@article {irs19-05, title = {Architecture of a client-server platform for IMATI cultural heritage applications}, number = {19-05}, year = {2019}, month = {07/2019}, pages = {48}, publisher = {CNR-IMATI}, type = {Technical Report}, address = {Genova}, abstract = {The final technological outcome of the GRAVITATE EU project is a software platform for the analysis and restoration of cultural heritage artefacts. The GRAVITATE platform is a client-server platform, composed of a back-end infrastructure, of a web interface and a desktop interface. The role of the web interface is to offer browsing and semantic and geometric similarity capabilities, while the desktop client is designed for graphical interaction and manipulation of high-resolution models. Both clients connect to a back-end infrastructure that offers access to 3D models and their geometric properties using a RESTful API. After the end of the project, a work of redesign of the original platform has been carried out, to exploit IMATI applications developed in the project, make them independent of the GRAVITATE servers and services, and putting the basis for future research in the CH domain. This technical report describes the internal organization of the client-server platform that was developed by reusing the parts already developed, and reimplementing the back-end and web interface. The back-end and the web interface were implemented in Python, using the Django framework, while the desktop interface was developed in C++, with the Qt library for the user interface. This technical report describes also the code organization and guides the developer in the task of performing changes and extensions to the platform. }, keywords = {3D semantic annotation, Client-server platform, Cultural heritage, Geometric similarity}, author = {A. Repetto and C. Catalano and M. Spagnuolo} } @article {irs18-06, title = {Adaptive sampling of environmental variables (ASEV)}, number = {18-06}, year = {2018}, month = {08/06/2018}, pages = {9}, publisher = {CNR-IMATI}, type = {Technical Report}, address = {Genova}, abstract = {In environmental surveys a large sampling effort is required to produce accurate geostatistical maps, representing the distribution of environmental variables and the analysis of each sample is often expensive. The standard way to plan a survey is by non-adaptive sampling, whose distribution is usually completely specified prior to data-collection. In general, the sampling points are located on a regular grid, or along directions that are selected following a priori knowledge. However, the contribution of each point to the final prediction accuracy is typically unknown, and it is likely that lesser points distributed in a different manner might reach the same accuracy results eliminating sampling redundancies. Adaptive sampling uses only few initial sampling points and then follows an iterative collection of data, learning and refining the distribution of the variables in order to optimize the uncertainty of the estimates. In this technical report, we describe the design of an adaptive sampling scheme, where a at each sampling step the next optimal point to be sampled is selected based on: i) an uncertainty map of the environmental parameter distribution, which is continuously updated when a new measure is acquired, and ii) the geometric and physics constraints given by the morphology of the surveyed area. The optimality criterion will take into account the trade-off between cost and prediction precision. This research is funded by the INTERREG-MATRAC-ACP project; its strategic objective is to enhance the protection of marine waters by improved real-time driving of ROVs equipped with a series of digital sensors. The project will lead to the definition of protocols for intervention in emergency situations with minimum risks for human operators, and more efficient monitoring procedures in routine conditions. Before starting experimentation on the field, a simulation study was developed to evaluate the effectiveness of this strategy. }, keywords = {Adaptive, Environmental, Gaussian Simulation, Sampling}, url = {http://irs.imati.cnr.it/reports/irs18-06}, author = {S. Berretta and D. Cabiddu and S. Pittaluga and M. Mortara and M. Spagnuolo and M. Vetuschi Zuccolini} } @article {irs16-10, title = {Comparing methods for the approximation of rainfall fields in environmental applications}, number = {16-10}, year = {2016}, month = {February}, pages = {26 p.}, publisher = {CNR-IMATI}, type = {Technical Report}, address = {Genova}, abstract = {Digital environmental data are becoming commonplace and the amount of information they provide is huge, yet complex to process, due to the size, variety, and dynamic nature of the data captured by sensing devices. The paper discusses an evaluation framework for comparing methods to approximate observed rain data, in real conditions of sparsity of the observations. The novelty brought by this experimental study stands in the geographical area and heterogeneity of the data used for evaluation, aspects which challenge all approximation methods. The Liguria region, located in the north-west of Italy, is a complex area for the orography and the closeness to the sea, which cause complex hydro-meteorological events. The observed rain data are highly heterogeneous: two data sets come from measured rain gathered from two different rain gauge networks, with different characteristics and spatial distribution over the Liguria region; the third data set come from weather radar, with a more regular coverage of the same region but a different veracity. Finally, another novelty of the paper is brought by the proposal of an application-oriented perspective on the comparison. The approximation models the rain field, whose maxima and their evolution is essential for an effective monitoring of meteorological events. Therefore, we adapt a storm tracking technique to the analysis of the displacement of maxima computed by the different methods. }, keywords = {Precipitation analysis, Storm tracking, Surface approximation}, url = {http://irs.imati.cnr.it/reports/irs16-10}, author = {G. Patan{\'e} and A. Cerri and V. Skytt and S. Pittaluga and S. Biasotti and D. Sobrero and T. Dokken and M. Spagnuolo} } @article {irs16-11, title = {Persistence-based tracking of rainfall field maxima}, number = {16-11}, year = {2016}, month = {July}, pages = {16 p.}, publisher = {CNR-IMATI}, type = {Technical Report}, address = {Genova}, abstract = {In this paper we propose a novel methodology for tracking the maxima of rainfall precipitation fields, whose changes in time may give interesting insights on the evolution of storms. Our approach is based on a topological analysis of rainfall data allowing for the extraction of the most prominent, and hence meaningful, rainfall field maxima. Then, an ad-hoc bottleneck matching is used to track the evolution of maxima along multiple time instances. The potential of our method is exhibited through a set of experiments carried out on a collection of observed punctual rainfall data and radar measurements provided by Genova municipality and Regione Liguria. }, keywords = {Critical points, Persistence analysis, Storm tracking}, url = {http://irs.imati.cnr.it/reports/irs16-11}, author = {D. Sobrero and A. Cerri and S. Biasotti and S. Pittaluga and M. Spagnuolo} } @article {irs16-04, title = {A study of the state of the art of process planning for additive manufacturing}, number = {16-04}, year = {2016}, month = {May}, pages = {16 p.}, publisher = {CNR-IMATI}, type = {Technical Report}, address = {Genova}, abstract = {In the manufacturing industry the term Process Planning (PP) is concerned with determining the sequence of individual manufacturing operations needed to produce a given part or product with a certain machine. In this technical report we propose a preliminary analysis of scientic literature on the topic of process planning for Additive Manufacturing (AM) technologies (i.e. 3D printing). We observe that the process planning for additive manufacturing processes consists of a small set of standard operations (repairing, orientation, supports, slicing and toolpath generation). We analyze each of them in order to emphasize the most critical aspects of the current pipeline as well as highlight the future challenges for this emerging manufacturing technology. }, keywords = {3D printing, Additive manufacturing, Process planning}, url = {http://irs.imati.cnr.it/reports/irs16-04}, author = {M. Livesu and M. Attene and M. Spagnuolo and B. Falcidieno} }