Coherent Population Trapping (CPT) atomic magnetometers hold broad application prospects in fields such as geological exploration, earthquake monitoring and early warning, marine magnetic anomaly detection, and space magnetic field mapping due to their significant advantages of all-optical configuration, omnidirectional detection, and ease of miniaturization and integration. However, when deployed on mobile platforms, the accuracy of CPT magnetometers is strongly constrained by the heading error. In the present work, we identify the vector light shift (VLS) as a key contributor to this heading error through theoretical analysis and experimental validation. To mitigate this effect, a VLS suppression method based on a double-pass configuration is proposed and experimentally validated. Compared to the conventional single-pass setup, this approach reduces the fictitious magnetic field corresponding to the VLS from 7.58 nT to 4.61 nT, achieving a suppression ratio of 39.2%. This method effectively mitigates the impact of the VLS on the systematic error and heading error of the CPT magnetometer. Furthermore, the experiment reveals that this method also enhances the measurement stability of the CPT magnetometer. At an integration time of 6.6 s, a 45.2% reduction is observed in the Allan deviation of the magnetic field reading. This study provides an effective heading error suppression scheme for the development of high precision CPT magnetometers for mobile platforms.

Abstract Dynamically tunable metasurface, a new generation of electromagnetic manipulation devices, has shown significant application potential in millimeter-wave communication, intelligent sensing, phased array antennas, and other related domains. Nonetheless, the majority of current dynamically tunable metasurface continue to face significant hurdles regarding compactness, tuning speed, and extensive angular operational range. Here, we propose a deep-subwavelength (≈ λ /11) metasurface modulator employing a 30 μ m-thick liquid crystal (LC) layer to improve the tuning speed, while providing capabilities of amplitude/phase modulation. The underdamped condition with maximum 2 π phase shift is investigated by the coupled mode theory (CMT). Subsequently, as a proof of concept, the finite element method is employed to establish the metasurface with specific pattern parameters to verify the establishment of the overdamping state. The simulations results indicate that the modulator’s capacity for amplitude and phase modification can be achieved by the tunable dielectric constant of LC. The amplitude modulation |r| 2 range is 0.126–0.938, while the phase modulation Δ ϕ of 277° is attained at 28.58 GHz. Furthermore, the modulation capacity of the amplitude and phase can be maintained under a broad angular range of 70°. This work establishes physics-based design guidelines for the design and downsizing of millimeter wavefront manipulation devices.

With the growing use of custom shielded cables in advanced electronic systems, evaluation of the transfer impedance of shielded cables with non-standard characteristic impedances is increasingly necessary; however, traditional measurement methods here are limited by the difficulty of obtaining matched loads. This letter presents a method for characterizing the transfer impedance of electrically short shielded cables with an arbitrary characteristic impedance. The proposed method can be used with mismatched loads in the experimental configuration. Validation is performed with cables of known transfer impedance. The presented results closely align with the theoretical formulas.

A novel elliptical metamaterial unit cell was developed, and a gyro-like backward-wave oscillator (BWO) using this elliptical metamaterial was constructed to release the electromagnetic energy into the desired TE11 mode. PIC simulations found that the gyro-like BWO could operate at TE11 mode below its cutoff frequency. When an applied voltage U=375 kV and a beam current I=1.77 kA, with an applied magnetic field B=0.51 T, the output power can be as high as 450 MW, while the electronic efficiency can be as high as 67.8% at 2.41 GHz. The results suggest a new technique to achieve high-efficiency gyro-like BWO operate below its cutoff frequency using a metamaterial unit cell, and also, this work seeks to design a compact high-power microwave source using metamaterial to radiate the Gaussian-like microwave wave beam.

Benefiting from the strong penetrative and anti-interference properties of low-frequency (LF, between 3 Hz and 300 kHz) electromagnetic (EM) waves, LF signals hold immense application potential in various fields including underwater communication, pipeline monitoring, and through-the-earth communication. Among these applications, Rotating Permanent Magnet Antenna (RPMA) stands out due to its compact size, low power consumption, high efficiency, and strong radiation intensity. Meanwhile, its omnidirectional characteristic leads the antenna with broad signal coverage. However, this omnidirectionality may lead to signal interference and increasing bit error rates when multiple RPMAs transmit signal simultaneously, which will reduce communication performance. Meanwhile current researches mainly focus on enhancing RPMA's radiation intensity with insufficient in-depth discussion on directionality. To meet the demand of directionality in different scenarios, this work proposes a directional pattern reconfigurable antenna based on RPMA array. The influence of factors such as the number of elements, spacing, distribution, and phase on the directional characteristics of the magnetic field intensity generated by RPMA arrays is comprehensively considered, and the antenna's directivity can be enhanced 2.67 times. Through experimentation with a designed three-element RPMA array, this work demonstrates the reconfigurability of directional characteristics and validates the communication robustness. By simple positional adjustments and phase control, the directionality can be changed flexibly between omnidirectional and directional characteristics. This directional pattern reconfigurable antenna offers significant advantage in improving communication quality, which will pave the way for future advancements and applications of RPMA.

The mechanical antenna (MA) is a potential solution for the extremely-low-frequency (ELF, 3-30 Hz) transmitter enabling industrial informatization. It can have the advantages of miniaturization and high efficiency compared to conventional transmitters. However, the current ELF MA has an issue with both high inertia and a long delay in symbol switching. Not only does this lower the data rate, but it also causes unnecessary power consumption in operation. This article proposes a compact ELF transmitter based on a heterogeneous architecture of piezoelectric cantilevers and oscillating electret, as well as a relevant information transfer program. The actuator fabricated from composite piezoelectric material exhibits a rapid dynamic reaction. Merging this with efficient spectrum utilization increases the data rate and reduces power consumption. A proof of concept demonstration conducted at a frequency of 26.4 Hz attained a data rate of 21 bit/s while consuming a mere 1.52 W of power. In addition, increasing the charge density of the electret can expand the transmission distance without requiring extra power consumption, thus adapting to more challenging applications such as Underwater Internet of Things, Through-the-Earth communication, pipeline inspection, and underground detection.

As the application of unmanned aerial vehicle (UAV) become increasingly widespread in various industries, its positioning in Global Navigation Satellite System (GNSS) denied environments plays an indispensable role in certain scenarios such as dense woods and enclosed underground environment. However, there are several existing defects of the conventional positioning method for UAV in GNSS-denied environments, such as the error accumulation and poor long-term accuracy in Inertial Navigation Systems and requirement for sufficient light and high computing power in vision-based localization. Therefore, a novel positioning method for UAV in GNSS-denied environments based on mechanical antenna (MA) is proposed in this work, which consists of MA installed on UAV to generated low-frequency (LF) magnetic signal, the three-dimensional magnetic field sensor in ground base station to receive signal, and the corresponding positioning algorithm based on particle swarm optimization. EM signals in LF bands is applied in positioning, therefore, this method has high propagation stability and anti-interference due to the characteristics of LF bands. Furthermore, because MA technology can greatly reduce the size and power consumption of the LF transmitting system, the LF signal used for positioning can be generated by a portable MA installed on UAV. Theoretical analysis and positioning experiments based on this method are carried out in detail. According to the results, the positioning method proposed in this work is of great feasibility with a mean error < 0.45 m in measurement, which will provide an alternative instrumentation for the positioning of UAV in GNSS-denied environments and can be used in a variety of industrial scenarios with complex electromagnetic environments in the future.

In this work, a flexible extremely low frequency mechanical antenna (ELFME) is fabricated by printing domain-editable magnets on macro-fiber-composite (MFC). When compared to existing magneto-electric composite antennas of similar volumes, our antenna exhibits an 18-fold increase in electromagnetic field emission intensity. Through an array design, we can further amplify the electromagnetic wave intensity. Moreover, this MFC driven, 3D printable design is ideal for making reconfigurable antenna that can be applied in complex-shaped mechanisms for geometrical reconfiguration and beam steering.

The low-frequency mechanical antenna shows promise in various applications such as underwater communication and navigation. Meanwhile, the design of stacked structure with multilayer unipolar electrets has significantly improved the communication distance. However, because it increases the risk of interlayer electric field breakdown caused by the unipolar electret, reducing the size of the mechanical antenna becomes necessary but challenging. In this work, an optimization method of stack spacing that can enhance the space utilization efficiency and radiation capability of mechanical antennas based on unipolar electret is presented. By analyzing the difference between unipolar and bipolar electret in the stacked layers, a non-equal spacing strategy for antenna structure design is implemented. This strategy takes advantage of the symmetric electric field between the layers of the stacked unipolar electret. In a verification scenario with four layers of unipolar electret, the size of the stack can be reduced by 22.3% compared with the equally spaced arrangement, and a theoretical communication distance of 30.2m can be achieved. The optimization method can be applied to stacked structures with any number of layers. It provides additional design guidance, especially when increasing the number of layers to achieve a greater communication distance.

As a novel pipeline monitoring technology, wireless sensor networks (WSN) have the advantages of low power consumption, flexibility, and robustness in aboveground, underground, and underwater pipeline monitoring systems. Using low-frequency (LF) rotating permanent magnet antennas (RPMA) for communications from sensor nodes (SN) to autonomous unmanned vehicles (AUV) has the advantages of low attenuation, low latency, and portable size. However, the communication ranges of different SNs may overlap and information from different SNs should be distinguished. Methods like time-division multiplexing (TDM) and frequency-division multiplexing (FDM) are not suitable in this case. Another way is to form RPMAs into an array so that directional communications from SNs to AUVs can be realized and there is no overlap between SNs. Therefore, this work analyzes and enhances the directivity of linear RPMA arrays in the near-field region. Based on the designed RPMA array prototype, the directional communication is realized at a distance of 5 m across concrete walls. This work paves the way for directional communications from SNs to AUVs in underground pipeline monitoring systems.

This article discovers a special mode conversion effect induced by dielectric coatings and asymmetric structures in floating multiconductor cables illuminated by a plane wave. Accordingly, a semianalytical model is proposed to predict the terminal response induced by this mode conversion effect. To this end, the modes on cables are decomposed into the common-mode (CM) and differential-mode (DM) components. First, the floating multiconductor cables are considered equivalent to a CM single conductor, and the current distribution along this conductor is obtained through full-wave simulations. Second, the transfer inductance matrix, which characterizes the conversion between the CM currents and DM equivalent induced sources, is derived, after which the transmission line model for the DM is constructed. Then, two application examples involving floating three- and four-conductor cables excited by a plane wave are examined to validate the performance. Furthermore, the effect of the dielectric coating thickness on the asymmetry/coating-induced mode conversion effect (ACIMCE) is analyzed by the Monte Carlo method, a regular polygon cross-sectional design method is proposed to eliminate the ACIMCE, and a low-frequency model is provided to understand the CM coupling mechanism. Notably, the existence of ACIMCE and the practical application of its elimination method to actual cables need further experimental validation.

Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.

We developed a novel spiral metamaterial (MTM) unit cell and constructed a slow wave structure (SWS) based on it at sub-1-GHz frequency and developed a compact relativistic Cherenkov oscillator with high efficiency under relatively low guide magnetic field. Particle-in-cell (PIC) simulations demonstrate that a circular waveguide with radius ro1=4.01 cm loaded with a spiral MTM SWS can radiate the TM 01 mode with output power 1.06 GW and frequency 945 MHz using a pulsed electron beam with energy 700keV ( U = 700 kV) and current 2kA with a magnetic field B=0.58 T, and the corresponding beam-to-microwave power conversion efficiency is 75.6%. When the applied voltage is varied from 400 to 700 kV, the output frequency of the TM 01 mode is centered at 940 MHz with a beam-tuning frequency bandwidth of 10 MHz, while the output power can be greater than 600 MW. This work seeks to design a compact backward wave oscillator (BWO) for high power for sub-1-GHz generation across a wide voltage range and with low dispersion

There are various advantages of low-frequency (LF) wireless communications such as stable propagation characteristics and low transmission attenuation, especially in lossy media. However, conventional LF especially ELF (extremely low frequency) transmitters have the bottleneck of excessive size and power consumption. Hence, this work proposes an LF signal transmitter based on rotating permanent magnets with miniaturization and portability to address the problem. Firstly, a theoretically analytical model of the transmitter’s radiation performance considering size factors and different working methods is established, which provides a more accurate model for the calculation of the magnetic field around the transmitter. Then, a prototype is developed and a test platform is established. Finally, this work explores the potential to apply the proposed rotating magnet-based transmitter (RMBT) in various industrial scenarios. The communication experiments in multi-scenario with complex media are conducted in this work, which completed LF signal transmissions and reception in 20 m air-seawater path (the deepest depth of underwater path in this field up to date). The results verify that RMBT is capable of communication in complex media and different electromagnetic environments, especially under harsh electromagnetic conditions (such as seawater, coal and concrete). RMBT proposed in this work provides a novel strategy for the next research of LF communications in the undersea and underground scenarios.

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

Twisted-wire pairs (TWPs) are a common signal transmission medium widely used to transmit the differential-mode (DM) signal. However, as the electromagnetic environment grows increasingly complex, there are more and more factors that can cause the common-mode (CM) noise. The CM noise can couple to TWPs, leading to an unwanted phenomenon known as mode conversion. In this study, data transmission is performed in the form of DM and CM signals, respectively. The DM signal is the normal signal transmission mode, while the CM signal simulates the worst conditions in which the mode-conversion effect is most pronounced. Based on a comparative analysis of experimental results, it is observed that there is a noticeable discrepancy in shielding effectiveness when confronted with different signal transmission modes. Therefore, the shielding effectiveness of TWPs becomes a critical factor in resisting the CM noise and maintaining signal integrity. It should be taken priority to improve the balance of TWPs and suppress the CM noise to ensure their reliable operation when surrounded by complex electromagnetic environments.

With the increasing popularity and complexity of electronic equipment, the problem of electromagnetic interference (EMI) has become increasingly prominent. Such interference can seriously affect the performance, reliability, and service life of the device and, in severe cases, may cause system failure or communication interruption. Existing EMI suppression techniques are widely used to address this issue, and this paper provides a comprehensive overview. Firstly, it introduces the impact and harm of EMI on us and then presents two forms of EMI, namely conducted EMI and radiated EMI, better to understand the sources and characteristics of these disturbances. Three different EMI filters, the advantages of soft switching compared to hard switching, the working principle of random modulation, and emerging trends in electromagnetic shielding are then analyzed and discussed. Finally, we comprehensively summarize the advantages and disadvantages of the above four methods and point out the possible limitations of each technique in practical applications. We also propose potential future research directions, which will help to overcome the shortcomings of existing technologies and promote the development of the field of EMI suppression.

In order to break through the bandwidth limitation of digital instantaneous frequency measurement for a real-valued waveform, we developed a frequency estimator with a high dynamic range and low computation cost using multiple sub-Nyquist analog to digital converter (ADC) channels. The procedures of the proposed scheme are as follows: (a) perform zero-crossing detection on the real-valued signal and use multiple sub-Nyquist ADC channels to sample the output signal; (b) take several samples of each channel to calculate the folded frequency; (c) use the frequency residuals of each channel to reconstruct the frequency based on the Chinese remainder theorem. Finally, the algorithm robust analysis and simulation experiments are performed, demonstrating that the scheme of this paper has relatively good anti-noise ability and dynamic adaptability.

Real world systems---such as robots, weather, energy systems and stock markets---are complicated and high-dimensional. Hence, without prior knowledge of the system dynamics, detecting or forecasting abnormal events from the sequential observations of the system is challenging. In this work, we address the problem caused by high-dimensionality via viewing time series anomaly detection as hypothesis testing on dynamical systems. This perspective can avoid the dimension of the problem from increasing linearly with time horizon, and naturally leads to a novel anomaly detection model, termed as DyAD (Dynamical system Anomaly Detection). Furthermore, as existing time-series anomaly detection algorithms are usually evaluated on relatively small datasets, we released a large-scale one on detecting battery failures in electric vehicles. We benchmarked several popular algorithms on both public datasets and our released new dataset. Our experiments demonstrated that our proposed model achieves state-of-the-art results.

We propose a novel 5° rectangular void metamaterial (MTM) ring in this article, which can introduce negative permeability without changing the volume of the vane structure much. Particle-in-cell (PIC) simulations demonstrate that the output power of the A24 (24 cavity) relativistic magnetron using the novel rectangular void MTM ring in the vane structure can be increased around 30%, while the electronic efficiency is nearly the same as for the A12 relativistic magnetron with diffraction output. The volume of the A24 cavity relativistic magnetron using the rectangular void MTM ring in the vane structure is the same as for the A12 relativistic magnetron. The Cherenkov threshold for 1-GW output power of this A24 magnetron can also be reduced to an applied voltage $U$ = 310 kV and with applied magnetic field of $B$ = 0.32 T at $S$ -band. The results presented in this article provide references for high-power microwave (HPM) system design for frequency agility and mode switching using MTMs. Also, the Cherenkov threshold for HPM devices can be reduced by using MTMs, which are lighter with less volume for pulsed power system design.