Abstract: The present disclosure relates to a method of anomaly detection using a trained artificial neural network (502) configured to implement at least an auto-associative function for replicating an input data sample at one or more outputs (A), the method comprising: a) injecting an input data sample into the trained artificial neural network (502) in order to generate a first replicated sample at the one or more outputs (A); b) performing at least one reinjection operation; c) computing a first parameter based on a distance between a value of an nth replicated sample present at the one or more outputs and a value of one of the previously injected or reinjected values; and d) comparing the first parameter with a first threshold (?), and processing the input data sample as an anomalous data sample if the first threshold is exceeded.

Abstract: An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells.

Abstract: A method for perceiving physical bodies comprises the following steps: a) acquisition of a plurality of distance measurements of the physical bodies arising from one or more sensors; b) acquisition or computation of a value of a priori probability of occupancy of the cells of an occupancy grid; and c) application of an inverse sensor model on the occupancy grid to determine a probability of occupancy of a set of cells of the grid; d) construction of a consolidated occupancy grid by fusing the occupancy probabilities estimated during step c); wherein each the inverse sensor model is a discrete model, associating with each cell of the corresponding occupancy grid, and for each distance measurement, a probability class chosen inside one and the same set of finite cardinality and identified by an integer index; and wherein step d) is implemented by means of integer computations performed on the indices of the probability classes determined during step c), and as a function of the value of a priori occupancy proba

Abstract: This asynchronous processor core comprises a loading unit for sequentially loading instruction lines, functional units for executing instructions and a decoder for decoding instruction lines loaded by the loading unit into instructions executable by the functional units. It comprises an execution control module configured as a two-state automaton: a sleep state (S1), wherein the asynchronous processor core awaits an interrupt control signal to execute an interrupt routine; an execution state (S2), wherein the decoder awaits a new interrupt routine instruction line to be decoded. The execution control module additionally manages a state variable (status) of the loading unit to selectively authorize or prevent, according to at least the value of this state variable (status), the loading of a new instruction line.

Abstract: An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells.

Abstract: A method for perceiving physical bodies comprises the following steps: a) acquisition of a plurality of distance measurements of the physical bodies arising from one or more sensors; b) acquisition or computation of a value of a priori probability of occupancy of the cells of an occupancy grid; and c) application of an inverse sensor model on the occupancy grid to determine a probability of occupancy of a set of cells of the grid; d) construction of a consolidated occupancy grid by fusing the occupancy probabilities estimated during step c); wherein each the inverse sensor model is a discrete model, associating with each cell of the corresponding occupancy grid, and for each distance measurement, a probability class chosen inside one and the same set of finite cardinality and identified by an integer index; and wherein step d) is implemented by means of integer computations performed on the indices of the probability classes determined during step c), and as a function of the value of a priori occupancy proba

Abstract: This asynchronous processor core comprises a loading unit for sequentially loading instruction lines, functional units for executing instructions and a decoder for decoding instruction lines loaded by the loading unit into instructions executable by the functional units. It comprises an execution control module configured as a two-state automaton: a sleep state (S1), wherein the asynchronous processor core awaits an interrupt control signal to execute an interrupt routine; an execution state (S2), wherein the decoder awaits a new interrupt routine instruction line to be decoded. The execution control module additionally manages a state variable (status) of the loading unit to selectively authorize or prevent, according to at least the value of this state variable (status), the loading of a new instruction line.