Abstract: A method of performing unsupervised detection of repeating patterns in a series (TS) of events (E21, E12, E5, . . . ), comprising the steps of: a) Providing a plurality of neurons (NR1-NRP), each neuron being representative of W event types; b) Acquiring an input packet (IV) comprising N successive events of the series; c) Attributing to at least some neurons a potential value (PT1-PTP), representative of the number of common events between the input packet and the neuron; d) Modifying the event types of neurons having a potential value exceeding a first threshold TL; and e) Generating a first output signal (OS1-OSP) for all neurons having a potential value exceeding a second threshold TF, and a second output signal, different from the first one, for all other neurons. A digital electronic circuit and system configured for carrying out the above method.

Abstract: A method of performing unsupervised detection of repeating patterns in a series (TS) of events (E21, E12, E5 . . . ), comprising the steps of: a) Providing a plurality of neurons (NRI-NRP), each neuron being representative of W event types; b) Acquiring an input packet (IV) comprising N successive events of the series; c) Attributing to at least some neurons a potential value (PTI-PTP), representative of the number of common events between the input packet and the neuron; d) Modify the event types of neurons having a potential value exceeding a first threshold TL; and e) generating a first output signal (OSI-OSP) for all neurons having a potential value exceeding a second threshold TF, and a second output signal, different from the first one, for all other neurons. A digital electronic circuit and system configured for carrying out such a method.

Abstract: A method of performing unsupervised detection of repeating patterns in a series of events, includes a) Providing a plurality of neurons, each neuron being representative of W event types; b) Acquiring an input packet comprising N successive events of the series; c) Attributing to at least some neurons a potential value, representative of the number of common events between the input packet and the neuron; d) Modify the event types of neurons having a potential value exceeding a first threshold TL; and e) generating a first output signal for all neurons having a potential value exceeding a second threshold TF, and a second output signal, different from the first one, for all other neurons. A digital electronic circuit and system configured for carrying out such a method is also provided.

Abstract: A method for searching for a digital image representing a graphical pattern generates the descriptor of an image as a 2D m×n matrix (m, n: whole numbers): resample digital image to produce normalized image of dimensions c.m×d.n (c, d: given whole numbers), process the normalized image in p processes, indexed 1-p (p: given whole number), producing p digital characteristic images (CI), each having m×n sub-images (SI), each S-I being c×d, each CI is reduced to m×n by replacing each SI by at least one representative value determined as a function of the c×d SI pixels, a selection function selects from p values, at least one value as selected value, each descriptor element (i, j), i?[l,m], j?[l,n] is allocated the processing index from which the CI is derived generating the reduced image including the selected value from the selection function applied to p pixels (i, j) of the P reduced images.