Abstract: The present invention provides a method and system for predicting tropospheric interference using a digital twin model for detecting tropospheric interference and suggesting actions for mitigating the effects of the tropospheric interference. The system (110) may include a machine learning engine (216) having a digital twin model to predict/score the likelihood of cell pairs having the tropospheric interference at a given point of time using cell configuration data, weather data, Hepburn data at that point of time by identifying cell pairs whose likelihood of interference is high. The system (110) is further configured to explore the effect of changing cell configuration parameters like remote electrical tilt and identify actions that can mitigate tropospheric interference with minimal impact on coverage.

Abstract: A system and method for prediction of failures and optimization, that can provide solution available for unsupervised learning models based on limited data that can predict different types of failure and pre-failure instances. The solution provides improvement upon previous methods of labelling by marking certain days data ahead of failure as belonging to failure data which will result in reduction of noisy data and improves good working condition data. The present invention helps with improved data quality due to labelling as the proposed method models complex distributions of feature vectors accurately and are better at finding deviations from normal data distribution which is used for detecting failures. The novel solution help to analyse and categorise the type of failures for PC Pumps currently deployed in CBM Fields for which failure days in advance can be predicted.

Abstract: Knowledge automation techniques may include generating, for each user of a plurality of users, a user vector associated with the user, and grouping the generated user vectors into clusters based on a clustering distance metric between the user vectors. For each cluster, the techniques may include determining a centroid of the cluster, and associating with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with. The techniques may further include comparing a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user, and providing one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user.

Abstract: Knowledge automation techniques may include generating, for each user of a plurality of users, a user vector associated with the user, and grouping the generated user vectors into clusters based on a clustering distance metric between the user vectors. For each cluster, the techniques may include determining a centroid of the cluster, and associating with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with. The techniques may further include comparing a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user, and providing one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user.

Abstract: Hand pose recognition comprises determining an initial hand pose estimate for a captured input hand pose and performing iterations based upon hand pose estimates and residues between such estimates and hand pose estimates. One or more control signals are generated based upon the hand pose recognition.

Abstract: Proposed is the use of a document widget for representing a property of a document. The document widget comprises: a human-readable portion for interpretation by a user; and a machine-readable portion representing the document property. By comprising information about a property of a document, a document widget may be processed in accordance with an optical recognition process so as to identify the document widget and enable extraction of the document property.

Abstract: A system and method for classification of moving objects and user authoring of new object classes is disclosed. In one embodiment, in a method of classification of moving objects, a moving object is inputted. Then, an object descriptor and a motion descriptor are extracted from the inputted moving object. Multiple initial candidate library object descriptors are identified from an object library and a motion library using the extracted object descriptor and the extracted motion descriptor. An initial object class estimate is identified based on the identified multiple initial candidate library object descriptors. Then, an initial residue is computed based on the extracted object descriptor and the identified multiple initial candidate library object descriptors associated with the initial object class estimate. The object class estimates are iteratively identified and it is determined whether the object class estimates converge based on a stopping criterion.

Abstract: Hand pose recognition comprises determining an initial hand pose estimate for a captured input hand pose and performing iterations based upon hand pose estimates and residues between such estimates and hand pose estimates. One or more control signals are generated based upon the hand pose recognition.

Abstract: Proposed is the use of a document widget for representing a property of a document. The document widget comprises: a human-readable portion for interpretation by a user; and a machine-readable portion representing the document property. By comprising information about a property of a document, a document widget may be processed in accordance with an optical recognition process so as to identify the document widget and enable extraction of the document property.