Search Results (313)

Artificial lighting is a central and resource-intensive component of controlled environment agriculture, directly regulating plant physiological processes while influencing energy efficiency and production outcomes. Chlorophyll fluorescence analysis, particularly pulse-amplitude-modulated fluorometry, provides a rapid and non-destructive method for assessing plants’ photosynthetic efficiency. However, the extent to which chlorophyll fluorescence reflects plant responses to different light spectra across species remains insufficiently understood. In this study, species-specific photoresponses of leafy vegetables (Amaranthus tricolor, Barbarea verna, Chrysanthemum coronarium, Perilla frutescens) to different light spectra were investigated by integrating chlorophyll fluorescence with growth, antioxidant, and pigment traits. Plants were cultivated under monochromatic red, blue, and combined red–blue light, with additional far-red supplementation. Correlation analysis was performed among growth, antioxidant parameters, pigment contents, and chlorophyll fluorescence parameters. The obtained results show that chlorophyll fluorescence parameters respond selectively, but species-specifically, to applied lighting-spectrum conditions. Relationships between fluorescence indices and physiological traits varied between species, and no single parameter consistently reflected plant performance across all crops. Therefore, to employ chlorophyll fluorescence as a useful proxy for assessing plant responses to lighting spectrum, a species-specific and context-dependent approach is required. Full article

In high-frequency 400 V/48 V matrix planar LLC resonant converters for data center power supplies, enlarged interwinding parasitic capacitance can induce significant transformer-related common-mode (CM) displacement currents. However, the effects of secondary-side rectifier commutation and local winding position on the resulting CM spikes have not been sufficiently clarified. This paper establishes a unified analytical expression for the transformer-related CM current in a converter with a half-bridge primary and a full-bridge synchronous-rectifier (SR) secondary. The analysis shows that asynchronous SR commutation shifts the secondary reference potential and introduces additional excitation through the interwinding parasitic capacitances, thereby producing double-pulse CM current spikes. The unequal spike amplitudes among different secondary-side rectifier units are further explained by the combined effects of local winding position and distributed parasitic coupling. Based on these findings, a shielding-layer scheme was then proposed and verified on a 400 V/48 V, 300 kHz, 3 kW prototype. The experimental results show average reductions of about 15 dB over 150 kHz–800 kHz and 20 dB over 800 kHz–6.5 MHz in the CM voltage spectrum, whereas the prototype achieves a peak efficiency of 97.78%. Full article

In ultra-wideband (UWB) synthetic aperture radar (SAR) imaging, in-band antenna standing waves (SW) can generate range ghosts, degrading image quality. To address this issue, an image-domain suppression method is proposed, leveraging the phase symmetry property (PSP) between the SW signal and its mirror SW (MSW) signal. Based on PSP, the MSW signal is rapidly constructed from the SW signal, ensuring that both share the same target region but exhibit different ghost regions. PSP is further extended to the image domain. Specifically, the SW-induced phase is extracted in the wavenumber domain. Based on the PSP, this phase is then used to construct the MSW signal, which exhibits a phase spectrum that is symmetric to that of the SW signal with respect to the origin. The MSW image is subsequently fused with the original SAR image, thereby effectively suppressing SW-induced ghosts. The experimental results demonstrate that the proposed method significantly mitigates ghosting while preserving the amplitude and structural integrity of the main signal, thereby enhancing overall imaging quality. Full article

Medical images commonly exhibit low contrast, weak boundaries, and complex textures. In addition, significant semantic differences exist between deep-level semantic features and shallow-level detail features, posing challenges for multi-scale feature fusion in terms of detail preservation and structural consistency. To address these issues, a frequency-enhanced and bidirectional feature-guided segmentation network (FBNet) is proposed. The network comprises two core components. The frequency-based enhancement (FBE) module employs the Fast Fourier Transform and applies adaptive modulation to the amplitude spectrum through a content-aware gating mechanism, enhancing detail expression and inter-structural contrast. The Bidirectional Guided Feature Fusion module (BGF) enables bidirectional interaction between shallow and deep features. Additionally, the Structure and Edge Awareness (SEA) module is constructed using directional and variance attention mechanisms to achieve collaborative optimization of structural modeling and edge perception. Experiments on four medical image segmentation datasets show that, compared to the second-best method, FBNet achieves improvements of 2.12, 1.57, 1.37, and 1.56 percentage points on the mIoU metric and 1.54, 1.11, 0.84, and 1.03 percentage points on the mDice metric. Full article

Objective biomarkers for neurodevelopmental disorders remain an unmet clinical need. The electroretinogram (ERG), a non-invasive recording of the retinal response to light, has shown promise as a physiological marker for autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD), yet existing classification approaches based on time-domain and time–frequency features achieve limited accuracy in clinically relevant multi-group scenarios. This study introduces ERG-Graph, a novel graph signal processing (GSP) framework that transforms each ERG waveform into a weighted, undirected graph through amplitude quantization and temporal-adjacency connectivity. Nine topological and spectral features, including total load centrality, clique number, algebraic connectivity, and clustering coefficient, were extracted from each graph to characterize the structural dynamics of the signal. Using light-adapted ERG recordings from 278 participants (ASD = 77, ADHD = 43, ASD + ADHD = 21, Control = 137), we evaluated these features across binary, three-group, and four-group classification scenarios using seven machine learning classifiers with 10-fold subject-wise cross-validation. The proposed ERG-Graph features achieved balanced accuracies of 0.91 (ASD vs. control, males) and 0.88 (ADHD vs. control, females). Critically, fusing ERG-Graph with time-domain features yielded a balanced accuracy of 0.81 for three-group classification (ASD vs. ADHD vs. control), representing an 11-percentage-point improvement over the previous benchmark of 0.70. Statistical analysis confirmed significant topological differences between groups (Kruskal–Wallis, p < 0.001; Cliff’s delta: large effect sizes), and SHAP analysis revealed that graph-theoretic features dominated the top-ranked predictors. These results demonstrate that graph-based topological features capture discriminative information in the ERG waveform that is inaccessible to conventional signal analysis methods, advancing the development of objective biomarkers for neurodevelopmental disorder screening. Full article

This study presents a characterization method for High-Frequency Current Transformers (HFCTs) intended for partial discharge (PD) measurement in on-line acquisition systems designed for AI-based processing and clustering. The primary objective is to analyze how key design parameters, ferrite core material, and number of turns, influence HFCT frequency response, attenuation, and sensitivity, thereby providing a basis for optimized sensor design when data analysis is to be performed by means of AI-based algorithms. The investigation focuses on the influence of different ferrite core materials and varying secondary turn numbers on the frequency spectrum and the response to IEC 60270-compliant calibrator impulses Both concentrated and well-distributed HFCT secondary winding configurations are analyzed to evaluate their impact on signal behavior and sensitivity. The experimental results are compared with a simplified theoretical model to validate performance trends and identify key design factors. The HFCT response to IEC 60270-compliant calibrator impulses is examined to assess its suitability for PD measurement systems and monitoring. The results highlight the critical role of core selection and the number of turns in shaping HFCT bandwidth, attenuation, and impulse response, which are essential for accurate and reliable PD detection in continuous monitoring systems to perform the diagnostic of the electrical insulation condition. This diagnostic approach is based on the detection of partial discharge (PD) activity over time, with the objective of identifying evolving phenomena by monitoring the amplitude and characteristics of the signals associated with different defects. Therefore, accurate separation of signals originating from different defects and from noise is essential. These results provide a foundation for designing HFCT sensors suitable for integration into advanced diagnostic frameworks, AI-aided for Condition-Based Maintenance (CBM). Full article

Ripple currents on the direct current (DC) bus in variable frequency drive (VFD) systems originate from motor load current fluctuations and the high-frequency switching of power devices. The resulting Joule heating within the DC-link capacitors is a primary driver of lifespan degradation. To address the lack of systematic models for multi-phase H-bridge inverters and the over-design caused by empirical methods, this paper proposes a novel analytical method that incorporates the 2kπ/N phase difference of parallel units for precise ripple current quantification. First, a dynamic DC-link capacitor model is established based on a single-phase H-bridge inverter, and the expressions for the instantaneous, average, and root mean square (RMS) input currents are derived. Furthermore, by introducing the 2kπ/N phase difference (where k = 0, 1, …, N − 1) among N parallel H-bridge units, a universal analytical expression for the RMS input current and its harmonic spectrum in a multi-phase system is obtained. The analysis reveals that ripple current harmonics concentrate at 2m × f_sw (where m is a positive integer and f_sw is switching frequency) and their sidebands (2m × f_sw ± f_o, f_o is output fundamental frequency), and the coupling influence of modulation index and power factor angle on ripple amplitude is quantitatively characterized. A 12 × 160 kW twelve-phase H-bridge inverter is taken as a case study, and MATLAB (v2023b) simulations and hardware experiments demonstrate that the theoretical calculations are in close agreement with the simulated and measured results, with the errors of input current harmonic amplitudes all below 5%. Compared with traditional empirical design, the proposed method reduces the capacitor volume and cost by approximately 15–20% while ensuring system reliability. This method is directly extensible to other multi-phase inverter topologies, providing a theoretical foundation for the accurate selection of DC-link capacitors. Full article

To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion properties via Doppler spectrum and phase sequences (direct physical level), and introduces the Gramian Angular Field (GAF) to map the echo amplitude sequence into two-dimensional (2D) structured images, thereby revealing the dynamic evolution characteristics of echo energy (abstract representation level). This approach integrates direct physical attributes and abstract system evolution features within a unified representation. To accommodate the structural differences among modalities, a heterogeneous branch processing network is designed: the Transformer is employed to capture long-range dependencies in one-dimensional (1D) sequences, while ResNet18 is used to extract spatial texture features from two-dimensional images. A self-attention mechanism is further introduced to achieve adaptive fusion of the multimodal data. Experimental results based on the IPIX dataset suggest that this cross-level strategy provides improved detection performance across various scenarios, as observed in complex marine environments. 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

Lyapunov-exponent-based diagnostics are widely used to quantify deterministic chaos in optically injected semiconductor lasers (OISLs). In most numerical implementations, the optical field is represented either in phasor coordinates $(A,\psi ,N)$ or in Cartesian quadrature coordinates $(X,Y,N)$. Although these representations are mathematically related through a smooth coordinate transformation away from vanishing field amplitude, their numerical realizations can exhibit markedly different robustness in variational calculations, directly impacting the reliability of Lyapunov exponent estimation and chaoticity maps. In this work, we present a systematic assessment of the numerical reliability of Lyapunov-based chaos analysis in master-slave optically injected semiconductor lasers using both phasor and quadrature formulations. The full Lyapunov spectrum was computed via a noise-free variational method that integrates the nonlinear dynamics together with the corresponding Jacobian equations using a fourth-order Runge-Kutta scheme combined with periodic QR orthonormalization. High-resolution Lyapunov maps were constructed in the injection strength-frequency detuning parameter space, and the consistency between both formulations was quantitatively evaluated. While both approaches reproduce the overall structure of chaotic and non-chaotic regions, the phasor formulation may generate spurious positive Lyapunov exponents in regimes where the optical field amplitude approaches low values. These discrepancies originate from singular terms proportional to $1/A$ and $1/A^2$ in the variational Jacobian of the phasor model, which can lead to numerical amplification and artificial chaotic signatures. The quadrature formulation avoids these singularities and provides numerically stable and physically consistent Lyapunov spectra across the explored parameter space. The results establish practical guidelines for robust chaos quantification in optically injected semiconductor lasers and highlight the importance of representation choice in variational Lyapunov analysis of nonlinear photonic systems. Full article

This article examines the behavior of seismic noise fields over the Japanese islands recorded by the F-net seismic network for 1997–2025. This paper uses nonlinear noise statistics: the entropy of the wavelet coefficient distribution, the Donoho–Johnston (DJ) wavelet index, and the multifractal singularity spectrum support width. These parameters were chosen because their changes reflect the complication or simplification of the noise structure. Changes in the structure of seismic noise properties are analyzed in comparison with a sequence of strong earthquakes. Using a model of the intensity of interacting point processes, the effect of the leading of local noise property extrema relative to the seismic event times is estimated. Using the Hilbert–Huang decomposition, the synchronization of the amplitudes of the envelopes of noise property time series for different IMF levels is estimated. A sequence of weighted probability density maps of extreme values of noise properties is analyzed in comparison with the mega-earthquake of 11 March 2011 and the preparation of another possible strong seismic event. Full article

Typically, response analysis of optical fiber biosensors focuses on changes in amplitude and wavelength shifts in the biosensor spectrum; therefore, not all of the spectral range is used for this analysis. On the other hand, if the entire spectrum is used, it is possible to leverage the current data in the spectrum and thus improve the performance of the biosensor. To do this, it is necessary to analyze a large amount of data present in each measured spectrum. This task can be made easier by using dimensionality reduction techniques. In addition, it is necessary to establish which spectral regions provide relevant information. Scaling techniques are mathematical data preprocessing tools used in machine learning to adjust the numerical scale of variables so that they have comparable weight and even highlight those characteristics that provide more information. To our knowledge, the use of these techniques in the development of optical fiber biosensors is not very common, which is why we believe they represent an attractive topic of study in this area. With the help of scaling techniques, we can modify the scale of the data so that all the information contained in the spectrum is used, regardless of its magnitude. In this work, two biosensors based on a chirped long period fiber grating (CLPFG) and a chirped Mach–Zehnder interferometer (CMZI) were developed for the detection of interleukin-10 (IL-10). Principal component analysis (PCA) was used as a dimensionality reduction technique together with a support vector machine (SVM) classifier with four different scaling techniques, standardization, minimum–maximum scaling, robust scaling, and a custom transformer, to compare the IL-10 detection performance of the biosensors. The results showed that robust scaling in CMZI performed best in detecting IL-10, with an F1-score equal to 1, as well as better reliability in detecting the protein. Full article

To promote the application of wrap-around reinforced soil structures in high-intensity seismic regions, this study systematically investigated the influence of different wrap-around facing types on the seismic performance of reinforced loess slopes. Through shaking table model tests, the dynamic responses of three wrap-around facing types—C-shaped wrap-around facing, secondary-reinforcement wrap-around facing, and self-wrap facing—under the excitation of two seismic waves (El Centro wave and Wenchuan Wolong wave) were compared and analyzed. The test introduced the marginal spectrum energy analysis method to accurately identify the location and evolution process of slope damage. The results indicated that reinforcement significantly enhances the global integrity of the slope, yet the influence of the wrap-around facing type on seismic performance is significant. The C-shaped wrap-around facing exhibited the best global stability and seismic performance, with damage initiating inside the slope body and a good energy dissipation mechanism. The secondary-reinforcement wrap-around facing is prone to stress release and local loosening in the slope crest region due to weak constraints. The self-wrap facing has insufficient restraint at the top, where the reinforcement tends to experience pullout. Compared with the El Centro wave, the Wolong wave, rich in long-period components, induced stronger dynamic responses, resulting in greater slope face displacement, acceleration amplification, marginal spectral amplitude, and reinforcement strain. Significant damage in the slopes initiated in the mid-upper region, and the damage pattern was directly related to the wrap-around facing type. The research findings provide a theoretical basis for the optimal design of reinforced loess slopes in high-intensity seismic zones. Full article

The high-frequency switching noise of insulated-gate bipolar transistors (IGBTs) limits the sensitivity of online partial discharge (PD) monitoring in ultra-high-voltage flexible DC (VSC-HVDC) transmission systems. To address this challenge, this study investigates the underlying mechanisms and evolution of this interference and develops an anti-interference signal separation method. Simulation and experimental results indicate that the energy of IGBT switching noise is concentrated in the 30–180 MHz range, which significantly overlaps with the ultra-high-frequency (UHF) band used for PD detection. This research further reveals the pronounced modulation effect of device aging on the interference spectrum: bond wire aging triggers “spectral reconstruction” via altered parasitic parameters, where severe collector aging leads to an abnormal surge in turn-off interference amplitude. In contrast, gate oxide layer degradation manifests as characteristic “global spectrum attenuation” and a shift in peak frequency toward lower bands. Confronted with the challenges of strong interference and spectrum drift induced by aging, this paper proposes an adaptive signal separation method based on feature optimization of the time–frequency cumulative energy function. This method constructs novel characteristic parameters—namely, oblique intercept width and morphological gradient steepness—to effectively capture the fundamental differences in the energy accumulation process of the signals. Experimental verification demonstrates that even under conditions of varying interference characteristics, the proposed method achieves high-precision separation of PD signals from IGBT noise, outperforming traditional equivalent time–frequency and wavelet principal component analysis methods. This research provides crucial theoretical and technical support for insulation condition monitoring and device aging diagnosis in VSC-HVDC converter valves. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article