Search Results (12,832)

Wireless Power Transfer (WPT) has emerged as a compelling alternative to wired charging; however, efficiency and safety are highly dependent on magnetic field distribution and leakage. This work presents a compact MEMS–based magnetic field induction sensor, including its design, fabrication, and electrical characterization, for real-time diagnosis in WPT systems. The sensor employs a Ni/Cr metallic inductor fabricated on a SiO₂ substrate using standard photolithography and occupies a footprint of 5 mm × 5 mm. The device is electrically characterized through impedance, quality factor, and frequency response measurements, followed by experimental validation using an industry-standard wireless charging system and dedicated signal-conditioning circuitry. The results from inductive coupling simulations, performed with the Magpylib Python library, align with experimental data showing that the sensor accurately follows theoretical magnetic field decay, detecting AC signals between 40 mV and 140 mV with a functional limit of 30 mm. Furthermore, experimental characterization through spatial mapping successfully identifies magnetic leakage hot spots, while thermal validation via infrared thermography correlates these magnetic readings with localized temperature increases. This integrated approach supports EMC optimization and thermal risk mitigation, providing a low–cost and effective diagnostic tool for enhancing safety and performance in WPT applications. Full article

Cycling places increasingly precise demands on cadence, pedaling angle and rider–bicycle coordination. The purpose of this study is to establish a rider–bicycle coupling model, describe the relationship between the rider and the bicycle, and characterize the predicted activation pattern of lower limb muscles during 60 rpm cadence cycling through the scaled model. A 3D motion capture system was used to record the cycling movements of a healthy male subject with 15 years of cycling experience (26 years old, height 168 cm, weight 56 kg, BMI 19.84 kg per square meter) during a cycling experiment. Pedal force information of vertical load components was acquired using plantar pressure sensors. By combining inverse kinematics and inverse dynamics, the movement trajectories and joint moments of the cycling motion were analyzed, and the activation characteristics of the major lower limb muscle groups were further examined through static optimization. The results showed that under the tested bicycle conditions, the participant model predicted that muscle groups with higher activation levels, including the hamstring, gluteus maximus, and gastrocnemius muscles, exhibited regular periodic changes. The proposed simulation framework provides a methodological reference for biomechanical analysis under the tested 60 rpm cycling condition and may provide a methodological reference for future biomechanical investigations of cycling performance and musculoskeletal function. Full article

This paper investigates the fault-detection and isolation problems for T-S fuzzy systems with actuator faults and sensor faults. To begin with, the zonotopic set-memberships of both the state and output are set up. After this, by taking the intersection of these two zonotopic set-memberships as the state-estimation zonotopic set-membership, an interval state estimation method is developed. In order to obtain an optimization-interval estimation, a parameterized correction matrix is introduced into the zonotopic set-membership construction. Aiming at some optimization goal, the computation of the parameterized correction matrix is given by LMIs. Moreover, fault detections for both actuator and sensor faults are accomplished, and fault isolation between actuator and sensor faults is also fulfilled by constructing proper residuals using the optimization-state interval estimation. Finally, a simulation example is given to verify the effectiveness of the proposed method. Full article

Diabetes is a major worldwide health concern, which emphasizes the critical need for precise and continuous glucose monitoring devices. This paper introduces a novel, non-invasive method for continuous blood glucose monitoring using on-chip multi-arm sensors designed as earbuds by using UMC 0.18 μm technology. The proposed sensor uses the dielectric characteristics of the earbud to detect variations in glucose levels while operating at various resonant frequencies, including 32, 42, 64, and 94 GHz. The sensitivity of the proposed method was evaluated using a reflection coefficient criterion of $\mid S_{11}\mid \le -6\mathrm{dB}$, confirming its ability to achieve accurate detection when implemented within an earbud device. A 3D electromagnetic high-frequency structure simulator (HFSS) is used to validate the simulation. Only |S₁₁| data are used to determine the glucose concentrations in the blinded prediction group. The results demonstrate a strong correlation between sensor responses and glucose levels. The sensor achieved a sensitivity of 12.4 $\mathrm{M}\mathrm{H}\mathrm{z}/\mathrm{m}\mathrm{g}/\mathrm{d}\mathrm{L}$, 6 $\mathrm{d}\mathrm{B}/\mathrm{m}\mathrm{g}/\mathrm{d}\mathrm{L}$. Moreover, the earbud’s homogeneous tissue architecture and naturally low eccrine sweat gland density lessen susceptibility to confounding physiological variables commonly observed in microwave-based glucose detection. As a major advancement in biomedical sensing technology, this wearable system provides a precise and useful method for non-invasive glucose monitoring. Full article

This study addresses the inherently low damping and vibration susceptibility of carbon fiber reinforced polymer (CFRP) laminated cantilever plates by developing a comprehensive dynamic modeling and active vibration control framework. An electromechanical coupling model incorporating macro-fiber composite (MFC) actuators and sensors is established using the first-order shear deformation theory (FSDT) and the assumed mode method, with virtual springs introduced to account for non-ideal clamped boundary conditions. A reduced-order state-space model is then derived through model reduction, and a linear quadratic Gaussian (LQG) controller is designed for optimal state estimation and feedback control. The theoretical model is systematically validated via convergence analysis, ANSYS finite element simulations, and LMS impact hammer testing. The results demonstrate that, with the relative errors of the first four natural frequencies controlled within 2%, the theoretical mode shapes are highly consistent with those obtained from ANSYS simulations. An active vibration control experimental platform is established, and the effectiveness of the control strategy is verified under dual-spectrum harmonic and impact excitations. The results show that the designed LQG controller can effectively suppress multi-modal vibrations, substantially attenuating the response amplitudes of dominant modes and significantly accelerating the transient vibration convergence. This study addresses the challenge of precisely characterizing actual non-ideal clamped boundary conditions. Through model order reduction and closed-loop LQG control experiments, it provides a comprehensive set of theoretical methodologies, numerical solution strategies, and engineering-oriented experimental schemes for the electromechanical coupling dynamic modeling and optimal vibration suppression of CFRP thin-walled composite structures. Full article

Precise control of dissolved oxygen is essential for reproducing physiologically relevant conditions in microfluidic cell culture systems. Here, we present a standalone, polydimethylsiloxane-free gas exchange unit (GEU) which enables controlled oxygenation and deoxygenation of perfused liquids and is suitable for integration with existing microfluidic platforms. The GEU employs a flat-sheet membrane contactor design to achieve efficient gas–liquid mass transfer while remaining independent of the downstream device. Oxygen transfer was experimentally characterised using optical oxygen sensors under different liquid and gas flow conditions. Reoxygenation efficiency decreased with increasing liquid flow rate because of reduced residence time, whereas active airflow through the gas compartment enhanced oxygen transfer. Controlled deoxygenation was achieved by flowing nitrogen through the gas compartment, with higher nitrogen pressures producing progressively lower oxygen levels. Numerical flow simulations demonstrated uniform flow distribution within the device, and a simplified analytical diffusion model accurately predicted the observed oxygen transfer trends. The proposed GEU provides a simple, robust and modular strategy for regulating dissolved oxygen upstream of microfluidic devices without requiring device redesign or specialised fabrication. This approach offers a practical solution for incorporating physiologically relevant oxygen control into a wide range of microfluidic and cell culture applications. Full article

In this paper, the design and simulation of a monolithically integrated silicon carbide (SiC) MEMS piezoresistive accelerometer with an on-chip SiC field-effect transistor (SiC-FET) readout circuit are presented for applications in harsh environments. The proposed device can be fully realized on a single SiC platform, ensuring robust operation at a very high temperature (1000 K) and removing the bottlenecks associated with heterogeneous integration and silicon-based sensor electronics. Finite-element-method (FEM) simulations were performed to investigate the mechanical performance of the accelerometer, including sensitivity, cross-axis response, resonant frequency, and thermal stability. The designed accelerometer exhibits a z-axis sensitivity of 35.8 $\Omega$/g with low cross-axis sensitivities of 0.27% and 2.12% along the x- and y-axes, respectively. The eigen frequency analysis shows a fundamental resonance frequency of 1640 Hz and an operating bandwidth of 328 Hz. Thermal simulations indicate excellent stability performance from 300 K to 1000 K with resistance and displacement variations of less than 0.21% and 0.009%, respectively. A 4H-SiC MOSFET was designed and analyzed by the TCAD process and device simulations. It was shown to operate stably in a wide temperature range without breakdown. The FEM and TCAD results were incorporated into a Verilog-A model and implemented in Cadence Virtuoso to evaluate the complete sensor-readout system. The integrated SiC-FET common-source amplifier achieved a sensitivity of up to 100 mV/V at 1000 K with a non-linearity of approximately 1%. The results demonstrate the feasibility of a fully integrated all-SiC accelerometer platform for high-temperature sensing applications in aerospace, automotive, energy, and industrial environments. Full article

Regenerative vibrations are a major hindrance to flexible cantilever structures during milling, resulting in a reduced tool life and diminished surface finish. In this research, two actively controlled methods are directly compared: a genetic algorithm (GA)-optimized classical proportional-integral-derivative (PID) controller and a GA-optimized fractional-order PID (FOPID) controller for a milling-dependent regenerative force on a flexible cantilever beam via numerical modeling, using piezoelectric actuator/sensor patches. The original aspect lies in synergistically combining fractional-order control with genetic algorithm-based optimization to actively reduce chatter and increase the machining stability of flexible milling systems. The simulation results from the GA-FOPID controller exhibited a reduction in vibration of approximately 92.70% compared with the open-loop system by reducing the RMS value from 1.5058 × 10⁻⁴ m to 1.0996 × 10⁻⁵ m. By reducing the vibration level and enlarging the predicted stable machining region, these improvements could potentially contribute to longer tool life, improved surface finish, and reduced post-processing requirements, although these technological benefits were not directly modeled in the present study. The main innovation of this work involves a unique combination of fractional-order control, PZT actuation, and genetic algorithm optimization in a regenerative milling delay architecture. To the best of the authors’ knowledge, based on the literature surveyed in this work, this combination of techniques has not previously been reported for active chatter suppression. The stability lobe diagram (SLD) analysis, conducted under the single-mode approximation that serves as the reference framework for the like-for-like comparison of the five investigated configurations, shows that the critical axial depth of cut at the representative spindle speed increases from a_p,crit(1500) = 0.061 mm for the uncontrolled system to 0.52 mm under GA-FOPID control. This enlargement of the predicted stable machining region was further confirmed, at a comparable order of magnitude, when the structural model was extended to include the two next bending modes, indicating that the trend is not an artifact of the single-mode simplification. Therefore, although the results were obtained exclusively from numerical simulation and have not yet been experimentally validated, they support the use of optimization-based methods to implement FOPID strategies as a means to increase both reliability and performance of flexible milling configurations. Full article

This study evaluates adaptive spatial matrix filtering (AMF) combined with MUSIC for underwater acoustic direction-of-arrival estimation under strong out-of-sector interference and extends the method to coherent sources by incorporating forward spatial smoothing (FSS) into the AMF design. Simulations compared AMF-MUSIC with conventional MUSIC and continuous matrix filter (CMF)-MUSIC. For a 20-sensor array with targets at −2° and 1°, an interferer at 50°, an SNR of −5 dB, and an INR of 20 dB, both AMF-MUSIC and CMF-MUSIC resolved the targets under a common −25 dB stopband bound, but AMF-MUSIC produced a smoother out-of-sector background. Tightening the CMF bound to −40 dB reduced background peaks but degraded target resolution. In coherent-source simulations using a 25-sensor array, AMF-FSS-MUSIC resolved the targets for all tested subarray lengths when the angular separation was at least 4.5°, achieving resolution probabilities of at least 95%. A 900–1100 Hz broadband simulation maintained an approximately −15 dB stopband response and a passband-response error below −12 dB. For the SWellEx-96 narrowband data, AMF-MUSIC reduced the DOA-estimation RMSE from 11.16° to 5.18° and increased the mean spatial-spectrum SIR from −0.41 dB to 13.18 dB, while the broadband results qualitatively demonstrated interference suppression. These results indicate a favorable configuration-dependent suppression–fidelity tradeoff, while robustness to other coherent-source conditions and broader measured-data validation require further investigation. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

Attitude estimation is critical for the stability and reliability of launch vehicle flight missions, especially under complex dynamic conditions with external disturbances and sensor uncertainties. To address the limitations of conventional estimation methods that ignore the coupling between thrust vector control (TVC) and attitude states, this paper proposes a robust attitude estimation framework based on the Right Invariant Extended Kalman Filter (IEKF). Two key innovations are incorporated: first, the control model of the launch vehicle is established as a TVC model, which explicitly characterizes the coupling between TVC inputs (thrust magnitude and gimbal deflections) and launch vehicle dynamics, instead of treating TVC effects as external disturbances. Second, TVC motion constraints are introduced into the classic IEKF filtering process, embedding TVC as a deterministic input into the state propagation model to enhance the structural rationality of the estimator. To verify the effectiveness of the proposed method, simulations of the launch vehicle ascent trajectory are conducted, with three comparative configurations tested under normal and sensor anomaly scenarios. The simulation results demonstrate that the proposed attitude estimation method, integrated with TVC modeling and motion constraints, is significantly superior to traditional methods in both accuracy and robustness, effectively suppressing state estimation drift and maintaining stable performance even under sensor degradation or outages. Full article

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

This study presents a sensor-fused field investigation and simulation-based analysis of four horizontal and vertical traffic-calming devices—two raised speed tables, a speed hump, and a raised crosswalk—installed along a 5250-ft two-lane residential collector in Nashville, TN, USA. A dual-sensor architecture combining a Miovision Scout video-based vehicle counter and WAAS/EGNOS-augmented GPS probe-vehicle logging (5 m 3-D RMS horizontal accuracy, 1 Hz sampling) was used to reconstruct 30 quality-controlled free-flow vehicle trajectories and 12-h per-lane volume counts. A spatial kinematic transform (a = v·dv/dx) was applied to extract device-specific approach-deceleration and post-device recovery-acceleration rates, and a three-parameter log-logistic cumulative-distribution function was fitted to the field-observed desired-speed percentiles (root-mean-square error below 0.043 for both speed-table devices). The camera- and GPS-derived observations were used to calibrate and statistically validate a PTV VISSIM microsimulation replica of the corridor, achieving a mean-speed calibration error of 0.71% or better at every device, a GEH statistic below 1.5 at all four analysis turning movements, and independent travel-time validation errors of 5.7–12.1%, within the accepted 15% threshold. The validated model was then used to reconstruct device- and spacing-specific May–Keller macroscopic speed–density–flow relationships, calibrated against simulated capacities of 650–775 vehicles per hour per lane at 350-, 700-, and 1050-ft device spacing. Results show capacity reductions of 20–33% relative to free-flow conditions and yield kinematically derived maximum recommended spacings of 265–630 ft to maintain crossing speeds at or below 15 mph, depending on device geometry. The findings demonstrate a reproducible, low-cost sensor-fusion workflow for quantifying the safety–capacity trade-off of traffic-calming corridors and for informing the design of sensor-in-the-loop adaptive-calming infrastructure. Full article

This paper proposes Non-Uniform Adaptive Acquisition (NUAA), a signal-feature-matched non-uniform adaptive photonic sampling framework that recovers broadband radio-frequency (RF) waveforms from highly sparse programmable non-uniform photonic sampling points. A 200 MHz mode-locked laser together with five electrical optical delay lines (EDLs; motor-actuated optical delay units) arranges the non-uniform sampling instants. Benefiting from the joint design of the photodetector/track-and-hold amplifier (PD/THA) response model and programmable non-uniform optical pulse spacing, a low-bandwidth PD infers neighboring pulse amplitudes from their deterministic superposition at the readout. In numerical simulations of this physical forward operator, that construction corresponds to a 1 THz equivalent sampling rate on the 1 ps EDL grid, while the electrical front end operates at a 1 GHz average sampling rate (cascaded PD–THA analog 3 dB bandwidth $\approx 0.676$ GHz). Under severe blocking interference and low signal-to-noise ratio (SNR), the numerical simulations show that the strongest broadband chirplet result uses a scene prior with support locking: with the NUAA–MU (Mamba–Unfolding) reconstructor at 0.1% multi-coset sparsity, all $N_{\mathrm{trial}}=50$ Monte Carlo trials succeed within 200 ms (Wilson 95% CI $[93, 100]\%$; cumulative-best NMSE $-28.0$ dB), whereas the configuration without a scene prior is substantially weaker in the same window. A scene prior may come from known radar or communication waveform families, coarse occupancy reported by a companion sensor, or accumulation across related tasks. Full article

Convective drying of Zingiberaceous roots, particularly ginger (Zingiber officinale), requires a uniform distribution of airflow and temperature to ensure energy efficiency and product quality. However, many drying systems exhibit aerothermal limitations that produce temperature gradients and non-uniform drying conditions. To address this issue, this study proposes a dual-swirl dryer featuring two air inlets: an upper helical inlet and a lower tangential inlet. Both inlet configurations generate swirling airflow patterns that enhance thermal uniformity and increase the residence time of hot air within the drying chamber. The internal flow behavior was investigated using Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent2025 R1 version. A three-dimensional polyhedral mesh was generated to improve computational efficiency and numerical accuracy. Turbulence and recirculation phenomena were modeled using the Realizable k–$ϵ$ turbulence model, while temperature distribution was analyzed through the energy conservation equation. Numerical predictions were experimentally validated using temperature sensors integrated into an automatic control system. The comparison between numerical and experimental results demonstrated that the dual-swirl configuration improves airflow redistribution, reduces thermal stagnation zones, and promotes a more homogeneous temperature field throughout the drying chamber. These findings confirm that the proposed system is an efficient alternative for agro-industrial drying applications. Full article