The use of projected fringes for the measurement of surface profile is a well-developed technique. In this paper, we present a surface profile measurement method for micro-components based on the combination of digital fringe projection, phase stepping, temporal phase unwrapping, and digital image acquisition. In this paper, a cost-effective machine is proposed. In particular, the paper describes the hardware and software realization. A simple procedure is described, which enables calibration of the optical set-up for subsequent quantitative measurement of micro-components of unknown shapes. In this paper, some examples are presented to demonstrate the potentiality of the system in measurement of microscopic surface profile.

We tackle the issue of measuring and analyzing the visitors dynamics in crowded museums. We propose an IoT-based system – supported by artificial intelligence models – to reconstruct the visitors trajectories throughout the museum spaces. Thanks to this tool, we are able to gather wide ensembles of visitors trajectories, allowing useful insights for the facility management and the preservation of the art pieces. Our contribution comes with one successful use case: the Galleria Borghese in Rome, Italy. Keywords. Pedestrian behaviour, floor usage, data acquisition, bluetooth, BLE, museums.

In the project ARCHEO 3.0 a Machine Learning (ML) system for automatic contouring of the stratigraphic units of an archaeological excavation has been experimented. In this research, we have applied the same ML algorithm to aerial color photographs that represent very important tools in the study of ancient topography and landscape archaeology. Aerials of the Vulci necropolis, one of the most important cities of ancient Etruria, have been used. These photos, both vertical and oblique, have been chosen because the marks had been studied and analyzed in a recent PhD work in Ancient Topography. In particular, the traditional mapping method has been compared with the results obtained by means of automated ML algorithm. This experiment has demonstrated that the developed ML algorithm can be applied to aerial photographs for the recognition of archaeological traces, with interesting development prospects. Keyword: Machine Learning, aerial photography, archaeological mapping, landscape archaeology, ancient topography, crop-marks, Vulci.

This study is a collaboration between the, Eforia Extraurban of Thessaloniki and the departments of Sciences of the Cultural Heritage of the University of Salerno. The paper describes the survey of Tomb II of Agios Athanasios, Thessaloniki, carried out with the technique of the uncalibrated terrestrial photogrammetry. The final purpose is the realization of a georeferenced digital model. The results show that the acquisition techniques, although characterized by further margins of error, show the advantage of acquisition s speed and secure overlap between the frames, allowing to monitor the cultural heritage with an extreme ease of operation.

Characterization of archaeological finds is a challenging case study for researchers, as the need of non-invasive measurements strongly affects the investigation strategies. Moreover, it is almost not possible to follow a fixed protocol, because the measurement conditions should be tailored to the samples under study. This paper presents a preliminary characterization of bronze coins coming from an archaeological site located in the south of Israel. The superficial corrosion layers have been analysed by means of Raman spectroscopy in order to investigate their composition and assess the conservation state of the finds. Reference samples of known composition have also been used to help peaks identification in the Raman spectra. Results coming from this study can help in defining the correct conservation strategies for the artefacts.