This paper presents a measurement system for measuring dynamic acceleration of infrastructure remotely using semipassive radio-frequency identification (RFID) tag. This measurement is critical to the vibration-based method for infrastructure health monitoring. Design considerations of accelerometer integrated ultrahigh-frequency RFID tag and dynamic acceleration measurements through an RFID wireless link are discussed. Measurement results of the system for a structural specimen have shown that it is capable of acquiring data which provides the information of natural frequency of the structural specimen. Moreover, the system can distinctively identify the state changes of the structural specimen by natural frequency shifts. These results are benchmarked against the results obtained with two commercial systems. It is shown that the standard deviation of the measurement of the natural frequency is ±0.01 Hz which is very close to the standard deviation of the commercial measurement systems.

This review will examine the rapidly growing field of soft robotics, with a special emphasis on soft robotic actuators and their applications in bioengineering. Bioengineering has increasingly utilized soft robotics due to their mechanical adaptability and flexibility, with applications including drug delivery, assistive and wearable devices, artificial organs, and prosthetics. Soft robotic applications, as well as the responsive mechanisms employed in soft robotics, include electrical, magnetic, thermal, photo-responsive, and pressure-driven actuators. Special attention is given to hydraulically amplified self-healing electrostatic (HASEL) actuators due to their biomimetic properties and innovative combination of dielectric elastomer actuators (DEAs) and hydraulic actuators, which eliminates the limitations of each actuator while introducing capabilities such as self-healing. HASEL actuators combine the fast response and self-sensing features of DEAs, as well as the force generation and adaptability of hydraulic systems. Their self-healing ability from electrical damage not only makes HASELs a unique technology among others but also makes them promising for long-term bioengineering applications. A key contribution of this study is the comparative analysis of the soft actuators, presented in detailed tables. The performance of soft actuators is assessed against a common set of critical parameters, including specific power, strain, maximum actuation stress, energy efficiency, cycle life, and self-healing capabilities. This study has also identified some important research gaps and potential areas where soft robotics may still be developed in the future. Future research should focus on improvements in power supply design, long-term material durability, and enhanced energy efficiency. This review will serve as an intermediate reference for researchers and system designers, guiding the next generation of advancements in soft robotics within bioengineering.

Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.

The development and integration of the Internet of Things (IoT) sensor technology across various domains have significantly transformed our work and daily lives, enriching society. However, the increase in the number of IoT devices leads to electronic waste (e-waste), which is a growing global concern. The continued development of sustainable IoT sensors utilizing biodegradable and renewable materials will not only help with reducing e-waste but also ensure wider adaptation of sensing applications thereby benefitting the global community. This review article examines the use of biodegradable and renewable materials in developing antennas for various sensing applications, emphasizing their sustainability, biodegradability, and recyclability. The main contributions of our work are six-fold. First, we review common bio-based materials used in microwave components, detailing the selection process for biodegradable and renewable materials, as well as their comparative advantages and limitations. Second, we examine biodegradable and renewable materials in antenna technologies for sensing applications, providing a comparative analysis based on microwave component type, material properties, dielectric constant, measurement method, relative permittivity, and relevant applications. Third, we analyze design requirements for antennas utilizing these materials, comparing antenna design type/technique, substrate and conductive materials, operating frequency band, size, and gain/directivity. Fourth, we evaluate antenna fabrication techniques, discussing their advantages and challenges. Fifth, we comprehensively review applications of biodegradable and renewable antennas in green IoT sensors, with focus areas including agriculture, environmental monitoring, healthcare, wearable electronics, logistics, and food processing. Finally, we address key research challenges, future prospects, and the potential of these technologies moving forward.