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This letter presents a frequency characteristics analysis for a magnetic anomaly detection (MAD) study. A magnetic dipole model is developed to study the energy distribution of the obtained MAD signal in the frequency domain, where fh is proposed to define the signal high-frequency boundary. The relations between fh and other parameters, such as the velocity of the target (or platform), closest path approach (CPA), magnetic moment strength, magnetic moment orientation (MMO), and sensor orientation, are analyzed. We find that the frequency response is independent of the moment strength. The combined effects of the MMO and sensor orientation on fh are defined by the functio n g(α , β , θ , φ ), which is studied by the Monte Carlo method, yielding a maximum value of max(g) ≈ 0.85. Thus, the function fh = 0.85v/CPA is advanced for frequency boundary estimation. The proposed theoretical analysis is examined in an experiment to aid in extracting the magnetic anomaly signal effectively.

This letter reports a noise suppression method for vector magnetic anomaly detection (MAD) based on noise spatial characteristics analysis. The environment noise indicates space anisotropy that is essential for noise depression. To find a projection direction e that allows the projected noise, denoted as Ne = e· N, to have minimum fluctuations, we compute the standard deviation σ of N_e. The projected signal is then processed by a vector orthogonal basis functions (OBFs) detector resulting in a higher signal to noise ratio (SNR) by 3 dB than the traditional OBFs filter. After noise space transformation (NST) processing, the compelling low-frequency noise is suppressed and the weak target signal can be obtained.

Usually, the signal-to-noise ratio is low and velocity spatial change is severe in areas where fault traps develop. Seismic data processing brings challenges to geophysicists. It is extremely difficult to improve imaging in deep parts were faults harm wave propagation. Traditional pre-stack data migration imaging methods based on common middle points or common shot points are generated by the geophysical theory hypothesis. To overcome such kind of challenge and improve imaging, full azimuth multi-rays pre-stack depth migration was applied in the B block. Full azimuth multi-rays pre-stack depth migration brings a new concept. Rays scan is generated from imaging point toward the surface, leading to a simultaneous emphasis on both continuous structure and discontinuous objects such as small faults and small-scale fractures. In this paper, a comparison was conducted between conventional Kirchhoff pre-stack depth migration and full azimuth multi-rays pre-stack depth migration. Sufficient tests have been carried out in the fault-developed areas. For geological attributes analysis, subsequent processing can be conducted on multi-domain gathers from full azimuth multi-rays pre-stack depth migration according to the request of geologists. Application of this method in B block in the Middle East shows that full azimuth multi-rays pre-stack depth migration can improve the imaging quality of medium and deep layers and improve the imaging of small faults and small-scale fractures. Full azimuth multi-rays pre-stack depth migration can use isotropic and anisotropic velocity models to perform reflection and directional imaging in the local angle domain. Migration radius is defined by both aperture and angle. Information from all azimuths and angles is fully involved in the imaging process, which is beneficial to imaging small faults and fractures. Structural attributes such as dip, azimuth and continuity of the target zone can be obtained directly from gathers after migration. High-quality structural imaging results can be obtained by effective reflection enhancement processing on gathers from full azimuth multi-rays pre-stack depth migration. Diffraction wave enhancement processing can be used for geological fracture prediction. This paper studies the principle of full azimuth multi-rays pre-stack depth migration, compares it with the conventional Kirchhoff prestack depth migration, analyses the advantages of this method in imaging principle and subsequent gathers processing, and applies it to seismic data of the Middle East B Block heterogeneous carbonate reservoir. The result shows that imaging accuracy of full azimuth multi-rays pre-stack depth migration technology is higher, especially in complex structural areas.

Epoxy resin and asphalt compatibility have a significant impact on the material qualities of epoxy-modified asphalt. At present, there is a shortage of the study on the compatibility mechanism from the standpoint of nano. This paper evaluated the influence of temperature on the compatibility of epoxy resin (ER) and asphalt using the molecular dynamics (MD) simulation approach to explore the compatibility between ER and asphalt in greater detail. First, a twelve-component molecular model of asphalt was built, as well as a model of the system following cross-linking of epoxy resin and curing agent. And model optimization and model validation were performed. Secondly, the model of ER and asphalt co-blended system was constructed by combining the optimized model with the Amorphous Cell module. Then, MD simulations were done at various temperatures to derive the solubility parameters and intermolecular interaction energy of the mixed system for each system. Finally, the compatibility of ER with asphalt at different temperatures was analyzed based on the calculation results. It was found that the compatibility of ER and asphalt was temperature dependent. As the temperature increased, the solubility parameters of ER and asphalt decreased, and the difference in solubility parameters Δδ between them took the minimum value at 170 °C. As the temperature rises, so does the total potential energy in each system, and the intermolecular interaction energy of ER and asphalt mixed system achieves the maximum value at 170 °C. Currently, the structure of ER and asphalt mixed system is the most stable, and the compatibility between the two is the best.

Tunneling magnetoresistive (TMR) magnetic sensors are broadly applied in various fields. However, to further improve the limit of detection (LOD), it is highly desirable to realize and calculate the noise limits of such sensor systems. Thus, techniques for noise elimination can be developed. In this paper, we propose an equivalent magnetic noise model to describe the LOD of a TMR magnetometer by considering intrinsic noise sources in the TMR sensing element and readout electronics. According to the model, the contribution of each noise source can be directly obtained. Therefore, the LOD of the magnetometer can be established theoretically. Moreover, the predicted detectivity of 390 pT/ $\surd $ Hz is also demonstrated by the experimental results. The model is applicable to all kinds of TMR sensors with a “Wheatstone bridge” configuration.

High-performance environment-friendly piezoelectric potassium sodium niobate (KNN)-based thin films have been emerged as promising lead-free candidates, while their substrate-dependent piezoelectricity faces the lack of high-quality information due to restraints in measurements. Although piezoresponse force microscopy (PFM) is a potential measuring tool, still its regular mode is not considered as a reliable characterization method for quantification. After combining machine-learning enabled analysis using PFM datasets, it is possible to measure piezoelectric properties quantitatively. Here we utilized advanced PFM technology empowered by machine learning to measure and compare the piezoelectricity of KNN based thin films on different substrates. The results provide a better understanding of the relationship between structures and piezoelectric properties of the thin films.

A procedure for the enhancement of alternating current field measurement (ACFM) detection performance is proposed based on a multi-parameter synergy analysis (MPSA) algorithm. Firstly, to gain the maximised ACFM signal characteristics, wavelet base property matching is adopted to choose the favourable wavelet bases. To this aim, the following six base properties should be considered: orthogonality, compact support, symmetry, discrete wavelet transform (DWT), vanishing moment and regularity. It is found that the applicable wavelet bases are Haar, Daubechies (DbN), Symlets (SymN) and Coiflets (CoifN). Secondly, the MPSA method is applied to select the optimal mother wavelet candidates. The candidate with the largest MPSA index value is regarded as the optimum wavelet base. Finally, the proposed MPSA denoising strategy is demonstrated using an ACFM experiment. The results indicate that wavelets Db4 with decomposition level (DL)9 and Sym7 with DL8 are most appropriate for x- and z-axis ACFM signal denoising, respectively. The enhanced ACFM detection performance is experimentally verified and it is found that the signal-to-noise ratio (SNR) is increased by 33.8 dB and 26.7 dB for the x- and z-axis signal, respectively.

A detection procedure by employing the induced magnetic signature characteristics for magnetic targets detection and identification is proposed in this paper. The highly sensitive tunneling magnetoresistive (TMR) sensor with low self-noise is adopted, while the magnetic target is traveling along a straight line at various speed and CPA (closest path approach) for magnetic anomaly detection (MAD). The triple-axis TMR sensor is characterized with a high sensitivity around 100 mV/V/Oe in a linear range of ±1 Oe and a self-noise of 170 pT/√Hz at 1 Hz. The signature waveform width and sensing range angle are used to analyze the signatures. It is found that the sensing range angle is independent of changing velocity and CPA. The proposed signature interpretation strategy is demonstrated by both experimental and simulated data.

This paper presents a novel low power security system based on magnetic anomaly detection by using tunneling magnetoresistance (TMR) magnetic sensors. In this paper, a smart light has been developed, which consists of TMR sensors array, detection circuits, a micro-controller, and a battery. Taking the advantage of low power consumption of TMR magnetic sensors, the smart light powered by Li-ion battery can work for several months. Power spectrum density of the obtained signal was analyzed to reject background noise and improve the signal-to-noise ratio effectively by 1.3 dB, which represented a 30% detection range improvement. Also, by sending the signals to PC, the magnetic fingerprints of the objects have been configured clearly. In addition, the quick scan measurement has been also performed to demonstrate that the system can discriminate the multiple objects with 30 cm separation. Since, the whole system was compact and portable; it can be used for security check at office, meeting room, or other private places without attracting any attention. Moreover, it is promising to integrate multiply such systems together to achieve a wireless security network in large-scale monitoring.

Multiferroic magnetoelectric (ME) materials with the capability of coupling magnetization and electric polarization have been providing diverse routes towards functional devices and thus attracting ever-increasing attention. The typical device applications include sensors, energy harvesters, magnetoelectric random access memories, tunable microwave devices and ME antennas etc. Among those application scenarios, ME sensors are specifically focused in this review article. We begin with an introduction of materials development and then recent advances in ME sensors are overviewed. Engineering applications of ME sensors are followed and typical scenarios are presented. Finally, several remaining challenges and future directions from the perspective of sensor designs and real applications are included.

In order to solve the problem of the performance degradation of the diagonal loading beamforming algorithm at low signal-to-noise ratio (SNR), a new beamforming method based on the covariance matrix of the received signal delay information is proposed. Firstly, the delay covariance matrix of the received signal is constructed to suppress the influence of the noise, then the delay covariance matrix is transformed to obtain the eigenvalues of the covariance matrix, and finally, the eigenvalues are processed by diagonal loading technology. The simulation results show that the proposed algorithm is productive.

Magnetoelectric (ME) based energy harvester has been attracting lots of interests due to its high output energy density at resonant frequency. However, the narrow bandwidth of the resonant frequency limits it for practical applications considering the wide frequency range of environmental energy source. This paper presents a hybrid multimodal energy harvester based on Metglas/PMN-PT ME composites. Specifically, the harvester uses 8 layers of Metglas to form a cantilever beam, and three PMN-PT plates are attached to the cantilever beam separately in order to work at different modes. The multimodal behaviors of energy harvester are demonstrated theoretically and experimentally. Such advanced energy harvester holds the promising for the wide frequency environmental energy utilization.

In this paper, a quantification method for sub-surface crack evaluation based on extreme learning machine (ELM) neural network is proposed to the application of alternating current field measurement (ACFM). According to simulation results of finite element model, the dimensions of cracks can be described by four features of the Bz and Bx curves. An optimized ELM feasible for ACFM detection is trained and validated based on the selected features. Taking the advantage of our proposed sub-surface crack sizing method, the experimental results indicate that the detection and quantification for sub-surface crack is conducted readily. The maximum errors of length and depth predicted by ELM can be limited to 5.65 % and 6.27 %, respectively.

In this paper, we proposed a measurement procedure to estimate the magnetic moment orientation (MMO) of an object. As vector magnetic field B i (i=x, y, z) in each orthogonal direction can be regarded as the superposition of the three basic elements along certain direction. We propose an orthogonal energy ratio (OER) algorithm to infer the MMO by calculating the signal energy distribution along each axis. The first step is to develop a theoretical model and obtain the dipole signature waveforms decomposed of 9 basic elements. The second step is to develop a standard OER database which provides a metric reference to compare with the measured OER. In this standard database, the one having the smallest variance is determined as the estimated MMO. Owing to symmetric property in space, it generally introduces 8 possible solutions. In the last step, an octant determination procedure is applied to select the unique MMO. In particular, the algorithm is independent of changes in CPA (closest path approach) and velocity. Through the complete processing of this algorithm, the target MMO can be estimated precisely with limited errors.

The presence of unexpected metal fragments is of major concern to some manufacturing lines, such as foodstuff and pharmaceutical products. Conventional coil metal detectors only have capability of detecting, which cannot localize metal pieces precisely. This paper presents an advanced metal detection system based on tunneling magnetoresistance (TMR) sensors array. To achieve the function of metal localization, the system is designed to contain 16 TMR sensors. Meanwhile, considering the electromagnetic interference (EMI) from manufacturing environment, the system is also integrated with matched filtering technique, which can eliminate EMI greatly. Experimental results demonstrate that such novel metal detection system can sense the metal particle with 1-mm diameter as the belt running in regular speed. Moreover, it can localize the metal particle with the spatial resolution of 3 cm.

Multiferroic magnetoelectric (ME) composites have been continually attracting researchers' attention because of the significant potential for building functional devices. Unlike conventional ME devices under quasi-static or low-power excitation, e.g., sensors, energy harvesters, and random memories, high-power ME devices like recently proposed ME antennas will perform complex dynamic behavior. The voltage-driven nonlinearity in 1–1-typed ME resonators was reported from the perspective of frequency-response curve in our last work. Here, we described both theoretically and experimentally a previously unobserved jumping and hysteresis effect in its magnetic bias characteristic. A direct link between the bias-response and the frequency-response characteristics was also experimentally provided to analyze the origin of the nonlinear behavior in the bias-response curve. In addition, the cubic spring constant k3 was theoretically calculated and compared to qualitatively explain the difference of our obtained threshold voltage that generated the first and second jumping resonances in the dual-peak bias-response curve. In contrast to our previous research, this work reveals the loading direction of the applied magnetic bias field, which plays a significant role when we consider the selection of an optimized bias field for high-power ME devices. Moreover, the sharp jumping in the bias-response curve is also a potential for opening dimensions for ME community.

This paper presents a novel alternating current field measurement (ACFM) system, which is realized by tunneling magnetoresistive (TMR) sensors and a tradeoff (TO) wavelet denoising algorithm. The TMR sensor is characterized with a high sensitivity of 30 mV/V/Oe and a self-noise of less than 9pT/√Hz at driving frequency f = 3 kHz. TO algorithm is proposed to select the optimum combination of mother wavelet family and decomposition level. The combinations of wavelet Db5 with decomposition level 8 and Sym7 with decomposition level 8 are determined as the most suitable ones for x- and z-axis signal denoising respectively, which have been demonstrated to be better than the traditional low-pass filtering method. Finally, the efficiency of the proposed system is proved by an experiment under more pressing test conditions, for example, with increased lift-off distance of 10 mm. In particular, it leads to increased SNR (signal-to-noise ratio) by 25.5 dB and 16.5 dB for x- and z-axis ACFM signal, respectively.

Pipeline systems, such as oil and natural gas pipelines, which are normally buried underground or distributed under the sea, are susceptible to corrosion and pressure destruction, and hence timely and reliable nondestructive testing (NDT) is necessary. Conventional nondestructive testing methods are generally based on ultrasonic, eddy current and magnetic flux leakage mechanism. With respect to eddy current testing (ECT) technologies, the current challenge is to develop magnetic sensors with high detection ability, extremely low power consumption and compatible with Internet of Things (IoT). Giant resonant magnetoelectric (ME) coupling has been previously reported in our proposed 1-1 ME composites. Here, we further presented an investigation on the eddy current testing (ECT) using 1-1 type ME sensor. The proposed ECT probe was built by integrating a small exciting coil surrounding the ME sensor. Simulation results indicate ECT based on ME sensors is especially applicable for non-ferromagnetic and low-conductivity materials, e.g. steel. One-dimensionally distributed cracks with varying crack sizes in a steel pipeline was then experimentally identified and located using ECT method. More importantly, the power consumption of our proposed ECT probe is as low as 0.625 u W , which shows 2–3 orders of magnitude improvement compared with other probes based on e.g. magnetoresistive (MR) sensors. Our research here provides a fundamental insight into NDT using ME sensors as a ECT probe and represents a crucial step towards online low-power monitoring of pipeline cracks.

A single-axis alternating current field measurement (ACFM) detection system is proposed for crack size estimation. A single tunneling magneto-resistive (TMR) sensor is used to detect Bz signal, which is able to determine both the length and depth of a crack simultaneously. First, a theoretical analysis is presented to evaluate the crack length and especially depth using Bz signature waveform. The underlying physics principle is supported using a finite element analysis (FEA) method. In the simulation, the cracks with various lengths and depths are analyzed for a given crack width, and corresponding Bzmax values are obtained. Next, a Bzmax characteristic polynomial surface is developed, which can be represented by a fitted polynomial interpolation equation. The crack depth can be inversed by this equation with reference to the measured Bzmax value and crack length. Finally, real ACFM experiments are conducted to demonstrate that the crack lengths and depths can be readily estimated.

In this paper, we propose a measurement procedure to estimate the magnetic moment orientation (MMO) of a dipole. Theoretical analysis indicates that magnetic field in each orthogonal direction Bi (i = x, y, z) is found to be a superposition of its three basic elements Bij (i = x, y, z and j = 1, 2, 3) along certain direction, allowing the obtained dipole signature to be decomposed of 9 basic elements. An orthogonal energy ratio (OER) algorithm is used to infer the MMO by calculating the signal energy distribution along each axis. A standard OER database is developed which provides a reference to be compared with the measured OER. The one having the smallest variance is determined as the estimated MMO. Owing to space symmetric property, it generally introduces 8 possible solutions. Then, a mathematic method is presented to define the common characteristics of the magnetic signature in the 8 octants, which is able to identify the octant of unknown target based on detected Bi field. As a result, the unique MMO can be estimated. The experimental results show good estimation accuracy for the dipole orientation with average error of 3.5°. In particular, the algorithm is independent of changing CPA and velocity, which is demonstrated in simulation and experiment.

On the effect of pore water pressure in a saturated asphalt pavement, fluid velocity and water infiltration rate will increase, which have a disadvantage influence on asphalt mixture strength. In order to measure the pore water pressure in pavement, a fiber optic hydraulic pressure sensor (FOHPS) is designed. The theoretical correlation between the applied pressure and the center wavelength shift of the FOHPS is derived by laboratory experiment. The pore water pressures in asphalt pavement at some running speeds were measured in situ. Furthermore, a falling head permeameter method was used to measure the permeability coefficients of asphalt mixture exposed to hydraulic pressures which were from 40 kPa to 350 kPa, and the correlation between permeability and hydraulic pressure was obtained. The experimental results showed that the pore water pressure would increase with the increasing car’s speed, but the lifetime of pore water pressure decreases with the increasing speed. The permeability coefficients of SMA-13 and AC-20 mixtures decrease with the increasing hydraulic pressure, but the water infiltration rate increases on an approximate linear curve as the hydraulic pressure increased from 40 kPa to 350 kPa.

A theoretical model for the multi-push-pull configuration of magnetoelectric (ME) laminated composites comprising magnetostrictive and piezoelectric layers with interdigitated electrodes encapsulated in polyimide film is presented. Analytical solutions for the ME voltage coefficient αE, ME charge coefficient αQ, noise charge density and equivalent magnetic noise were derived. Parametric studies are presented to evaluate the influence of material properties and polyimide film geometries. The results show that the value of αE was determined by the parameters of the magnetostrictive and piezoelectric phases, and that the values of αQ and noise charge density were determined not only by the component parameters, but also by the volume fraction of the piezoelectric phase and polyimide film geometry. The equivalent magnetic noise had no dependence on the polyimide film geometry, but rather was determined by the component parameters and the volume fraction of the piezoelectric phase. Theoretical and experimental results are compared and shown to have good agreement with each other.

The magnetic equivalent noise for a 4×4 Metglas/Pb(Zr,Ti)O3 magnetoelectric (ME) sensor array unit has been optimized. Such optimization holds good for practical sensor application in open (unshielded) environments. The self-noise estimation for the array unit was about 15–16.8 pT/√Hz at f=1 Hz, as estimated by the coherence between each unit and their beam. To verify the efficiency of this optimization technique, self-noise tests were also performed in a magnetic shielding chamber and shown to be close to that of units in an open environment. After employing a noise optimization method, the 4×4 array can achieve a 27 dB common mode noise rejection and an equivalent magnetic noise floor of 8.2 pT/√Hz at f=1 Hz.

We present a frequency multiplication in Metglas/Pb(Zr,Ti)O 3 (PZT) magnetoelectric (ME) composites which can be tuned by geomagnetic field. Such effect was observed under small dc magnetic biases, and can be operated over a broad frequency range. Moreover, the geomagnetic field can serve as the “ON–OFF” switch for control of the frequency multiplication in ME composites. Thus, a guidance device could be developed to lock onto the direction with largest component of Earth’s field.

In this paper, we present a quasi-static charge amplifier method to detect the extremely low frequency response of Metglas/Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 magnetoelectric (ME) laminated composites. Using this technique, ME effect can be detected at f −2 Hz, and ME charge coefficients show good stability over wide frequency range from 10 mHz to 1 kHz. Thus, it allows us to develop a quasi-static magnetic sensor based on ME composites which can work at frequencies as low as 10 mHz, with equivalent magnetic noise floors of about 3 nT/√Hz.

A sensitive tuning of dielectric properties under the application of magnetic field in Fe74.4Co21.6Si0.5B3.3Mn0.1C0.1/0.7Pb(Mg1/3Nb2/3)O3-0.3PbTiO3 multiferroic composites is presented. We demonstrate large variations of the effective permittivity at resonance region under extremely low magnetic field of 4 Oe near room temperature. The tuning of the maximum dielectric permittivity at resonance and the corresponding frequency was systematically studied. We suggest that the magnetic dependence of the effective permittivity is consistent with a strain-mediated magnetic-elasto-dielectric effect. Our results suggest a route towards future applications of the dielectric properties tuning effect in advanced devices.

A differential structure which has the ability to reject external vibrational noise for Metglas/Pb(Zr,Ti)O3 (PZT) fiber-based magnetoelectric (ME) heterostructures has been studied. This type of ME structure functions better than conventional sensors as a magnetic sensor when used in an environment in which vibrational isolation is impractical. Sensors fabricated with this differential mode structure can attenuate external vibrational noise by about 10 to 20 dB at different frequencies, while simultaneously having a doubled ME voltage coefficient. Interestingly, in addition to offering a means of mitigating vibrational noise, this ME structure offers the potential to be a hybrid sensor, separating magnetic and acoustical signals.

A magnetoelectric (ME) bending-mode structure based on Metglas/Pb(Zr,Ti)O3 fiber laminates has been studied. This bending mode had a fundamental resonance (FBR) of about 210 Hz, which was much lower than that of the longitudinal mode. Near the FBR, the ME voltage coefficient was about 400 V/cm·Oe. Magnetic sensors based on this bending mode had an equivalent magnetic noise floor of ≤0.3 pT/√Hz at f = 210 Hz.

As a result of the coupling between their dual order parameters, multiferroic materials exhibit unusual physical properties and, in turn, promise new device applications. [ 1 , 2 ] Of particular interest is the existence of a cross-coupling between the magnetic and electric orders, termed the magnetoelectric (ME) effect. [ 3–5 ] Because no single-phase material has been put forward demonstrating a practical capacity for such coupling at room temperature, [ 8 ] many of the most promising applications offered by the ME effect, including magnetic fi eld sensors and electric write-magnetic read memory devices, have not been forthcoming. [ 6 , 7 ] Furthermore, the exploitation of high magnetic fi eld sensitivity in two-phase ferromagnetic/ferroelectric composites requires development and identifi cation of end users. [ 7 ]