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 reconstructing a causation process from time varying data describing an event, the data consisting in a certain number of entities each having a position in a space, and each of the entities being characterized by at least a quantity or value relatively to at least one feature and in the quantity or value relatively to at least one of the features of the entities at least at two different times or at each time instant of a sequence of time instants; the method describing the higher likelihood transition of all entities i, j from the time n to the time n+1 as a function of the position coordinate of the entity I and of the entity j and the quantity of the at least one feature of the entity I and of the entity j at the time n and at the time n+1: Mi,j[n,n+1]=?(xi,yi,qi[n],qi[n+1],xj,yj,qj[n],qj[n+1]).

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 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 reconstructing a causation process from time varying data describing an event, the data consisting in a certain number of entities each having a position in a space, and each of the entities being characterized by at least a quantity or value relatively to at least one feature and in the quantity or value relatively to at least one of the features of the entities at least at two different times or at each time instant of a sequence of time instants; the method describing the higher likelihood transition of all entities i, j from the time n to the time n+1 as a function of the position coordinate of the entity I and of the entity j and the quantity of the at least one feature of the entity I and of the entity j at the time n and at the time n+1: Mi,j[n,n+1]=?(xi, yi, qi[n], qi[n+1], xj, yj, qj[n], qj[n+1]).

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 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 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 neural network for processing arrays of data with pertinent topology includes a n-dimensional array of cells (Ki) corresponding to the knots of the neural network, each cell having connections to the directly adjacent cells (Kj) forming the neighborhood of a cell (Ki), Each cell (Ki) has inputs for each connection to directly adjacent cells; an output for the connection to one or more of the directly adjacent cells (Kj), the connection between the cells being determined by weights (wij), and each cell being characterized by an internal value and being able to carry out signal processing for generating a cell output signal (ui), The output signal (ui) of a cell (Ki) is a function of its internal value and of the input signals from the neighboring cells, each cell being associated univocally to a record of a n-dimensional database (Pi) with pertinent topology and the value of each data record being the starting value of the corresponding cell.

Abstract: An algorithm for projecting information data belonging to a multidimensional space into a space having fewer dimensions, a method for the cognitive analysis of multidimensional information data based on said algorithm, and a program comprising said algorithm stored on a recordable support.

Abstract: An artificial neural network comprises at least one input layer with a predetermined number of input nodes and at least one output layer with a predetermined number of output nodes or also at least one intermediate hidden layer with a predetermined number of nodes between the input and the output layer. At least the nodes of the output layer and/or of the hidden layer and/or also of the input layer carry out a non linear transformation of a first non linear transformation of the input data for computing an output value to be fed as an input value to a following layer or the output data if the output layer is considered.

Abstract: A method of processing multichannel and multivariate signals as described hereinbefore, wherein the signals from each channel are subjected to a first processing step by a recirculation artificial neural network being trained to generate the recorded multichannel and multivariate signals; and a second processing step in which the weights of the connections between the knots of the recirculation neural network determined in the first processing step are processed by an artificial neural network, the recirculation neural network being preferably of the non supervised kind. A particular family of recirculation neural network which can be used according to the present invention is a so called auto-associative neural network.