Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach.

In this paper, we present a deep learning approach for identifying current intensity and frequency. The reconstruction is based on measurements of the magnetic field generated by the current flowing in a conductor. Magnetic field data are collected using a magnetic probe capable of generating a spectrogram, representing the spectrum of frequencies of the magnetic field over time. These spectrograms are saved as images characterized by color density proportional to the induction field value at a given frequency. The proposed deep learning approach utilizes a convolutional neural network (CNN) with the spectrogram image as input and the current or frequency value as output. One advantage of this approach is that current estimation is achieved contactless, using a simple magnetic field probe positioned close to the conductor.

Analytic Continuation, Phase Unwrapping, and Retrieval of the Refractive Index of Metamaterials from S-Parameters.

The heuristic homogenization approach is intensively employed to characterize electromagnetic metamaterials (MMs). The effective parameters are extracted within this framework using the Nicolson-Ross-Weir (NRW) method. Special attention must be devoted to handling this procedure because of the branch ambiguity issue affecting it, i.e., the lack of uniqueness in the evaluation of the effective refractive index neff rooted in the use of the multivalued complex logarithm to invert the Airy-Fresnel relation. Over the years, several techniques based on the phase-unwrapping approach have been introduced, but without any theoretical justification. In this paper, we aim to clarify the theoretical connection between the phase unwrapping method and the analytic continuation theory framework. Furthermore, three-phase-unwrapping approaches, which descend directly from the theory we discussed, are compared to identify which approach is best suited to reconstruct the complex refractive index of metamaterials when the NRW method is applicable.

Finite Element Evaluation of the Electric Field Distribution in a Non-Homogeneous Environment.

Finite element analysis is used in this study to investigate the effect of media inhomogeneity on the electric field distribution in a sample composed of cells and their extracellular matrix. The sample is supposed to be subjected to very high pulsed electric field. Numerically computed electric field distribution and transmembrane potential at the cell membrane in electroporation conditions are considered in order to study cell behavior at different degrees of inhomogeneity. The different inhomogeneity grade is locally obtained using a representative model of fixed volume with cell-cell distance varying in the range of 1-283 um. The conductivity of the extracellular medium was varied between plain collagen and a gel-like myxoid matrix through combinations of the two, i.e., collagen and myxoid. An increase in the transmembrane potential was shown in the case of higher aggregate. The results obtained in this study show the effect of the presence of the cell aggregates and collagen on the transmembrane potential. In particular, by increasing the cell aggregation in the two cases, the transmembrane potential increased. Finally, the simulation results were compared to experimental data obtained by culturing HCC1954 cells in a hyaluronic acid-based scaffold. The experimental validation confirmed the behavior of the transmembrane potential in presence of the collagen: an increase in electroporation at a lower electric field intensity was found for the cells cultured in the scaffolds where there is the formation of collagen areas.

Learning-Based Approaches to Current Identification from Magnetic Sensors.

Direct measurement of electric currents can be prevented by poor accessibility or prohibitive technical conditions. In such cases, magnetic sensors can be used to measure the field in regions adjacent to the sources, and the measured data then can be used to estimate source currents. Unfortunately, this is classified as an Electromagnetic Inverse Problem (EIP), and data from sensors must be cautiously treated to obtain meaningful current measurements. The usual approach requires using suited regularization schemes. On the other hand, behavioral approaches are recently spreading for this class of problems. The reconstructed model is not obliged to follow the physics equations, and this implies approximations which must be accurately controlled, especially if aiming to reconstruct an inverse model from examples. In this paper, a systematic study of the role of different learning parameters (or rules) on the (re-)construction of an EIP model is proposed, in comparison with more assessed regularization techniques. Attention is particularly devoted to linear EIPs, and in this class, a benchmark problem is used to illustrate in practice the results. It is shown that, by applying classical regularization methods and analogous correcting actions in behavioral models, similar results can be obtained. Both classical methodologies and neural approaches are described and compared in the paper.

Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model.

Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell's equations and numerical models of the body are used for this purpose. This approach is time-consuming, especially in the case of high frequencies, for which a fine discretization of the model should be used. In this paper, the surrogate model of electromagnetic wave absorption in human body, utilizing Deep-Learning, is proposed. In particular, a family of data from finite-difference time-domain analyses makes it possible to train a Convolutional Neural Network (CNN), in view of recovering the average and maximum power density in the cross-section region of the human head at the frequency of 3.5 GHz. The developed method allows for quick determination of the average and maximum power density for the area of the entire head and eyeball areas. The results obtained in this way are similar to those obtained by the method based on Maxwell's equations.

Curvature-Dependent Electrostatic Field as a Principle for Modelling Membrane-Based MEMS Devices. A Review.

The evolution of engineering applications is increasingly shifting towards the embedded nature, resulting in low-cost solutions, micro/nano dimensional and actuators being exploited as fundamental components to connect the physical nature of information with the abstract one, which is represented in the logical form in a machine. In this context, the scientific community has gained interest in modeling membrane Micro-Electro-Mechanical-Systems (MEMS), leading to a wide diffusion on an industrial level owing to their ease of modeling and realization. Physically, once the external voltage is applied, an electrostatic field, orthogonal to the tangent line of the membrane, is established inside the device, producing an electrostatic pressure that acts on the membrane, deforming it. Evidently, the greater the amplitude of the electrostatic field is, the greater the curvature of the membrane. Thus, it seems natural to consider the amplitude of the electrostatic field proportional to the curvature of the membrane. Starting with this principle, the authors are actively involved in developing a second-order semi-linear elliptic model in 1D and 2D geometries, obtaining important results regarding the existence, uniqueness and stability of solutions as well as evaluating the particular operating conditions of use of membrane MEMS devices. In this context, the idea of providing a survey matures to discussing the similarities and differences between the analytical and numerical results in detail, thereby supporting the choice of certain membrane MEMS devices according to the industrial application. Finally, some original results about the stability of the membrane in 2D geometry are presented and discussed.

Temperature and Torque Measurements of Switched Reluctance Actuator with Composite or Laminated Magnetic Cores.

Soft magnetic composite (SMC) materials made of iron powder are more frequently used in construction of electric actuators and motors because of their advantages with respect to Fe-Si electric steel sheets and because they have almost no powder loss. The study deals with measurements of temperature and torque of a low-power rotary switched reluctance actuator, with reference to a commercial actuator and a prototype actuator characterized by stator and rotor cores made of soft magnetic composite materials. Further power loss analysis was also conducted. To assess the actuators, magnetization characteristics and iron loss vs. magnetic flux density at a given frequency were measured according to IEC standards. Results show that the actuator made of soft magnetic composites exhibits higher efficiency and a lower temperature rise of stator and windings in comparison with the commercial actuator.

Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks.

In the paper, we present a novel approach to the optimum design of wearable antenna arrays intended for off-body links of wireless body area networks. Specifically, we investigate a four-element array that has a switchable radiation pattern able to direct its higher gain towards a signal source and a lower gain towards an interference. The aim is to increase the signal to interference ratio. We apply a genetic algorithm to optimize both the spatial placement and the feed phasing of the elementary on-body antennas. We propose a simplified, computationally efficient model for the simulation of the array radiation pattern. The model is based on full-wave simulations obtained with a simplified cylindrical model of the human body. We also propose, implement, and evaluate four objective functions based on signal to interference ratio, i.e., min-max, nadir point distance maximization, utopia point distance minimization, and full Pareto-like. Our optimized design obtained with this approach exhibits a significant performance improvement in comparison to the initial heuristic design.

Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions.

The purpose of this research was to improve the performance of a wireless body area sensor network, operating on a person in the seated and standing positions. Optimization-focused on both the on-body transmission channel and off-body link performance. The system consists of three nodes. One node (on the user's head) is fixed, while the positions of the other two (one on the user's trunk and the other on one leg) with respect to the body (local coordinates) are design variables. The objective function used in the design process is characterized by two components: the first controls the wireless channel for on-body data transmission between the three sensor nodes, while the second controls the off-body transmission between the nodes and a remote transceiver. The optimal design procedure exploits a low-cost Estra, which is an evolutionary strategy optimization algorithm linked with Remcom XFdtd, a full-wave Finite-Difference Time-Domain (FDTD) electromagnetic field analysis package. The Pareto-like approach applied in this study searches for a non-dominated solution that gives the best compromise between on-body and off-body performance.

Many-Objective Automated Optimization of a Four-Band Antenna for Multiband Wireless Sensor Networks.

This paper describes a new design and an optimization framework for a four-band antenna to be used in wireless sensor networks. The antenna is designed to operate effectively in two open frequency bands (ISM-Industrial, Scientific, Medical), 2.4 GHz and 5.8 GHz, as well as in two bands allocated for the fifth-generation (5G) cellular networks, 0.7 GHz and 3.5 GHz. Our initial design was developed using the trial and error approach, modifying a circular disc monopole antenna widely used in ultra wideband (UWB) systems. This initial design covered the three upper bands, but impedance matching within the 700 MHz band was unsatisfactory. The antenna performance was then improved significantly using an optimization algorithm that applies a bi-objective fully-Paretian approach to its nine-parameter geometry. The optimization criteria were impedance matching and radiation efficiency. The final design exhibits good impedance matching in all four desired bands with the Voltage Standing Wave Ratio (VSWR) value below 2 and radiation efficiency of 88%. The simulated antenna performance was verified experimentally.

Optimal Design of Electromagnetically Actuated MEMS Cantilevers.

In this paper we present the numerical and experimental results of a design optimization of electromagnetic cantilevers. In particular, a cost-effective technique of evolutionary computing enabling the simultaneous minimization of multiple criteria is applied. A set of optimal solutions are subsequently fabricated and measured. The designed cantilevers are fabricated in arrays, which makes the comparison and measurements of the sensor properties reliable. The microfabrication process, based on the silicon on insulator (SOI) technology, is proposed in order to minimize parasitic phenomena and enable efficient electromagnetic actuation. Measurements on the fabricated prototypes assessed the proposed methodological approach.

Effect of Tissue Inhomogeneity in Soft Tissue Sarcomas: From Real Cases to Numerical and Experimental Models.

Electrochemotherapy is an established treatment option for patients with superficially metastatic tumors, mainly malignant melanoma and breast cancer. Based on preliminary experiences, electrochemotherapy has the potential to be translated in the treatment of larger and deeper neoplasms, such as soft tissue sarcomas. However, soft tissue sarcomas are characterized by tissue inhomogeneity and, consequently, by variable electrical characteristic of tumor tissue. The inhomogeneity in conductivity represents the cause of local variations in the electric field intensity. Crucially, this fact may hamper the achievement of the electroporation threshold during the electrochemotherapy procedure. In order to evaluate the effect of tissue inhomogeneity on the electric field distribution, we first performed ex vivo analysis of some clinical cases to quantify the inhomogeneity area. Subsequently, we performed some simulations where the electric field intensity was evaluated by means of finite element analysis. The results of the simulation models are finally compared to an experimental model based on potato and tissue mimic materials. Tissue mimic materials are materials where the conductivity can be suitably designed. The coupling of computation and experimental results could be helpful to show the effect of the inhomogeneity in terms of variation in electric field distribution and characteristics.

Effect of Electrode Distance in Grid Electrode: Numerical Models and In Vitro Tests.

Electrochemotherapy is an emerging local treatment for the management of superficial tumors and, among these, also chest wall recurrences from breast cancer. Generally, the treatment of this peculiar type of tumor requires the coverage of large skin areas. In these cases, electrochemotherapy treatment by means of standard small size needle electrodes (an array of 0.73 cm spaced needles, which covers an area of 1.5 cm2) is time-consuming and can allow an inhomogeneous coverage of the target area. We have previously designed grid devices suitable for treating an area ranging from 12 to 200 cm2. In this study, we propose different approaches to study advantages and drawbacks of a grid device with needles positioned 2 cm apart. The described approach includes a numerical evaluation to estimate electric field intensity, followed by an experimental quantification of electroporation on a cell culture. The electric field generated in a conductive medium has been studied by means of 3-dimensional numerical models with varying needle pair distance from 1 to 2 cm. In particular, the electric field evaluation shows that the electric field intensity with varying needle distance is comparable in the area in the middle of the 2 electrodes. Differently, near needles, the electric field intensity increases with the increasing electrode distance and supply voltage. The computational results have been correlated with experimental ones obtained in vitro on cell culture. In particular, electroporation effect has been assessed on human breast cancer cell line MCF7, cultured in monolayer. The use of 2-cm distant needles, supplied by 2000 V, produced an electroporation effect in the whole area comprised between the electrodes. Areas of cell culture where reversible and irreversible electroporation occurred were identified under microscope by using fluorescent dyes. The coupling of computation and experimental results could be helpful to evaluate the effect of the needle distance on the electric field intensity in cell cultures in terms of reversible or irreversible electroporation.

Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors.

We investigate a case of automated energy-budget-aware optimization of the physical position of nodes (sensors) in a Wireless Body Area Network (WBAN). This problem has not been presented in the literature yet, as opposed to antenna and routing optimization, which are relatively well-addressed. In our research, which was inspired by a safety-critical application for firefighters, the sensor network consists of three nodes located on the human body. The nodes communicate over a radio link operating in the 2.4 GHz or 5.8 GHz ISM frequency band. Two sensors have a fixed location: one on the head (earlobe pulse oximetry) and one on the arm (with accelerometers, temperature and humidity sensors, and a GPS receiver), while the position of the third sensor can be adjusted within a predefined region on the wearer's chest. The path loss between each node pair strongly depends on the location of the nodes and is difficult to predict without performing a full-wave electromagnetic simulation. Our optimization scheme employs evolutionary computing. The novelty of our approach lies not only in the formulation of the problem but also in linking a fully automated optimization procedure with an electromagnetic simulator and a simplified human body model. This combination turns out to be a computationally effective solution, which, depending on the initial placement, has a potential to improve performance of our example sensor network setup by up to about 20 dB with respect to the path loss between selected nodes.

Field models and numerical dosimetry inside an extremely-low-frequency electromagnetic bioreactor: the theoretical link between the electromagnetically induced mechanical forces and the biological mechanisms of the cell tensegrity.

We have implemented field models and performed a detailed numerical dosimetry inside our extremely-low-frequency electromagnetic bioreactor which has been successfully used in in vitro Biotechnology and Tissue Engineering researches. The numerical dosimetry permitted to map the magnetic induction field (maximum module equal to about 3.3 mT) and to discuss its biological effects in terms of induced electric currents and induced mechanical forces (compression and traction). So, in the frame of the tensegrity-mechanotransduction theory of Ingber, the study of these electromagnetically induced mechanical forces could be, in our opinion, a powerful tool to understand some effects of the electromagnetic stimulation whose mechanisms remain still elusive.