Search Results (94)

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Polyether ether ketone (PEEK) is a high-performance thermoplastic with potential applications in aerospace, automotive, and biomedical components, owing to its exceptional specific strength, thermal stability, and biocompatibility. However, its moderate hardness and limited wear resistance in dry sliding severely constrain its use in highly loaded tribological contacts. In this study, PEEK-based reinforced hybrid composites were produced utilizing a powder metallurgy technique, with reinforcement fractions of 10 wt.% graphite (Gr), boron (B), hydroxyapatite (HAp), and zirconium (Zr). The processing sequence included homogeneous wet-mixing, uniaxial cold compaction at pressures of 10–30 MPa, and sintering at 250–300 °C. The composition and microstructures were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Mechanical and tribological performances were assessed by Vickers microhardness, uniaxial compression and dry sliding wear tests. The best-performing Gr-B hybrid composite increased hardness by 240% and compressive strength by 175% compared with unreinforced PEEK. Tribologically, boron-containing PEEK demonstrated up to a 34.7% reduction in the coefficient of friction and approximately a 90% drop in wear-induced mass loss compared with unreinforced PEEK. The resulting Gr-B-reinforced PEEK hybrids are excellent choices for demanding load-bearing and tribological components like aerospace bushings, automotive sliding elements, spinal cages, and orthopedic fixation devices in biomedical applications because of their balanced combination of high hardness, superior wear resistance, and high compressive strength. Full article

Aero engine surge diagnosis is a key technology in engine health management, and its diagnostic accuracy is of great significance for ensuring operational safety. Traditional threshold-based diagnostic methods are significantly affected by working conditions, which makes it difficult to achieve full working condition coverage. Moreover, due to issues such as varying feature thresholds across conditions, weak signal characteristics, and low identifiability, the diagnostic accuracy remains limited. To address these challenges, this paper proposes an STFF-CANet (Spatio-Temporal Feature Fusion Cross-Attentional Network) diagnosis model of aero engine surge based on spatio-temporal feature fusion. The model first employs a Convolutional Neural Network (CNN) to extract spatial features from the frequency domain of dynamic signals via Fast Fourier Transform (FFT). Simultaneously, a Bidirectional Long Short-Term Memory (BiLSTM) network is used to capture temporal features from signals optimized by Variational Mode Decomposition (VMD). A cross-attention mechanism is further introduced to achieve deep fusion of spatiotemporal features, thereby enhancing the capability to identify weak fault characteristics. In addition, the sliding window slice method is used to expand the sample size for the small sample fault data of the engine surge of an aero engine. This ensures both informational continuity between slices and statistical stability of features, effectively mitigating the difficulty of diagnosing early and weak surge characteristics under small-sample conditions. Experimental results demonstrate that the model achieves an F1-score, Recall, Precision, and Accuracy of 97.96%, 97.52%, 98.43%, and 99.01%, respectively, in surge fault classification. These outcomes meet the practical requirements for aero engine surge diagnosis and provide an effective solution for early fault warning in complex industrial equipment. Full article

This study investigates the two-dimensional (2D) steady-state frictional contact behavior of functionally graded piezoelectric material (FGPM) coatings under a high-speed rigid cylindrical punch. An electromechanical coupled contact model considering inertial effects is established, while a layered model is employed to simulate arbitrarily varying material parameters. Based on piezoelectric elasticity theory, the steady-state governing equations for the coupled system are derived. By utilizing the transfer matrix method and the Fourier integral transform, the boundary value problem is converted into a system of coupled Cauchy singular integral equations of the first and second kinds in the frequency domain. These equations are solved semi-analytically, using the least squares method combined with an iterative algorithm. Taking a power-law gradient distribution as a case study, the effects of the gradient index, relative sliding speed, and friction coefficient on the contact pressure, in-plane stress, and electric displacement are systematically analyzed. Furthermore, the contact responses of FGPM coatings with power-law, exponential, and sinusoidal gradient profiles are compared. The findings provide a theoretical foundation for the optimal design of FGPM coatings and for enhancing their operational reliability under high-speed service conditions. Full article

With the ongoing miniaturization of smart devices, fine-grained hand gesture recognition using millimeter-wave radar has attracted increasing attention, yet practical deployment remains challenging in continuous-gesture segmentation, robust feature extraction, and reliable classification. This paper presents an end-to-end fine-grained gesture recognition framework based on frequency modulated continuous wave(FMCW) millimeter-wave radar, including gesture design, data acquisition, feature construction, and neural network-based classification. Ten gesture types are recorded (eight valid gestures and two return-to-neutral gestures); for classification, the two return-to-neutral gesture types are merged into a single invalid class, yielding a nine-class task. A sliding-window segmentation method is developed using short-time Fourier transformation(STFT)-based Doppler-time representations, and a dataset of 4050 labeled samples is collected. Multiple signal classification(MUSIC)-based super-resolution estimation is adopted to construct range–time and angle–time representations, and instance-wise normalization is applied to Doppler and range features to mitigate inter-individual variability without test leakage. For recognition, a variable-channel deep residual shrinkage network (DRSN) is employed to improve robustness to noise, supporting single-, dual-, and triple-channel feature inputs. Results under both subject-dependent evaluation with repeated random splits and subject-independent leave one subject out(LOSO) cross-validation show that DRSN architecture consistently outperforms the RefineNet-based baseline, and the triple-channel configuration achieves the best performance (98.88% accuracy). Overall, the variable-channel design enables flexible feature selection to meet diverse application requirements. Full article

Foot gesture recognition using a continuous-wave (CW) radar requires implementation on edge hardware with strict latency and memory budgets. Existing structured and unstructured pruning pipelines rely on iterative training–pruning–retraining cycles, increasing search costs and making them significantly time-consuming. We propose a NAS-guided bisection hybrid pruning framework on foot gesture recognition from a continuous-wave (CW) radar, which employs a weighted shared supernet encompassing both block and channel options. The method consists of three major steps. In the bisection-guided NAS structured pruning stage, the algorithm identifies the minimum number of retained blocks—or equivalently, the maximum achievable sparsity—that satisfies the target accuracy under specified FLOPs and latency constraints. Next, during the hybrid compression phase, a global L1 percentile-based unstructured pruning and channel repacking are applied to further reduce memory usage. Finally, in the low-cost decision protocol stage, each pruning decision is evaluated using short fine-tuning (1–3 epochs) and partial validation (10–30% of dataset) to avoid repeated full retraining. We further provide a unified theory for hybrid pruning—formulating a resource-aware objective, a logit-perturbation invariance bound for unstructured pruning/INT8/repacking, a Hoeffding-based bisection decision margin, and a compression (code-length) generalization bound—explaining when the compressed models match baseline accuracy while meeting edge budgets. Radar return signals are processed with a short-time Fourier transform (STFT) to generate unique time–frequency spectrograms for each gesture (kick, swing, slide, tap). The proposed pruning method achieves 20–57% reductions in floating-point operations (FLOPs) and approximately 86% reductions in parameters, while preserving equivalent recognition accuracy. Experimental results demonstrate that the pruned model maintains high gesture recognition performance with substantially lower computational cost, making it suitable for real-time deployment on edge devices. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

Metal–organic frameworks (MOFs) are a class of highly ordered nanoporous crystals that possess a designable framework and unique chemical versatility. MOF thin films are ideal for nanotechnology-enabling applications, such as optoelectronics, catalytic coatings, and sensing. Mg-MOF-74 has been drawing increasing attention due to its remarkable CO₂ uptake capacity among MOFs and other commonly used CO₂ absorbents. Mg-MOF-74 thin films are currently fabricated by immersing selected substrates in precursor solutions, followed by a traditional solvothermal synthesis process. Herein, we introduce a rapid, easy, and cost-effective synthesis protocol to fabricate MOF thin films in an additive manner. In this work, the controllable synthesis of Mg-MOF-74 thin films directly on optical supports is reported for the first time. Dense, continuous, and uniform Mg-MOF-74 thin films are successfully fabricated on bare glass slides, with an average growth rate of up to 85.3 nm min⁻¹. The structural and optical properties of the resulting Mg-MOF-74 thin films are characterized using X-ray diffraction, atomic force microscopy, scanning electron microscopy, UV-Vis-NIR spectroscopy, and Fourier Transform Infrared Spectroscopy (FTIR). The CO₂ adsorption performance of the resulting Mg-MOF-74 thin films is studied using FTIR for the first time, which demonstrates that, as per the length of the light path for gas absorption, 1 nm Mg-MOF-74 thin film could provide 400.9 ± 18.0 nm absorption length for CO₂, which is achieved via the extraordinary CO₂ adsorption by Mg-MOF-74. The synthesis protocol enables the rapid synthesis of MOF thin films, highlighting Mg-MOF-74 in more CO₂-related applications, such as enhanced CO₂ adsorption and MOF-enhanced infrared gas sensing. Full article

Against the backdrop of global energy transition and strict environmental regulations, supercritical carbon dioxide (scCO₂) fracturing and oil displacement technologies have emerged as pivotal green approaches in shale gas exploitation, offering the dual advantages of zero water consumption and carbon sequestration. However, the inherent low viscosity of scCO₂ severely restricts its sand-carrying capacity, fracture propagation efficiency, and oil recovery rate, necessitating the urgent development of high-performance thickeners. The current research on scCO₂ thickeners faces a critical trade-off: traditional fluorinated polymers exhibit excellent philicity CO₂, but suffer from high costs and environmental hazards, while non-fluorinated systems often struggle to balance solubility and thickening performance. The development of new thickeners primarily involves two directions. On one hand, efforts focus on modifying non-fluorinated polymers, driven by environmental protection needs—traditional fluorinated thickeners may cause environmental pollution, and improving non-fluorinated polymers can maintain good thickening performance while reducing environmental impacts. On the other hand, there is a commitment to developing non-noble metal-catalyzed siloxane modification and synthesis processes, aiming to enhance the technical and economic feasibility of scCO₂ thickeners. Compared with noble metal catalysts like platinum, non-noble metal catalysts can reduce production costs, making the synthesis process more economically viable for large-scale industrial applications. These studies are crucial for promoting the practical application of scCO₂ technology in unconventional oil and gas development, including improving fracturing efficiency and oil displacement efficiency, and providing new technical support for the sustainable development of the energy industry. This study innovatively designed an amphiphilic modified amino silicone oil polymer (MA-co-MPEGA-AS) by combining maleic anhydride (MA), methoxy polyethylene glycol acrylate (MPEGA), and amino silicone oil (AS) through a molecular bridge strategy. The synthesis process involved three key steps: radical polymerization of MA and MPEGA, amidation with AS, and in situ network formation. Fourier transform infrared spectroscopy (FT-IR) confirmed the successful introduction of ether-based CO₂-philic groups. Rheological tests conducted under scCO₂ conditions demonstrated a 114-fold increase in viscosity for MA-co-MPEGA-AS. Mechanistic studies revealed that the ether oxygen atoms (Lewis base) in MPEGA formed dipole–quadrupole interactions with CO₂ (Lewis acid), enhancing solubility by 47%. Simultaneously, the self-assembly of siloxane chains into a three-dimensional network suppressed interlayer sliding in scCO₂ and maintained over 90% viscosity retention at 80 °C. This fluorine-free design eliminates the need for platinum-based catalysts and reduces production costs compared to fluorinated polymers. The hierarchical interactions (coordination bonds and hydrogen bonds) within the system provide a novel synthetic paradigm for scCO₂ thickeners. This research lays the foundation for green CO₂-based energy extraction technologies. Full article

Pathological analysis of tissue biopsies remains the gold standard for diagnosing cancer and other diseases. However, this is a time-intensive process that demands extensive training and expertise. Despite its importance, it is often subjective and not entirely error-free. Over the past decade, pathology has undergone two major transformations. First, the rise in whole slide imaging has enabled work in front of a computer screen and the integration of image processing tools to enhance diagnostics. Second, the rapid evolution of Artificial Intelligence has revolutionized numerous fields and has had a remarkable impact on humanity. The synergy of these two has paved the way for groundbreaking research aiming for advancements in digital pathology. Despite encouraging research outcomes, AI-based tools have yet to be actively incorporated into therapeutic protocols. This is primary due to the need for high reliability in medical therapy, necessitating a new approach that ensures greater robustness. Another approach for improving pathological diagnosis involves advanced optical methods such as spectral imaging, which reveals information from the tissue that is beyond human vision. We have recently developed a unique rapid spectral imaging system capable of scanning pathological slides, delivering a wealth of critical diagnostic information. Here, we present a novel application of spectral imaging (SI) for virtual Hematoxylin and Eosin (H&E) staining using a custom-built, rapid Fourier-based SI system. Unstained human biopsy samples are scanned, and a Pix2Pix-based neural network generates realistic H&E-equivalent images. Additionally, we applied Principal Component Analysis (PCA) to the spectral information to examine the effect of down sampling the data on the virtual staining process. To assess model performance, we trained and tested models using full spectral data, RGB, and PCA-reduced spectral inputs. The results demonstrate that PCA-reduced data preserved essential image features while enhancing statistical image quality, as indicated by FID and KID scores, and reducing computational complexity. These findings highlight the potential of integrating SI and AI to enable efficient, accurate, and stain-free digital pathology. Full article

Elastic full waveform inversion (EFWI) is a crucial technique for retrieving high-resolution multi-parameter information. However, the lack of low-frequency components in seismic data may induce severe cycle-skipping phenomena in elastic full waveform inversion (EFWI). Recognizing the approximately linear relationship between the phase components of seismic data and the properties of subsurface media, we propose an Elastic Wave Phase Inversion in local-scale frequency–wavenumber domain (LFKEPI) method. This method aims to provide robust initial velocity models for EFWI, effectively mitigating cycle-skipping challenges. In our approach, we first employ a two-dimensional sliding window function to obtain local-scale seismic data. Following this, we utilize two-dimensional Fourier transforms to generate the local-scale frequency–wavenumber domain seismic data, constructing a corresponding elastic wave phase misfit. Unlike the Elastic Wave Phase Inversion in the frequency domain (FEPI), the local-scale frequency–wavenumber domain approach accounts for the continuity of seismic events in the spatial domain, enhancing the robustness of the inversion process. We subsequently derive the gradient operators for the LFKEPI methodology. Testing on the Marmousi model using a land seismic acquisition system and a simultaneous-source marine towed seismic acquisition system demonstrates that LFKEPI enables the acquisition of reliable initial velocity models for EFWI, effectively mitigating the cycle-skipping problem. Full article

Synthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and limited engineering applicability under low signal-to-noise ratio (SNR) conditions, an enhanced algorithm named dual-input feature fusion ShuffleNet (DIFF-ShuffleNet) based on intercepted SAR signal data is proposed. First, the SAR signal is processed by combining pulse compression and time–frequency analysis technology to enhance anti-noise robustness. Then, an improved lightweight ShuffleNet architecture is designed to fuse range pulse compression (RPC) maps and azimuth time–frequency features, significantly improving recognition accuracy in low-SNR environments while maintaining practical deployability. Moreover, an improved coarse-to-fine search fractional Fourier transform (CFS-FRFT) algorithm is proposed to address the chirp rate estimation required for RPC. Simulations demonstrate that the proposed SAR operating mode recognition algorithm achieves over 95.00% recognition accuracy for SAR operating modes (stripmap, spotlight, sliding spotlight, and scan) at an SNR greater than −8 dB. Finally, four sets of measured SAR data are used to validate the algorithm’s effectiveness, with all recognition results being correct, demonstrating the algorithm’s practical applicability. Full article

Anomaly detection plays a vital role in the processing of hyperspectral images and has garnered significant attention recently. Hyperspectral images are characterized by their “integration of spatial and spectral information” as well as their rich spectral content. Therefore, effectively combining the spatial and spectral information of images and thoroughly mining the latent structural features of the data to achieve high-precision detection are significant challenges in hyperspectral anomaly detection. Traditional detection methods, which rely solely on raw spectral features, often face limitations in enhancing target signals and suppressing background noise. To address these issues, we propose an innovative hyperspectral anomaly detection approach based on the fractional optimal-order Fourier transform combined with a multi-directional dual-window detector. First, a new criterion for determining the optimal order of the fractional Fourier transform is introduced. By applying the optimal fractional Fourier transform, prominent features are extracted from the hyperspectral data. Subsequently, band selection is applied to the transformed data to remove redundant information and retain critical features. Additionally, a multi-directional sliding dual-window RAD detector is designed. This detector fully utilizes the spectral information of the pixel under test along with its neighboring information in eight directions to enhance detection accuracy. Furthermore, a spatial–spectral combined saliency-weighted strategy is developed to fuse the detection results from various directions using weighted contributions, further improving the distinction between anomalies and the background. The proposed method’s experimental results on six classic datasets demonstrate that it outperforms existing detectors, achieving superior detection performance. Full article

Background: Ultrasonography is commonly employed during thoracic regional anesthesia; however, its accuracy can be affected by factors such as obesity and poor penetration through the rib window. Needle-sized ultrasound transducers, known as intra-needle ultrasound (INUS) transducers, have been developed to detect the pleura and fascia using a one-dimensional radio frequency mode ultrasound signal. In this study, we aimed to use time-frequency analysis to characterize the pleural signal and develop an automated tool to identify the pleura during medical procedures. Methods: We developed an INUS system and investigated the pleural signal it measured by establishing a phantom study, and an in vivo animal study. Signals from the pleura, endothoracic fascia, and intercostal muscles were analyzed. Additionally, we conducted time- and frequency-domain analyses of the pleural and alveolar signals. Results: We identified the unique characteristics of the pleura, including a flickering phenomenon, speckle-like patterns, and highly variable multi-band spectra in the ultrasound signal during the breathing cycle. These characteristics are likely due to the multiple reflections from the sliding visceral pleura and alveoli. This automated identification of the pleura can enhance the safety for thoracic regional anesthesia, particularly in difficult cases. Conclusions: The unique flickering pleural signal based on INUS can be processed by time-frequency domain analysis and further tracked by an auto-identification algorithm. This technique has potential applications in thoracic regional anesthesia and other interventions. However, further studies are required to validate this hypothesis. Key Points Summary: Question: How can the ultrasound pleural signal be distinguished from other tissues during breathing? Findings: The frequency domain analysis of the pleural ultrasound signal showed fast variant and multi-band characteristics. We suggest this is due to ultrasound distortion caused by the interface of multiple moving alveoli. The multiple ultrasonic reflections from the sliding pleura and alveoli returned in variable and multi-banded frequency. Meaning: The distinguished pleural signal can be used for the auto-identification of the pleura for further clinical respiration monitoring and safety during regional anesthesia. Glossary of Terms: intra-needle ultrasound (INUS); radio frequency (RF); short-time Fourier transform (STFT); intercostal nerve block (ICNB); paravertebral block (PVB); pulse repetition frequency (PRF). Full article

In this paper, we investigate the thermal friction sliding contact of a functionally graded piezoelectric material (FGPM)-coated half-plane subjected to a rigid conductive cylindrical punch. This study considers the effect of the thermal convection term in heat conduction. The thermo-electro-elastic material parameters of the coating vary exponentially along its thickness direction. Utilizing thermoelastic theory and Fourier integral transforms, the problem is formulated into Cauchy singular integral equations of the first and second kinds with surface stress, contact width, and electric displacement as the unknown variables. The numerical solutions for the contact stress, electric displacement, and temperature field of the graded coating surface are obtained using the least-squares method and iterative techniques. It can be observed that the thermo-electro-elastic contact behavior of the coating surface undergoes significant changes as the graded index varies from −0.5 to 0.5, the friction coefficient ranges from 0.1 to 0.5, and the sliding velocity changes from 0.01 m/s to 0.05 m/s. The results indicate that adjusting the graded index of the coating, the sliding speed of the punch, and the friction coefficient can improve the thermo-electro-elastic contact damage of the material’s surface. Full article