Design of Double-Layer Electrically Extremely Small-Size Displacement Sensor

Deep-Learning-Assisted Simultaneous Target Sensing and Super-Resolution Imaging

Metasurfaces have recently experienced revolutionary progress in sensing and super-resolution imaging fields, mainly due to their manipulation of electromagnetic waves on subwavelength scales. However, on the one hand, the addition of metasurfaces can multiply the complexity of retrieving target information from detected electromagnetic fields. On the other hand, many existing studies utilize deep learning methods to provide compelling tools for electromagnetic problems but mainly concentrate on resolving one single function, limiting their versatilities. In this work, a multifunctional deep learning network is demonstrated to reconstruct diverse target information in a metasurface-target interactive system. First, a preliminary experiment verifies that the metasurface-involved scenario can tolerate the system noises. Then, the captured electric field distributions are fed into the multifunctional network, which can not only accurately sense the quantity and relative permittivity of targets but also generate super-resolution images precisely. The deep learning network, thus, paves an alternative way to recover the targets' information in metasurface-target interactive systems, accelerating the progression of target sensing and superimaging areas. Besides, another new network that allows forward electromagnetic prediction is also proposed and demonstrated. To sum up, the deep learning methodology may hold promise for inverse reconstructions or forward predictions in many electromagnetic scenarios.

A quartz crystal microbalance (QCM)-based easy setup device for real-time mass change detection under high-power RF plasma

Sensing technologies serve a crucial role in monitoring and testing surface properties in biosensors, thin films, and many other industries. Plasma treatments are routinely used in most of these technologies to modify the surfaces of materials. However, due to the high radio frequency (RF) noise in plasma processes, real-time surface tracking is still rather difficult. In this study, we aim to construct an easy-to-set up mass change detection system capable of operating under RF plasma conditions. For this purpose, we have presented a novel technique that utilizes the quartz crystal microbalance sensor to detect mass changes in different plasma environments. The constructed device was then tested under 13.56 MHz, 100 W plasma atmosphere. The results showed that the resonance frequency of a crystal was successfully measured with 1.0 Hz resolution under the impact of plasma-induced high power of RF noise. Moreover, as a preliminary study, we used ethylenediamine (EDA) to track changes in resonance frequency under plasma conditions and observed noise-free signals in frequency-voltage curves. Furthermore, the system's sensitivity was found to be 3.8 ng/Hz, with a test molecule (EDA) deposition of about 380 ng in the RF plasma atmosphere. Overall, this study focused on creating a relatively new approach for detecting the real-time mass change in a strong RF environment, which we believe could be an improved and easy-to-set up technique for plasma-based processes such as surface coating, etching, and activation.

Microwave-sensor-node integrated into a short-range wireless sensor network

This paper presents the first microwave-sensor-node integrated into a short-range wireless sensor network based on ZigBee technology. The node includes an analog front-end circuit, a Frequency Modulated Continuous Wave generator, an Analog-to-Digital-Converter module, a transceiver, a power unit, a processing unit and a new one-port dielectric permittivity sensor which is able to measuring the separation of structural cracks by the reflection coefficient measured in microwave frequencies. The analog front-end is composed of a pair of power dividers, an isolator and a mixer. The dielectric permittivity sensor is based on a patch antenna of variable length. The processing unit and transceiver are implemented with an Arduino UNO and an XBee module respectively. Additionally, the methodology for data processing is presented and the results of the measurement of a synthetic crack are presented. The results show that the system was successfully implemented with a sensitivity of 0.07 GHz/mm, for an opening range of between 0 and 5 mm and for a frequency range ranging from 2.782 GHz to 3.131 GHz. It is important to mention that the measurement was done remotely, placing the sensor 3 m from the client PC.