Abstract: Retrieving saved information relative to a first pack of boards, which belongs to a plurality of known packs of boards, entails a preparation step comprising a generation step and a saving step for saving reference features in a digital memory together with the information relative to the known pack of boards, creating a digital dataset relative to the known pack of boards, the set of the digital datasets constituting a first digital archive, and a retrieval step comprising an observation step in which at least one digital photograph of the first pack of boards is acquired, a comparison step for comparing first features linked to the digital photograph with the saved reference features, to identify a match with one of the saved reference images, and an extraction step for extracting from the first digital archive information relative to the known pack of boards the comparison step identified the match.

Abstract: Computer implemented method for training a software infrastructure based on machine learning techniques and intended for analysis of data obtained from a three-dimensional tomographic inspection of objects of a predetermined type, such as logs, with the aim of determining information about internal characteristics of interest of the self-same objects, wherein, once a training set comprising a plurality of objects of the same predetermined type has been selected, for each object the software infrastructure is supplied with training input data and corresponding training output data, which are processed by the software infrastructure for setting internal processing parameters of the software infrastructure which correlate the training input data with the training output data; where the training input data comprise data obtained from a three-dimensional tomographic inspection of the object, and the training output data comprise information about internal characteristics of interest assessed at internal points of

Abstract: A method for identifying a log of origin of a first board, comprising an identification step, during which a second board (12) is identified which was obtained from the same log (2) as the first board (11) was obtained from, a studying step, during which identifying features of the second board (12) are identified, and a recognition step during which the log (2) of origin of the first board (11) is recognised, among a plurality of known logs (2) about which saved information is available, and this is done by identifying the log of origin of the second board (12), using the identifying features of the second board (12) itself.

Abstract: A method for establishing a posteriori a match between a piece of wood and a log from which the piece of wood has been obtained, comprising the following operating steps of performing a tomographic scan of the wooden log, of calculating or selecting a log cutting pattern, of defining, starting with the tomographic information available, one or more virtual individualising characteristics which are linked to the distribution and/or size of physical characteristics of the log inside and/or on the surface of the self-same virtual piece of wood, of saving them in a database, together with information about the identity of the log, of dividing the log into real pieces of wood according to the cutting pattern, of acquiring real information about the distribution and/or size of physical characteristics of the log inside and/or on the surface of a real piece of wood and of defining corresponding real individualising characteristics to be compared with virtual individualising characteristics saved and of identifying a

Abstract: Computer implemented method for training a software infrastructure based on machine learning techniques and intended for analysis of data obtained from a three-dimensional tomographic inspection of objects of a predetermined type, such as logs, with the aim of determining information about internal characteristics of interest of the self-same objects, wherein, once a training set comprising a plurality of objects of the same predetermined type has been selected, for each object the software infrastructure is supplied with training input data and corresponding training output data, which are processed by the software infrastructure for setting internal processing parameters of the software infrastructure which correlate the training input data with the training output data; where the training input data comprise data obtained from a three-dimensional tomographic inspection of the object, and the training output data comprise information about internal characteristics of interest assessed at internal points of

Abstract: This invention relates to a method for non-destructive inspection of a log (1) to identify inner zones of sapwood (14) of the log (1) that have not been attacked by fungi that cause bluestain in the wood. The method comprises a first step of carrying out a tomographic scan of the log (1) to be inspected using X-ray beams that pass through the log (1) and a second step of obtaining a three-dimensional representation of the log (1) that is representative of the local moisture content of the log, the local moisture content being correlated with attenuation of the X-ray beams through the log. The method comprises the step of processing the three-dimensional representation of the log (1) to identify inner regions (145) of the log (1), in which the local moisture content is greater than or equal to a moisture threshold value for a spatial extent greater than an extent threshold.

Abstract: A method for establishing a posteriori a match between a piece of wood and a log from which the piece of wood has been obtained, comprising the following operating steps of performing a tomographic scan of the wooden log, of calculating or selecting a log cutting pattern, of defining, starting with the tomographic information available, one or more virtual individualising characteristics which are linked to the distribution and/or size of physical characteristics of the log inside and/or on the surface of the self-same virtual piece of wood, of saving them in a database, together with information about the identity of the log, of dividing the log into real pieces of wood according to the cutting pattern, of acquiring real information about the distribution and/or size of physical characteristics of the log inside and/or on the surface of a real piece of wood and of defining corresponding real individualising characteristics to be compared with virtual individualising characteristics saved and of identifying a

Abstract: This invention relates to a method for non-destructive inspection of a log (1) to identify inner zones of sapwood (14) of the log (1) that have not been attacked by fungi that cause bluestain in the wood. The method comprises a first step of carrying out a tomographic scan of the log (1) to be inspected using X-ray beams that pass through the log (1) and a second step of obtaining a three-dimensional representation of the log (1) that is representative of the local moisture content of the log, the local moisture content being correlated with attenuation of the X-ray beams through the log. The method comprises the step of processing the three-dimensional representation of the log (1) to identify inner regions (145) of the log (1), in which the local moisture content is greater than or equal to a moisture threshold value for a spatial extent greater than an extent threshold.

Abstract: A method and system for scheduling a plurality of concurrent job sequences with constraints of precedence and mutual exclusion is described. In particular, a scheduler for a system performing biological analyzes on clinical samples including non-preemptible jobs which must use a set of resources (machines) and having constraints on release times and execution times is described. The scheduling method uses on-purpose algorithmic solution that extracts the core of DBM data structures and Floyd-Warshall algorithm from the engine of a full-fledged Model Checking tool and tailors it to the specific requirements of the case.

Abstract: A method and system for scheduling a plurality of concurrent job sequences with constraints of precedence and mutual exclusion is described. In particular, a scheduler for a system performing biological analyses on clinical samples including non-preemptible jobs which must use a set of resources (machines) and having constraints on release times and execution times is described. The scheduling method uses on-purpose algorithmic solution that extracts the core of DBM data structures and Floyd-Warshall algorithm from the engine of a full-fledged Model Checking tool and tailors it to the specific requirements of the case.

Abstract: A method for identifying a piece of wood amongst a plurality or for determining its rotation includes the operating steps of: acquiring at least one piece of real X-ray information about the piece of wood; calculating, based on a tomographic reconstruction, a piece of virtual X-ray information in the same way as in the acquisition of the real X-ray information; comparing the real and virtual X-ray information to verify whether they match; and repeating the calculation and comparison steps up to obtaining the matching, and/or, for identification method only, selecting a different tomographic reconstruction and reiterating the calculation and comparison steps and, if necessary, the repetition and/or selection steps.

Abstract: A method for identifying a piece of wood amongst a plurality of starting pieces of wood comprises the operating steps of: acquiring a tomographic reconstruction of each piece of wood of said plurality; acquiring at least one piece of real X-ray information about a first piece of wood to be identified; calculating, based on a tomographic reconstruction, a piece of virtual X-ray information in the same way as acquisition of the real X-ray information; comparing the real and virtual X-ray information to verify whether they at least substantially match; and repeating the calculation and comparison steps for a plurality of separate pieces of virtual X-ray information which can be obtained based on the selected tomographic reconstruction, and/or selecting a different tomographic reconstruction and reiterating the calculation and comparison steps and, if necessary, the repetition and/or selection steps.