been increased to over 1 km, outperforming similar neural networks based on magnetic dipole models.