Yao Wang

Flexible magnetic sensors have become a hot research topic due to their non-contact human–machine interaction capabilities in areas such as motion recognition and posture detection of intelligent robots, virtual reality (VR) space reconstruction, and the Internet of Things. This study proposes a flexible, low-power three-dimensional (3D) magneto-impedance (MI) sensor based on a planar FeSiB/PI/graphene microcoil/PI/FeSiB heterostructure. Through the magneto-impedance effect of soft magnetic materials and the magnetoresistance effect of graphene under the synergistic modulation of weak current excitation, this sensor can decouple the magnetic field components in the X, Y, and Z directions with a single measurement, thus guaranteeing the real-time detection capability of a 3D magnetic field. Experimental results show that the proposed 3D magnetic sensor possesses the obvious advantages, such as the low power consumption of 76 μW, high resolutions of 31, 36, and 6992 nT/Hz^1/2 in the X, Y, and Z directions, respectively. Additionally, the magnetic sensor exhibits excellent anti-bending performance and can adapt to complex mechanical deformation environments. These characteristics endow it with great application potential in the field of intelligent wearable devices and provide new ideas for the future flexible electronics technology.

Low-frequency (LF) magnetoelectric (ME) antennas based on the acoustic wave drive principle provide the broad potential application prospects in the Internet of Underground Things (IoUT) with the tight energy budget and high-loss medium. However, the frequency reconfigurable (FR) ME antennas with ultralow energy consumption have not received the deserved attention, which can enhance the adaptability in different IoUT scenarios. In this study, a nonvolatile FR ME antenna based on the Metglas/[Pb(Mg_1/3 Nb_2/3)O₃]_1-x- [PbTiO₃]_x/Metglas/Pb(Zr,Ti)O₃ /Metglas/[Pb(Mg_1/3 Nb_2/3)O₃]_1-x- [PbTiO₃]_x/Metglas laminate is proposed for the first time. By applying specific depolarized electric field pulses to PMN-PT, the varied nonvolatile polarization and piezo-stress change its own permittivity and neighboring Metglas’ permeability, respectively. Thus, the multimode reactance of the ME antenna can be modulated nonvolatilely, resulting in the operating frequency of the antenna tuned from 52.16 to 73.57 kHz. The proposed antenna realizes the frequency reconfiguration more conveniently and energy efficiently than the current tuning methods of ME heterojunction. Specifically, the reconfiguration energy consumption is reduced by three orders of magnitude compared with the varactor-based tuning method requiring bias voltages. Meanwhile, the transmission energy is reduced by three orders compared with the LoRa antenna by leveraging the low underground attenuation property of the LF magnetic field. Moreover, the antenna’s frequency reconfiguration capability ensures the high transmission rates and signal-to-noise ratio (SNR) in underground scenarios with different losses, reducing the energy consumption by 90.11% compared with the conventional ME antenna. Correspondingly, the proposed antenna provides an environment-aware and energy-efficient method for the underground wireless sensing application with varying media and a tight energy budget.

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 one hand, the reference signal is estimated using the proposed correlation detection 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 stochastic resonance and correlation detection. 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.

Recently acoustically driven magnetoelectric (ME) antennas provide a promising candidate for portable low frequencies (LF 30-300 kHz) transceivers in the high-loss medium. However, single ME antenna constraints the transmitted magnetic field due to the weak magnetic moment and low strain modulated magnetization efficiency. Hence a multi-physical field driven magnetoelectric antenna array consisting of double elastic plates sandwiched between multiple arranged PZT8/Metglas/Ni laminates is 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 magnetoelectric 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 multi-physical field driven effect of fabricated antenna array, the 3 × 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 × 10 antenna array. This provides a power-efficient method to enhance the magnetic field transmission efficiency at low frequency.

Multiple magnetic targets localization (MMTL) has been widely applied in geologic prospecting and vehicle surveillance due to its fast response speed and low cost. However, the MMTL of closely spaced targets is challenging because it is difficult to distinguish the signal of a target submerged in the magnetic anomalous fields of neighboring targets. In addition, current MMTL methods require large numbers of measuring points and precise numbers of targets, making these methods laborious and unfeasible. In this study, we propose a novel MMTL method based on a multi-scale cluster matching pursuit (MCMP) algorithm that does not require the prior information of target number and is robust to noises. In this method, the MMTL task is first modeled as a sparse signal recovery problem that is solved with the proposed MCMP algorithm. Specifically, the target is iteratively searched at multiple scales, and the dispersed sources of target are clustered during updating to avoid false target reconstruction. Using this method with only 4.7% of the measuring points required by previous methods, the localization error of closely spaced targets was reduced by 76% and 94% compared with the point cloud clustering method and data fitting method, respectively. The experimental application of proposed method to the detection of unexploded ordnances resulted in localization errors of only 0.09 m. Interestingly, the targets could be still distinguished even if their magnetic moments were aligned or their separation distance was approximately equal to the targets’ dimensions.

Internet of Underground Things (IoUT) requires numerous devices performing different tasks such as sensing, computation, storage and transmission etc., which is crucial for various industrial and agricultural applications. However, the IoUT nodes based on conventional von Neumann architecture and radio frequency antenna bring severe power consumption and short communication distance issues due to such factors as separated computation, storage and communication units, and severely attenuated radio frequency electromagnetic wave in the high loss underground environment, respectively. Therefore, a low frequency (LF) magnetoelectric (ME) antenna with memory in computing (MIC) ability is proposed in this study, which consists of Ni/Metglas/Pb(Zr0.3Ti0.7)O3-1 mol% Mn/Metglas/Ni laminate. First, a nonlinear electromechanical equivalent circuit model is studied to understand the operational principles of MIC antenna. Meanwhile the remanent impedance of MIC antenna modulated with electric field pulses is leveraged to enable functions of multistate nonvolatile memory and all 16 nonvolatile Boolean logic operations with a single device, facilitating the power efficient edge computing application beyond von Neumann architecture. Furthermore, the stored information of MIC antenna can be directly modulated into the radiated magnetic field through nonlinear converse magnetoelectric effect without additional modulator. Specifically, the stored information of MIC antenna buried underground can be wirelessly transmitted with the distance of 13 m at the power consumption of 884 mW, which outperforms the state-of-art antennas adopted by the Lora and ZigBee technologies. The proposed antenna provides a promising candidate to realize the low-power IoUT system integrating nonvolatile edge computing and LF communication technology.

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.

Low limit of detection (LOD) and highly sensitive probing of functional magnetic nanobead labels is the cornerstone of magnetic label molecular detection technology, which is of great significance in analytical chemistry and biomolecular analysis. Here we present a probing technology based on quartz crystal microbalance (QCM) combines bimodal magnetic fields to accurate and rapid probing of magnetic beads. The proposed technique uses QCM to detect the change of viscosity and density of tested solution under a bimodal magnetic field, thereby enabling the quantification of magnetic bead concentration. The experimental findings demonstrate that the technique can detect magnetic beads accurately and rapidly within 0 ng/mL to 100 μg/mL without anchoring magnetic beads on the QCM. Furthermore, when the particle size of magnetic beads is 1 μm and 0.28 μm, the LOD is reached at 4.62 ng/mL and 2.31 ng/mL, respectively. The versatility of the no-modification probing platform yet the wide detection range and low LOD to various magnetic particles, showcase the potential of the proposed technology to enable swift, repeatable, and dependable assays across a broad range of applications, including but not limited to biomolecule analysis, Molecular genetics, pre-symptomatic diagnosis, and biosafety.

The performance of magnetic anomaly detection (MAD) with magnetic gradient tensor (MGT) measurement system is severely limited by both the measurement errors and accuracy of localization algorithm. However, the robust calibration and localization method against various interferences and noises has not received the deserved attention. This study proposes a joint calibration and localization method with MGT to improve the accuracy of non-cooperative target (NCT) detection with environmental magnetic interferences and noise. First, compared to the previous calibration method requiring a uniform magnetic field environment and precise triaxial nonmagnetic turntable, a new calibration method based on multi-invariant constraints is studied, which is insensitive to the magnetic interferences and movement of array center during the calibration. Then, an adaptive multi-pair of MGTs localization (AMML) method is proposed for the MAD of NCT. On one hand, the corresponding multi-pair of MGTs are chosen adaptively depending on the condition number of MGT, which is far more robust against varying movement directions of NCT compared to previous methods. On the other hand, the AMML method is optimized with the Levenberg–Marquardt (LM) algorithm guided by the prior information of analytical scalar triangulation and ranging (STAR) method, which enhances the efficiency and accuracy of localization method significantly. The experimental results demonstrate that the AMML method reduces the localization error by 39%, 54.3%, and 55.4% compared to the new STAR method (NSM), Lv-STAR method (LSM) and conventional STAR method, respectively.

Recent developments in wireless sensor networks have empowered low-power, intelligent sensor nodes to be distributed over a large area. However, wireless sensor nodes that actively emit electromagnetic waves for energy-hungry communication are limited by the volumes and capacities of batteries, requiring periodic maintenance in widespread applications. To solve this problem, we propose a passive wireless sensor platform, WiSensor, which makes wireless sensor nodes take use of ambient Wi-Fi waves instead of actively emitting electromagnetic waves to transmit sensor data. By affecting the Wi-Fi channels through backscattering, the sensor node modulates the sensor data onto the Wi-Fi channels established by a Wi-Fi transmitting node and a Wi-Fi receiving node. The receiving node acts as the sensor receiver and the sensor data are extracted from the Wi-Fi channel state information. We define the signal-to-noise ratio at the sensor data receiver and analyze the WiSensor performance dependencies, providing guidance for the system design and implementation. A prototype of WiSensor is established, in which sensor data can be transmitted at 200 bps with a bit error rate of 0.01% while the sensor node is placed as far as 5 m from the Wi-Fi transmitting node and 2 m from the Wi-Fi receiving node at 2.37- $\mu \text{W}$ power consumption for the wireless connection. WiSensor is a novel and general wireless sensor platform that embeds the utilization of Wi-Fi channels to practical sensing tasks without the need for wireless sensor nodes to follow Wi-Fi protocols, empowering ubiquitous commodity Wi-Fi devices in our daily life with general sensor connectivity. We believe WiSensor demonstrates promising potential in the field of next-generation smart homes and other applications that urgently require ultralow-power sensor data transmission.