Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, multistatic GPR could potentially be employed to quickly produce subsurface maps for public and private stakeholders, enabling rational and more efficient planning of underground infrastructure inspection, maintenance, and construction. An application of interest in such context is a faster identification of underground utilities location and depth by innovative data visualization methods, such as augmented reality. A 3-D model of the subsurface asset is desirable for such applications. However, raw GPR data is often hard to interpret. Imaging algorithms are applied to improve GPR data readability and signal-to-noise ratio by focusing the spread energy. Here, a processing pipeline that takes raw 3-D multistatic GPR data as input and yields a 3-D model as output is proposed. Initially, a 3-D back-projection algorithm is applied to air-coupled, multistatic GPR data to recover buried target localization. An enhancement filter, tailored for tubular structures, is applied to reduce background noise and highlight structures of interest in the 3-D image. This process is successfully applied to three laboratory scenarios of plastic buried targets with different sizes and shapes.

This paper describes the development of new air-coupled ultrawideband ground-penetrating radar (GPR) for highway pavement and bridge deck inspections that can achieve high spatial resolution and high inspection performance while operating on vehicles driving at regular highway speeds. The major design features include dual-frequency band operation, 8 gigasamples per second high-speed real-time data acquisition, high-speed large volume data transmission and storage, and customized signal-processing algorithms for GPR image enhancement and feature extractions. For design validations, steel reinforcing bar detection experiments in various setups are conducted. The measurement results show good agreement with the subsurface feature configurations.

Recent advances in precision manufacturing technology and a thorough understanding of the properties of piezoelectric materials have made it possible for researchers to develop innovative microrobotic systems, which draw more attention to the challenges of utilizing microrobots in areas that are inaccessible to ordinary robots. This review paper provides an overview of the recent advances in the application of piezoelectric materials in microrobots. The challenges of microrobots in the direction of autonomy are categorized into four sections: mechanisms, power, sensing, and control. In each section, innovative research ideas are presented to inspire researchers in their prospective microrobot designs according to specific applications. Novel mechanisms for the mobility of piezoelectric microrobots are reviewed and described. Additionally, as the piezoelectric micro-actuators require high-voltage electronics and onboard power supplies, we review ways of energy harvesting technology and lightweight micro-sensing mechanisms that contain piezoelectric devices to provide feedback, facilitating the use of control strategies to achieve the autonomous untethered movement of microrobots.

Harmful algal blooms (HABs) frequently occur in coastal and inland water bodies, resulting in detrimental health and economic consequences. In freshwater, HABs are often dominated by cyanobacteria (CyanoHABs or cHABs), which produce a suite of secondary metabolites ranging from taste and odor compounds to liver and neurological toxins. Recently, Uncrewed Aerial Vehicles (UAVs) have emerged as a cost-effective remote sensing solution for using imaging cameras to monitor the distribution of HABs. However, most of these systems are unable to concurrently measure crucial water parameters, requiring additional in situ measurements and sample collection. The UAV system developed in this work presents a comprehensive platform that integrates multiple water sensors, including near-infrared (NIR) multispectral cameras and temperature, pH, and turbidity sensors. The integrated sensor system can not only monitor HAB distributions but also measure essential water quality parameters. In addition, the electromechanical 3D-printed water sampling structure is designed to collect water samples for laboratory analysis of chemical and biological samples. To ensure real-time data transmission, a Long-Range Wide Area Network (LoRaWAN) is developed. An ArcGIS database is implemented for water quality and HAB distribution mapping. Comprehensive field tests have been performed for system performance validation and assessment.

Railroad ballast inspection is critical for the safety of both passenger and freight rail. Ground-penetrating radar (GPR) has been utilized as a highly efficient nondestructive evaluation and structural health monitoring technique in bridge and roadway inspection for many years. However, the development of robust GPR technologies for railroad ballast inspection is still at its early stage due to the complex scattering characteristics of ballast and the lack of efficient algorithms to process big GPR data. An efficient unsupervised method for detecting the region of interest in ballast layer based on the Hilbert transform and Renyi entropy analysis is developed. Both laboratory test and field test are set up and conducted. The data interpretation results demonstrate that the developed region of interest detection algorithm is an efficient and valuable tool for GPR data processing.

This paper presents the design and analysis of an automated traffic signage inventory management system employing the radio-frequency identification (RFID) technology. Traffic signage inventory management faces the operational challenge of recognizing a large number of traffic signs spread in a large area without obvious identifications on them. Thanks to the remote sensing ability of RFID, the operational efficiency can be significantly improved by scanning traffic signs remotely in motion. The system consists of an in-vehicle RFID reader and a handheld RFID reader, both of which connect to mobile applications with user-friendly interfaces. Additionally, user-friendly control software and a database are developed. In this paper, the adequacy of the RFID technology for the task is assessed. On the theoretical side, this paper investigates the radio-wave propagation model and the antenna radiation range. Additionally, the influences of the vehicle speed and the sign-to-reader distance on the reading performance are studied and empirical tests of the tag positioning are conducted. Finally, a system protocol is validated by a field test.