Low-frequency Characteristics Research Articles

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Ship-Radiated Noise Separation in Underwater Acoustic Environments Using a Deep Time-Domain Network

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

Salient object detection in low-light RGB-T scene via spatial-frequency cues mining

Low-light conditions pose significant challenges to vision tasks, such as salient object detection (SOD), due to insufficient photons. Light-insensitive RGB-T SOD models mitigate the above problems to some extent, but they are limited in performance as they only focus on spatial feature fusion while ignoring the frequency discrepancy. To this end, we propose an RGB-T SOD model by mining spatial-frequency cues, called SFMNet, for low-light scenes. Our SFMNet consists of spatial-frequency feature exploration (SFFE) modules and spatial-frequency feature interaction (SFFI) modules. To be specific, the SFFE module aims to separate spatial-frequency features and adaptively extract high and low-frequency features. Moreover, the SFFI module integrates cross-modality and cross-domain information to capture effective feature representations. By deploying both modules in a top-down pathway, our method generates high-quality saliency predictions. Furthermore, we construct the first low-light RGB-T SOD dataset as a benchmark for evaluating performance. Extensive experiments demonstrate that our SFMNet can achieve higher accuracy than the existing models for low-light scenes.

DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion

The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, QAB/F, FMI, and Qs metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are 2.06%, 11.95%, 21.04%, 21.52%, 1.04%, and 0.09% higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods.

Frequency-Separated Attention Network for Image Super-Resolution

The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep networks are challenging to train, thus failing to exhibit the expressive capability commensurate with their depth. High-frequency and low-frequency features in images play different roles in image super-resolution. Networks based on CNNs, which should focus more on high-frequency features, treat these two types of features equally. This results in redundant computations when processing low-frequency features and causes complex and detailed parts of the reconstructed images to appear as smooth as the background. To maintain long-term memory and focus more on the restoration of image details in networks with strong representational capabilities, we propose the Frequency-Separated Attention Network (FSANet), where dense connections ensure the full utilization of multi-level features. In the Feature Extraction Module (FEM), the use of the Res ASPP Module expands the network’s receptive field without increasing its depth. To differentiate between high-frequency and low-frequency features within the network, we introduce the Feature-Separated Attention Block (FSAB). Furthermore, to enhance the quality of the restored images using heuristic features, we incorporate attention mechanisms into the Low-Frequency Attention Block (LFAB) and the High-Frequency Attention Block (HFAB) for processing low-frequency and high-frequency features, respectively. The proposed network outperforms the current state-of-the-art methods in tests on benchmark datasets.

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMGs), two piezoelectric nanogenerators (PENGs), and two multilayer-structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm−3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

A joint feature refining and multi-resolution feature learning approach for bandwidth extension of speech coded by EVS codec

The bandwidth of the speech signals is often limited in speech communications due to the specifications of the standardized codecs or insufficient bitrates. So, we present a bandwidth extension (BWE) framework of speech signals coded by the enhanced voice services (EVS) codec. Previous studies on speech bandwidth extension based on convolutional neural network (CNN) which exist channel and spatial redundancy feature information. This work proposes an end-to-end architecture which combines novel channel and spatial reconstruction module. Specifically, the spatial reconstruction module utilizes a mask to distinguish high-frequency and low-frequency features. Group convolutions is then employed to enhance high-frequency features and suppress low-frequency features. The channel reconstruction module is introduced to reduce unnecessary feature information. Additionally, we introduce a novel time–frequency loss function, incorporating time-domain loss and frequency-domain loss based on Mel-spectrum and multi-resolution representation, to optimize the network. To assess the model and loss function’s performance, we conducted experiments on subdatasets encoded at rates of 6.6kbps, 7.2kbps, and 8kbps, respectively. The experimental results demonstrated that our proposed model surpassed other baseline models in terms of LSD, SNR, PESQ, and MOS scores.

Active feedback control on acoustic radiation of metastructure shell with low-frequency and broadband characteristics

Due to the tunable characteristics of elastic waves, the vibroacoustic coupling behavior of a mechanical metastructure is a hot topic of underwater vehicles. In this work, a metastructure shell with active feedback control is presented and fabricated. The dynamic effective density and sound pressure level are derived to find the influences of acceleration and displacement feedback control. Different from a single cylinder, a double cylinder structure has both in-phase and anti-phase modes. Numerical results are obtained by Fourier transform and harmonic series expansion. With the introduction of an active feedback control system, the reduction of acoustic radiation shows low-frequency and broadband characteristics. In addition, finite element simulation is applied to support numerical results and present vibroacoustic characteristics. Finally, an experiment is performed in the anechoic chamber to illustrate the quiet metastructure shell, which can be applied to new designs of underwater vehicles.

DF3Net: Dual frequency feature fusion network with hierarchical transformer for image inpainting

In recent years, researchers have made significant strides in computer vision by leveraging transformers, achieving remarkable breakthroughs in low-level vision tasks. The inherent long-range dependency of transformers grants them potent remote modeling capabilities, surpassing those of Convolutional Neural Networks (CNNs) and enabling the extraction of global features and accurate semantic structures. However, it has been observed that single transformer frameworks lack sensitivity to high-frequency information in images, resulting in the generation of blurry reconstructed regions. To address this limitation, this paper proposes a novel two-branch Dual Frequency Feature Fusion Network (DF3Net) for image inpainting based on the hierarchical atrous transformer (HAT). Specifically, the head of the dual-frequency convolution (DFC) module decouples the feature maps into low and high-frequency components. The low-frequency factor goes through the proposed HAT branch, while the high-frequency component is input into the gated convolution module branch, effectively capturing both global structural information and local texture details. Finally, the DFC tail fuses the high and low-frequency features to output the reconstructed image. Moreover, the feature fusion between high and low-frequency branches is performed layer-wise within the network, enabling mutual learning between the two branches interactively, ensuring coherence in image semantic structure and texture details. Experimental evaluations on Places2, Paris StreetView, and CelebA-HQ with different mask ratios demonstrate that the proposed method outperforms state-of-the-art methods in enhancing the structural accuracy of image inpainting. It generates semantically reasonable images with fine texture details.

Hollow Fe3O4/Fe@C nanocubes for broadband microwave absorption spanning low- and high-frequency bands

Rationally constructing hollow structures with heterogeneous interface is usually an effective strategy to regulate the electromagnetic response behavior for lightweight and strong absorption, but achieving low-frequency and broadband absorption is a major challenge. This work adopts a novel Fe(II)/Fe(III) ions regulation strategy to design and synthesize hollow Fe(OH)X (x = 2, 3) nanocubes, which addresses the disadvantages of time-consuming or using etchant. Subsequently, a unique hollow Fe3O4/Fe@C nanoboxes with adjustable magnetic composition and uniformly coated shell have been successfully synthesized by in-situ carbonization with the assistance of phenolic resin and hydrogen thermal reduction. Notably, the different magnetic components induce the magnetic coupling assembly and play a crucial role in determining its electromagnetic parameters, whereas Fe3O4 contributes to more dipole polarization and stronger low-frequency response characteristics. For Fe3O4@C hollow nanoboxes, the minimum reflection loss (RLmin) is as low as –119.9 dB at 2.3 mm, and extremely wide effective absorption bandwidth (EAB) of 11.4 GHz is achieved spanning low-frequency band of 2.8 ∼ 7.2 GHz and high-frequency band of 11.0 ∼ 18 GHz at 5.0 mm. Surprisingly, the Fe3O4@C also has satisfactory EAB20 (RL＜ –20 dB) reaching 7.1 GHz (14.9 ∼ 18 GHz) at a thin thickness of 1.8 mm. The RLmin of other hollow nanoboxes is also below –30 dB, and the EAB is greater than 9 GHz. This study provides a competitive candidate for efficient microwave absorption in complex frequency bands, especially low-frequency bands.

A Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation

Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.

Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics

To address the issues of high concealment and difficult positioning of loose bolts in transmission towers, this paper proposes a new method for locating loose bolts in transmission towers. In this method, we divide the vibration response of the transmission tower into low-frequency signals of 2–25 Hz and high-frequency signals of 25–75 Hz. For the low-frequency signals, the single signal component is obtained by adaptive Chirp mode decomposition and uses the general demodulation transformation to deeply denoise the non-modal information. Since frequency characteristics themselves do not contain time information, considering the importance of time information for positioning, we propose a low-frequency time-varying frequency feature that preserves time characteristics based on synchronous wavelet transform and peak search. For the high-frequency signals, we use singular value decomposition to remove signal outliers caused by pulse excitation and eliminate forced vibrations through wavelet packet transform. Without altering its inherent characteristics, this method enables high-frequency time-domain signals to better represent the nonlinear characteristics of transmission towers. Furthermore, based on the powerful capabilities of Timesnet and Transformer in dealing with time series data, we propose a fault diagnosis model, which ultimately achieves the positioning of loose bolts in transmission towers. The bolt node model proves that this approach can better represent the loose bolt characteristics, and the transmission tower model verifies the effectiveness of this approach in locating loose bolts in transmission towers. Finally, bolt-loosening tests were conducted on a 110 kV transmission tower, and the accuracy of the positioning results reached 92.8%, demonstrating the effectiveness and efficiency of this method in practical positioning applications.

The multi-dimensional joint response of the electric potential time–frequency-field in the fracture process of flawed coal under dynamic load

The fracture failure of flawed coal has always been a problem in underground engineering. Therefore, it is necessary to monitor crack propagation and damage evolution during coal fracture. In this study, the split Hopkinson pressure bar (SHPB) and digital image correlation (DIC) were combined to analyze the impact failure electric potential (EP) of flawed coal samples. The multi-dimensional joint response characteristics of coal EP time–frequency-field under dynamic load were studied and the differential expression and multi-fractal characteristics of EP responses at different measuring points were analyzed. Based on the EP signals, the evolution of damage to the coal was calculated. The results show that the time series evolution of EP and stress–strain curves during coal failure is consistent. The generation of fracture zones will excite the high-value area of local EP, and the spectral distribution of EP signals in this area shows the characteristics of low frequency and high amplitude. Each channel’s EP signal and the degree of deformation at the corresponding place are perfectly matched. The multi-fractal spectrum of the EP signal close to the fracture zone has a higher Δα and a lower Δf, which demonstrates the dominance of the large-scale EP signal produced in this region by the destruction of sample. Theoretical study indicates that the main causes of the anomalous distribution of the EP field are crack tip potential, diffusion potential, etc. The results can provide theoretical basis for the evaluation of coal fracturing by EP technology.

SDFIE-NET – A self-learning dual-feature fusion information capture expression method for birdsong recognition

Bird recognition is important for the monitoring of bird populations and the protection of ecosystems. Identifying birds through image forms can be difficult due to the complexity of natural environments. Song-based bird recognition allows for bird identification with only a small amount of background noise introduced, however, efficiently recognizing bird songs remains a challenging task. Based on this problem, this paper proposed a self-learning dual-feature fusion information capture expression method (SDFIE-NET) for recognizing birdsong. Firstly, using the Mel filter excerpt the low-frequency characteristics of the bird song. Since fixed-parameter filters are incapable of achieving different feature extraction effects based on different birdsong. In this paper, we incorporate a fully learnable audio classification front-end Leaf architecture for the extraction of bird song feature information, which can self-learn different extraction parameters for the birdsong. Effectively combining the high-frequency feature information and low-frequency differences acquired by the two approaches corresponds to the declared dual-feature fusion module (SCDFF), reducing information redundancy and improving characterization capability. Secondly, the backbone network utilizes SDFIE-NET, which is composed of the Fused-MBConv module and modified CA-MBConv module. The Criss-Cross Attention module is added after each layer composed of Fused-MBConv modules. This improves the speed and accuracy of effective information transfer between internal modules and increases the expressive power of the model at the pixel level. To enhance the anti-interference and generalization ability of the model, we constructed a self-made dataset (Bird_alldata) consisting of 30 kinds of birdsong. On this dataset, we performed a variety of experiments, and recognition accuracy reached 95.77 % and the F1-score reached 95.52 %. Generalization experiments were conducted on the environmental sound dataset Urbansound8K and the bird song dataset Birdsdata, and the model achieves recognition accuracies of 94.05 % and 94.10 % on the two datasets, with F1-scores of 94.21 % and 94.05 %, respectively.

Multi-feature Learning Adaptive Network for Underwater Image Enhancement

Underwater image enhancement faces variety of challenges owing to the diversity of underwater scenes (viewed as water types) and the rich multi-frequency information. To deal with these challenges, this paper proposes a multi-feature learning adaptive underwater image enhancement network comprising an adaptive module and a dual-layer synchronous enhancement network. First, we design an adaptive module which enables the determination of water type inside the model and eliminates the negative effect of water type diversity by building water type related features. Then, the model learns high-frequency and low-frequency features through a dual-layer synchronous enhancement network to extract more comprehensive information. Finally, the outputs of the dual-layer network are merged to obtain more realistic underwater enhanced images. Numerous experiments have shown that the proposed method outperforms the comparison method for visual perception and assessment metrics.

Numerical study on acoustic characteristics of flow boiling in a helical tube

Helically coiled steam generators have advantages of strong heat transfer capacity, compact structure, and low thermal stress, and are widely used in many engineering fields. However, the heat transfer performance is still limited by the critical heat flux, beyond which boiling deterioration is an important cause of safety accidents. Both engineering and experimental studies have found that complex noises could be frequently generated during the occurrence of boiling crisis. In this paper, the acoustic characteristics of both subcooled and saturated flow boiling in a helical tube were numerically investigated, by using the volume of fluid scheme and acoustic model. The distribution of the vapor-liquid phase, flow pattern transition, and acoustic frequency band characteristics during boiling were analyzed under different wall temperatures (varied superheat), inlet subcooling, and flowrates. It’s found that the flow pattern under low mass flowrate was plug flow, slug flow, and wavy flow. All the three flow patterns have unique boiling acoustic features. The plug flow, consisted by a large number of small bubbles, shows the distinct high-frequency band characteristics of the boiling acoustics. The wavy flow, which is composed mainly by large bubbles, exhibits a significant low-frequency band characteristics of the boiling acoustics.

Learning Generalized Segmentation for Foggy-Scenes by Bi-directional Wavelet Guidance

Learning scene semantics that can be well generalized to foggy conditions is important for safety-crucial applications such as autonomous driving. Existing methods need both annotated clear images and foggy images to train a curriculum domain adaptation model. Unfortunately, these methods can only generalize to the target foggy domain that has seen in the training stage, but the foggy domains vary a lot in both urban-scene styles and fog styles. In this paper, we propose to learn scene segmentation well generalized to foggy-scenes under the domain generalization setting, which does not involve any foggy images in the training stage and can generalize to any arbitrary unseen foggy scenes. We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style. As the content (e.g., scene semantic) rests more in low-frequency features while the style of urban-scene and fog rests more in high-frequency features, we propose a novel bi-directional wavelet guidance (BWG) mechanism to realize the above three objectives in a divide-and-conquer manner. With the aid of Haar wavelet transformation, the low frequency component is concentrated on the content enhancement self-attention, while the high frequency component is shifted to the style and fog self-attention for de-correlation purpose. It is integrated into existing mask-level Transformer segmentation pipelines in a learnable fashion. Large-scale experiments are conducted on four foggy-scene segmentation datasets under a variety of interesting settings. The proposed method significantly outperforms existing directly-supervised, curriculum domain adaptation and domain generalization segmentation methods. Source code is available at https://github.com/BiQiWHU/BWG.

Frequency Shuffling and Enhancement for Open Set Recognition

Open-Set Recognition (OSR) aims to accurately identify known classes while effectively rejecting unknown classes to guarantee reliability. Most existing OSR methods focus on learning in the spatial domain, where subtle texture and global structure are potentially intertwined. Empirical studies have shown that DNNs trained in the original spatial domain are inclined to over-perceive subtle texture. The biased semantic perception could lead to catastrophic over-confidence when predicting both known and unknown classes. To this end, we propose an innovative approach by decomposing the spatial domain to the frequency domain to separately consider global (low-frequency) and subtle (high-frequency) information, named Frequency Shuffling and Enhancement (FreSH). To alleviate the overfitting of subtle texture, we introduce the High-Frequency Shuffling (HFS) strategy that generates diverse high-frequency information and promotes the capture of low-frequency invariance. Moreover, to enhance the perception of global structure, we propose the Low-Frequency Residual (LFR) learning procedure that constructs a composite feature space, integrating low-frequency and original spatial features. Experiments on various benchmarks demonstrate that the proposed FreSH consistently trumps the state-of-the-arts by a considerable margin.

Low-frequency forbidden band characteristics of metamaterials based on combined resonant units

With a view to increasing the width of the local resonant band gaps and obtaining damping at low-frequency, a metamaterial structure with multiple resonant units and an adjustable band gap is proposed. The single-unit structure of the metamaterial consists of a main system and three resonant units. Simultaneously, the energy band structure and frequency response characteristics of the metamaterial are investigated with the finite element method and the dispersion equation. Further, the effects of natural frequency, mass of resonant units, mass ratio, and stiffness ratio on the energy band structure are demonstrated. Also, the effect of damping on the band gap in the metamaterial structure is examined. The low-frequency vibration isolation performance of the structure is verified on experimental platform. In conclusion, the designed metamaterial structure provides three adjustable band gaps in the low-frequency range with good vibration damping.

Improving adversarial transferability through frequency enhanced momentum

The emergence of adversarial examples seriously affects the practical security deployment of convolutional neural networks. The existing attack algorithms perform brilliantly under white-box scenarios, but they show weak transferability when faced with unknown black-box models. Recent studies have revealed that models have different interests in different frequency components of images, and the low-frequency characteristics play a non-negligible role in the decision-making of models. In this article, we present the frequency enhanced momentum iterative attack, called FE-MI-FGSM. Specifically, we use multiple convolution kernels for Gaussian filtering of the image before each gradient update to push the processed image closer to the common decision boundaries of multiple models. Then, the average gradient of the white-box model to these processed images is obtained and used as the perturbation direction to generate adversarial examples with a high success rate of white-box attack and high transferability. The empirical results show that compared with the current mainstream gradient-based methods, our method performs better on both normally trained and adversarially trained models. Besides, our method can combine with gradient-based methods which integrate convergence algorithms or input transformations for the sake of reaching satisfactory improvement of the transferability.