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This paper aims to discuss the classification of targets based on their radar cross-section (RCS). The wavelength, the dimensions of the targets and the distance from the antenna are in the order of 1 mm, 1 m and 10 m, respectively.

The near-field RCS is considered, and the physical optics approximation is used for its numerical calculation. To model real scenarios, the authors assume that the incident angle is a random variable within a narrow interval, and repeated observations of the RCS are made for its random realizations. Then, the histogram of the RCS is calculated from the samples. The authors use a nearest neighbor rule to classify conducting plates with different shapes based on their RCS histogram.

This setup is considered as a simple model of traffic road sign classification by millimeter-wavelength radar. The performance and limitations of the algorithm are demonstrated through a set of representative numerical examples.

The proposed method extends the existing tools by using near-field RCS histograms as target features to achieve a classification algorithm.

This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process.

In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system.

By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process.

The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.

Straightness measurement of rail weld joint is of essential importance to railway maintenance. Due to the lack of efficient measurement equipment, there has been limited in-depth research on rail weld joint with a 5-m wavelength range, leaving a significant knowledge gap in this field.

In this study, the authors used the well-established inertial reference method (IR-method), and the state-of-the-art multi-point chord reference method (MCR-method). Two methods have been applied in different types of rail straightness measurement trollies, respectively. These instruments were tested in a high-speed rail section within a certain region of China. The test results were ultimately validated through using traditional straightedge and feeler gauge methods as reference data to evaluate the rail weld joint straightness within the 5-m wavelength range.

The research reveals that IR-method and MCR-method produce reasonably similar measurement results for wavelengths below 1 m. However, MCR-method outperforms IR-method in terms of accuracy for wavelengths exceeding 3 m. Furthermore, it was observed that IR-method, while operating at a slower speed, carries the risk of derailing and is incapable of detecting rail weld joints and low joints within the track.

The research compare two methods’ measurement effects in a longer wavelength range and demonstrate the superiority of MCR-method.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

The paper seeks to bridge the already familiar benefits of 3D printing (3DP) to the rehabilitation of cultural heritage, still based on the use of complex and expensive handcrafted techniques and scarce materials.

A compilation of different information on frequent anomalies in cultural heritage buildings and commonly used materials is conducted; subsequently, some innovative techniques used in the construction sector (3DP and 3D scanning) are addressed, as well as some case studies related to the rehabilitation of cultural heritage building elements, leading to a reflection on the opportunities and challenges of this application within these types of buildings.

The compilation of information summarised in the paper provided a clear reflection on the great potential of 3DP for cultural heritage rehabilitation, requiring the development of new mixtures (lime mortars, for example) compatible with the existing surface and, eventually, incorporating some residues that may improve interesting properties; the design of different extruders, compatible with the new mixtures developed and the articulation of 3D printers with the available mapping tools (photogrammetry and laser scanning) to reproduce the component as accurately as possible.

This paper sets the path for a new application of 3DP in construction, namely in the field of cultural heritage rehabilitation, by identifying some key opportunities, challenges and for designing the process flow associated with the different technologies involved.