Search Results (244)

Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. 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

Monitoring mechanical vibration is crucial for ensuring the structural integrity and optimal performance of unmanned aerial vehicles (UAVs). This study introduces a portable and low-cost system that enables integrated acquisition and analysis of UAV vibration data in a single step, using a Raspberry Pi 4B, data acquisition (DAQ) through a MCC128 DAQ HAT card, and six accelerometers positioned at strategic structural points. Ground-based engine tests at 2700 RPM allowed vibration data to be recorded under conditions similar to those of real operation. Data was processed with a Kalman filter, a Hann window function application, and frequency analysis via Fast Fourier Transform (FFT). The first and second wing bending natural frequencies were identified at 12.3 Hz and 17.5 Hz, respectively, as well as a significant component around 23 Hz, which is a subharmonic of the propulsion system excitation frequency near 45 Hz. The results indicate that the highest vibration amplitudes are concentrated at the wingtips and near the engine. The proposed system offers an accessible and flexible alternative to commercial equipment, integrating acquisition, processing, and real-time visualization. Moreover, its implementation facilitates the early detection of structural anomalies and improves the reliability and safety of UAVs. Full article

Fourier transform infrared (FTIR) spectroscopy-based gas remote sensing has been widely applied for long-range atmospheric composition analysis. However, when deployed for longwave infrared methane detection, spectral features of methane are significantly interfered by water vapor variations at the edge of atmospheric window, which compromises detection performance. To address the spectral fitting degradation caused by relative changes between methane and water vapor signals, this study incorporates temperature, relative humidity, and sensing distance into the cost function, establishing a continuous optimization space with concentration path lengths (CLs) as variables, which are the product of the concentration and path length. A hybrid differential evolution and Levenberg–Marquardt (D-LM) algorithm is developed to enhance parameter estimation accuracy. Combined with a three-layer atmospheric model for real-time reference spectrum generation, the algorithm identifies the optimal spectral combination that provides the best match to the measured data. Algorithm performance is validated through two experimental configurations: Firstly, adaptive detection using synthetic spectra covering various humidity–methane concentration combinations is conducted; simulation results demonstrate that the proposed method significantly reduces the mean squared error (MSE) of fitting residuals by 95.8% compared to the traditional LASSO method, effectively enhancing methane spectral feature extraction under high-water-vapor conditions. Then, a continuous monitoring of controlled methane releases over a 500 m open path under high-outdoor-humidity conditions is carried out to validate outdoor performance of the proposed algorithm; field measurement analysis further confirms the method’s robustness, achieving a reduction in fitting residuals of approximately 57% and improving spectral structure fitting. The proposed approach provides a reliable technical pathway for adaptive gas cloud detection under complex atmospheric conditions. Full article

Zn-based ternary deep eutectic solvents (TDESs) have attracted significant attention due to their good biodegradability, stability, and sustainability. In this work, TDESs composed of choline chloride:urea (ChCl:U) and zinc salts, ZnCl₂, Zn(CH₃COO)₂, and ZnSO₄ were synthesized and characterized by Fourier transform infrared (FTIR) spectroscopy and laser desorption ionization mass spectrometry (LDI MS). Their antibacterial activity against cariogenic Streptococcus species isolates was determined by microdilution assay, while their cytotoxic potential and effect on the intracellular reactive oxygen species (ROS) induction were analyzed on the MRC-5 fibroblast cell line by XTT, trypan blue, and DCF assays, respectively. FTIR confirmed that hydrogen bonds prevail in the molecular structure of ChCl:U:Zn salts, while LDI MS revealed the interactions between zinc salts and ChCl:U. The antibacterial TDES potential was high, especially against Streptococcus sanguinis, with ChCl:U:ZnCl₂ displaying the most promising effects (MICs 1.13–18.12 µg/mL). Cytotoxicity assessment showed that concentrations up to 100 µg/mL of all TDESs did not display significant cytotoxicity, while higher concentrations significantly reduced cell viability by increasing ROS production and cell membrane damage, outlining the safety window of up to 100 µg/mL. Strong antibacterial activity of low TDESs concentrations combined with their good biocompatibility highlights their potential as innovative candidates for biomedical application. Full article

Debate persists over whether classical econometric or modern machine learning (ML) approaches provide superior forecasts for volatile monthly price series. Despite extensive research, no systematic cross-domain comparison exists to guide model selection across diverse asset types. In this study, we compare traditional econometric models with classical ML baselines and hybrid approaches across financial assets, futures, commodities, and market index domains. Universal Python-based forecasting tools include month-end preprocessing, automated ARIMA order selection, Fourier terms for seasonality, circular terms, and ML frameworks for forecasting and residual corrections. Performance is assessed via anchored rolling-origin backtests with expanding windows and a fixed 12-month horizon. MAPE comparisons show that ARIMA-based models provide stable, transparent benchmarks but often fail to capture the nonlinear structure of high-volatility series. ML tools can enhance accuracy in these cases, but they are susceptible to stability and overfitting on monthly histories. The most accurate and reliable forecasts come from models that combine ARIMA-based methods with Fourier transformation and a slight enhancement using machine learning residual correction. ARIMA-based approaches achieve about 30% lower forecast errors than pure ML (18.5% vs. 26.2% average MAPE and 11.6% vs. 16.8% median MAPE), with hybrid models offering only marginal gains (0.1 pp median improvement) at significantly higher computational cost. This work demonstrates the domain-specific nature of model performance, clarifying when hybridization is effective and providing reproducible Python pipelines suited for economic security applications. Full article

Yuanjiang County is one of the most important gem-producing areas in China. The authors of this study discovered and collected gem-quality andradite Garnsts in the epidote amphibolite from the periphery of the ruby deposit in Shaku Village, Yuanjiang County. After careful observation of the collected andradite, it was found that these andradite samples appear green when the thickness is less than 2 mm and reddish-brown when the thickness is greater than 2 mm, exhibiting the typical Usambara effect. To investigate the gemological and spectroscopic characteristics of Yuanjiang andradite, this study conducted basic gemological tests, microscopic observation, electron probe microanalysis (EPMA), ultraviolet–visible (UV-Vis) absorption spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and laser Raman spectroscopy on the collected samples. Tests show that Yuanjiang andradite has a lower specific gravity than typical andradite, which is due to the presence of epidote inclusions inside. EPMA results indicate that the samples contain a certain amount of Cr element. The crystal chemical formula of the samples calculated from the EPMA results is (Ca_2.89–2.93, Mn_0.01–0.02, Fe_0.15–0.10)(Fe_1.69–1.95, Al_0.00–0.23, Cr_0.00–0.23, Si_0.05–0.08)(SiO₄)₃. UV-Vis tests show that the samples have transmission windows in both the green- and red-light regions, with Fe³⁺ and Cr³⁺ acting as the main chromogenic ions, among which Cr³⁺ is crucial for the occurrence of the Usambara effect. The FTIR and Raman test results are basically the same as previous research results, but some peak positions related to metal cations differ from the theoretical values, which may be caused by the presence of a certain amount of Cr³⁺ in the samples. Full article

Radar waveform design is an important approach to radar system performance enhancement. For a long time, synthetic aperture radar (SAR) systems have utilized linear frequency modulation (LFM) waveforms as transmitted signals and have relied on window functions to suppress sidelobes. However, this approach significantly degrades system signal-to-noise ratio (SNR) and resolution. Nonlinear frequency modulation (NLFM) waveforms can suppress sidelobes without SNR loss and have been widely applied in the SAR field in recent years. Nonetheless, they still cannot completely avoid resolution loss. To address this, this article, based on an advanced High-Freedom Parameterized Frequency Modulation (HFPFM) coding model, constructs a waveform sidelobe optimization model constrained by mainlobe widening and solves it using a gradient descent method. Through detailed experiments, we found that the optimized waveform, compared to the LFM waveform, can reduce sidelobes by more than 9 dB without widening the mainlobe, thereby simultaneously avoiding the resolution and SNR losses caused by window function weighting. In addition, this optimization method can efficiently and rapidly optimize all parameters simultaneously using only matrix multiplication and fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT). The SAR point target imaging simulation results verify that the optimized waveform can clearly image weak targets near strong targets, which proves the effectiveness of the proposed method. Full article

Cognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge. Hence, a novel “Deep Wavelet Cyclostationary Independent Gaussian Markov Fourier Transform Dempster–Shafer Network” is proposed. When the signal waveform is submerged within the noise envelope and residual correlation emerges in the noise, it violates white Gaussian assumptions, leading to misidentification of signal presence. To resolve this, the Adaptive Continuous Wavelet Cyclostationary Denoising Autoencoder (ACWC-DAE) is introduced, in which the Adaptive Continuous Wavelet Transform (ACWT), Cyclostationary Independent Component Analysis Detection (CICAD), and Denoising Autoencoder (DAE) are introduced into the first hidden layer of a Deep Q-Network (DQN). It restores the bursty signal structure, separates the structured noise, and reconstructs clean signals, leading to accurate signal detection. Additionally, bursty and fading-affected primary user signals become fragmented and dip below the noise floor, causing conventional fixed-window sensing to fail in accumulating reliable evidence for detection under intermittent and low-duty-cycle conditions. Therefore, the Adaptive Gaussian Short-Time Fourier Transform Dempster–Shafer Model (AGSTFT-DSM) is incorporated into the second DQN layer, Adaptive Gaussian Mixture Hidden Markov Modeling (AGMHMM) tracks the hidden activity states, Adaptive Short-Time Fourier Transform (ASFT) resolves brief signal bursts, and Dempster–Shafer Theory (DST) fuses uncertain evidence to infer occupancy, thereby detecting an accurate user signal. The results obtained by the proposed model have a low error and detection time of 0.12 and 30.10 ms and a high accuracy of 97.8%, revealing the novel insight that adaptive wavelet denoising, along with uncertainty-aware evidence fusion, supports reliable spectrum detection under low-SNR conditions where existing models fail. Full article

Nickel ions (Ni²⁺) are persistent heavy metal pollutants that pose significant risks to human health due to their toxicity. Conventional treatment technologies, while effective, are often costly, energy-intensive, and limited in removing emerging pollutants. In this study, we report an eco-friendly, fluorescence-based sensing platform using carbon nanostructures (CNs) synthesized from coffee waste via pyrolysis at 600 °C. The CNs were characterized by Fourier transform infrared (FTIR) spectroscopy and evaluated for their fluorescence response toward Ni²⁺, Co²⁺, Cu²⁺, and Cd²⁺ ions. Distinct ion-specific behaviors were observed, with Ni²⁺ inducing the strongest fluorescence quenching. Sensitivity studies revealed reliable detection across 10⁻⁸–10⁻³ M, with a detection limit of 10⁻⁴ M (≈5.9 mg/L). Fluorescence stability was maintained for up to six hours, with one hour identified as the optimal detection window. Performance in real water samples highlighted consistent responses in mineral water, reflecting reliable sensing capability in a realistic aqueous matrix. While the current detection limit is above the World Health Organization guideline for drinking water, the CNs show promise for monitoring Ni²⁺ in contaminated or industrial effluents. Overall, this work demonstrates that coffee waste-derived CNs provide a cost-effective, sustainable approach to heavy metal sensing, linking waste valorization with environmental monitoring. Full article

One of the key challenges with developing pulsed induction (PI) electromagnetic induction (EMI) sensors for use in the Arctic is the inaccessibility of the environment, which makes in situ testing prohibitively expensive. To mitigate this, sensor development can be streamlined through the creation of a robust simulation strategy with which to optimize features such as coil turns and geometry. Building on work that previously presented a method for simulating an Arctic PI sensor via a time-domain finite element model (FEM), this paper presents a method for approximating a time-domain simulation with multiple frequency-domain simulations. A comparison between the fast Fourier transform (FFT) of a time-domain simulation and a collection of frequency-domain simulations is presented. These are validated against empirical data with a PI sensor over seawater, with an air gap used as a proxy for sea ice. Using the method described, a range of coils is simulated with dimensions from $0.5\times 0.5$ m up to $1.0\times 2.0$ m, demonstrating the ability of this approach to enable comparison of sensor performance over a wider parameter space. For a parametric sweep over 10 sensor-to-seawater lift-off distances, the improvement from the time-domain simulation (of a 402 $\mathsf{\mu }$s window) to the frequency-domain simulation (comprising 100 discrete frequencies) represents a reduction in simulation time from 38,013 min to 141 min. Full article

Within a power hardware-in-the-loop (PHIL) controller design automation (CDA) framework for voltage feedback grid-forming inverters, a scaled-down inverter system is developed for time-domain response solving. This hardware-based approach effectively addresses the conflicting demands of accuracy, computational efficiency, and modeling cost that are commonly encountered in simulation-based methods. Conventional synchronous sampling in digitally controlled pulse-width modulation (PWM) inverters introduces severe low-frequency distortion and significant ripple components in the step response, leading to non-decaying oscillations that compromise the extraction of settling time and steady-state error. By analyzing the sideband aliasing mechanism in capacitor-voltage sampling and associated harmonic-cancellation conditions, aliasing-free sampling is achieved using 90° phase-shifted anti-aliasing filters combined with synchronous sampling. Although Fast Fourier Transform (FFT) filtering offers the highest fidelity, it suffers from window-boundary distortions and is unsuitable for online use; therefore, four practical filtering schemes are evaluated against the FFT benchmark, among which oversampling with moving-average filtering (MAF) retains dynamics closest to the FFT result while avoiding its distortions. An objective function incorporating step-response metrics is constructed to optimize single-variable active damping and multiple resonant controllers, mitigating severe overshoot encountered in conventional integral-based approaches. Experimental results verify the aliasing mechanism and the effectiveness of the proposed CDA method. Full article

Magnetic Resonance Imaging (MRI) serves as a pivotal medical diagnostic technique widely deployed in clinical practice, yet high-resolution reconstruction frequently introduces motion artifacts and degrades signal-to-noise ratios. To enhance imaging efficiency and improve reconstruction quality, this study proposes a Transformer network-based super-resolution framework for MRI images. The methodology integrates Nonuniform Fast Fourier Transform (NUFFT) with a hybrid-attention Transformer network to achieve high-fidelity reconstruction. The embedded NUFFT module adaptively applies density compensation to k-space data based on sampling trajectories, while the Mixed Attention Block (MAB) activates broader pixel engagement to amplify feature extraction capabilities. The Interactive Attention Block (IAB) facilitates cross-window information fusion via overlapping windows, effectively suppressing artifacts. Evaluated on the fastMRI dataset under 4× radial undersampling, the network demonstrates 3.52 dB higher PSNR and 0.21 SSIM improvement over baselines, outperforming state-of-the-art methods across quantitative metrics. Visual assessments further confirm superior detail preservation and artifact suppression. This work establishes an effective pipeline for high-quality radial MRI reconstruction, providing a novel technical pathway for low-field MRI systems with significant research and application value. Full article

Within the framework of an extensive conservation project involving multiple stained-glass windows of the Basilica of San Petronio in Bologna, Italy, this study reports the results of the diagnostic campaign on the rose window depicting Sant’Ambrogio between two angels holding the coats of arms of the Marsili family. The rose window is located in the homonymous chapel and, based on recent studies attributing the cartoon to the Bolognese painter Biagio Pupini, who was active in San Petronio from 1519, is dated to the early sixteenth century. No evidence was found regarding the workshop responsible for the production of the stained-glass window. The window showed no significant conservation issues, either in the glass elements or in the lead cames. However, the extensive degradation of the grisaille—likely caused by a low-quality mixture, improper firing, or aggressive cleaning—resulted in the loss of the original drawing. This study presents the results of non-invasive investigations on the glass tiles of the rose windows and the analyses of deposits present on their surfaces. Fiber Optic Spectroscopy (FOS) in transmittance, X Ray Fluorescence (XRF), and Hyper Spectral Imaging (HIS) in transmittance were used to investigate the glass composing the rose window. Fourier Transform Infrared Spectroscopy (FT-IR) was applied to study deposit samples collected from the external surface of the window. Additionally, only four glass samples, obtained from hidden areas or already detached fragments, were analyzed using Scanning Electron Microscope with Energy-Dispersive Spectroscopy (SEM-EDS). In addition, a photographic processing method is described, which enabled the recovery of the ghost image, the faint trace or imprint left by the grisaille on the glass during firing, allowing the conservators to faithfully reintegrate the original drawing. Full article

Background/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior^® perfusion fluid can discriminate kidneys recovered from donation after circulatory death (DCD) versus donation after brain death (DBD). Methods: Preservation solution from isolated kidney allografts (n = 10; 5 DCD/5 DBD) matched on demographics was analyzed in the Amide I and fingerprint regions. Several spectral preprocessing steps were applied, and feature extraction was based on the Fast Correlation-Based Filter. Support vector machines and Naïve Bayes were evaluated. Unsupervised structure was assessed based on cosine distance, multidimensional scaling, and hierarchical clustering. Two-dimensional correlation spectroscopy (2D-COS) was used to examine band co-variation. Results: Donor cohorts were well balanced, except for higher terminal serum creatinine in DCD. Quality metrics were comparable, indicating no systematic technical bias. In Amide I, derivatives improved classification, but performance remained modest (e.g., second derivative with feature selection yielded an area under the curve (AUC) of 0.88 and an accuracy of 0.90 for support vector machines; Naïve Bayes reached an AUC of 0.92 with an accuracy of 0.70). The fingerprint window was most informative. Naïve Bayes with second derivative plus feature selection identified bands at ~1202, ~1203, ~1342, and ~1413 cm⁻¹ and achieved an AUC of 1.00 and an accuracy of 1.00. Unsupervised analyses showed coherent grouping in the fingerprint region, and 2D correlation maps indicated coordinated multi-band changes. Conclusions: Performance in this 10-sample pilot should be interpreted cautiously, as perfect leave-one-out cross-validation (LOOCV) estimates are vulnerable to overfitting. The findings are preliminary and hypothesis-generating, and they require confirmation in larger, multicenter cohorts with a pre-registered analysis pipeline and external validation. Full article