Frequency Domain Research Articles

Weak bond evaluation of lap bonding structures by SH guided waves using EMATs

ABSTRACT In this research, simulations and experiments were conducted for the bonding quality evaluation of an aluminum bonding structure, based on the SH guided wave analysis. In order to simulate the propagation of SH waves in the bonded structure, a 3D finite element model was established based on interfacial spring stiffness modeling. The frequency-wavenumber diagram was obtained by doing Fourier transform along the spatial axis in the time frequency domain simulation results. At the same time, the influence of the bonding interface weakening on the propagation characteristics of the guided wave was analyzed. The simulated dispersion curves of SH-guided waves in different bonding states were in good agreement with the theoretically results. In addition, a periodic permanent magnet electromagnetic acoustic transducer (PPM-EMAT) 3D finite element model was established. An optimized transducer was fabricated to excite pure SH0 mode. Then, a series of aluminum lap bonding structures with different bonding states were prepared. The effect of bonding interfaces on the propagation characteristics of SH waves was investigated. The interfacial spring stiffness can be determined when the STFT of the SH waves matches the theoretical dispersion curves. Thus, this method demonstrated an ultrasonic way to distinguish the weakening of the bonding structures.

An improvement in block matching algorithms using a dissimilarity measure in frequency domain transform

In this work, the image block matching based on dissimilarity measure is investigated. Moreover, an unsupervised approach is implemented to yield that the algorithms have low complexity (in numbers of operations) compared to the full search algorithm. The state-of-the-art experiments only use discrete cosine transform as a domain transform. In addition, some images were tested to evaluate the algorithms. However, these images were not evaluated according to specific characteristics. So, in this paper, an improved version is presented to tackle the problem of dissimilarity measure in block matching with a noisy environment, using another’s domain transforms or low-pass filters to obtain a better result in block matching implementing a quantitive measure with an average accuracy margin of ± 0.05 is obtained. The theoretical analysis indicates that the complexity of these algorithms is still accurate, so implementing Hadamard spectral coefficients and Fourier filters can easily be adjusted to obtain a better accuracy of the matched block group.

Military Expenditures and Economic Growth: Evidence from NATO and Non-NATO Alliances

ABSTRACT Given its significant socioeconomic implications, the nexus between military expenditures and economic growth has been the subject of an extensive theoretical and empirical debate. The present study focuses on NATO vs. non-NATO member countries (i.e. 141 countries) as a case study to empirically examine the aforementioned complex relationship during the period 1992–2020. Using annual data, we employ a panel model, as well as spectral preliminary analysis that takes into account the nonlinear behaviour of our series, positive or negative causal relationships and their coherence characteristics across both time and frequency domains. Findings reported herein from an extended panel of several states suggest that military expenditure enhances economic growth under the NATO alliance case, whereas it becomes harmful for growth in the case of non-NATO alliances such as the SCO & CSTO. Our study contributes to the understanding on the crucial role of a military alliance membership in the formation of the economic growth and military spending relationship. Hence, our findings can be beneficial for policymakers to account for the existence of possible spillovers that may arise from country participation in a military alliance.

Multiple-access ultrastable frequency dissemination based on optical frequency combs via a fiber link

We demonstrate an optical fiber-based, multiple-access frequency transmission using two optical frequency combs. The experimental results using the Allan deviation analysis show that with the phase compensation technique, the frequency instabilities at the remote site are 8.7 × 10−15/1 s and 1.0 × 10−17/103 s, and at the accessing node along the fiber link, the frequency instabilities are 6.9 × 10−15/1 s and 1.1 × 10−17/103 s. Similarly, the power spectral density of phase noise was analyzed in the frequency domain. These experimental results demonstrate that the compensation scheme improved the performance by two to three orders of magnitude. Thus, the proposed frequency transmission technique has potential application for disseminating ultrastable frequency references in the optical fiber network.

A measurement method of in-bore projectile motion acceleration based on bottom pressure correction

Due to factors such as high temperatures, elevated pressures, and severe high-frequency shocks in the bore, there is considerable noise interference in acceleration test signals. This makes it challenging to accurately measure projectile motion acceleration using existing methods. To address this issue, we propose an advanced measurement approach that uses bottom pressure correction. Our model suggests a significant correlation between projectile motion acceleration and thrust. By utilizing the correlations between bottom pressure and motion acceleration in both temporal and frequency domains, we can improve the accuracy of acceleration measurements. Based on these insights, we have developed a novel testing system that synchronously measures bottom pressure and acceleration, using bottom pressure as a corrective mechanism for the measured acceleration signals. Empirical results show that the maximum relative error in peak motion acceleration is only 4.86%, demonstrating the effectiveness of our proposed method.

Baroreflex Sensitivity as a Surrogate Biomarker for Concurrently Assessing the Severity of Arterial Stiffness and Cardiovascular Autonomic Neuropathy in Individuals with Type 2 Diabetes

This study aimed to investigate whether baroreflex sensitivity (BRS) could serve as a reliable metric for assessing cardiovascular autonomic neuropathy (CAN) and concurrently act as a surrogate biomarker for evaluating the severity of arterial stiffness and CAN in individuals diagnosed with type 2 diabetes mellitus (T2DM). Participants underwent brachial–ankle pulse wave velocity (baPWV) as well as autonomic function evaluations encompassing the Sudoscan-based modified composite autonomic scoring scale (CASS), baroreflex sensitivity, and heart rate variability in time domains and frequency domains. Linear regression analysis was performed to evaluate the influence of independent variables on baPWV and modified CASS. Participants with higher baPWV values were older, with longer diabetes duration, lower body weight, body mass index, waist circumference, elevated systolic and diastolic blood pressure, and mean arterial blood pressure. They also exhibited a higher prevalence of retinopathy as the underlying disease and reduced estimated glomerular filtration rate. Multiple linear regression analysis revealed that age and BRS were significantly associated with baPWV while diabetes duration, UACR, and BRS were significantly associated with modified CASS. Our study confirms the significant association of BRS with baPWV and modified CASS in T2DM, highlighting its pivotal role in linking microvascular and macrovascular complications. This supports BRS as a surrogate marker for assessing both the severity of arterial stiffness and cardiovascular autonomic neuropathy in T2DM, enabling the early identification of complications.

The SQUID Controller Unit for the LiteBIRD Space Mission: Description, Functional Tests and Early Performance Assessment

AbstractLiteBIRD is a satellite mission to be launched by JAXA in the early 2030s. It will measure the Cosmic Microwave Background (CMB) primordial B-modes with an unprecedented sensitivity. Microwave radiation will be detected by Transition Edge Sensors (TESs) arrays multiplexed in frequency domain and read by Superconducting QUantum Interference Devices (SQUIDs). The LiteBIRD SQUID Controller Unit (SCU), based on the heritage of the successful design used for the ground-based SPT3G experiment, presents some novel elements that make it suitable for a space-borne application. We compare our first breadboard model with the ground-based, Off-The-Shelf Components (COTS) version, by driving the same SQUID Array Amplifier (SAA) at 4 K, measuring relevant quantities such as noise, gain and bandwidth. We demonstrate that the noise added by our first prototype (including a switching part for redundancy purposes) never exceeds the noise added by the COTS-based electronics board, representing our benchmark. We also present the first noise estimates with the SAA cooled below 1 K, going closer to the conditions expected for LiteBIRD operation.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

Fractional order modelling and optimal control of dual active bridge converters

As the construction of new power systems centred around renewable energy gains traction, the installed capacity of new energy sources has experienced explosive growth. Research has shown that the actual external characteristics of inductors and capacitors in circuits exhibit fractional order characteristics. As a core device in dual active bridge converters, the non-integer order of inductors and capacitors has an important impact on their dynamic performance, frequency domain characteristics, and control system design. This paper conducts modelling and control research on dual active bridge converters based on fractional-order calculus theory. First, based on the definition of fractional-order calculus, according to the operating mode of the fractional-order dual active bridge converter and the characteristics of fractional-order components, The fractional-order state space average model was established, and the influence of the order of inductance and capacitance on the amplitude frequency characteristics, phase frequency characteristics and dynamic performance of the converter was analyzed. Secondly, the average model and circuit model were built on the MATLAB/Simulink simulation platform, and the simulation results under different fractional orders were compared. Finally, in order to further improve the control performance of the converter, a fractional-order P I λ control strategy is designed based on the established state space average model of the fractional-order dual active bridge converter and based on the transfer function from the control to the output of the fractional-order dual active bridge converter. The research results show that the fractional-order model can more accurately describe the actual characteristics of the converter. In addition, the fractional-order controller P I λ enables the fractional-order dual active bridge converter to obtain better robustness and improve the dynamic performance of the converter.

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.

Design of ultra-thin multi-band sub-terahertz metamaterial absorber using three concentric ring resonators with same opening direction

Abstract In this paper, an ultra-thin sub-terahertz multi-band terahertz metamaterial absorber is proposed, its cell consists of three concentric ring resonators with the same opening direction, which can realize three discrete absorption peaks in the sub-terahertz frequency domain. The absorption peaks are mainly formed when the three open circular resonators interact with each other, and the superposition of the three discrete peaks creates a three-band absorption. The physical mechanisms of three-band absorption are illustrated by the electric fields and surface current distributions of three absorption peaks. Adjustment of the absorption effect could be obtained by altering the dimensions of the cleft circular resonator. By fixing the dimensions and changing the opening direction, these resonators will couple to each other and play an important role in regulating the frequency, intensity, and number of absorption peaks. Moreover, the multi-band metamaterial structure could be combined with vanadium dioxide, a phase change material, to actively modulate the absorption. In addition, the three-band metamaterial absorber has the characteristics of polarization-sensitive and wide-angle, which has extensive applications in the domains of security detection, biomedicine and communication.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings.

Electrical Impedance Tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. 
Approach: Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated 1) in the time domain 2) in the frequency domain 3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from fifteen adult and neonatal ICU patients. 
Main result: Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. 
Significance: Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question. &#xD.

Time-domain slicing optical frequency domain reflectometry

A time-domain slicing (TDS) optical frequency domain reflectometry is proposed for large strain sensing with better spatial resolution. Compared with the conventional frequency domain slicing (FDS) method, the TDS with a Burg spectrum estimation is capable of enhancing the similarity of a local spectrum under large strain and mostly suppressing the fake peaks during the strain resolving. The experimental results demonstrated that it enables measurements of strain ranging from 600 to 4200 µε with a spatial resolution of 2.4 mm and a narrow optical frequency scanning range of only 10 nm. Moreover, the measurement accuracy is improved by six times by decreasing the root mean square error (RMSE) from 8.6611 to 1.3396 µε without any hardware modification.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system. 

Gaining Insights into the Interplay between Optical and Magnetic Properties in Photoexcited Coordination Compounds.

We describe early and recent advances in the fascinating field of combined magnetic and optical properties of inorganic coordination compounds and in particular of 3d-4f single molecule magnets. We cover various applied techniques which allow for the correlation of results obtained in the frequency and time domain in order to highlight the specific properties of these compounds and the future challenges towards multidimensional spectroscopic tools. An important point is to understand the details of the interplay of magnetic and optical properties through performing time-resolved studies in the presence of external fields especially magnetic ones. This will enable further exploration of this fundamental interactions i.e. the two components of electromagnetic radiation influencing optical properties.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Temperature insensitive distributed wide-dynamic-range strain sensing based on polarization-maintaining photonic crystal fiber

In this paper, a temperature insensitive wide-dynamic-range distributed strain sensor with centimeter-level spatial resolution based on optical frequency domain reflection technology is proposed. It adopts a polarization-maintaining photonic crystal fiber as the sensing fiber, and experimental results demonstrate that its temperature sensitivity is much smaller than that of the standard polarization-maintaining fiber. Benefited from the specially designed cross-correlation algorithm, the sensor achieves the maximum measurable strain reaches 7000 μϵ with spatial resolution of 1.66 cm. This provides a new approach to address current issues with special fiber sensors, such as single-point sensing and low spatial resolution, which shows great significance for structural health monitoring of buildings, bridges and tunnels with urgent requirement of large dynamic range.

Cutting-edge tool wear monitoring in AISI4140 steel hard turning using least square-support vector machine

ABSTRACT This research addresses the critical challenge of tool wear monitoring in AISI4140 steel hard turning through the innovative application of a Least Square-Support Vector Machine (LS-SVM) prediction model. While acknowledging the growing significance of hard turning over conventional grinding techniques, the background also recognizes the challenges posed by accelerated tool wear and decreased productivity. The main goal is to create a reliable tool wear prediction model that is tailored to harsh turning circumstances. Material selection, cutting force measurement, acceleration measurement, and accurate tool wear measurement are all included in the experimental procedures. Using time and frequency domain analyses, the research methodically examines how cutting force and acceleration affect tool wear. For feature selection, non-linear regression is used to determine which parameters have the greatest influence on wear. A comprehensive training and validation procedure, structural clarification, and a detailed mathematical foundation are used in the development of the LS-SVM-based tool wear prediction model. The presentation of the findings and related discussions explores the impact of acceleration and cutting force on wear. The LS-SVM model’s successful application for precise tool wear prediction in AISI4140 steel hard turning is highlighted in the conclusions, demonstrating the model’s potential to improve tool lifespan and manufacturing efficiency.

Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.

Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead. For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required. Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks. Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected. To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.

Evaluation of the variation of the GPR frequency spectra created by the activities of earthworms

The analysis and utilization of frequency spectrum in emission signals from ground penetrating radar (GPR) are essential for investigating soil characteristics and enhancing urban management. This research focuses on assessing the effectiveness of earthworm activities in improving rainwater management in regions with clay soil, where water infiltration is challenging. Specifically, we examined the impact of two earthworm species, Lumbricus terrestris and Aporrectodea caliginosa, as well as a combination of both, on the permeability of clay loam soil. To evaluate earthworm activities, a gravity-driven film flow setup with nine tanks was employed. Furthermore, we suggest utilizing the Discrete Fourier transform to analyse the frequency spectra of different data types (traces, transects, and the average of tanks) and comprehend their influence on earthworm behavior. To investigate the relationship between fluctuations in infiltration velocity and the frequency spectrum signal over time and activity position, a correlation analysis was performed using linear regression models. The goodness of fit was assessed based on the coefficient of determination (R2). For the earthworm species L1, L2, and L12, the R2 values were determined as 0.8858, 0.847, and 0.9493, respectively. These results demonstrate that the frequency domain was derived from variations in ground penetrating radar signals, and a significant correlation exists between infiltration velocity and spectra influenced by earthworm activity.