Abstract: A computer implemented method, computer program product and computer system for sensor selection. The computer system can run the computer program to execute the method by dividing a two-dimensional area into cells, wherein the cells are arranged in a grid; receiving a selection trigger for a subset of cells of the grid, wherein at least one cell of the subset has at least one sensor and the at least one cell has a sampling frequency associated; determining a set of constraints for the at least one sensor; selecting the at least one sensor if the at least one sensor complies with the set of constraints; and calculating a sampling frequency of the at least one sensor dependent on the sampling frequency of the at least one cell.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: A method and system of recognizing a face image comprising a plurality of processing nodes. Nodes obtain parts of the face image and extract features of the obtained part thereby generating a feature template. Nodes compare the feature template with stored subject templates and calculate an initial similarity score in respect of each comparison, thereby generating an initial score vector associated with a plurality of subjects. Nodes average the initial similarity score vectors generated by it and by at least two predefined nodes, giving rise to an intermediate score vector. The intermediate score vector is repeatedly averaged until a convergence condition is met, thereby generating a final score vector. A node associates the face image to the subject corresponding to the highest score in the final score vector thereby recognizing the face image.

Abstract: Methods and systems for computerized modeling of dispersion of pollution originating from vehicles travelling on a road network are provided. Traffic-related data for road segments useable to calculate a pollution emission estimate for the segments is captured and weather-related data for segments are obtained. A pollution emission estimate is calculated for each segment. Segments are associated with a pollution dispersion model based on a traffic flow label of the segment. A pollution density map is calculated based on the superpositions of segment dispersions. The pollution density map is updated in response to detecting changes affecting one or more segment dispersions.

Abstract: A system and method to perform multisensory data fusion in a distributed sensor environment for object identification classification. Embodiments of the invention are sensor-agnostic and capable handling a large number of sensors of different types via a gateway which transmits sensor measurements to a fusion engine according to predefined rules. A relation exploiter allows combining sensor measurements with information on object relationships from a knowledge base. Also included in the knowledge base is a travel model for objects, along with a graph generator to enable forecasting of object locations for further correlation of sensor data in object identification. Multiple task managers allow multiple fusion tasks to be performed in parallel for flexibility and scalability of the system.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: A computer implemented method (200), computer program product, and computer system for updating a data structure reflecting a spatio-temporal phenomenon in a physical area, wherein the spatio-temporal phenomenon is estimated at any location of the physical area by a Gaussian Process with a mean function and a covariance function, the method (200) comprising: storing (210) a data set adapted to represent fixed locations of the physical area, wherein the data set has a mean vector and a covariance matrix according to the Gaussian Process, and wherein the data structure includes the mean vector and the covariance matrix; receiving (222, 224) sensor measurement data of the spatio-temporal phenomenon from at least one sensor node out of a plurality of sensor nodes located at specific arbitrary locations of the physical area; and merging (230, 232, 234, 236, 238) the specific arbitrary locations and the received measurement data into the data structure by using exact recursive Bayesian regression.

Abstract: A computer implemented method, computer program product and computer system for sensor selection. The computer system can run the computer program to execute the method by dividing a two-dimensional area into cells, wherein the cells are arranged in a grid; receiving a selection trigger for a subset of cells of the grid, wherein at least one cell of the subset has at least one sensor and the at least one cell has a sampling frequency associated; determining a set of constraints for the at least one sensor; selecting the at least one sensor if the at least one sensor complies with the set of constraints; and calculating a sampling frequency of the at least one sensor dependent on the sampling frequency of the at least one cell.

Abstract: The invention provides a coating system for coating substrates in a cyclic mode. The process stations of the coating system are disposed in a circular fashion. A handling mechanism is provided for transferring the substrates between the process stations. The process stations comprise a lock for loading and unloading the substrates, at least two coating chambers, each of which comprises a plasma source for stationary coating of the substrate, and preferably a heating station.