Search Results (81)

In this paper, we propose a single-layer metasurface structure with ultra-wideband operation and high polarization conversion efficiency, capable of transforming linearly polarized waves into cross-polarized waves. This structure excites additional electromagnetic resonance modes by integrating two symmetrical square patches within an anisotropic split-ring resonator (SRR). These new modes couple with the inherent resonance modes of the SRR, forming closely spaced multi-resonance characteristics across a wide frequency band. This multi-resonance capability enables broadband polarization conversion. This metasurface achieves an ultra-wideband performance spanning 10.89 GHz to 30.12 GHz, covering part of the X-band, the entire Ku-band, and the K-band, while maintaining a high polarization conversion efficiency exceeding 90%. Its broadband characteristics are attributed to the resonator’s ability to generate multiple resonances within a single unit cell. Both experimental and simulation results demonstrate the metasurface’s excellent polarization conversion performance. Furthermore, the proposed metasurface maintains acceptable oblique-incidence performance over a large portion of the operating band, although localized degradation appears at some frequencies. This structure offers significant advantages over traditional multilayer or active designs, featuring simple fabrication without assembly or welding. It may be useful for broadband polarization conversion and may also provide potential for scattering-control applications. Full article

Accurate measurement of spool displacement is essential for achieving high-performance closed-loop control and condition monitoring in hydraulic systems. However, conventional inductive displacement transducers typically rely on sinusoidal excitation and complex analog signal conditioning circuits, resulting in higher hardware cost and limited system integration. To address these issues, this paper proposes a software-based demodulation method for a differential inductive displacement transducer under symmetric complementary square-wave excitation. First, the structure and operating principle of the transducer are analyzed, and an electromagnetic model describing the nonlinear relationship between coil inductance and the position of the inductive core is established, along with its electrical characteristics. Then, a simplified signal acquisition circuit is designed to enable digital extraction of inductance variations using a microprocessor. Compared with conventional approaches, the proposed scheme significantly reduces hardware complexity and cost while being more suitable for embedded system integration. A simulation model is developed to analyze the inductance variation and to validate the proposed hardware circuit. In addition, a test platform is built to conduct static calibration and dynamic response experiments. The experimental results show that the proposed method achieves a linearity of 2.36% and a sensitivity of 155.6 mV/mm and exhibits strong robustness against switching noise. Finally, application tests in a hydraulic valve system demonstrate that the proposed transducer and demodulation method enable accurate and stable spool position measurement, providing a low-cost and easily integrated solution for embedded hydraulic control systems. Full article

In mechanical material evaluation and biomechanical studies, Young’s modulus is commonly used to describe the elastic response of materials. Existing measurement approaches are mainly based on contact loading or large-scale experimental instruments, which may limit excitation controllability and system integration in practical applications. In this work, a Young’s modulus measurement system based on electromagnetic excitation and capacitive sensing is designed and experimentally implemented. The system is composed of an electromagnetic driving unit and a capacitive sensing unit. In the driving unit, a coaxial copper wire coil is arranged with a ring-shaped neodymium–iron–boron permanent magnet assembly. When a square-wave electrical signal is applied, the coil generates a Lorentz force, which produces transient mechanical excitation on the tested sample. The resulting micro-scale deformation of the material surface is monitored using a coaxial passive capacitive sensor. The sensor records the relative capacitance variation (ΔC/C₀) induced by deformation during excitation. Based on the measured capacitance response, a force–capacitance coupling model is established to relate the electrical signal to the mechanical behavior of the material, enabling the inverse calculation of Young’s modulus. Commercial standard hardness blocks were used for system calibration and performance verification. The experimentally obtained Young’s modulus values are consistent with reference data within an acceptable deviation range, indicating that the proposed system can be used for quantitative evaluation of elastic properties. Due to its compact configuration and controllable excitation, the system is suitable for non-invasive surface mechanical characterization of soft materials, including biological tissues. Full article

In response to the urgent requirement for sustainable power supply for deep-sea or offshore underwater sensing equipment, this work investigates autonomous power generation aboard marine vessels. The vertical vibrations induced by wave excitation at the bottom of the vessel are utilized to drive the vibration energy harvesters on the deck for power generation. In a scenario involving automatic steering, a multiplicity of magnetoelectric harvesters mounted on the deck would move vertically in response to surface wave motion, enabling continuous conversion of wave energy into electrical power. The key feature of this study is that the ship-based self-power generation system is simple to install and safe, with the vibration energy harvesters mounted above the sea surface to avoid the unpredictable underwater sea conditions. This study presents a numerical case analysis of a three-magnet energy harvester designed to generate induced electrical power under wave conditions characterized by a speed of V = 3.0 m/s, amplitude of Z_o = 0.4 m, and wavelength of λ = 2.0 m. Prior to optimizing the ship-based energy harvester, the mathematical model of a three-magnet vibration system was validated against experimental data to ensure accuracy. Subsequently, a sensitivity study was performed to evaluate the influence of wave parameters (e.g., amplitude and wavelength) and the harvester’s geometric parameters on the electrical power output. To maximize power generation, the flower pollination algorithm—an efficient bio-inspired optimization method known for its robustness in global search—was integrated with the objective function defined as the root-mean-square electrical power. Simulation results indicate that the optimized harvester is capable of producing up to 0.1943 W. These findings highlight the potential of ship-based energy harvesters as a sustainable and reliable source of electrical power. Full article

In order to simplify the stator winding structure of traditional variable reluctance (VR) resolvers and enhance their performance under high-speed operating conditions, this paper proposes a design for a variable reluctance self-coupling resolver based on ultrahigh-frequency (UHF) square wave excitation. The proposed solution optimizes the traditional winding structure by eliminating the separate excitation winding and integrating both excitation and detection functions into the two-phase sine and cosine windings. By optimizing the arrangement of the sine and cosine windings, a single-layer equal-turn winding design is successfully implemented, significantly simplifying the winding layout and reducing copper usage. In terms of excitation signal, this paper innovatively replaces the traditional sinusoidal excitation with UHF square wave excitation. Compared to sinusoidal excitation, square wave excitation not only generates higher electromotive force (EMF) peaks but also simplifies engineering implementation, reducing the complexity of system hardware. To validate the feasibility and advantages of the proposed structure, a complete experimental testing platform was built, and comparative experiments were conducted under various rotational speeds. The experimental results show that the proposed self-coupling resolver can achieve high-precision rotor position detection across the entire speed range, significantly improving the detection accuracy and dynamic response of traditional methods under high-speed conditions. Ultimately, the design demonstrates strong engineering application potential and provides a new solution for high-precision, high-dynamic response rotor position detection. Full article

An integrated control strategy is proposed for direct-drive wave energy conversion (DDWEC) systems to address displacement safety constraints and improve the robustness of sensorless position estimation. Under strong wave excitation, buoy displacement may exceed its stroke limit due to conventional amplitude control, leading to mechanical risks. To mitigate this, a displacement-constrained damping regulation law is introduced, incorporating a displacement-dependent correction factor that retains optimal damping within a safe region and increases additional damping smoothly as the displacement approaches its limit. For sensorless operation, a dual-time-scale adaptive amplitude modulation strategy is developed, based on high-frequency square-wave voltage injection. By decoupling the fast position-estimation loop from the slow injection-amplitude adjustment, the demodulated high-frequency current remains within an optimal band, ensuring a high signal-to-noise ratio (SNR) under disturbances and parameter variations. Simulation results show that displacement boundary violations are eliminated, with a 25.7% reduction in peak displacement and only a 7.65% reduction in average captured power. The injection amplitude is adaptively regulated to maintain the demodulated current within the measurement band, enhancing position-estimation stability and accuracy. A fail-safe boundary for extreme sea states (H_s ≈ 2.2 m) is also identified, ensuring robust operation under varying conditions. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

The ageing of elevator travelling cables results in the breakage of inner copper strands, leading to communication and control faults in the elevator system. In this paper, a travelling cable state evaluation method based on time-frequency transformation and a deep learning fitting method is proposed. The cable diagnosis is based on the transmission line theory and finite element simulation results, which indicate that the number of broken strands of copper wires in twisted cables is positively related to the amplitude of fluctuation in the cable’s transmission spectrum. To evaluate this fluctuation with low cost and high accuracy, we acquired the 500 Msps time-domain signal after a square wave with different periods was transmitted through the detected cable; the transmission in base frequency and harmonics is calculated and combined into the total transmission spectrum. A deep learning model with a two-layer 1-D CNN and squeeze-excitation channel attention is utilized to fit the spectrum data, and cross-entropy is applied to estimate the departure between the fitting results and the experimental data, which serves as the cable’s broken-state index. Experiments demonstrate that the proposed method is able to detect minor cable faults such as one or two copper strands broken and could distinguish different broken states with a sensitivity of 16.42 ± 1.39 per break strand. Full article

Millimeter-wave (mmWave) massive multiple-input, multiple-output (MIMO) systems are a cornerstone technology for integrated sensing and communication (ISAC) in sixth-generation (6G) mobile networks. These systems provide high-capacity backhaul while simultaneously enabling high-resolution environmental sensing. However, accurate channel estimation remains highly challenging due to intrinsic noise sensitivity and clustered sparse multipath structures. These challenges are particularly severe under limited pilot resources and low signal-to-noise ratio (SNR) conditions. To address these difficulties, this paper proposes HASwinNet, a deep learning (DL) framework designed for mmWave channel denoising. The framework integrates a hierarchical Swin Transformer encoder for structured representation learning. It further incorporates two complementary branches. The first branch performs sparse token extraction guided by angular-domain significance. The second branch focuses on angular-domain refinement by applying discrete Fourier transform (DFT), squeeze-and-excitation (SE), and inverse DFT (IDFT) operations. This generates a mask that highlights angularly coherent features. A decoder combines the outputs of both branches with a residual projection from the input to yield refined channel estimates. Additionally, we introduce an angular-domain perceptual loss during training. This enforces spectral consistency and preserves clustered multipath structures. Simulation results based on the Saleh–Valenzuela (S–V) channel model demonstrate that HASwinNet achieves significant improvements in normalized mean squared error (NMSE) and bit error rate (BER). It consistently outperforms convolutional neural network (CNN), long short-term memory (LSTM), and U-Net baselines. Furthermore, experiments with reduced pilot symbols confirm that HASwinNet effectively exploits angular sparsity. The model retains a consistent advantage over baselines even under pilot-limited conditions. These findings validate the scalability of HASwinNet for practical 6G mmWave backhaul applications. They also highlight its potential in ISAC scenarios where accurate channel recovery supports both communication and sensing. Full article

This paper presents a compact and accurate readout circuit for remote two-wire resistive sensors, based on an inverting operational amplifier with a fixed bias voltage, diode steering, and unidirectional square-wave excitation generated by a microcontroller. The proposed method determines the sensor resistance by directly sampling two steady-state voltage plateaus at the op-amp output during alternating excitation phases. This approach enables fast, lead-wire-insensitive measurements without the need for analog filtering or precise PWM duty-cycle control. The architecture supports sensor array multiplexing via analog switches, allowing scalable, low-power implementation. Experimental results demonstrate a maximum relative error of 0.23% across a wide resistance range (0.5–3.5 kΩ), confirming the method’s suitability for low-cost, embedded, and remote sensing applications. Full article

Wearable antennas for wireless sensor network (WSN) applications require circularly polarized (CP) radiation to maintain stable communication link under human body movement and complex environments. However, many existing wearable CP antennas rely on either linearly polarized (LP) or CP radiator with a single axial ratio (AR) mode combined with external polarization conversion structures, which limit the achievable axial ratio bandwidth (ARBW). In this work, an all-textile wideband CP antenna with a square-ring notched slot radiator, a 50 Ω microstrip line, and a 3 × 3 nonuniform metasurface (MTS) is proposed for 5.85 GHz WSN applications. Unlike conventional CP generation approaches, the square-ring notched slot, analyzed using characteristic mode analysis (CMA), directly excites three distinct AR modes, enabling potential wideband CP radiation. The nonuniform MTS further improves IBW performance by exciting additional surface wave resonances. Moreover, the nonuniform MTS further enhances ARBW by redirecting the incident wave into an orthogonal direction with equivalent amplitude and a 90° phase difference at higher frequency region. The proposed antenna is composed of conductive textile and felt substrates, offering flexibility for wearable applications. The proposed antenna is measured in free space, on human bodies, and fresh pork in an anechoic chamber. The measured results show a broad IBW and ARBW of 84.52% and 43.56%, respectively. The measured gain and radiation efficiency are 4.47 dBic and 68%, respectively. The simulated specific absorption rates (SARs) satisfy both US and EU standards. Full article

Multi-beam and beam-scanning antennas enable extensive communication coverage while mitigating multipath fading and enhancing spectrum utilization efficiency. This paper presents a transmissive metasurface antenna design, which utilizes a microstrip square-ring patch antenna with four feed ports as the excitation source. A 7 × 7 square patch metasurface is positioned above the feed source, facilitating the generation of four independently steerable beams by switching activation among the four feed ports. Operating at 12.6 GHz, the antenna achieves a gain of 10.4 dB. The 3 dB beamwidth of the beams from all four ports exceeds 23°. The proposed design offers advantages of structural simplicity, low profile, and cost-effectiveness. By leveraging transmissive metasurfaces, this approach combines the benefits of low profile and low cost with flexible manipulation of electromagnetic wave radiation, thereby providing a novel methodology for designing multi-beam communication antennas. Full article

We investigate the excitation and variability of migrating and non-migrating diurnal and semi-diurnal tides in the mesosphere and lower thermosphere (MLT) during the 2021 Northern Hemisphere sudden stratospheric warming (SSW). Zonal wind data from MERRA-2 reanalysis are decomposed into tidal components using a two-dimensional least-squares harmonic fitting technique. The migrating diurnal tide (DW1) strengthens at low latitudes following the SSW onset, whereas the migrating semi-diurnal tide (SW2) intensifies at high latitudes. Non-migrating diurnal tides (D0, DW2, DW3) arise from nonlinear interactions between DW1 and stationary planetary waves (SPWs), while non-migrating semi-diurnal tides (SW1, SW3) are modulated by stratospheric ozone variability linked to planetary-wave activity. The zonally symmetric semi-diurnal tide (S0) responds primarily to dynamical perturbations associated with the SSW. Eastward non-migrating diurnal tides (DE2, DE3) correlate strongly with total precipitable water vapor (TPWV), indicating tropospheric latent-heat forcing, whereas DE1 exhibits weak coupling. These results reveal distinct, latitude-dependent excitation pathways connecting stratospheric and tropospheric dynamics to tidal variability in the MLT during major SSW events. Full article

In the field of structural engineering, accurate identification of modal damping ratio is the key to structural dynamic response analysis. In order to accurately identify the modal damping ratio of the structure, this study proposes a method to identify the first-order modal damping ratio of the structure by analyzing the free attenuation response of the acceleration signal. By intercepting the free attenuation section from the structural dynamic response output, the amplitude is extracted, and the logarithmic estimation slope of the amplitude is fitted by the least square method to establish a theoretical model for identifying the first-order modal damping ratio. The results show that the method has high accuracy and good stability when the modal damping ratio is in the range of 0.00500~0.06400, and different nodes have little effect on the accuracy of identification. When the modal damping ratio is in the range of 0.06400~0.07000, the accuracy of the method is relatively low and the stability is relatively poor, but it is still within the acceptable range. When the damping ratio is greater than 0.07000 or less than 0.00500, the accuracy may be reduced. In order to further verify the effectiveness of the method, it is applied to the damping identification of a steel arch bridge project. The dynamic response of the bridge under random excitation and El Centro seismic wave excitation is analyzed by using the recommended value and identification value of the first-order damping ratio. The results show that the method can accurately and reliably identify the first-order modal damping ratio, which is significantly different from the empirical modal damping ratio. The identified modal damping ratio can more accurately describe the dynamic response of the structure after long-term use, while the recommended value is not applicable. This method can be applied to the modal damping ratio identification of other structural types, which reflects that the modal damping ratio identification method proposed in this study has certain engineering significance. It is worth noting that the accuracy of identification will be reduced when the modal damping ratio is less than 0.00500 or more than 0.07000, and it may not even be applicable if the modal damping ratio is too small or too large. This method has higher requirements for acceleration signals. In engineering, it may be affected by noise and other factors, resulting in reduced identification accuracy. In practical engineering, it is necessary to improve the identification accuracy of first-order modal damping ratio by changing the interception point of the free attenuation section of the acceleration signal and the screening of the amplitude. Full article

Lock-in thermography is a widely adopted infrared nondestructive testing technique that detects subsurface defects by applying modulated thermal waves and analyzing the resulting surface temperature variations. However, quantitatively characterizing subsurface defects at varying depths remains a significant challenge. This study explores the lateral resolution of subsurface defect detection using phase-based lock-in thermography, integrating analytical modeling, finite element simulation, and phase difference analysis. The results demonstrate that defect visibility and boundary definition are highly influenced by the excitation frequency. The thermal diffusion length, which is inversely proportional to the square root of the excitation frequency, governs both the penetration depth and the lateral spread of thermal energy. Higher frequencies enhance lateral resolution, whereas lower frequencies improve the detectability of deeper defects. Detection becomes particularly difficult for defects with small radii or low radius-to-depth ratios. A critical radius-to-depth threshold of 2 is identified as essential for reliable boundary delineation. These findings offer practical guidance for selecting excitation frequencies to achieve an optimal balance between depth sensitivity and lateral resolution in thermal-wave-based nondestructive evaluation. Full article