Frequency-domain Spectrum Research Articles

Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS)

Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify three different gluten wheats (high gluten, medium gluten, and low gluten). After collecting the time-domain spectral information of the samples, the frequency-domain spectra, refractive index spectra and absorption coefficient spectra of the samples were obtained by calculating the optical parameters. The experimental results showed that there were differences in the refractive indices and absorption coefficients of wheat with different gluten levels. More importantly the differences in refractive index spectra were more significant. The Competitive Adaptive Reweighted Sampling (CARS) method was applied to select characteristic frequencies from the refractive index spectra within the frequency range of 0.1 to 1.5 THz, to establish a discrimination model for wheat gluten strength. We analysed and compared four discriminative models of Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), Improved Convolutional Neural Networks (Improved CNN) and Sparrow Algorithm Optimised Support Vector Machines (SSA-SVM). The final results indicated that the SSA-SVM model demonstrated the optimal discrimination performance, achieving an accuracy rate of 100% as reflected in the confusion matrix. In summary, this study provides an efficient, accurate, and non-destructive discrimination method for wheat gluten strength, offering a theoretical basis for differentiating wheat with varying gluten strengths in production processes. It holds practical significance for industrial production reference.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Evaluation of the mechanical shock testing standards for electric vehicle batteries

The safety of Li-ion batteries (LIB) has become an important issue with the continuously increased use of electric vehicles (EV) in the world. In a survivable vehicle crash, when the vehicle needs to maintain structural integrity, crash-induced shock may damage EV's LIB. Therefore, an evaluation of commonly used mechanical shock test standards for EV battery module and pack is performed in this study against the crash-induced shock signals collected from National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) tests. Various shock analysis methods including signal characteristics in time domain, power spectral density (PSD) and shock response spectrum (SRS) in frequency domain, and the acceleration/velocity-change diagram are used for the evaluation. It is found that most peak accelerations of NCAP shock signals significantly exceed the peak accelerations specified in shock testing standards. Crash-induced shocks cannot be fully represented by the half-sine pulse adopted in shock testing standards. In both time and frequency domains, the existing shock testing standards generally underestimate the severities of the crash-induced shocks, and therefore, are non-conservative. It also shows that the correct selection of a filter for the processing of the original crash-induced shock signal is crucial for the specification of EV battery shock environment and shock response analyses. The results obtained in this research can support the development of more reliable shock testing standards for EV batteries.

Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), USA. The detection method is a two-step searching method, called Deep Scanning (DS), which includes a time-domain energy analysis and a frequency-domain spectrum analysis. In the time domain, acoustic signals with higher energy, measured by sound pressure level (SPL), are labeled for the potential existence of moving vessels. In the frequency domain, the labeled acoustic signals are examined against a predefined training dataset using a neural network. This research builds training data using diverse vessel sound features obtained from real measurements, with a duration between 5.0 seconds and 7.5 seconds and a frequency between 800 Hz to 10,000 Hz. The proposed method was then evaluated using all acoustic data in the years 2017, 2018, and 2021, respectively; a total of approximately 171,262 2-minute.wav files at three deployed locations in May River, SC. The DS detections were compared to human-observed detections for each audio file and results showed the method was able to classify the existence of vessels, with an average accuracy of around 99.0%.

Intelligently optimized arch-honeycomb metamaterial with superior bandgap and impact mitigation capacity

Engineering applications concurrently face challenges related to vibration, impact, load-bearing and energy absorption. Here, an arch-honeycomb metamaterial with split-ring resonators is proposed for wave attenuation and impact mitigation, with better energy absorption and lower initial peak stress. The split-ring resonators effectively induce bandgaps below 3 kHz, and the mechanisms of bandgap generation and polarization are studied through mode shapes, frequency contours and equivalent mass densities. Subsequently, a machine learning-based optimization framework is introduced to tailor the bandgaps, leading to a 155.59% increase in bandgap width. Furthermore, superior vibration isolation and impact mitigation are confirmed through experiments. Obvious attenuation peaks are observed in the bandgaps of the acceleration transmission spectrum in frequency domain. In time domain, the impact test demonstrates peak weakening and delay. The metamaterials offer advantages in wave attenuation, impact mitigation, load-bearing and energy absorption, providing valuable insights for the advancement of multifunctional metamaterials and their practical engineering applications.

Qualitative and Quantitative Detection of Typical Reproductive Hormones in Dairy Cows Based on Terahertz Spectroscopy and Metamaterial Technology.

Progesterone (PROG) and estrone (E1) are typical reproductive hormones in dairy cows. Assessing the levels of these hormones in vivo can aid in estrus identification. In the present work, the feasibility of the qualitative and quantitative detection of PROG and E1 using terahertz time-domain spectroscopy (THz-TDS) and metamaterial technology was preliminarily investigated. First, the time domain spectra, frequency domain spectra, and absorption coefficients of PROG and E1 samples were collected and analyzed. A vibration analysis was conducted using density functional theory (DFT). Subsequently, a double-ring (DR) metamaterial structure was designed and simulated using the frequency domain solution algorithm in CST Studio Suite (CST) software. This aimed to ensure that the double resonance peaks of DR were similar to the absorption peaks of PROG and E1. Finally, the response of DR to different concentrations of PROG/E1 was analyzed and quantitatively modeled. The results show that a qualitative analysis can be conducted by comparing the corresponding DR resonance peak changes in PROG and E1 samples at various concentrations. The best R2 for the PROG quantitative model was 0.9872, while for E1, it was 0.9828. This indicates that terahertz spectral-metamaterial technology for the qualitative and quantitative detection of the typical reproductive hormones PROG and E1 in dairy cows is feasible and worthy of in-depth exploration. This study provides a reference for the identification of dairy cow estrus.

A Low-Complexity Widely-Linear MMSE Equalizer for DFT-Spread OFDM With Frequency-Domain Spectrum Shaping

A Low-Complexity Widely-Linear MMSE Equalizer for DFT-Spread OFDM With Frequency-Domain Spectrum Shaping

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum

Estimation Method for Nonuniform State of MOIP in Traction Transformer via Curve Reconstruction of Frequency Domain Spectrum

Classification of wheat grain varieties using terahertz spectroscopy and convolutional neural network

Wheat quality and quantity differ in diverse climates. Therefore, it is essential to identify the variety before purchasing and warehousing. In this study, a study on variety discrimination for 12 wheat varieties (strong-gluten wheat, medium-gluten wheat, weak-gluten wheat) using the Terahertz time-domain spectroscopy (THz-TDS) technology in combination with a Convolutional neural network (CNN). Firstly, the original Time-domain spectra (TDS) of wheat in the range of 0.1–2.0 THz were acquired, and the Frequency domain spectra (FDS), the absorption coefficient spectra in the range of 0.2–1.0 THz were obtained through Fourier Transform. Then, Competitive adaptive reweighted sampling (CARS) algorithms were applied to screen the feature spectrum. Finally, the Support vector machine (SVM), the Least square support vector machine (LS-SVM), the Back-propagation neural networks (BPNN) and CNN models were constructed using feature spectral data. By comparing the four models, it was found that the calibration set accuracy and prediction set accuracy of the CNN model reached 98.7% and 97.8% respectively, with an error recognition rate of only 2.2%. The research results show that combining THz-TDS technology with CNN has the advantages of accurate recognition and high efficiency. It can identify different wheat varieties and can be used for seed classification and quality detection.

Non-contact viscoelasticity measurements based on impulsive stimulated Brillouin spectroscopy

The mechanical properties of cells and tissues play a crucial role in determining biological functions. As a label-free and non-contact mechanical imaging method, Brillouin spectroscopy can characterize viscoelastic changes in samples with high spatial resolution. To sensitively identify small mechanical differences among biological systems, it is important to improve Brillouin scattering efficiency while combining various viscoelastic contrast mechanisms in measurement. This paper presents a high-speed Brillouin spectroscopy based on impulsive stimulated Brillouin scattering. The acoustic oscillation can be excited in a single shot with a pulsed pump laser and detected by a continuous probe laser in the time domain. This time-domain signal can then be transferred to the frequency-domain Brillouin spectrum with high precision. With this method, various viscoelastic information including sound velocity, sound attenuation coefficient, elastic longitudinal storage modulus, and loss modulus can be obtained simultaneously based on derived spectral information. Owing to stimulated scattering and time-domain detection, spectra with a signal-to-noise ratio of 26 dB can be achieved within a millisecond-level spectral integration time. The average measurement precision for storage modulus and loss modulus of the longitudinal elastic modulus are 0.1% and 1%, respectively. With this method, the Brillouin spectra and viscoelastic parameters of typical liquids and polymer materials are measured and compared, providing a comprehensive reference for viscoelastic parameters. We also study the elastic changes in different curing stages of PDMS and make a comparison of viscoelasticity with agarose gel. Moreover, six edible oils are identified based on various viscoelastic contrast mechanisms, which not only provides a new perspective for material identification but also expands the measurement capabilities of Brillouin spectroscopy and enhances the sensitivity of viscoelasticity measurements.

Investigation on road underground defect classification and localization based on ground penetrating radar and Swin transformer

In response to the low detection efficiency and accuracy of traditional manual methods for detecting road underground defects, this paper proposes an intelligent detection method based on ground penetrating radar (GPR). This method integrates the detection, classification, and localization of road underground defects. The approach uses Swin Transformer as a feature extraction network and utilizes the YOLOX object detection algorithm as a road underground defect detection model. It enables the detection of defect regions in three types of defect images: voids, non-compact areas, and underground pipelines. In addition, the collected radar signals are processed by Fourier transformation to obtain time-domain spectra and frequency-domain spectra, which are further analyzed to generate signal classification data set to achieve the defect classification. Finally, based on the relative positional relationship between the detected defect images and the GPS information collected by the GPR, the real positions of the defects on the map are automatically determined using the APIs provided by Amap (AutoNavi map). Experimental results show that this method achieves a precision and recall rate of 94.2% and 99.1%, respectively, for the detection of road underground defects, with an average precision of 94% and an average classification accuracy of 90%. The method significantly improves the accuracy and speed of road underground defect detection while meeting engineering requirements, making it highly valuable for practical road underground defect detection tasks.

Optical analysis of aligned Ni nanowire arrays with different degree of oxidation for terahertz polarizer application

The optical properties of aligned nickel nanowire arrays (NiNWAs) with different degrees of oxidation for terahertz (THz) polarizer applications have been investigated by using THz time-domain spectroscopy. In frequency-domain spectra, the full width at half maxima of transmitted peaks was broadened and the peak positions have a blue shift with increasing oxidation levels, besides the enhancement in peak intensity. It is indicated that the oxidation of Ni nanowires (NWs) has a significant influence on the interaction between Ni NWs and THz wave. The transmittance of the aligned NiNWAs increases with annealing temperature increasing. Conversely, the degree of polarization and extinction ratio (ER) decreases. A corresponding relationship between the change of ER and degree of oxidation is summarized by means of thermogravimetric analysis. The change of ER for the annealing sample with the degree of oxidation of 0.507% is 27.32%, which induced the polarization properties of aligned NiNWAs to be sensitive to the oxidation of Ni NWs. These findings can provide new positive features in the development of future polarization-based device applications for THz electronics and photonics.

An electromechanical dynamic stiffness matrix of piezoelectric stacks for systematic design of micro/nano motion actuators

Piezoelectric stacks have proved to be effective for micro/nano motion actuators with large blocking forces. A critical problem is to build their electro-mechanical model for systematic design of statics and dynamics including piezoelectric hysteresis and elasto-kinematics of compliant mechanisms. To ease this issue, this paper proposes a new electro-mechanical dynamic stiffness matrix of piezoelectric stacks to enable a systematic analysis. Positive and inverse piezoelectric effects are included into the dynamic stiffness matrix of Timoshenko beams in the form of Taylor’s series with a clear definition of physical parameters. Consequently, the Jacobian matrix, input/output stiffness, natural frequencies, frequency-domain spectrums of mechanical displacement and electrical impedance as well as the time-domain response of piezoelectric hysteresis can be fully obtained with a single modeling process. Particularly, the time-domain response in the presence of piezoelectric rate-dependent hysteresis and dynamic resonance behaviors of compliant mechanisms is captured in a parameter-insightful way but not the manner in Hammerstein hysteresis model with a black-box transfer function. Experiments on a proof-of-concept prototype of precision positioning stage verify the easy operation and satisfying prediction accuracy of the presented approach.

In Vivo Dynamic and Static Analysis of Lymphatic Dysfunction in Lymphedema Using Near-Infrared Fluorescence Indocyanine Green Lymphangiography.

Near-infrared fluorescence indocyanine green lymphangiography, a primary modality for detecting lymphedema, which is a disease due to lymphatic obstruction, enables real-time observations of lymphatics and reveals not only the spatial distribution of drainage (static analysis) but also information on the lymphatic contraction (dynamic analysis). We have produced total lymphatic obstruction in the upper limbs of 18 Sprague-Dawley rats through the dissection of proximal (brachial and axillary) lymph nodes and 20-Gy radiation (dissection limbs). After the model formation for 1 week, 9 animal models were observed for 6 weeks using near-infrared fluorescence indocyanine green lymphangiography by injecting 6-μL ICG-BSA solution of 20-μg/mL concentration. The drainage pattern and leakage of lymph fluid were evaluated and time-domain signals of lymphatic contraction were observed in the distal lymphatic vessels. The obtained signals were converted to frequency-domain spectrums using signal processing. The results of both static and dynamic analyses proved to be effective in accurately identifying the extent of lymphatic disruption in the dissection limbs. The static analysis showed abnormal drainage patterns and increased leakage of lymph fluid to the periphery of the vessels compared with the control (normal) limbs. Meanwhile, the waveforms were changed and the contractile signal frequency increased by 58% in the dynamic analysis. Specifically, our findings revealed that regular lymphatic contractions, observed at a frequency range of 0.08 to 0.13 Hz in the control limbs, were absent in the dissection limbs. The contractile regularity was not fully restored for the follow-up period, indicating a persistent lymphatic obstruction. The dynamic analysis could detect the abnormalities of lymphatic circulation by observing the characteristics of signals, and it provided additional evaluation indicators that cannot be provided by the static analysis. Our findings may be useful for the early detection of the circulation problem as a functional evaluation indicator of the lymphatic system.

Identification of hydration stages: An innovative study of ultrasonic coda waves using integrated sensing element (ISE)

In this study, a piezoelectric-based integrated sensing element (ISE) is prepared and preliminary employed for monitoring the hydration process based on the dominant mechanism of acoustic impedance variation of cement and coda wave idea. The results of the signal for a period of 24 h show that three different stages (0–2 h/2–12 h/12–24 h) of the cement during hydration can be distinguished by the reflected coda wave combined with its frequency-domain component, while the first arrival waveform shows almost the same pattern due to the wave trajectory only in packing material. Moreover, the wavelet analysis of the coda wave can distinguish these three stages, in line with the results from the time-domain waveform, energy, and frequency-domain spectrum.

Degradation Diagnosis of Power Module Based on Frequency Characteristics in Heat Flow

With the growing demand for highly reliable power electronic systems, the degradation diagnosis of the power modules is becoming increasingly important. Due to its multi-layer structure and complex operating conditions, power modules are subject to several different degradation mechanisms and locations. However, most existing approaches for degradation diagnosis are designed to identify only one particular type of degradation. In this paper, a novel approach for diagnosing degradations in power modules is proposed based on the frequency-domain heat flow spectrum analysis. Different degradation modes can be clearly distinguished based on the discovery that the frequency-domain heat flow characteristics will change in different frequency bands under different locations of degradation. Simulations and experiments are provided to validate the proposed analysis and method.

The Combination of Spectrum Subtraction and Cross-power Spectrum Phase Method for Time Delay Estimation

In order to solve the problem of large error of delay estimation in low SNR environment, a new delay estimation method based on cross power spectral frequency domain weighting and spectrum subtraction is proposed. Through theoretical analysis and MATLAB simulation, among the four common weighting functions, it is proved that the cross-power spectral phase weighting method has a good sharpening effect on the peak value of the cross-correlation function, and it is verified that the improved spectral subtraction method generally has a good noise reduction effect under different SNR environments. Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. The results of this paper can provide useful help for sound source localization in complex environments.

Improved peak-to-peak method for cavity length measurement of a Fabry-Perot etalon using a mode-locked femtosecond laser.

Differing from the conventional peak-to-peak method using two neighboring spectral peaks in the frequency-domain fringe spectrum of the spectral response of a Fabry-Perot etalon to a femtosecond laser, which contains N spectral peaks equally spaced with a spacing of the etalon free spectral range (FSR), the proposed method employs a pair of spectral peaks with a spacing of an integer multiple k (k ≫ 1) of FSR for measurement of the etalon cavity length d with a reduced measurement error. Under the constrain of the total N spectral peaks obtainable in the finite spectral range of the femtosecond laser, the optimized k is identified to be N∕2 in consideration of an averaging operation using N - k samples of d to achieve the minimum measurement error. The feasibility of the proposed method is demonstrated by experimental results with an uncertainty analysis based on "Guides to the Expression of Uncertainty in Measurement".

Investigation of the time-domain linear viscoelastic response of warm mix asphalt mixture via discrete and continuous spectrum

Asphalt mixture is a temperature-sensitive material, and its viscoelastic properties are essential for pavement design and performance evaluation. This paper presents a method for investigating the time domain linear viscoelastic parameter response of asphalt mixture after adding different warm mix agents. The approach utilizes the generalized Sigmoidal function to construct master curves of storage modulus and loss modulus in the frequency domain, and both discrete and continuous spectrum analyses are used to analyze the viscoelastic behavior of the asphalt mixture. The effects of varying Kelvin and Maxwell element numbers on the discrete spectrum are compared to the spectral intensity and master curve obtained from the continuous spectrum. Results show that the generalized Maxwell model with 10−3 or 10−2 as the center point and the generalized Kelvin model with 103 or 104 can obtain more accurate results. When used as input for simulation software, the number of 11 elements can balance computational efficiency and accuracy. The study evaluates the impact of different warm mix agents on the relaxation modulus and creep compliance of asphalt mixture and discusses the practical implications of these findings for engineering applications. Adding Foam warm mix agent significantly lowers the asphalt mixture's relaxation modulus by approximately 44%, while Sasobit and Evotherm marginally raise it by 14% and 22%, respectively. The Foam warm mix agent also increases the equilibrium modulus of creep compliance to 0.091 MPa, 80% higher than that of HMA. The findings of this paper provide guidance for selecting the appropriate warm mix agent to improve pavement performance.

Dynamic monitoring of dielectric properties during two phase immiscible displacements in sand packs using frequency domain electromagnetic sweeps

Electromagnetic heating is a promising soil remediation method especially in thin formations. The lack of a wide-spread adoption of this method stems from insufficient knowledge of how the complex dielectric properties, that govern propagation of the electromagnetic waves through porous media, change with changing frequency, water saturation, displacement types and flow regimes. To breach these gaps several sets of spontaneous deionized (DI) water imbibition experiments, followed by the primary drainage floods, that were followed by the secondary DI water imbibition floods in confined uniform sand packs were performed. The frequency domain relative dielectric constant and conductivities were extracted from the two-port complex S-parameter measurements taken with the vector network analyzer during these immiscible displacements at various water saturation levels at ambient conditions. A novel coaxial transmission line core holder was designed and commissioned, and a modified version of a plane-invariant dielectric extraction algorithm was developed for this purpose. Series, parallel and semi-disperse mixing models were applied to fit the water saturation dependent relative dielectric constant and conductivity values sampled at 500 MHz from the extracted frequency domain spectra. The Maxwell-Garnett parallel model was proved to be the most flexible because it could capture the sampled conductivity values in all secondary imbibition floods before and after the breakthroughs, where the inflection points were observed. These inflection points were attributed to silica production and a potential shear-stripping flow. This observation was further confirmed by conducting a single-phase Darcy's law analysis of two DI water imbibition floods.