Search Results (2,120)

A low-latency, low-complexity digital demodulator is presented for chirp spread spectrum (CSS)-modulated RF packets targeting low-power IoT wireless systems operating in spectrally congested environments. Conventional CSS receivers rely on fast-fourier transform (FFT)-based synchronization and long preamble sequences, resulting in increased latency and computational complexity. To address these limitations, the proposed receiver employs amplitude-domain synchronization using oversampled sub-chirp windows and maximum likelihood estimation without requiring FFT processing. A digital demodulator co-designed with receiver’s fractional-N phase-locked loop (PLL) architecture enables rapid sub-chirp generation and fast frequency settling, while compensation techniques mitigate symbol boundary offset (SBO) error due to PLL non-idealities during synchronization. The proposed system achieves packet synchronization within 17.5 preamble symbol cycles while maintaining symbol boundary offset estimation error below ±1%. Simulation results demonstrate a syncword misdetection probability below 10⁻³ at SNRs of 9 dB and 1 dB without and with 8× repetition, respectively. In the presence of interferences, the receiver tolerates worst-case in-band signal-to-noise ratio (SIR) levels down to −16.2 dB while consuming 877 µW and 830 µW average power at the digital demodulator, and fractional-N PLL, respectively. Implemented in 65 nm CMOS, the proposed architecture occupies 0.195 mm² active area. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

Impulse-based impedance sensing with capacitively coupled electrodes is introduced as a fast, non-contact, and simplified complementary method to conventional capacitive impedance measurements. Unlike frequency-domain methods, the proposed approach derives effective resistive and capacitive properties of a sample from the transient response to a single rectangular pulse. The equivalent circuit model comprises three elements: sample resistance, sample capacitance, and electrode coupling capacitance. From this model, analytical expressions of the transient response were derived, enabling accurate simulation of measured signals and providing the basis for both phantom verification and machine learning training. Importantly, the coupling capacitance, typically considered a limitation in contactless methods, is estimated alongside the sample parameters, providing insight into electrode–object coupling conditions. A machine-learning model trained on simulated circuit responses, including noise and temporal variability, is employed as a low-latency estimator for extracting parameters from measured transient signals. Experimental validation was carried out using a configurable lumped-element equivalent circuit and NaCl solutions of controlled conductivity, cross-verified with conductometric measurements and numerical probe simulations. Across a tested conductivity range, the method achieved estimation errors of 2–8%. The proposed approach is intended as a low-latency measurement strategy for simplified capacitively coupled impedance sensing, with potential relevance to future capacitively coupled electrical impedance tomography systems, where rapid acquisition of boundary measurements is prioritized over full frequency-resolved impedance spectroscopy. Full article

This paper presents a simulation-based 16-quadrature amplitude modulation (16-QAM) direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) visible-light optical wireless system using a 520 nm InGaN directly modulated laser (DML) and direct detection over 500 m. A 1024-point transform with 511 data subcarriers provides approximately 15 Gb/s gross and 14.82 Gb/s payload rates without external optical modulators or amplifiers. Under the adopted static line-of-sight model, the simulated bit-error rate (BER) falls below ${10}^{-3}$ at a receiver-side equivalent optical signal-to-noise ratio (OSNR) of about 17 dB and remains below this threshold for beam divergence up to 9 mrad. Gamma–Gamma simulations show that a 5 cm aperture maintains $\mathrm{BER}<{10}^{-3}$ at 20 dB OSNR up to $C_n^2\approx 5\times {10}^{-14}{\mathrm{m}}^{-2/3}$. Pointing-error analysis gives per-axis angular-jitter standard deviations of 0.425, 0.515, and 0.564 mrad at 1% outage for 5, 10, and 15 cm apertures. The clear-air margin is exhausted at $V_{2\%}\approx 0.66\mathrm{km}$, corresponding to $V_{5\%}\approx 0.50\mathrm{km}$, or near 107 mm/h rain. For a 1.5 GHz bandwidth-limited DML, adaptive bit loading reaches 16.5 Gb/s at 28 dB OSNR. The results support a low-complexity medium-range architecture but remain numerical estimates requiring experimental validation under practical device, alignment, and weather conditions. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

Frequency instability caused by reduced system inertia in inverter-dominated islanded microgrids represents a critical challenge in renewable-integrated power systems. Conventional fixed-parameter controllers exhibit limited adaptability to uncertain and time-varying low-inertia conditions. This paper proposes an LSTM–MPC + VIC framework that embeds a Long Short-Term Memory (LSTM) surrogate predictor directly within a Model Predictive Control (MPC) optimisation loop, coordinated with a Virtual Inertia Controller (VIC) for immediate transient support. The LSTM provides data-driven frequency predictions without requiring precise analytical system modelling, while the VIC supplies reactive inertial damping within the same control cycle. The proposed controller is evaluated against Proportional–Integral–Derivative (PID), PSO-optimised PID, and standard MPC baselines on a 50 Hz islanded microgrid. Results demonstrate the lowest maximum frequency deviation of 0.009748 Hz, fastest settling time of 36.34 s, and minimum integral absolute error of 0.12283 Hz·s among all controllers. A Lyapunov-based Input-to-State Stability (ISS) analysis, incorporating the load disturbance term via Young’s inequality, confirms an ISS ultimate bound of 0.057866 Hz and an effective decay rate of 1.2952 s⁻¹. Robustness is further validated through multi-scenario testing, parametric sensitivity analysis, component ablation, and computational feasibility assessment, confirming suitability for real-time deployment in low-inertia microgrid systems. Full article

To address the critical challenge of 0–100 Hz low-frequency vibration control for marine hydraulic pipelines, this paper proposes a dedicated fiber-reinforced magnetorheological elastomer (MRE) isolator and a genetic algorithm-optimized fuzzy control strategy utilizing the magnetically tunable properties of MREs. An upper-lower split-type isolator is designed to suppress axial and radial vibrations through the shear and Compression Modes of MRE, respectively, and a two-degree-of-freedom (2-DOF) dynamic model is established to analyze the effects of mass ratio and natural frequency ratio on the system’s amplitude magnification factor. A Mamdani-type fuzzy controller, with acceleration error and its rate of change as inputs and control voltage as output, is optimized via a genetic algorithm. Simulation and experimental results show that 31–56.5% amplitude attenuation is achieved under 25–35 Hz single-frequency excitation; 12 dB isolation in the 5–23 Hz band at the input end and a maximum 15 dB isolation in multiple bands for the suspended pipeline section are obtained without external forced excitation; and efficient 0–100 Hz full-band isolation is realized at an applied current of 1.5 A. This work verifies the effectiveness of the proposed scheme for low-frequency vibration control of marine hydraulic pipelines. Full article

Electrostatic MEMS micromirrors provide a compact and low-power beam-steering solution for miniaturized laser communication terminals. However, when they are used for quasi-static beam pointing rather than resonant scanning, the nonlinear voltage–angle relationship, bidirectional actuation asymmetry, and terminal-level installation errors can significantly degrade pointing accuracy. In this paper, a nonlinear modeling and differential-voltage control method is investigated for a two-axis electrostatic MEMS micromirror used in a miniaturized laser communication terminal. The device under test is a bonded aluminum MEMS micromirror with a 5.0 mm aperture. Static and dynamic characterization results show that the micromirror achieves maximum mechanical deflection angles of 5.215° and 5.161° along the X and Y axes, respectively, with resonant frequencies of 317 Hz and 319 Hz. To improve the accuracy of quasi-static pointing, the differential-voltage actuation principle is analyzed, and a nonlinear voltage–angle model is established based on measured deflection data. Compared with a first-order linear model, the cubic nonlinear model reduces the root-mean-square fitting error from 0.142° to 0.0127° for the X axis and from 0.132° to 0.0109° for the Y axis. Furthermore, a terminal-level calibration architecture based on a quadrant detector is introduced to map the MEMS angular deflection to the received spot position. The proposed modeling and calibration approach provides an actuator-level basis for accurate beam pointing and closed-loop acquisition in miniaturized laser communication terminals. Full article

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

In radio-over-fiber (RoF) links, optical single-sideband (OSSB) modulation is an effective method to mitigate power fading caused by chromatic dispersion. However, its low modulation efficiency leads to suboptimal link performance. To address this, we propose a tunable optical carrier-to-sideband ratio (OCSR) OSSB modulation scheme based on a dual-electrode Mach–Zehnder modulator (DEMZM) in a Sagnac loop. Firstly, by adjusting the OCSR, higher radio-frequency (RF) transmission efficiency can be achieved. The experimental results demonstrate that the proposed link provides a 6 dB improvement in received RF power compared to conventional SSB modulation schemes. Furthermore, this approach effectively optimizes nonlinear distortions in the link, achieving a 12.14 dB enhancement in spurious-free dynamic range (SFDR). For tests conducted with a broadband signal featuring a 15 GHz carrier frequency and 500 MHz bandwidth, the optimal error vector magnitude (EVM) reaches 4.88%. Additionally, the link performance can be flexibly improved by adjusting the polarization controller configurations for each channel, making it suitable for multi-user application scenarios. Full article

Time-fractional diffusion equations (TFDEs) are essential for modeling anomalous transport in heterogeneous media, but high-fidelity long-time simulations face two bottlenecks: the $O\left(N^2\right)$ complexity of non-local fractional derivatives, and the spatial truncation error of polynomial-based finite element methods (FEMs) when resolving oscillatory plumes or singular sources. We propose a framework combining a C-Bézier FEM for spatial approximation with a fast L1 temporal discretization. By coupling the shape parameter of the C-Bézier basis to the mesh size ($\mu =\pi h$), the scheme reproduces trigonometric profiles of the corresponding frequency exactly; for solutions whose spatial part lies in the C-Bézier space this eliminates the spatial truncation error and drives the associated error constant to near zero. A sum-of-exponentials (SOE) approximation reduces the temporal complexity from $O\left(N^2\right)$ to $O\left(N\right)$ and storage to $O\left(1\right)$, enabling scalable 3D simulation. We prove the optimal $O({\tau }^{2-\alpha }+h^{k+1})$ convergence, and numerical experiments confirm these rates. For profiles matched by the basis, the method yields substantially smaller errors than Lagrange FEM; for a general solution outside the C-Bézier space, the two methods share the same order and comparable error magnitudes, so the gains are specific to fields reproduced by the basis. We further examine low-regularity scenarios, including discontinuous interfaces and Dirac-delta injections. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

The increasing penetration of renewable energy sources introduces significant variability, low-inertia behaviour, and operational uncertainty into modern power systems, resulting in frequent frequency deviations and degraded dynamic stability. Conventional Load Frequency Control (LFC) approaches based on fixed-parameter PID controllers often exhibit limited disturbance rejection capability under nonlinear and stochastic operating conditions. This study proposes an enhanced LFC framework that integrates a PID controller optimised using the Flower Pollination Algorithm (FPA) with support from a Unified Power Flow Controller (UPFC) and a Redox Flow Battery (RFB) to improve frequency regulation, damping, and robustness in renewable-integrated low-inertia power systems. This study developed a MATLAB/Simulink single-area power system model comprising governor, turbine, and generator-load dynamics to evaluate controller performance under a 0.01 pu step disturbance, stochastic load variations, renewable energy fluctuations, and ±20% parameter uncertainty conditions. The FPA optimally tuned the PID controller gains using the Integral Time Absolute Error criterion to enhance transient response and disturbance rejection capability. Comparative analyses were conducted against conventional PID and fuzzy-based controllers using settling time, overshoot, RMS deviation, ITAE, and mean frequency deviation indices. Simulation results demonstrate that the proposed FPA–PID + UPFC framework significantly outperforms the conventional PID controller by achieving approximately 66.6% settling-time reduction, 72.1% RMS reduction, and 75.5% ITAE reduction. The proposed framework reduced settling time from 18.46 s to 6.16 s and substantially improved damping performance under stochastic disturbances. The coordinated integration of the UPFC and RFB further enhanced transient stability through dynamic power-flow regulation and rapid active-power compensation during disturbances. Sensitivity analysis under parameter uncertainty and stochastic operating conditions confirmed stable and reliable operation under stochastic disturbances and parameter uncertainty conditions. The proposed architecture, therefore, provides an effective, practically applicable solution for secondary frequency regulation in renewable-rich smart grids, low-inertia transmission systems, microgrids, and future distributed power networks. Full article

Objective: Preanalytical errors account for the vast majority of preanalytical incidents and remain a fundamental threat to the reliability of test results. Although the types and frequencies of these errors have been extensively studied in the literature, their time-dependent variability has received comparatively little attention. This study aimed to evaluate how preanalytical specimen rejection rates vary across intraday time intervals and to assess the independent influence of time on preanalytical quality. Methods: This retrospective observational study included a total of 579,845 specimens accepted by the central laboratory of Istanbul Atlas University Hospital between January 2024 and December 2025. Specimens were analyzed with respect to preanalytical rejection reasons, the distribution and rate of these reasons across clinical units, and time of day. Each day was divided into six equal four-hour intervals: Z1 (00:00–04:00), Z2 (04:00–08:00), Z3 (08:00–12:00), Z4 (12:00–16:00), Z5 (16:00–20:00), and Z6 (20:00–24:00). Statistical analyses were performed using the Pearson chi-square test, and effect sizes were quantified using Cramér’s V coefficient. Results: Of the 579,845 specimens examined, 4365 were rejected, yielding an overall rejection rate of 0.79%. Rejection rates were found to be non-uniformly distributed across the day (p < 0.001). The highest rejection rate was observed during the Z2 interval (04:00–08:00) at 1.98%, whereas the lowest was recorded during Z3 (08:00–12:00) at 0.45%. Negative binomial regression analysis identified the Z2 interval as the only time period independently associated with an increased rejection risk Incidence Rate Ratio (IRR) = 1.63; 95% Confidence Interval (CI): 1.22–2.19. Among clinical units, the highest rejection rate was recorded in the emergency department (1.92%). Analysis of error types revealed that the majority of rejections were attributable to hemolysis (47.5%) and clotted specimens (26.3%). Hemolysis rates peaked in the emergency department, while clotted specimens occurred more frequently within intensive care units. Analysis of time and error interactions revealed that clotted specimens peaked during Z1 and Z2, whereas hemolysis became the primary cause of rejection during Z3 and Z4. Conclusions: Preanalytical specimen rejection rates exhibited significant variation according to time of day, clinical unit, and error type, with time emerging as a factor independently associated with preanalytical quality. The coexistence of elevated rejection risk during Z2 (04:00–08:00) and markedly low rejection rates during Z3 (08:00–12:00) indicates that the relationship between workload and error frequency is not linear. Although hemolysis and clotted specimens constituted the dominant error types, their distribution followed distinct patterns depending on the clinical unit and time interval. These results underscore the necessity of time-based monitoring to pinpoint unit-specific risks, providing a clear roadmap for targeted quality improvement interventions. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth-generation (5G) networks can support UAV connectivity through high bandwidth and low-latency communication; however, rapid three-dimensional UAV mobility creates handover-management challenges that can increase signalling overhead, service interruption, and Quality of Service (QoS) degradation. This paper presents an integrated framework that combines LSTM-based multi-UAV trajectory prediction with proactive handover optimization using an Advantage Actor–Critic (A2C) Deep Reinforcement Learning (DRL) agent. The LSTM predictor is evaluated on a real-world UAV trajectory dataset and reports a root mean square error (RMSE) of 4.37 m over a 5 s prediction horizon after conversion to a local East–North–Up coordinate frame. A lightweight simulation-level coordination mechanism is included to reduce simultaneous target-cell contention among multiple UAVs; it is not claimed as a new standardized 3GPP signalling procedure. Handover performance is evaluated by replaying 180 held-out flight trajectories in a controlled 5G simulation across ten independent random seeds. Under these stated assumptions, the proposed framework achieves a handover success rate of $94.2\pm 0.8$%, an average SINR of $15.8\pm 0.2$ dB, a handover delay of $45.2\pm 1.1$ ms, and a handover frequency of $0.85\pm 0.05$ HOs/min, outperforming the tuned 3GPP A3, reactive SINR, and CASH baselines in the reported simulation results (Wilcoxon signed-rank test, $p<0.01$, Bonferroni-corrected). The experimental setup is described in detail to support methodological transparency and facilitate future replication, but the handover results should be interpreted as simulation-based evidence rather than live-network validation. Full article