Abstract: A method of determining features of events or processes having a dynamic evolution in space and/or time using measurements of parameters that calculate the most probable consequences of the event or process at a certain time includes: defining a set of measurable parameters describing the effects of the event or process, characteristic of the event or process, and measurable at a certain time; defining a n-dimensional space where the parameters describing the event or process are represented by entity points; determining, as a function of the measured values of the characteristic parameters describing the event or process at the certain time, a geometrical point in the n-dimensional space forces accumulate that are generated by the evolution of the event of process in time; and displaying or printing the n-dimensional space where the characteristic parameters are shown as entity points and as a geometrical point.

Abstract: A method of determining implicit hidden features of phenomena, representable by a point distribution in a space, includes the following steps: defining a set of first parameters describing effects of a phenomenon such as an event or process; defining a n-dimensional space, wherein the first parameters are represented by entity points; determining, as a function of measured values of the first parameters, additional geometrical points in the n-dimensional space, which are expected to provide additional characteristic parameters describing the phenomenon or additional locations where the phenomenon will produce its effects; adding the additional parameters or points, in recurrent sequence, to the first parameters or points, to define at each iterative step a shorter minimum spanning tree than at the preceding step; and displaying or printing the n-dimensional space, wherein the additional characteristic parameters or points are shown together with the first parameters and the geometrical point.

Abstract: A model simulating the evolutionary dynamics of events or processes includes a non-linear adaptive mathematical system simulating spatial and temporal dynamics by using measured values of parameters describing the evolutionary condition of an event or process at different times. The model enables the definition of a n-dimensional array of points in a n-dimensional reference system having an axis that represents the values of the parameters being measured. The displacements of each of the points are computed as a function of their displacements in the array of points between a first time a second time and as a function of the distance of each of the points of the array from each of the points representing the measured parameters. The evolution of the event and or the model in time is visualized by displaying the points of the array of points at different times.

Abstract: A method of fusing images includes the steps of providing at least two images of the same object, each image being a digital image or being transformed in a digital image formed by an array of pixels or voxels, and of combining together the pixels or voxels of the at least two images being combined to obtain a new image formed by the combined pixels or voxels.

Abstract: A method of determining implicit hidden features of phenomena, representable by a point distribution in a space, includes the following steps: defining a set of first parameters describing effects of a phenomenon such as an event or process, which are measurable and characteristic of the event or process; defining a n-dimensional space, wherein the first parameters are represented by entity points; determining, as a function of measured values of the first parameters, additional geometrical points in the n-dimensional space, which are expected to provide additional characteristic parameters describing the phenomenon or additional locations where the phenomenon will produce its effects; adding the additional parameters or points, in recurrent sequence, to the first parameters or points, to define at each iterative step a shorter minimum spanning tree than at the preceding step; and displaying or printing the n-dimensional space, wherein the additional characteristic parameters or points are shown together w

Abstract: A model simulating the evolutionary dynamics of events or processes includes a non linear adaptive mathematical system simulating the spatial and temporal dynamics of the event or processes by using measured values of a certain number of parameters describing the evolutionary condition of the event or process at certain different times. The values of such parameters are measured at a first time and at least a second time different from and following the first time. The model enables the definition of a n-dimensional array of points in a n-dimensional reference system having an axis that represents the values of the parameters being measured, the parameters in the array being represented by special points in the array of points.

Abstract: A method of determining features of events or processes having a dynamic evolution in space and/or time using measurements of parameters that calculate the most probable consequences of the event or process at a certain time includes: defining a set of measurable parameters describing the effects of the event or process, characteristic of the event or process, and measurable at a certain time; defining a n-dimensional space where the parameters describing the event or process are represented by entity points; determining, as a function of the measured values of the characteristic parameters describing the event or process at the certain time, a geometrical point in the n-dimensional space forces accumulate that are generated by the evolution of the event of process in time; and displaying or printing the n-dimensional space where the characteristic parameters are shown as entity points and as a geometrical point.