Frequency Domain Research Articles

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.

Evaluation of transformation invariant loss function with distance equilibrium in prediction of imaging photoplethysmography characteristics

Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative. Approach. In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC). Main results. The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively. Significance. This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

The link between the acoustic characteristics of worship spaces and their emotional impact.

The emotional impact of sound depends on its characteristics. Given that such characteristics can be modified when the sound is played in a room, it is crucial to examine how the acoustic characteristic of a room can impact the emotional experience. Researchers have investigated the links between the acoustic parameters of concert halls and the emotional impact; however, the applicability of such links to other building types, such as worship spaces, needs to be studied. In a previous study, the author analyzed the emotional response to the acoustic environments of worship spaces using self-report and physiological indicators. This paper builds on the previous work by analyzing the acoustic parameters of the buildings from the previous study and establishing links between such parameters and the emotional response indicators. It also analyzes the frequency domain to study the role of the interaction between the room and sound source in enhancing the emotional experience and introduces resonance quality (Q) and resonance width (Δf) as important parameters to consider in room acoustics. The results showed the significance of considering frequencies < 1000 Hz in analyzing the acoustics of the studied worship spaces and demonstrated that amplifying the dominant frequency range of the sound source was linked to enhancing the emotional experience.

Non pilot data-aided carrier and sampling frequency offsets estimation in fast time-varying channel

This paper considers the non pilot data-aided estimation of the carrier frequency offset (CFO) and sample frequency offset (SFO) of orthogonal frequency division multiplexing (OFDM) signals in fast time-varying channel. The main obstacle is the time-variant channel response, which deteriorates the estimation validity. A practical approach to mitigate this impact is to reduce the time consumption of one-shot estimation. In this way, we propose a method to reduce the time consumption to within one OFDM symbol duration. The maximum likelihood (ML) estimator is derived based on the observations of frequency domain constellations output of two FFTs on one symbol; its closed-form approximation is then derived to reduce the calculation burden. Remarkably, our method does not require any training symbol or pilot tone embedded in the signal spectrum, therefore achieves the highest spectral efficiency. Theoretical analysis and simulation results are employed to assess the performance of proposed method in comparison with existing alternatives.

Resonance Detection Method of AC-DC Hybrid Micro-grid Based on Wavelet Transform and Fast Fourier Transform

The grid connection of a mass of distributed power sources in the AC/DC hybrid micro-grid will cause harmonics, voltage sags, voltage fluctuations, and flicker in the grid, which seriously affects the power quality of the grid. Therefore, the power quality problems such as the resonance generated by the AC/DC hybrid micro-grid need to be studied urgently. In this paper, a resonance detection technology based on the combination of wavelet transform (WT) and fast Fourier transform (FFT) is proposed. This method uses wavelet transform to process the high-frequency and low-frequency parts of the signal separately, and eliminates part of the interference signal. The method then uses the FFT to obtain the resonance content. The AC/DC hybrid micro-grid resonance detection method based on WT and FFT can not only determine the time and amplitude of the resonance signal, but also filter the interference signal and improve the accuracy of frequency domain analysis. It is verified by simulation that the algorithm can detect the resonance of the AC/DC hybrid micro-grid.

Refining Localized Attention Features with Multi-Scale Relationships for Enhanced Deepfake Detection in Spatial-Frequency Domain

The rapid advancement of deep learning and large-scale AI models has simplified the creation and manipulation of deepfake technologies, which generate, edit, and replace faces in images and videos. This gradual ease of use has turned the malicious application of forged faces into a significant threat, complicating the task of deepfake detection. Despite the notable success of current deepfake detection methods, which predominantly employ data-driven CNN classification models, these methods exhibit limited generalization capabilities and insufficient robustness against novel data unseen during training. To tackle these challenges, this paper introduces a novel detection framework, ReLAF-Net. This framework employs a restricted self-attention mechanism that applies self-attention to deep CNN features flexibly, facilitating the learning of local relationships and inter-regional dependencies at both fine-grained and global levels. This attention mechanism has a modular design that can be seamlessly integrated into CNN networks to improve overall detection performance. Additionally, we propose an adaptive local frequency feature extraction algorithm that decomposes RGB images into fine-grained frequency domains in a data-driven manner, effectively isolating fake indicators in the frequency space. Moreover, an attention-based channel fusion strategy is developed to amalgamate RGB and frequency information, achieving a comprehensive facial representation. Tested on the high-quality version of the FaceForensics++ dataset, our method attained a detection accuracy of 97.92%, outperforming other approaches. Cross-dataset validation on Celeb-DF, DFDC, and DFD confirms the robust generalizability, offering a new solution for detecting high-quality deepfake videos.

Dual-domain faster Fourier convolution based network for MR image reconstruction

Deep learning methods for fast MRI have shown promise in reconstructing high-quality images from undersampled multi-coil k-space data, leading to reduced scan duration. However, existing methods encounter challenges related to limited receptive fields in dual-domain (k-space and image domains) reconstruction networks, rigid data consistency operations, and suboptimal refinement structures, which collectively restrict overall reconstruction performance. This study introduces a comprehensive framework that addresses these challenges and enhances MR image reconstruction quality. Firstly, we propose Faster Inverse Fourier Convolution (FasterIFC), a frequency domain convolutional operator that significantly expands the receptive field of k-space domain reconstruction networks. Expanding the information extraction range to the entire frequency spectrum according to the spectral convolution theorem in Fourier theory enables the network to easily utilize richer redundant long-range information from adjacent, symmetrical, and diagonal locations of multi-coil k-space data. Secondly, we introduce a novel softer Data Consistency (softerDC) layer, which achieves an enhanced balance between data consistency and smoothness. This layer facilitates the implementation of diverse data consistency strategies across distinct frequency positions, addressing the inflexibility observed in current methods. Finally, we present the Dual-Domain Faster Fourier Convolution Based Network (D2F2), which features a centrosymmetric dual-domain parallel structure based on FasterIFC. This architecture optimally leverages dual-domain data characteristics while substantially expanding the receptive field in both domains. Coupled with the softerDC layer, D2F2 demonstrates superior performance on the NYU fastMRI dataset at multiple acceleration factors, surpassing state-of-the-art methods in both quantitative and qualitative evaluations.

A New Biorthogonal Spline Wavelet-Based K-Layer Network for Underwater Image Enhancement

Wavelet decomposition is pivotal for underwater image processing, known for its ability to analyse multi-scale image features in the frequency and spatial domains. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCS-SW), based on the Cohen–Daubechies–Feauveau (CDF) wavelet construction method and the cubic special spline algorithm. BCS-SW has better properties in compact support, symmetry, and frequency domain characteristics. In addition, we propose a K-layer network (KLN) based on the BCS-SW for underwater image enhancement. The KLN performs a K-layer wavelet decomposition on underwater images to extract various frequency domain features at multiple frequencies, and each decomposition layer has a convolution layer corresponding to its spatial size. This design ensures that the KLN can understand the spatial and frequency domain features of the image at the same time, providing richer features for reconstructing the enhanced image. The experimental results show that the proposed BCS-SW and KLN algorithm has better image enhancement effect than some existing algorithms.

ОСНОВНІ РЕЖИМИ РОБОТИ І ГРАФІЧНИЙ ІНТЕРФЕЙС КОРИСТУВАЧА КОМПЛЕКСУ ДЛЯ ЕКСПЕРИМЕНТАЛЬНИХ ДОСЛІДЖЕНЬ МАГНІТНИХ ПОЛІВ І ДІАГНОСТУВАННЯ ЕЛЕКТРОЕНЕРГЕТИЧНОГО ОБЛАДНАННЯ

Hardware and software monitoring complex is designed to study the relationship between changes in the patterns of the magnetic field intensity distribution with power equipment failures. In experimental studies with predetermined techni-cal faults artificially introduced into the object the complex will provide useful and reliable results by registering changes in the magnetic field induction in the spatial, temporal and frequency domains. The choice and flexible adjust-ment of data collection tools by the researcher will ensure the use of the most appropriate method for measuring, con-verting and displaying information about the investigated faults. The article presents the main operating modes and graphical user interfaces of a personal computer and a mobile data acquisition system that jointly convert, store, ana-lyze and display measurement information received from various magnetic sensors. The generalized functional specifi-cation of the complex software, the design of the graphical user interface (GUI) as parts of the real-time operating sys-tem (RTOS) of the mobile data acquisition system (DAQ) and PC software will also be used in the development of prob-lem-oriented computer diagnostic systems for electric power equipment. Ref. 12, fig. 3.

On performance evaluation of high‐power, high‐bandwidth current measurement technologies for SiC switching devices

AbstractSilicon carbide (SiC) power metal‐oxide‐semiconductor field‐effect transistors (MOSFETs) switch at an unprecedented speed, even at high currents. For accurate dynamic characterization, current sensors must measure high currents at a high bandwidth. Moreover, at high switching speeds, parasitic impedances in the commutation loop become critical. To ensure high‐accuracy measurements, the current sensor insertion impedance must be minimal. Here, a two‐step current sensor evaluation method is proposed. This method serves the characterization and suitability assessment of high‐power, high‐bandwidth current sensors for fast‐switching applications using SiC power MOSFETs. Conducting a small‐ and a large‐signal transmission behaviour analysis separately results in holistic information about the current sensor behaviour in both time and frequency domain. The proposed method is validated using four commercially available current sensors that are widely used for SiC power MOSFET characterization. The work concludes transferring the knowledge derived in the conducted experiments to a practical, application‐oriented sensor selection guide.

Trend change analysis in the assessment of body balance during posture adjustment in reaction to anterior-posterior ground perturbation.

Postural adjustments (PA) occur to counteract predictable perturbations and can be impaired as a result of musculoskeletal and neurological dysfunctions. The most common way to detect PA is through electromyography measurements or center of pressure (COP) position measurements, where analysis in time domain and frequency domain are the most common. Aim of the research was to determine whether a new method of analyzing stabilographic measurements-the COP trend change analysis (TCI) of temporary posture corrections- can expand understanding of changes in balance strategy connected with PA. The study group involved 38 individuals (27women, 11men) aged 23±2.6 years. Measurements were performed using a stabilographic platform placed on a perturbation platform. The tests involved three measurements with forward and backward momentary movements of the platform. Participants were tested in three conditions-knowing the nature, time and direction of perturbation (Tr3), knowing only the nature of perturbation (Tr2) and without any information about the perturbation (Tr1). Statistically significant differences were revealed in the last second of Tr3 for the mean velocity of COP (p<0.05) and for two TCI parameters-TCI_dV (p<0.05) and TCI_dS (p<0.01). The increase in TCI_dV was related to the increase in the mean distance between trend changes (TCI_dS) and constant value of the mean time between trend changes (TCI_dT). The increase of the mean value of TCI_dS was the result of smaller number of posture corrections with the distance 0-2 mm and lager number with the distance 4-6 mm. Obtained results proved that the TCI analysis is a method enabling an extended analysis of PA, indicating the nature of changes occurring in posture corrections-longer momentary jumps of COP-related to a change in the strategy of maintaining balance before a known disorder, which has not been analyzed in this type of research so far.

Measuring crisis from climate risk spillovers in European electricity markets

This paper studies how climate risks spill over to European electricity markets across time and frequency domains using the connectedness network approach. By introducing three climate risk measures—the climate policy uncertainty index, climate physical risk index, and climate concern index—the empirical results reveal the vulnerability of European electricity markets to volatile climate policies and extreme climate crisis events. Overall, the spillover effects in the climate–electricity nexus are primarily concentrated in the medium- and long-term frequency domains, indicating that the impact of climate risk shocks is persistent. Compared with the impact of climate physical risks and climate concern shocks, the spillovers from climate policy uncertainty shocks to the electricity market are higher and more persistent. These findings highlight the need to consider the heterogeneous impacts of climate risks on electricity markets at different frequency bands.

Optimization Analysis of the Arrangement of the Submerged Floating Tunnel Subjected to Waves

The motion responses, mooring tensions, and submergence depth are the dominant factors for the arrangement of the Submerged Floating Tunnel (SFT) subjected to waves. Generally, the maximum values of motion responses, mooring tensions, and absolute submergence depth are mainly focused on. In the present study, experiments are implemented to measure the motion responses and mooring tensions of the SFT with different mooring patterns and submergence depths under waves with different characteristic wave heights and periods. In order to evaluate the arrangement of the SFT more effectively and comprehensively, besides the maximum values, several new characteristic parameters are introduced. Such parameters account for the motion responses in the frequency domain, the uniformity of the tension distribution, the length of time during which the cable reaches a relaxed condition during wave action, the KC number, the dimensionless period, the wave height, and the submergence depth. The results from the optimization analysis show the following: according to the characteristic values of motion responses and mooring tensions, the pattern of diagonal cables is better than that of diagonal cables + vertical cables; and within the range of the present experiments, there are optimal dimensionless parameters—the dimensionless submergence depth d0/LP ≥ 0.15, the KC number ≤ 0.8, or the dimensionless wave height Hs/d0 ≤ 0.10—for the condition of which the dynamic responses and mooring tensions vary slightly.

Comparison of autonomic dysfunction in patients with Parkinson's Disease, progressive supranuclear palsy, and multiple system atrophy.

To assess and compare autonomic nervous system (ANS) dysfunction, especially cardiovascular dysautonomia, in Parkinson's Disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and healthy controls. Assessment of ANS can be useful in differential diagnosis. Dysautonomia affects quality of life and can lead to potentially life-threatening complications. There is very little literature data regarding dysautonomia in PSP in relation to other parkinsonian syndromes. This study expands the knowledge about ANS dysfunction in parkinsonisms, especially PSP. Patients with PD, MSA and PSP were prospectively recruited to our study. Demographic data and information about clinical and neuropsychological assessment, medication and comorbidities was collected. SCOPA-AUT questionnaire, 5-minute tilt test, and 5-minute heart rate variability (HRV) analysis in time and frequency domains were used to assess ANS. Analysis was also performed in patients with PSP-RS and PSP-P phenotypes, and in a subgroup with eliminated confounding factors, including age and disease duration. 76 PD, 25 PSP, and 12 MSA patients, and 20 controls, were included. Symptoms of dysautonomia revealed by a SCOPA-AUT questionnaire were present in all groups of patients. Urinary dysfunction was more pronounced in atypical parkinsonisms, and cardiovascular symptoms in α-synucleinopathies. HRV was disrupted in all groups of patients. However, when PSP-P and PSP-RS phenotypes were considered, HRV was diminished in PSP-RS, but there were no differences in HRV parameters between PSP-P and controls. Neurogenic orthostatic hypotension was present in 25% of PD and 58% of MSA patients, but it was absent in PSP patients and the control group. 13 PD and nine PSP patients and 16 controls were included in subanalysis. This revealed that PSP, but not PD, patients had significantly more symptoms of dysautonomia and lower HRV indices compared to controls, and that orthostatic hypotension was even more common in PD than in controls. Our study suggests that dysautonomia is common in PD, MSA and PSP, even though it has different profiles in the different diseases. NOH is present in PD and MSA, but not in PSP.

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

Optical Parametric Amplification in Crossed Fabry‐Perot Cavities

AbstractOptical parametric amplification (OPA) is a promising method of producing extremely intense light. A new OPA scheme with comprehensively high performance is urgently required for future development. In this study, an amplification scheme known as crossed‐Fabry‐Perot‐cavity OPA (XOPA) is proposed. It is based on the principle of periodic idler elimination, which prevents energy back‐conversion among the three coupling waves, resulting in a monotonically increasing overall conversion efficiency. Using a signal at 808 nm and a pump at 532 nm, a chirped pulse XOPA is experimentally demonstrated with a conversion efficiency of 56.28% and a gain bandwidth of 120 nm. The measured pulse duration after compression is 19.2 fs, which is comparable to the Fourier‐transform‐limited 16.8 fs. Further investigations revealed several advantages. Stable pulse shaping in spatial, temporal, and frequency domains is realized by a spatiotemporally modulated pump. Pulse contrast adjustability on the front edge of the signal is verified in the XOPA of different Fabry‐Perot cavity lengths. These results indicate astringency and precise regulation of output in nonlinear processes. Considering numerous crystals suitable for noncollinear configurations from the near‐infrared to mid‐infrared regions, XOPA has a universal potential application in laser systems with extreme intensity, few‐cycle duration, and internal confinement fusion drivers.

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Permafrost temperature dynamics and its climate relations in various Tibetan alpine grasslands

Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004–2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time–frequency analysis. The findings revealed that in the time–frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10–11, 8–9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time–frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths.

Marathi Speech Emotion recognition using Deep Learning techniques.

Marathi Speech Emotion recognition using Deep Learning techniques.