Magneto-acousto-electrical tomography (MAET) is an imaging modality to image the electrical conductivity of biological tissues. It is based on electrical current induction by using ultrasound under a static magnetic field. The aim of this study is to develop a data acquisition system for MAET based on magnetic field measurements. The static magnetic field is generated by six permanent neodymium magnets. A 16-element linear phased array (LPA) transducer is utilized to generate acoustic pressure waves inside the phantom. To measure the magnetic field intensity generated by the induced currents, contactless receiver sensors are developed using two similar disk multiple layer coils, which are Helmholtz coil sensor. Physical properties and electrical characteristics of the sensors are assessed. A two-stage cascaded amplifier is designed and utilized in the receiving system. The gain of the cascaded amplifier at 1 MHz is adjusted to be 96 dB. Experimental studies are conducted with two different phantoms, having 3 S m ⁻¹ and 58 S m ⁻¹ electrical conductivity, respectively. A-scan and B-scan images of phantoms are obtained with the LPA transducer. Comparison of the ultrasound (A-scan) and MAET signals reveals that 3 S m ⁻¹ conductive inhomogeneity can be detected with this data acquisition system. Furthermore, the front and rear interfaces of an inhomogeneity ( ) of 58 S m ⁻¹ conductivity are detectable.

Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design and reduces energy consumption, the PMS reflection magnitude is unity and thus a global optimization of the reflection phases over the PMS unit cells is required in each beamforming scenario. We propose an implementation of such an optimization method rooted in the traditional Fourier transform-based beamforming and evaluate its performance. Additionally, we show that a pair of trained feed-forward neural networks (FFNN) with one input, one hidden, and one output layer can replace time-consuming global optimizations in the case of a PMS comprising $3\times 10$ unit cells. We train the FFNNs on a dataset obtained for typical single- and dual-beam beamforming scenarios. After training, the FFNNs perform requested beamforming tasks within a fraction of second and with about the same accuracy as the original optimization algorithm. The proposed methodology may find applications in future mobile telecommunication systems that require real-time beamforming on low-end hardware. The same beamforming methodology can be also employed in short-range wireless power transfer systems.

Transformer oil serves as an essential insulating medium for high-power electrical equipment such as transformers. Its degradation can result in transformer malfunction and potential disruptions in the power distribution network. Therefore, real-time monitoring of transformer oil quality is crucial to prevent equipment failure and maintain network reliability. In this study, a Microstripline-Split Ring Resonator (M-SRR)-based microwave sensor is designed and fabricated to assess the quality of transformer oil through variations in its dielectric constant. Unlike previously reported passive sensor structures, the proposed configuration enables continuous and non-invasive monitoring of transformer oil aging, providing a viable alternative to conventional chemical analysis techniques. The simulated and experimental results of the forward transmission coefficient (S₂₁) for the Oil Under Test (OUT) exhibit a strong correlation, confirming the validity of the proposed design. The sensor demonstrates a normalized sensitivity range of approximately -38.02 to -5.48% for oil Sample 1 and -22.47 to -5.77% for oil Sample 2, indicating its effectiveness in detecting subtle changes in the oil’s electrical properties during the degradation process. Furthermore, the loss tangent of transformer oil can be evaluated by analyzing the magnitude of the S₂₁ parameter at the resonance frequencies, providing additional insight into the dielectric losses and overall condition of the oil.