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: Image processing method that includes the steps of considering each image point as a node of an artificial neural network, and of processing the image as function of values of the nodes and of connections of each image point undergoing processing with neighboring image points, the image points of the processed image being obtained by iterative evolution steps of parameters defining the appearance as evolution steps of the value of nodes or by iterative evolution steps of values of the set of connections or by a combination of the evolutions, wherein the processing occurs by evolution iterative steps that are functions of connections of neighboring image points with the image point under examination, each of the neighboring image points being further considered as neighboring one or more or all adjacent image points, the functions providing immediate feedback contributions for determining appearance values of all other image points.

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 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 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 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.

Abstract: Image processing method wherein each image is composed of an array of image points, so called pixels or voxels particularly in a two-, three-, or more dimensional space respectively each image point being univocally defined by its position within the array of image points and by one or more numerical parameters defining the image point appearance as regards characteristics of brightness, grey, colour shade or the like, and wherein each image point is considered to be a node of an artificial neural network, the image being processed as a function of parameters defining the appearance of each pixel as values of the nodes of said artificial neural network and as a function of connections of each pixel under processing with neighbouring pixels composed of pixels of a predetermined subset of pixels, particularly with neighbouring pixels of said pixel under processing, so called pixel window, while pixels of the new image i.e.

Abstract: A system and method are provided for the training and testing of prediction algorithms. According to an exemplary embodiment of the invention the method generates optimum training, testing and/or validation data sets from a common general database by applying a genetic algorithm to populations of testing and training subsets used in connection with a given prediction algorithm. In exemplary embodiments the prediction algorithm operated upon is an artificial neural network. As well, in preferred exemplary embodiments, the most predictive independent variables of the records of the common database are automatically selected in a pre-processing phase.

Abstract: A method for encoding pixels of digital or digitized images, i.e., images consisting of a set of image dots, named pixels in two-dimensional images and voxels in three-dimensional images, each of said pixels or voxels being represented by a set of values which correspond to a visual aspect of the pixel on a display screen or in a printed image. According to the invention, the pixels or voxels of at least one portion of interest of the digital or digitized image or each pixel or voxel of the set of pixels or voxels which form the image is uniquely identified with a vector whose components are given by the date of the pixels or voxels to be encoded and by the data of at least one or at least some or of all of the pixels around the pixels to be encoded and arranged within a predetermined subset of pixels or voxels included in the whole set of pixels or voxels which form the image.

Abstract: A clinical trial phase simulation method for drug trials, which method allows to predict the trend of the results of a clinical trial phase of a drug with the steps of providing a database comprising for each of a certain number of individuals a predefined number of independent variables each of which corresponds to a certain clinical parameter relevant or characteristic for a disease condition against which the drug to be tested is oriented and at least a further independent variable describing the specific treatment to which the individual has been subjected between at least two different treatments one with the said drug and the second with a placebo or with another known drug, the database comprising also for each individuals one or more dependent variables describing the effects of the said treatments; carryings out an input variable selection; adding to the independent variables selected as input variables the dependent variables describing the effects of the treatments; training and validating an artif

Abstract: A system and method are provided for the training and testing of prediction algorithms. According to an exemplary embodiment of the invention the method generates optimum training, testing and/or validation data sets from a common general database by applying a genetic algorithm to populations of testing and training subsets used in connection with a given prediction algorithm. In exemplary embodiments the prediction algorithm operated upon is an artificial neural network. As well, in preferred exemplary embodiments, the most predictive independent variables of the records of the common database are automatically selected in a pre-processing phase.