Frequency Domain Research Articles

Contactless defects detection using modulated photoluminescence technique: model for a single Shockley-Read-Hall trap in a semiconductor thin layer

Studying defects in semiconductors is, in practice, a very important topic for opto-electronic applications. It involves advanced characterization tools able to quantify and qualify the defect densities present in the materials. In the present article we focus on the use of a contactless frequency domain technique: modulated photoluminescence (MPL), and show its potential to detect defects. MPL has been used for the measurement of differential lifetime for several decades in silicon wafers. By extending it to low lifetime/highly defective materials we discovered its potential to become a defect spectroscopy method, measuring time constants close to the ones governing impedance spectroscopy measurements. Proofs of concept and an analytical model for doped materials have been presented already. Here, we reformulate the analytical model more explicitly and check its applicability by extensive numerical simulations for the case of a low illumination for a thin layer with a single defect. We present a parametric numerical study simulating the response of a single Shockley-Read-Hall center, showing the appearance of so-called V-Shapes in the MPL phase patterns as predicted by the analytical model, and valid beyond small-signal approximation. We discuss the difference between these two approaches and extend the analytical model and numerical investigations to intrinsic materials.

A CNN-based Fault Diagnosis Method of Multi-function Integrated RF System using Frequency Domain Scanning with Lasso Regression

Multi-function Integrated RF System (MIRFS) is a key unit for improving the reliability and safety of advanced aircraft. Single-frequency point data are easily affected by various interferences and noises resulting in unreliable and insufficient information. To reduce the low fault diagnosis rate caused by this effect, a novel fault diagnosis method is proposed based on frequency domain scanning and Lasso regression in this paper. Firstly, performance parameter sequences under each fault condition are extracted using frequency domain scanning. By extracting the performance parameters of a specific frequency band as raw fault data, the reliability of the data is greatly increased. Secondly, Lasso regression prioritizes the performance parameter sequences according to their correlation with faults. This approach selects the optimal performance parameter sequences as the data source for fault diagnosis, resolving the issue of excessively high dimensions in fault data. Finally, convolutional neural networks (CNNs) achieve precise fault diagnosis for both soft and hard faults. The results confirm that the proposed method overcomes the low distinguishability of fault states for both single frequency point data and sequence feature data. Compared with diagnostic models using sequence feature data as datasets, the diagnostic accuracy for hard faults increased by 7.92%, and for soft faults by 5.34%.

Motion adaptive vision-based vibration measurement and modal identification for the roof masts of a tall building

On May 18, 2021, occupants in Saige Plaza Building felt significant building motions together with its roof masts caught in obvious vibrations (May 18 vibration event), which were recorded by a surveillance camera installed on the roof of the building. This study aims to investigate the vibration characteristics of masts by processing the video data using computer vision technique. A motion adaptive vision-based vibration measurement method (M-DAVIM) is firstly proposed, focusing on dealing with the adverse motion effects of the camera itself on measuring dynamic displacements. The M-DAVIM incorporates time-domain and frequency domain correction procedures to reduce noise caused by camera self-vibration, and utilizes a CNN-based object tracking method to search objects that blurred by camera shaking. Indoor periodic vibration tests and field tests of photovoltaic panels demonstrated that M-DAVIM outperforms previous vision-based methods in accurately measuring displacements under unfavorable conditions, such as target rotation, motion blurring, and background interference. The proposed M-DAVIM was then applied to measure the dynamic displacements of the roof masts of Saige Building and identify their modal parameters (frequencies and damping ratios) based on the limited video data. A vibration component of 7.60 Hz was identified as the camera self-vibration and was effectively corrected by the M-DAVIM method. Based on finite element analysis and given the wind condition, the twin-mast might mainly experience the vortex-induced vibration at 2.12 Hz with two masts vibrating synchronously in-plane along opposite directions during May 18 to 22, 2021. This study demonstrates the robustness and effectiveness of the M-DAVIM and shows its potential application for long-term field monitoring of large-scale structures under severe outdoor environments.

A 2-channel TIADC frequency response mismatch calibration method based on frequency domain adaptive filtering

A 2-channel TIADC frequency response mismatch calibration method based on frequency domain adaptive filtering

Speech Separation in Time–frequency Domain by Deep Learning with High Performance and Reducing Parameters

IntroductionTraditional speech separation algorithms used time–frequency (T-F) masks. Recently, deep neural networks, which are generally called deep learning, have been used to estimate T-F masks, and the separation performance has been improved owing to it. However, a conventional deep learning approach that achieved the high separation performance requires a large number of parameters because its network is composed of recurrent neural networks (RNNs) which have recurrent and full connections. This is a disadvantage because the low memory consumption is desired in the case of using speech separation algorithms for actual applications. Because of this shortcoming, we proposed a novel network architecture that balances both the high separation performance and the low memory cost.MethodsThe proposed network is composed of convolutional neural networks (CNNs) which have a sparse connection instead of RNNs to reduce the number of parameters. Additionally, aiming to achieve the high separation performance, we designed the proposed network for speech separation to make it possible to learn the feature of speech. We evaluated the separation performance for each network on the task of separating two-speaker mixture.ResultsIn the simulation results, the separation performance of the proposed network was competitive with one of conventional ones with the highest separation performance regardless of realizing the more than 80% reduction of the number of parameters.ConclusionThe results indicated that the proposed network is superior to a conventional one considering the cost-performances of both the speech separation performance and the memory cost.

Frequency domain fault ranging method based on parameter estimation for single-ended faults with anti-transition resistance in distribution networks

Frequency domain fault ranging method based on parameter estimation for single-ended faults with anti-transition resistance in distribution networks

FAITH: Frequency-domain Attention In Two Horizons for time series forecasting

Time Series Forecasting (TSF) plays a crucial role in various sectors, including industrial maintenance, weather prediction, energy management, traffic control, and financial planning. Current deep learning-based predictive models often exhibit significant deviations between their forecasting outcomes and the ground truth because they lack an efficient method to extract both the global frequency-domain information of each variable and the relationships between different variables. To tackle this challenge, we introduce an innovative model—Frequency-domain Attention In Two Horizons (FAITH). FAITH decomposes time series into trend and seasonal components using a multi-scale adaptive decomposition and fusion architecture and processes them separately. It utilizes two modules—the Frequency Channel Feature Extraction Module (FCEM) and the Frequency Temporal Feature Extraction Module (FTEM)—to capture inter-channel relationships at a finer granularity and to extract global temporal information from the frequency domain of different variables. This significantly enhances its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments across six benchmarks for long-term forecasting and five benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in various domains, such as electricity, weather, and traffic. These results validate the effectiveness and superiority of FAITH in both long-term and short-term time series forecasting tasks. Our codes and data are available at https://github.com/LRQ577/FAITH

Corrigendum to “Application of selected methods of computational intelligence to recognition of the liquid–gas flow regime in pipeline by use gamma absorption and frequency domain feature extraction” [Measurement 238 (2024) 115260

Corrigendum to “Application of selected methods of computational intelligence to recognition of the liquid–gas flow regime in pipeline by use gamma absorption and frequency domain feature extraction” [Measurement 238 (2024) 115260

Real-Time Distributed Strain Sensing Using FPGA-Based Optical Frequency Domain Reflectometry

Real-Time Distributed Strain Sensing Using FPGA-Based Optical Frequency Domain Reflectometry

Distributed Strain Sensing based on Sensing Range Enhanced Optical Frequency Domain Reflectometry by Modified Longest Common Substring Algorithm

Distributed Strain Sensing based on Sensing Range Enhanced Optical Frequency Domain Reflectometry by Modified Longest Common Substring Algorithm

Effects of spectral manipulations of music mixes on musical scene analysis abilities of hearing-impaired listeners.

Music pre-processing methods are currently becoming a recognized area of research with the goal of making music more accessible to listeners with a hearing impairment. Our previous study showed that hearing-impaired listeners preferred spectrally manipulated multi-track mixes. Nevertheless, the acoustical basis of mixing for hearing-impaired listeners remains poorly understood. Here, we assess listeners' ability to detect a musical target within mixes with varying degrees of spectral manipulations using the so-called EQ-transform. This transform exaggerates or downplays the spectral distinctiveness of a track with respect to an ensemble average spectrum taken over a number of instruments. In an experiment, 30 young normal-hearing (yNH) and 24 older hearing-impaired (oHI) participants with predominantly moderate to severe hearing loss were tested. The target that was to be detected in the mixes was from the instrument categories Lead vocals, Bass guitar, Drums, Guitar, and Piano. Our results show that both hearing loss and target category affected performance, but there were no main effects of EQ-transform. yNH performed consistently better than oHI in all target categories, irrespective of the spectral manipulations. Both groups demonstrated the best performance in detecting Lead vocals, with yNH performing flawlessly at 100% median accuracy and oHI at 92.5% (IQR = 86.3-96.3%). Contrarily, performance in detecting Bass was arguably the worst among yNH (Mdn = 67.5% IQR = 60-75%) and oHI (Mdn = 60%, IQR = 50-66.3%), with the latter even performing close to chance-levels of 50% accuracy. Predictions from a generalized linear mixed-effects model indicated that for every decibel increase in hearing loss level, the odds of correctly detecting the target decreased by 3%. Therefore, baseline performance progressively declined to chance-level at moderately severe degrees of hearing loss thresholds, independent of target category. The frequency domain sparsity of mixes and larger differences in target and mix roll-off points were positively correlated with performance especially for oHI participants (r = .3, p < .01). Performance of yNH on the other hand remained robust to changes in mix sparsity. Our findings underscore the multifaceted nature of selective listening in musical scenes and the instrument-specific consequences of spectral adjustments of the audio.

Reduced Order Model Coupling Frequency Domain Method for Calculation of Core Loss in SPM Machine Considering PWM Current Harmonics

Reduced Order Model Coupling Frequency Domain Method for Calculation of Core Loss in SPM Machine Considering PWM Current Harmonics

High frequency domain enhancement and channel attention module for multi-view stereo

Multi-view stereo based on deep learning is increasingly popular as a method for 3D reconstruction. Existing methods have made significant advancements in pixel-level depth estimation. However, challenges such as occlusions and non-Lambertian surfaces in images hinder accurate confidence estimation. Moreover, cost volume regularization often results in excessive smoothing at object boundaries. To tackle these challenges, we propose integrating the High Frequency Information Compensator and 3D Channel Attention Module into the Multi-View Stereo Network, termed HFCA-MVS. Firstly, in the feature volume aggregation stage, we introduce a high-frequency information compensator module to enhance the correlation between 2D semantics and 3D space. Subsequently, in the cost volume regularization stage, a 3D channel attention module is introduced to enhance the representation of channel features by capturing relationships among different channels. Lastly, the 3DCNN network employs the GELU activation function to boost the activation response and mitigate excessive object boundary smoothing. HFCA-MVS demonstrates competitive performance in 3D reconstruction across three benchmark datasets: DTU, BlendMVS, and Tanks&Temples. Particularly, compared to CasMVSNet, MVSTER, and Geo-MVSNet on the DTU benchmark, HFCA-MVS achieves a relative improvement in completeness of 33%, 6.5%, and 0.4%, respectively, and an enhancement in overall performance of 15% and 4.2% compared to CasMVSNet and MVSTER. Furthermore, our model yields comparable reconstruction results to existing models on the Tanks&Temples dataset.

Unsupervised health indicator fusing time and frequency domain information and its application to remaining useful life prediction

Unsupervised health indicator fusing time and frequency domain information and its application to remaining useful life prediction

Heart rate variability is more sensitive to stress than heart rate in specialist police undergoing selection.

Police tactical group (PTG) officers respond to the most demanding and high-risk police situations. As such, PTG personnel require exceptional physical fitness, and selection for employment often evaluates fitness both directly and indirectly. While heart rate (HR) is often used to measure physical effort, heart rate variability (HRV) may be a valuable tool for measuring stress holistically. The primary aim of this research was to investigate whether HRV was more sensitive than HR at monitoring workload during key PTG selection activities. As aerobic fitness is associated with workload during these tasks, a secondary aim was to investigate relationships between HRV, HR and aerobic fitness during the same tasks. The relationships between HRV (percentage of adjacent R-R intervals varying by 50% or more; pRR50%) and HR, as measured by ambulatory electrocardiograms obtained during a specialist police selection course, as well as aerobic fitness, as determined via total shuttles completed on the 20-meter multistage fitness test (MSFT; 'beep test'), were investigated. This study included a cohort of six male PTG candidates (n = 6) undergoing selection. As illustrated by a time-series plot, HR values were generally unremarkable, but HRV values were potentially depressed, and tentatively indicated overstress when count data from consecutive short-term analyses were derived. The MSFT was significantly, positively, correlated with pRR50% (ρ (6) = 0.812, p = 0.050, Fisher's z = 1.132). The MSFT and nonlinear HRV, frequency domain HRV, and HR were not significantly correlated. When assessed by linear regression, neither HRV nor HR were predicted by MSFT score. These findings indicate that HR alone is likely not sufficiently sensitive to provide detail on the stress response of candidates undertaking essential tactical tasks that combine physical stressors with cognitive load in adverse conditions. HRV analysis may provide additional insights regarding candidate suitability, particularly during dynamic and multifaceted assessments, though the causal direction of the relationship between HRV and aerobic fitness remains unclear.

Graph convolution for large-scale graph node classification task based on spatial and frequency domain fusion

Graph convolution for large-scale graph node classification task based on spatial and frequency domain fusion

FMambaIR: A Hybrid State Space Model and Frequency Domain for Image Restoration

FMambaIR: A Hybrid State Space Model and Frequency Domain for Image Restoration

A time-domain finite element formulation of the equivalent fluid model for the acoustic wave equation

Sound-absorptive materials such as foam can be described by the equivalent fluid (EF) model. The homogenized fluid’s acoustic behavior is thereby described by complex-valued, frequency-dependent acoustic material parameters. When transforming the acoustic wave equation for the EF model from the frequency domain to the time domain, convolution integrals arise. The auxiliary differential equation (ADE) method is used to circumvent the direct calculation of these convolution integrals. The wave equation and the coupled set of ordinary ADEs are solved in the time domain using the finite element (FE) method. The approach relies on approximating the complex-valued frequency response functions of the inverse equivalent bulk modulus and density by a sum of rational functions consisting of real and complex poles. The order of the rational function approximation defines the number of additionally introduced auxiliary variables per nodal degree of freedom. The presented FE formulation includes a narrow-band non-reflecting boundary condition (NRBC) for normal incidence. The implementation in openCFS shows optimal temporal and spatial convergence for a semi-infinite duct based on the analytic plane wave solution for harmonic excitation. The simulation of a pressure pulse propagating in an infinite EF domain with a scatterer demonstrates the capability for multidimensional, actual transient problems.

Frequency Domain Prediction of Tonal Signals With Time-Varying Pitches

Frequency Domain Prediction of Tonal Signals With Time-Varying Pitches

Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across different cases. Therefore, this study proposes a novel dynamic spectrum-driven hierarchical learning model (DSHNet), the first to specifically leverage image frequency domain information to explore region-level salience differences among and within polyps for precise segmentation. A novel spectral decoupler is advanced to separate low-frequency and high-frequency components, leveraging their distinct characteristics to guide the model in learning valuable frequency features without bias through automatic masking. The low-frequency driven region-level saliency modeling then generates dynamic convolution kernels with individual frequency-aware features, which regulate region-level saliency modeling together with the supervision of the hierarchy of labels, thus enabling adaptation to polyp heterogeneous and illumination variation simultaneously. Meanwhile, the high-frequency attention module is designed to preserve the detailed information at the skip connections, which complements the focus on spatial features at various stages. Experimental results demonstrate that the proposed method outperforms other state-of-the-art polyp segmentation techniques, achieving robust and superior results on five diverse datasets. Codes are available at https://github.com/gardnerzhou/DSHNet.