Search Results (703)

Hyperspectral image (HSI) classification has achieved substantial progress with deep learning. However, existing methods still underexploit frequency-domain information, particularly the complementary roles of high- and low-frequency components. The recently proposed Kolmogorov-Arnold Network (KAN) shows strong nonlinear feature extraction ability for HSI classification, but its lack of frequency-domain learning and reliance on B-spline activation functions often causes unstable training and convergence issues. To address these limitations, this study introduces a Frequency-aware Filtering Activation-based KAN (FFAKAN) for HSI classification. In this framework, the conventional B-spline activation functions in KAN are replaced with learnable high-pass and low-pass spatial filters, enabling explicit frequency decomposition while preserving spectral sequence modeling capacity. Specifically, the proposed framework includes three modules: spectral-spatial feature embedding (S2FE), adaptive filtering KAN (AFKAN), and sequence feature extraction (SeqFE) modules. First, the S2FE module generates highly discriminative feature representations, providing a strong foundation for subsequent processing. Second, the AFKAN module, serving as the core component, employs learnable cutoff frequencies together with cosine-based smooth transition functions to achieve physically interpretable high-low frequency separation, adaptively capturing fine-grained details and structural characteristics in HSI data. Finally, the SeqFE module leverages multi-layer stacked 3D convolutions to perform deep spectral-spatial correlation modeling, extracting high-level discriminative joint features for the classification task. Experiments on four public HSI datasets demonstrate that FFAKAN consistently outperforms state-of-the-art methods. Overall, the proposed method achieves significant performance gains, with maximum improvements of 6.82%, 1.83%, 4.35%, and 5.93% compared with conventional approaches. Moreover, compared with strong baseline models, FFAKAN further improves the overall accuracy by 1.70%, 0.10%, 0.02%, and 0.37%, respectively. These results clearly demonstrate the effectiveness, robustness, and superior generalization capability of the proposed method across different datasets. This study introduces a new paradigm that incorporates physically interpretable frequency-domain priors, showing strong adaptability and promising potential in complex land-cover scenarios. Full article

Filters supporting dynamic reconfiguration that use the spectral parameter approximation (SPA) technique, together with other methodologies, and the interpolated spectral parameter approximation (ISPA) technique offer dynamic adjustment of the cutoff frequency (f_c) with a narrow transition bandwidth and a very wide f_c range. However, they suffer from a high multiplier requirement, leading to increased hardware resource usage. With fewer multipliers, we suggest the Multiply and Accumulate (MAC)-based SPA (MAC-SPA) and MAC-based interpolated SPA (MAC-ISPA) filter in this article. This article describes a unified MAC structure utilizing Time-Division Multiplexing (TDM) that uses the resource-sharing concept to implement an MAC-SPA and MAC-ISPA filter. The developed dynamically reconfigurable filter is implemented and realized using a 0.18 µm CMOS process. Additional testing was done on the Xilinx xc6vlx760-1ff1760 FPGA device. Relative to the filter that incorporates SPA along with the modified coefficient decimation method (MCDM), the obtained results reveal that the proposed MAC-SPA and MAC-ISPA channel filters, synthesized on FPGA, achieve a reduction in occupied slice count by approximately 7% and 4.76%, respectively. Although their operating speeds are slightly lower by about 9.4% for the MAC-SPA filter and 13.89% for the MAC-ISPA filter, this tradeoff is offset by significant savings in hardware resources, making both designs more area-efficient with only a modest reduction in speed. Full article

Measurement results of train-induced vibrations are evaluated for characteristic frequencies, amplitudes and spectra, leading to a prediction which is based on transfer functions of the vehicle–track–soil system, the soil, and the building–soil system. The characteristic frequencies of train-induced vibrations are discussed following the propagation of vibrations from the source to the receiver: out-of-roundness frequencies of the wheels, the sleeper passage frequency, the vehicle–track eigenfrequency, the car-length frequency and multiples, axle-distance frequencies, bridge eigenfrequencies, the building–soil eigenfrequency, and floor eigenfrequencies. Amplitudes and spectra are compared for different train and track types, for different train speeds, and for different soft and stiff soils, where high frequencies are typically found for stiff soil and low frequencies for soft soil. The ground vibration is between the cut-on frequency due to the layering and the cut-off frequency due to the material damping of the soil, but the dominant frequency range also changes with distance from the track. The frequency band of the axle impulses due to the passing static loads obtains a signature from the axle sequence. The high amplitudes between the zeros of the axle-sequence spectrum are measured at the track, the bridge, and also in the ground vibrations, which are even dominant in the far field. A prediction software is presented, which includes all three parts: the excitation by the vehicle–track interaction, the wave transmission through the soil, and the transfer into a building. Full article

Introduction: In patients with conductive and mixed hearing loss, implantable hearing devices such as active middle ear implants (AMEIs) and bone conduction implants (BCIs) are established alternatives when conventional hearing aids fail. Although bone conduction (BC) thresholds are routinely used as eligibility criteria, their role as frequency-specific predictors of postoperative functional outcomes remains poorly defined. This study aimed to evaluate the influence of preoperative BC thresholds across the audiometric spectrum on postoperative speech recognition outcomes after implantation with AMEIs and BCIs. Methods: A retrospective observational study was conducted at a tertiary referral center including patients implanted with BCIs or AMEIs. Pre- and postoperative audiological data were analyzed, including air and bone conduction thresholds, frequency-segmented BC measures (low, mid, and high frequencies), cochlear frequency gradient (ΔBC Slope), and speech recognition scores (SRSs) at 65 dB HL one year after implantation. Results: 102 patients were included (50 BCI, 52 AMEI). Both implant types achieved significant postoperative improvements in tonal thresholds and SRS compared with pre-implantation values (all p < 0.001). High-frequency BC thresholds (BC-High, 4–6 kHz) showed a significant inverse correlation with postoperative SRS in both BCI (r = −0.382, p = 0.001) and AMEI users (r = −0.398, p < 0.001), and emerged as the only independent predictor in multivariable models (BCI: β = −0.533, p = 0.022; AMEI: β = −0.491, p = 0.020). Low- and mid-frequency BC measures were not associated with postoperative speech outcomes (all p > 0.05). ROC analyses demonstrated excellent discriminative performance of BC-High for identifying suboptimal outcomes, with area under the curve values of 0.92 for BCI (p = 0.001) and 0.94 for AMEI (p = 0.002), and implant-specific cutoff values of >47 dB HL and >61 dB HL, respectively. Conclusions: High-frequency BC thresholds showed the strongest association with postoperative speech recognition after implantable hearing rehabilitation. BC-High could function as a prognostic marker of functional outcome rather than an eligibility criterion, providing clinically meaningful information to refine preoperative counseling and individualized decision-making within current indication frameworks. Full article

Frequency-selective attenuation is widely needed in integrated analog front-ends, yet conventional on-chip RC low-pass filters occupy unfeasibly large silicon areas for low-frequency cutoffs and inherently introduce cumulative phase lag. Motivated by the nonlinear, frequency-dependent state evolution of memristive devices, this work experimentally demonstrates a highly compact, capacitor-free memristor–resistor network that functions as a variable-cutoff, zero-phase-lag resistive attenuator. An Au/HfO₂/Au memristor (15 µm × 15 µm) is connected in series with a load resistor and characterized over a wide frequency range. By leveraging the finite time constant of internal ionic drift, the attenuation bandwidth is strictly programmable via the device’s initial resistance. Cutoff frequencies of approximately 10 Hz, 1 kHz, and 10 kHz are achieved for initial resistances of 400 k$\Omega \pm$30 k$\Omega$, 300 k$\Omega \pm$30 k$\Omega$, and 200 k$\Omega \pm$30 k$\Omega$, respectively. Remarkably, the nonlinear state-switching mechanism enables a steep post-cutoff attenuation rate approaching −60 dB/dec—equivalent to a cascaded third-order RC network—using only a single nanoscale device. Rather than functioning as a strictly linear time-invariant (LTI) filter, the proposed circuit operates as a state-adaptive edge-processor. Its inherent amplitude-dependent dynamics and total absence of reactive poles make it exceptionally suited for highly specialized, area-constrained applications, including zero-phase closed-loop noise suppression, frequency-to-amplitude conversion, and amplitude-aware event-driven sensory preprocessing. Full article

Wind power generation exhibits pronounced volatility and intermittency, and direct grid connection may cause instability in grid frequency. To address this issue, this paper proposes an optimisation strategy for hybrid energy storage systems to mitigate wind power fluctuations, integrating lithium-ion batteries with supercapacitors within wind power systems. Firstly, the grid-connected power of wind turbines and the reference power of the energy storage system are determined through dynamic weight adjustment using a weighted filtering algorithm combining adaptive exponential smoothing and recursive averaging algorithms. Secondly, the fish-eagle optimisation algorithm is employed to refine variational modal decomposition parameters. The modal components derived from decomposing the energy storage system’s reference power are converted into Hilbert marginal spectra. Following determination of the cut-off frequency, high-frequency signal components are managed by supercapacitors, while low-frequency components are handled by lithium-ion batteries. Finally, an optimised configuration model for the hybrid energy storage system is constructed to minimise the annual lifecycle target cost. Case study analysis demonstrates that this approach effectively smooths fluctuations in wind power output while fully leveraging the complementary characteristics of both energy storage types, achieving a balance between system economics and overall performance. Full article

Under the combined effects of vibrations from train operations and wind loads, the dynamic response monitoring data of masonry partition walls in subway stations are often contaminated with high-frequency noise, which hinders the accurate identification of the structure’s true dynamic characteristics. To tackle this problem, this paper proposes employing a Butterworth low-pass filter to process the on-site monitoring data. The paper initially elaborates on the monitoring theory grounded in the pulsation method, followed by a detailed explanation of the rationale for selecting the Butterworth filter, as well as data processing techniques such as Fast Fourier Transform (FFT) and self-power spectrum analysis. By incorporating a field monitoring case from a subway station in Guangzhou, the paper compares and analyzes the acceleration time-history curves before and after filtering. Additionally, finite element analysis is performed to assess the mechanical response of the masonry wall under wind loads, train-induced vibrations, and their combined effects. The results demonstrate that after applying a 4th-order Butterworth low-pass filter with a 46 Hz cutoff frequency, the high-frequency noise in the data is effectively suppressed, thereby accentuating the main trend and low-frequency vibration characteristics of the signal. This provides a reliable data foundation for subsequent precise analysis of the dynamic response and fatigue performance of the masonry walls. Full article

Background/Objectives: Macular telangiectasia type 2 (MacTel2) is a progressive parafoveal retinal disorder with emerging evidence supporting broader neurodegenerative and metabolic involvement. Given the vulnerability of cochlear structures to systemic and microvascular stressors, this study aimed to investigate whether MacTel2 is associated with measurable auditory dysfunction. Methods: This prospective case–control study included 42 participants: 21 patients with clinically and multimodally confirmed MacTel2 and 21 age- and sex-matched healthy controls. All participants underwent standardized audiological assessment, including tympanometry, conventional and extended high-frequency pure-tone audiometry (0.5–16 kHz), distortion product otoacoustic emissions (DPOAE; 0.5–8 kHz), and click-evoked auditory brainstem response (ABR). Hearing loss was graded using the World Health Organization (WHO) classification based on PTA4 (0.5, 1, 2, and 4 kHz), and a clinically relevant cutoff of PTA4 > 25 dB HL was additionally applied. DPOAE responses were considered absent when the signal-to-noise ratio (SNR) was <6 dB. Results: The MacTel2 and control groups were comparable with respect to age and sex distribution. Patients with MacTel2 demonstrated significantly higher air-conduction thresholds than controls across both conventional and extended high frequencies, with the largest differences observed in the extended high-frequency range (10–16 kHz). PTA4 values were significantly higher in the MacTel2 group in both better- and worse-hearing ears, and the prevalence of clinically relevant hearing loss (PTA4 > 25 dB HL) was significantly greater among MacTel2 patients. DPOAE amplitudes were markedly reduced at all tested frequencies (0.5–8 kHz) in the MacTel2 group, and frequency-specific DPOAE absence/reduction (SNR < 6 dB) was substantially more frequent in MacTel2 than in controls. In contrast, ABR wave I and wave V latencies and the I–V interpeak interval did not differ significantly between groups, suggesting preserved brainstem-level auditory conduction. Within the MacTel2 cohort, no significant correlations were observed between the disease grade and audiological measures. Conclusions: MacTel2 was associated with significantly impaired peripheral auditory function, characterized by elevated conventional and extended high-frequency thresholds and pronounced reductions or the absence of DPOAE responses, while ABR parameters remained comparable to those of controls. These findings support a predominantly cochlear (outer hair cell-related) involvement in MacTel2 and suggest that auditory screening including conventional pure-tone audiometry, with consideration of extended high-frequency audiometry and otoacoustic emissions when feasible, may be clinically relevant in this population. Full article

Background/Objectives: The attenuation coefficient (AC) is a quantitative ultrasound parameter that describes the frequency-dependent reduction of acoustic energy as ultrasound waves propagate through biological tissues. Recently, AC has gained increasing relevance in abdominal ultrasound as an objective and reproducible biomarker for tissue characterization, particularly in the assessment of diffuse parenchymal diseases. Unlike conventional qualitative B-mode imaging, AC provides standardized numerical measurements that improve interobserver reproducibility and facilitate longitudinal monitoring. Methods: This review provides a comprehensive and critical overview of the current clinical applications of AC measurements in abdominal ultrasound, mainly focusing on liver steatosis quantification. Emphasis is placed on the comparative evaluation of commercially available AC-based technologies, highlighting their methodological differences, validation evidence, and diagnostic performance to support future efforts toward harmonization and standardization across ultrasound platforms. Results: Several studies have demonstrated a strong correlation between AC values and established reference standards, including magnetic resonance imaging–proton density fat fraction (MRI-PDFF) and histopathological grading, supporting its role in the noninvasive evaluation of liver steatosis. The growing clinical adoption of AC has been accompanied by the development of multiple vendor-specific software implementations integrated into modern ultrasound systems. Although these platforms share a common physical basis, they differ substantially in algorithmic design, signal processing strategies and region-of-interest selection. These differences may influence absolute AC values and diagnostic cutoff thresholds, therefore limiting direct comparability across systems. Another factor that further contributes to the heterogeneity of reported cutoff values is the variability in validation approaches, with some technologies validated against liver biopsy and others against MRI-PDFF. Conclusions: AC is a promising quantitative ultrasound biomarker for noninvasive liver steatosis assessment, showing strong correlation with histology and MRI-PDFF. However, inter-vendor variability currently limits cross-platform comparability. Standardized acquisition protocols, unified quality-control criteria, phantom-based cross-calibration, and consistent vendor-specific reporting are essential to ensure reliable longitudinal monitoring and broader clinical implementation. Full article

Background/Objectives: Primary ciliary dyskinesia (PCD) is a rare inherited disorder caused by dysfunction of motile cilia, leading to chronic respiratory disease. Diagnosis is challenging due to heterogeneous and non-specific clinical manifestations and the absence of a single definitive diagnostic test. Current diagnostic strategies rely on a combination of functional, ultrastructural, and genetic analyses. The objective of this study was to evaluate whether ciliary beat frequency (CBF), combined with ciliary beat pattern (CBP) assessment using digital high-speed video microscopy (DHSV), could serve as an effective first-line screening tool to identify patients requiring further diagnostic investigations. Methods: This single-center retrospective study included 65 patients (52 children and 13 adults) with clinical suspicion of PCD. Ciliary beat analysis was performed on nasal or bronchial samples using DHSV and Sisson–Ammons Video Analysis software. CBF and CBP were assessed and compared between patients with confirmed PCD and those in whom PCD was excluded based on transmission electron microscopy (TEM) and/or molecular genetic analysis. Results: Fifteen patients were diagnosed with PCD. Mean CBF was significantly lower in the PCD group compared with the non-PCD group (3.3 Hz vs. 8.1 Hz; p < 0.001). A CBF cut-off value of 5.25 Hz yielded a sensitivity of 78.6% and a specificity of 95.7%. Three patients with PCD had CBF values above this threshold; however, two of them exhibited abnormal CBP. Sample type, patient age, and the presence of airway pathogens did not significantly influence CBF measurements. Conclusions: CBF and CBP analysis using DHSV represents a useful first-line screening tool within a multifaceted diagnostic approach for PCD, allowing rapid identification of patients who should undergo further confirmatory testing. Full article

Background/Objectives: Although discrepancies between bioelectrical impedance analysis (BIA) devices are well documented, their clinical relevance in vulnerable populations remains unclear. This study aims to assess the impact of device choice on muscle mass classification criteria in patients with cancer or obesity and to identify modifiers of device variability. Methods: BIA data from 224 adults (85 with cancer, 139 with obesity) measured with two segmental multi-frequency devices (seca mBCA 515 and InBody 970) were analyzed. Device differences were assessed using the Wilcoxon signed-rank test and agreement analyses. Differences in classification of body composition cut-offs cited in the GLIM criteria for malnutrition and the ESPEN and EASO criteria for sarcopenic obesity were evaluated using McNemar’s test. The impact of disease type, sex, and age on device differences was examined through multivariable models. Results: Significant device differences were found for all parameters (all p ≤ 0.005). Discrepancies were largest for skeletal muscle mass (kg and %), with effect sizes r > 0.8 and poor agreement (Lin’s CCC < 0.90). A significant impact of device choice on muscle mass classification was observed for both cancer and obesity patients (p < 0.001), with seca classifying more patients as having low fat-free mass (50% vs. 20%) and as having a body composition consistent with sarcopenic obesity (90% vs. 50%) than InBody. Discrepancies were more pronounced in cancer patients and females. Conclusions: Muscle mass assessment by BIA is highly dependent on device choice, potentially leading to clinically relevant discrepancies in classification when rigid cut-offs are applied. An individualized interpretation of BIA data and further validation of prediction equations in disease-specific subpopulations is warranted. Full article

The momentum transport and scale-dependent motion characteristics within vegetation canopies play a crucial role in shaping near-surface turbulent structures and exchange processes, yet the interactions among different turbulent scales and their statistical representations remain insufficiently understood. Based on a series of controlled wind tunnel experiments, this study identifies coherent turbulent structures using a phase-space algorithm constructed from streamwise velocity fluctuation u′, acceleration a, and jerk j, and compares transport efficiency (exuberance η). This study uses scale-wise (cut-off frequency) momentum flux contribution analysis, natural visibility graph (NVG), and large–small-scale amplitude modulation to examine transport and multiscale behaviors across different canopy densities, array layouts, and inflow conditions. Results show that canopy density (different C_d drag coefficient) is a primary factor governing transport efficiency. Under low-wind staggered configurations, increasing canopy density strengthens the contribution of low-frequency large-scale motions to total momentum flux. In contrast, high-wind aligned configurations intensify canopy-top shear, enhancing small-scale motions and thereby reducing the relative contribution of large-scale motions. NVG analysis further reveals that in high-density canopies, large-scale acceleration and deceleration events tend toward equilibrium, whereas deceleration events dominate consistently in low- and medium-density cases. Amplitude modulation results indicate that high-density cases exhibit highly consistent modulation behavior, followed by low-density cases, while medium-density cases display a pronounced height-dependent variation, characterized by a distinct modulation critical point. This study proposes a unified analytical framework integrating coherent structure detection, graph-theoretic analysis, multiscale transport characterization, and large–small-scale modulation, providing a comprehensive description of momentum transport and scale motions within canopy flows, and it offers new insight into the mechanisms governing complex vegetation canopy turbulence. Full article

Rotor-induced micro-Doppler signatures are essential for radar-based characterization of rotary-wing UAVs, but practical echoes are often dominated by a strong quasi-static body return concentrated near zero Doppler. In hovering or low-speed scenarios, rotor-induced components may intermittently overlap this near-zero region, where hard DC suppression discards informative rotor content and fragments micro-Doppler structures. Data-driven decompositions such as EMD and VMD avoid fixed cutoffs, yet without explicit constraints on rotor periodicity they are vulnerable to mode mixing and residual leakage under low-SNR conditions. This paper proposes a Cyclic-Power-Guided Soft Mask (CPGSM) framework that exploits cyclostationary periodicity as a physically grounded prior for rotor–body separation. A CPS-guided soft masking procedure consisting of a DC-dominant overlap band is first identified from quasi-static dominance; within this band, cyclic power spectrum analysis yields a continuous rotor-consistency score that guides smooth time–frequency soft allocation, while deterministic assignment is applied elsewhere. Simulations demonstrate improved micro-Doppler continuity, reduced body leakage, and more stable performance from 5–30 dB SNR compared with hard DC isolation and EMD/VMD, together with consistent rotor-speed estimates across sensing configurations. Full article

Schottky barrier diodes (SBDs) based on low-dimensional materials are of interest for high-speed electronics due to their intrinsic nonlinear transport characteristics. In this work, aligned carbon nanotube Schottky barrier diodes (ACNT-SBDs) were systematically studied through electrical characterization, small-signal modeling, and large-signal nonlinear measurements. Devices with channel widths ranging from 50 to 500 µm were fabricated to examine size-dependent direct-current and high-frequency behavior. Clear Schottky rectification and pronounced geometry-dependent characteristics were observed, with the widest device achieving an intrinsic cutoff frequency of up to 282 GHz. Based on measured S-parameters, a refined small-signal model incorporating a parallel resistance–constant phase element (CPE) branch was developed, providing substantially improved agreement with measured S- and Y-parameters and phase response compared with the classical model. The extracted CPE parameters exhibit systematic dependence on channel width, indicating distributed junction charge dynamics associated with carbon nanotube interfaces. Furthermore, the large-signal nonlinear behavior was evaluated using an anti-parallel diode configuration, achieving a third-harmonic output power of −22.58 dBm at 30 GHz under zero-bias operation. This work provides a comprehensive experimental and modeling framework for understanding the high-frequency and nonlinear behavior of ACNT-SBDs. Full article

Accurate and reliable current measurement is a key prerequisite for ensuring the safe operation of power systems. Conventional through-core and wound current transformers require power outage for installation or modification of line structures, which are plagued by high installation difficulty and cost, and fail to meet the digital development needs of smart grids. To address the demand for non-intrusive installation of current transformers, this paper proposes a non-intrusive current transformer with PCB coils in reverse-series connection. First, a magnetic coupling current calculation model is established to design a reverse-series double-layer coil structure, and a mathematical model of the equivalent circuit for the sensing and measurement system is constructed. The influence of circuit parameters on the output response is analyzed, yielding an optimization method for the system operating state and completing the hardware circuit design. Subsequently, a simulation model of the reverse-series double-layer coil is built to calculate and analyze the amplitude-frequency characteristics, steady-state and transient performance, as well as anti-interference capability of the transformer. The results demonstrate that the designed transformer, combined with an active integrating circuit, achieves an upper cutoff frequency of 13,169 Hz and a lower cutoff frequency approaching 0 Hz, which satisfies the requirements of wide-frequency measurement while ensuring high sensitivity and anti-interference capability. Finally, a current-sensing experiment platform is built for comparative verification with conventional invasive current transformers. Experimental results show that after correction with a proportional coefficient of 1.317, the fitting squared error is only 0.0038. The linearity remains excellent under different conditions with a wide dynamic measurement range, and the phase error is less than 15°. Within the range of 2–120% of the rated current, the ratio error is less than 0.9%, indicating high measurement accuracy. This study provides a new high-precision and convenient method for current measurement in smart grids. Full article