Ultra-low-frequency (ULF) receiving antenna has held great promise in underwater communication systems due to their low propagation loss. However, current methods for miniaturizing ULF antennas are accompanied by a decrease in efficiency and sensitivity. This paper presents an upconversion-resonance-matching (URM) method to improve the efficiency and sensitivity of the portable ULF seawater receiving antenna (SRA). The ULF electric field was upconverted to the higher frequency field, relying on an electronic switch nonlinear controlling the electric field around the modulation metal rod. The modulated signal was resonantly received by a compact receiver based on a high-Q quartz resonator. This solution provides a possibility for the compact SRA to achieve a high-Q resonance-enhanced output at ULF electric field. Experimental results for a 635 Hz electric field demonstrate that the URM method achieves a Q value of 33,330.3 for an SRA with a 10 cm size. The efficiency of the SRA for receiving a 635Hz electric field is 8 orders of magnitude higher than that of a dipole antenna at the same size. Furthermore, the sensitivity and limit of detection for 635 Hz electric field are observed to be 0.127 V/V•m-1 and 59.3 μV/m, respectively. These results show significant potential for use in portable marine monitoring networks.

The three-axis magnetometers are widely utilized in automotive electronics, industrial robotics, and intelligent navigation for motion recognition and attitude detection. But the conventional 3D magnetic field detection is realized with multiple orthogonal sensors, causing the spatial errors, non-orthogonality, and bulky issue. This study presents an ultra-low power 3D magneto-impedance (MI) sensor based on a planar [FeSiBC 20 nm/Cu 6 nm]50/graphene microcoil/[FeSiBC 20 nm/Cu 6 nm]50 heterostructure. Utilizing the synergetic effects of soft magnetic material's magnetoimpedance and graphene's Hall effect under the weak current excitation, it decouples the X, Y, Z magnetic field components in one measurement, avoiding the multiple steps required by the recent planar sensors. It possesses the ultra-low power of 25 mu W, high resolutions of 20, 20, 7587 nT/Hz1/2 at the X, Y, Z direction, and real-time detection capability. Experiments verify that the miniature sensor array improves the magnetic field imaging accuracy by around 65% compared to the conventional 1D Hall sensors in the magnetic field leakage test.

In single-frequency injection topology identification, a low-amplitude diagnostic tone (e.g., 833 Hz) is superimposed onto the network, and the widespread deployment of battery-free measurement units enables detection of the injected signal. Each node operates under strict power constraints, typically on the microwatt level, which limits raw waveform transmission. As a result, amplitude and phase information must be extracted locally. Furthermore, improvements in front-end noise characteristics can be exploited by the lock-in amplifier to achieve proportional reductions in the amplitude of the injected diagnostic current, thereby substantially mitigating network perturbations. We therefore present a system-in-package lock-in amplifier that dissipates 30.6 mu W at 3.3 V and is tailored to demodulate an 833 Hz probe signal for distribution-grid topology identification. The circuit employs a duty-cycle-controlled multiplier with enable/disable gating to suppress quiescent consumption, achieving an input-referred noise density of 1.3 mu V/ root Hz and an approximately 19% reduction in average power consumption to support battery-less operation. This work thus establishes a field-deployable, maintenance-free sensing platform for large-scale topology monitoring and other energy-constrained applications, such as fault prevention and condition diagnosis in power networks.

Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models.

Accurate and synchronized assessment of biochemical parameters, such as biomarker concentration and body fluid viscosity, is crucial for advancing early disease detection and health management. Conventional biomolecular multiparameter detection methods often rely on multiple sensors or analytical techniques, which introduce cross-talk between sensing modalities, data inconsistencies, and complex calibration requirements, ultimately compromising detection precision and adaptability. We propose a streamlined detection approach that leverages a single uncoated Quartz Crystal Microbalance (QCM) sensor to monitor the dynamic magnetized motion of biomolecules under multimodal magnetic field modulation. Unlike conventional QCM methods that rely on static mass loading effects, this approach enables the sensor to capture motion signals that encode information about biomolecule concentration and base liquid viscosity. A backpropagation (BP) neural network is employed to model the nonlinear coupling between these motion-derived signal characteristics and the target biochemical parameters. The proposed method is validated using prostate-specific antigen (PSA) as a biomolecular model analyte. Experimental results from blind tests, where both concentration and viscosity were simultaneously unknown, demonstrate a prediction accuracy of 90 % for concentrations ranging from 0.01 to 1000 ng/mL and 87 % for viscosities between 1 and 6 cP. By integrating multimodal magnetic modulation with QCM-based motion sensing and machine learning, the BP-MMM-QCM technique provides a versatile and high-precision solution for biomolecule analysis. Accurate detection of biomolecule concentrations is essential for early disease diagnosis as well as monitoring disease progression and therapeutic responses. This approach overcomes the limitations of conventional QCM methods and enables real-time, multi-parameter detection in a single assay, making it a promising tool for disease diagnostics and health monitoring applications.

Recently, acoustically driven magnetoelectric (ME) antennas provide a promising candidate for portable low frequencies (LFs 30-300 kHz) transceivers in the high-loss medium. A single ME antenna, hence, constrains the transmitted magnetic field due to the weak magnetic moment and low strain-modulated magnetization efficiency. A multiphysical field driven ME antenna array consisting of double elastic plates sandwiched between multiple arranged PZT8/Metglas/Ni laminates is, hence, proposed, which can enhance the transmitted magnetic field nonlinearly with increased antennas. Here, the accumulated ac strain and magnetic field of multiple antennas intensify with increased antennas and couple in a positive feedback way. Specifically, the ac piezostrain of adjacent antennas couples through the elastic plate along the length direction, enhancing the modulation of neighboring antennas' magnetization oscillations; meanwhile, the enhanced magnetic coupling among adjacent antennas along the width direction produces additional magnetostrictive strains, which enhance the Q values of converse ME (CME) effect and effective strains propagated in the elastic plate, thereby further improving the transmitted magnetic field. First, an equivalent circuit model based on magnetic and strain-modulated motion equations is proposed to understand and optimize the ME antenna array. Furthermore, due to the multiphysical field driven effect of the fabricated antenna array, the $3 \times 3$ array can enhance the transmitted magnetic field to 19.8 times compared to that of a single antenna. Such nonlinear improvements can further reach 1337 times for the $10 \times 10$ antenna array. This provides a power-efficient method to enhance the magnetic field transmission efficiency at LF.

A 4-probe laser interferometric displacement sensor (4pLIDS) has advantages over a single-probe LIDS in a micro-thruster calibration system. By synchronously measuring the displacements at four points and differentiating the measured data, the common-mode noise can be greatly suppressed, thereby improving the measurement accuracy. Based on the design requirements of the nanometer-resolution 4pLIDS, we discussed the connections among various performance aspects of LIDS, designed evaluation schemes, and conducted tests. We devised the schemes to evaluate the 4-probe performances related to synchronicity using a single nanopositioning stage as the displacement reference, and considering the agreement and output temporal uniformity from each probe. We further analyzed the influences of the non-ideal performances of the displacement reference on the evaluation results to ensure that the results truly reveal the LIDS's performances. The evaluation results show that the resolution of the 4pLIDS reaches 0.7 nm, the standard deviation (SD) of the 4pLIDS repeatability is no more than 0.15 nm and the inconsistency error among the four probes is within +/- 0.4 nm. The actual differential measurement results using the 4pLIDS conform to the predictions from the evaluated performances and demonstrate the effectiveness of the evaluation.

In-situ marine electric field sensing is key to building the construction of the Internet of Underwater Things (IoUT). However, in-situ measurements require both measurement stability and energy supply.In this paper, bubbles present in nature are used to achieve stable measurements and stable energy supply. Both theory and experiments show that the low dielectric constant of bubbles can change the electric field strength between electrodes to achieve stable modulation measurements. At the same time, the bubble has a high energy density, and the energy it provides is sufficient to complete the acquisition of the front-end signal. Experiments have shown that the bubble-modulated polarization measurements have a sensitivity of 2.1 mu A/(V/m) and a high stability not found in static measurements. The energy consumption of the system after discarding to the motor modulation system is only on the order of a few hundred mu W, which is fully capable of being fed by an underwater energy harvester. The use of this characteristic of bubbles can effectively promote the construction of IoUT at cold springs and hydrothermal fluids.

Integrated Sensing and Communication (ISAC) has emerged as the next-generation of wireless sensing technology. While Wi-Fi backscatter enables the low-cost passive sensing without dedicated RFID readers, the current methods suffer from the degraded sensor data detection performance due to the asynchrony between the direct communication link of Wi-Fi packets and backscatter link of sensing data. In this study, a novel demodulation scheme named WiSensor 2.0 is proposed, which achieves the synchronization-free detection by extracting edge features of backscattered data. This approach combines differential weighted wavelet decomposition with Miller encoding to realize the edge feature extraction, followed by the SVM detector. Experimental results demonstrate that the bit error rates of proposed method is decreased significantly compared to the previous work at the challenging conditions of asynchrony, noises, and multipath fading effect, which is promising for the low-cost and low-power sensing applications.

Due to the severe attenuation of magnetic anomaly signal (MAS) over distance and inevitably dominant environmental noise, the measured MAS typically suffers from a low signal-to-noise ratio (SNR), which degrades the performance of target tracking, localization, and recognition applications. When the noise and signal are heavily overlapped in the time or frequency domain, conventional denoising methods often distort the MAS by separating them. To address this issue, a reference signal-guided adaptive FitzHugh-Nagumo stochastic resonance (RS-AFHN-SR) method is proposed to restore weak MAS under low SNR conditions. Instead of decomposing the noisy signal and subtracting corresponding noise components, the weak MAS is restored by transferring part of the noise energy to the signal based on stochastic resonance (SR) theory. On the one hand, the reference signal is estimated using the proposed correlation detection (CD) method based on the magnetic dipole model, which guides the adaptive optimization of FHN model parameters and enhances the accuracy of parameter estimation. On the other hand, residual noise in the SR-enhanced signal is further suppressed by integrating it with the reference signal, resulting in the more precise signal shape. Accordingly, the proposed denoising method provides the superior signal restoration performance under low SNR conditions by combining the advantages of SR and CD. Specifically, the RS-AFHN-SR method provides an SNR improvement of 27.8 dB for the noisy MAS with an initial SNR of -19.6 dB. Furthermore, experimental results show that the proposed approach effectively reduces false alarms and localization errors compared to previous denoising methods.

In this paper, we introduce a novel electro-thermo-mechanical simulation framework utilizing the unified FDTD method for efficient and accurate analysis of electrical, thermal, stress, and displacement responses in chiplet architectures. To enable precise stress simulation in heterogeneous system such as redistribution layers (RDL) structures, we developed equilibrium differential equations for multi-material systems and derived finite difference equations for displacement equilibrium using central differencing, including formulations for free boundary conditions. Numerical simulations demonstrate the framework’s effectiveness through thermo-mechanical coupled analyses of block models and simplified RDL structures. Compared with COMSOL commercial software, our FDTD based solver achieves computational errors within 2% for block models and 5% for RDL structures, while demonstrating superior simulation efficiency and significantly reduced memory consumption. The proposed framework offers substantial advantages in modeling complex chiplet based heterogeneous integration systems under strong electromagnetic interference. It effectively characterizes spatiotemporal distributions of electromagnetic fields, temperature fields, mechanical stress, and displacement, providing essential insights for evaluating electromagnetic compatibility (EMC) and signal integrity (SI) in chiplet integration systems.

Harvesting energy from subsea bubbles, such as those produced by photosynthesis of benthic plants or submarine methane seepage, is a promising solution for powering subsea environment perception devices, but the low gas flux brings significant challenges. Herein, we propose a passive mechanical self-adaptive porous valve with self-adaptive mechanical properties and high gas permeability. It improves energy harvesting performance from low-flux bubbles by controlling bubble accumulation and high-speed release. Unlike traditional active mechanical metamaterials, this passive design utilizes gas-liquid interface deformation (rather than metamaterial actuation) to generate self-adaptive Laplace pressure counteracting bubble buoyancy, and thus requires no external energy. The porous valve has a stable opening threshold inversely proportional to its structural pore diameter. Compared with a bubble energy harvesting device with no valve, the instantaneous gas-intake rate of the device equipped with the porous valve is increased by one to four orders of magnitude, and the maximum output power and electrical energy production are enhanced by factors of 36.6 and 16.4, respectively. The energy of underwater biological metabolic gas with a low flux (28 μL $$\cdot \, {\min }^{-1}$$ ⋅ min − 1 ) is effectively harvested and supplied to an underwater sensor. This work is expected to provide in situ energy for subsea self-powered sensing and autonomous exploration.

In situ marine electric field sensing is an effective means of exploration for marine geological activities. Dynamic modulation measurement is commonly used in measurement solutions due to stability requirements in in situ measurements. However, the high energy requirement of modulation measurement is a challenge underwater field. In this article, we realize passive modulation measurements using the low permittivity of bubbles. Furthermore, we also use the energy of the bubbles to power the measurements, so to realize a self-powered in situ electric field measurement system. Experiments show that the limit of detection (LOD) of the proposed passive bubble-modulated polarization method is 0.1 mV/m and the drift (1.42 x 10(-4) (V/m)/day) is 1.2% of that of the conventional static measurement methods (1.1 x 10(-2) (V/m)/day). The energy consumption of the proposed passive modulated measurement system (0.825 mW) is 2.75 x 10(-5) of the conventional modulated measurement system (30 W). This article truly realizes an in situ long-term detection system that integrates energy supply and measurement, which can promote the exploitation of cold springs and hydrothermal fluids and the construction of the Internet of Underwater Things (IOUT) in these regions.

The traditional quartz crystal microbalance (QCM) technology is primarily used for measuring load mass and requires the load to be in a static state, making it difficult to capture particle motion under the action of external force fields. This study to overcome the constraints of traditional QCM technology by proposing the use of QCM to detect particle motion in liquid loads. This work delves into the principle of QCM sensing particle motion in liquid loads and presents sensing signal models. By investigating the motion mechanism of magnetic particles driven by a magnetic field and generating controllable particle motion, the modulation effect of particle motion on QCM vibration is demonstrated. Experimental results show that particle motion influences the surface strain of the QCM through the liquid medium, modulating the thickness-shear vibration of the QCM. Consequently, particle motion signals can be obtained from the QCM output. Compared to traditional QCM methods that detect static loads, sensing particle motion enables higher sensitivity and stability in detecting parameters (including mass) and allows for the simultaneous detection of multiple load parameters. This study aims to overcome the limitations of traditional QCM technology by proposing a novel approach for detecting particle motion, not only enabling the simultaneous detection of multiple characteristics of the load but also significantly improving detection performance.

To realize "FPGA-like" microwave components and enhance the reconfiguration ability of microwave links, this article proposes a redefinable microwave component that breaks the limitations of conventional microwave components with single functionality. The redefinable microwave components adopt a "combining tangram" design approach to identify the "greatest common divisor" among several microwave functional structures. The patch + gap structure is used as the multiplex unit, and the RF switches are used to control the current flow direction, forming different electromagnetic distributions, thus achieving dynamic reconfiguration of microwave functions. The resonant performance is adjusted using variable capacitors, enabling the reconfiguration of the performance. The test results of the sample indicate that the microwave passive component can achieve software-defined antenna, filtering, and coupling functions, with adjustable center frequencies for all three functions. Compared with commonly used antenna, filter, or coupler, the performance metrics of the redefinable microwave components have not decreased. Furthermore, the component has also been applied for practical validation in radar systems. The demonstration results show that the redefinable microwave component significantly improves the reconfiguration performance of the microwave link, enabling the microwave system to meet the requirements of multiple application scenarios such as imaging, communication, and distance measurement.

Magnetically labeled biomolecule detection technology is playing an increasingly important role in disease diagnosis, drug development, and other fields. Biomolecules labeled with magnetic particles can be regulated to move under a magnetic field, and the motion signals are feasible to be acquired using quartz crystal microbalance (QCM). By analyzing the motion signals of biomolecules, various property parameters that determine the motion characteristics can be detected. Based on this mechanism, we developed a magnetically labeled biomolecule multiparameter detection instrument system. To accommodate the detection of various molecular parameters, the design of the instrument system provides ample flexibility for application: the magnetic field excitation source in the system can generate arbitrary waveform excitations and the sensor QCM is free of biochemical decoration. To enhance the detection sensitivity and resolution of the system, the QCM output signal is demodulated first and only the molecular motion signal is collected for analysis, with digital signal processing and analysis being completed by a computer. Test results indicated that the system has achieved the detection of parameters such as the magnetic bead (MB) concentration (ng/mL level), and the hydrodynamic dimension (HD) of magnetically labeled biomolecules (nm level). This instrument system functions as an open platform for biomolecule multiparameter detection, capable of completing more magnetically labeled molecule parameter detections and biochemical dynamic process monitoring. It represents a new instrument technology for biochemical molecule detection.

This letter proposes an ultra-low-power grid signal analog processor (UGSP) for magnetoelectric sensor systems. It is designed in accordance with the principle of a lock-in amplifier (LIA) and employs an intermittent operation mode by dynamically controlling the duty cycle of the enable signal. As a portable sensor interface platform, it achieves low-power, high signal-to-noise ratio (SNR) signal detection in wireless sensor networks (WSN) for grid network topology analysis. For validation, the UGSP chip is fabricated with a compact size of 15 x 15 x 4 mm(3). Its power consumption is only 35 mu W at a 3.3 V supply, and is significantly lower than that of conventional AD630-based LIA (600 mW). It has an SNR improvement of 55 dB. The utilization of this UGSP considerably prolongs the operational lifespan of the battery-powered detection circuitry on the WSN node. It is also applicable to other sensor technologies requiring low consumption and high sensitivity, self-powered monitoring systems in smart grids, early warning of power system faults, real-time diagnostics, and other related applications.

Electromagnetic (EM) inverse problems have always been an important research topic in applied electro-magnetics. They have great application values in EM characterization and inverse design by reconstructing unknown EM parameters from measured or user-defined EM field information. Some typical problems include inverse scattering, inverse source reconstruction, antenna array synthesis, fault diagnosis, and direction-of-arrival (DoA) estimation. In recent years, the compressive sensing (CS) technique has shown great potential in solving EM inverse problems. It allows faithful reconstruction from undersampled measurements by exploiting the sparse property of EM parameters, thus leading to significant improvement in efficiency and accuracy of reconstructions. The Bayesian compressive sensing (BCS) algorithm, based on a probabilistic CS framework, does not rely on inconvenient Restricted Isometry Property (RIP) verifications to yield stable results. It is preferred in EM problems where the RIP conditions are hardly satisfied.

Acoustically driven magnetoelectric (ME) antenna operating at low frequency reduces the antenna size and enhances the transmission efficiency significantly compared to electric antennas. This study reports a two-dimensional (2D) electro-magneto-elastic equivalent circuit model and experimental verification of nonvolatile pattern reconfigurable ME antenna (i.e., Ni/Metglas/ PZT/Metglas/Ni) for the first time. The proposed ME antenna modulates the magnetic moment oscillation with an adjustable built-in magnetic field, which results in a nonvolatile reconfigurable pattern. Specifically, the proposed model is constructed with constitutive equation characterizing 2D stress coupling, magnetic charge theory, 2D Newton’s equation, and Maxwell’s equations. Both the theoretical model and experiment indicate that the proposed antenna could provide nonvolatile reconfigurable B-field patterns within 360° with gain ranging between -121.3 and -120.6 dBi. The antenna with aspect ratio of around 1 facilitates the reconfigurability of pattern due to more convenient modulations, which provides the robust communication performance at various scenarios.

Stability of marine electric fields measurements is a problem of broad interest in relation to long-term in situ observations, especially in the construction of the Internet of Underwater Things (IoUT), geological exploration, and disaster monitoring. The root of this problem is the drift in electrode potential caused by imbalances in the electrodes' electrical processes, which are unavoidable in potential-difference measurements based on the conduction current. Here, we propose the variable dielectric polarization (VDP) measurement method, which uses the variation of a dielectric between the electrodes to measure the external electric field. This method modulates the electric field signal to higher frequencies, allowing separation of the signal from the output by demodulation. This not only separates the signal from low-frequency noise, but it also increases the measurement sensitivity by frequency boosting. The results of our experiments show that the VDP system we constructed has a measurement sensitivity of 2.63 V/(V/m) and a measurement resolution of 22.4 mu V/m. Under long-term measurements, the measured drift of the VDP system (106 ( mu V/m)/day) was found to be only 1/320 of the self-potential (SP) method (34 (mV/m)/day). The proposed VDP method, thus, provides a new approach to the construction of future IoUT devices and the prediction of marine disasters.