Abstract: Method and system for managing handover decisions while maintaining quality-of-service (QoS) of active user application sessions in a wireless network is disclosed. The method includes determining QoS requirement for each of the active user application sessions (UASs) based on user entitlements for relevant services associated with the active UASs, assessing the active UASs to determine instantaneous QoS level for each of the active UASs, and identifying impacted user equipment (UE) based on an overall QoS level for a set of active UAS, from among the active UASs, running on the impacted UE. The overall QoS level may be based on the instantaneous QoS level and the QoS requirement for each UAS in the set of active UAS. The method further includes managing a handover decision for the impacted UE based on a need for a handover of the impacted UE and a feasibility of the handover of the impacted UE.

Abstract: Embodiments of the present disclosure relate to a method (300) and a system (103) for delivering content to a Human Machine Interface (HMI) (101) of a vehicle (100). The system (103) receives a content to be delivered from a third-party application server (104), and vehicle and other data when an event occurs. Once the content is received, the system (103) determines a driver distraction score for the content based on the received data and one or more driver distraction rules defined by an OEM server (102). Further, the system (103) creates a content package based on the driver distraction score and the received data. Further, the system assigns a priority score to the content package based on a type of event. Thereafter, the system delivers the content package to the vehicle for providing to the HMI.

Abstract: The present disclosure discloses a method and a system for generating AR/VR content in real-time. The system upon receiving a user query or gestures, identifies query attributes and action attributes associated with IOT devices. Thereafter, system identifies IOT data corresponding to the query attributes and the action attributes from the IOT devices. Based on the IOT data, system generates multimedia content using trained machine learning model. The machine learning model is trained to provide multimedia content based on the IOT data associated with the query attributes or the action attributes. Thereafter, the system generates AR/VR content in real-time based on the multimedia content. In this manner, present disclosure supports interactive queries over AR/VR content and provides AR/VR content in real-time thereby reducing storage requirement for storing various images for interpolation.

Abstract: This disclosure relates to system and method for managing cache data in a network through edge nodes. The method includes receiving set of first node parameters, set of Mobile Edge Computing (MEC) node association metrics, and set of data request parameters from the user device; identifying a variation in at least one of the set of first node parameters, the set of MEC node association metrics, and the set of data request parameters; selecting a second edge node from the plurality of edge nodes to store the cache data based on a set of second node parameters, the set of MEC node association metrics, and the set of data request parameters, when variation is above predefined threshold; associating the second edge node with the user device to store the cache data for the user device; and dynamically managing cache data for the user device in the second edge node.

Abstract: Embodiments of present disclosure discloses system and method for managing applications in electronic device. Initially, relation tree associated with applications is identified. The relation tree is generated based on learning technique implemented for applications, parameters, enablers associated with electronic device. Based on identified relation tree, enablers are identified from plurality of enablers, corresponding to each of applications. Further, current status of parameters based on current status of enablers is retrieved. The relation tree is updated based on learning technique implemented for at least one of the current status of the parameters, new applications, new parameters, enablers associated with the electronic device. An application from the applications is identified based on the current status of the parameters and the relation tree. The electronic device is instructed to perform operations associated with the identified application.

Abstract: Disclosed herein is a method and system for predicting water leakage events in district metered area of water distribution network. The method comprises receiving historic data of water flow and water pressure associated with DMA for predicting flow data and pressure data of water in the DMA for predefined future time period. Thereafter, method comprises retrieving plurality of cluster points corresponding to the predicted flow data, the pressure data, and an anomaly score identified for each of the plurality of cluster points from machine learning model. The method detects one or more cluster points as anomaly points by comparing anomaly scores with lower bound value and upper bound value and thereafter predicts leakage events corresponding to each of the detected anomaly points. In this manner, present disclosure predicts water leakage events in DMA for future time intervals and hence necessary leakage maintenance actions may be taken to avoid such events.

Abstract: Memory management in communication networks is disclosed. The method includes generating a memory allocation trigger for an event based on an event type and estimating a memory size required by the event based on the event type of a user service and at least one configuration parameter. The event type includes a new user service request, a modified user service request, or a network event. The method includes allocating a memory section to the event based on the estimated memory size for the event of the user service and availability of memory blocks in a memory pool, the memory section comprises at least one memory block. The method includes verifying performance of the communication network. Verifying further includes receiving performance feedback for the communication network in response to allocating the memory section for the user service and modifying at least one of the set of configuration parameters.

Abstract: Disclosed herein is a method and a data transfer system for providing seamless data transfer between communication devices. Properties of data to be transferred, status of network parameters and power associated with the communication devices are monitored in real time. Further, communication protocols available at the communication devices and a need to switch between the communication protocols are determined. Splitting of the data into subsets of data and sequencing the subsets of data are performed using a neural network, which is trained based on properties of the data, data storage space of the communication devices, speed of data transfer and a communication channel available for the data transfer. The optimum communication protocols are identified based on order of priority value and contention value. The subsets of data are transferred using identified optimum communication protocols, until a change in the monitored status is detected to switch the optimum communication protocols.

Abstract: A method, device, and system for predicting and caching user activity for seamless user experience within vehicles is disclosed. The method includes identifying a contextual information based on current activity associated with a user within the vehicle. The method further includes retrieving predictive information from a remote server or a local cache within the vehicle, based on a type of the contextual information. The method includes rendering the predictive information to the user within the vehicle in response to retrieving. The method further includes capturing response of the user to the predictive information rendered to the user as a user feedback.

Abstract: The present subject matter is related in general to the field of image processing, disclosing method and system for evaluating an image quality for Optical Character Recognition (OCR) Image evaluation system receives image comprising optical character data. The image evaluation system determines image parameter value for each of one or more image parameters of the image. The image parameter value for each of the one or more image parameters is determined for plurality of binary image segments identified in the image. The image evaluation system determines suitability value and impact value of the image, based on the image parameter value for each of the image parameters determined for the image. The image evaluation system determines quality score for the image, based on the suitability value and the impact value. The image is transmitted for processing before the OCR, upon determining the quality score to be above overall pre-defined threshold value.

Abstract: A method and system are described for secure communication. The system receives a first secure protocol data packet during an authenticated session to communicate with a user computing device using a secure protocol. The encrypted linked data in first secure protocol data packet is decrypted using a secure key that is dependent on encrypted linked data and user credentials. The system retrieves a second secure protocol data packet from a distributed ledger network that is associated with first secure protocol data packet. The encrypted linked data in second secure protocol data packet is decrypted. The system identifies changes between decrypted linked data of first secure protocol data packet and decrypted linked data of second secure protocol data packet and if changes are identified then first secure protocol data packet is considered as tampered and actions in first secure protocol data packet are not executed, thereby preventing fraudulent execution of transactions.

Abstract: The disclosure relates to method and system for determining network congestion level. The method includes determining a round trip time (RTT) for each high priority and for each low priority packet transaction in the precision time protocol (PTP) protocol. The high priority and the low priority packet transactions are conducted between a master device and a slave device within a pre-defined time window. The method further includes determining an average high priority RTT and an average low priority RTT based on the RTT for each high priority packet transaction and for each low priority packet transaction respectively, computing an average delta RTT based on a difference between the average high priority RTT and the average low priority RTT, and determining an instantaneous network congestion level based on the average delta RTT and a reference delta RTT for a number of pre-defined time windows since a start of the slave device.

Abstract: Disclosed subject matter relates to a field of vehicle navigation system that performs a method for determining an optimal trajectory for navigation of an autonomous vehicle. A trajectory determining system associated with the autonomous vehicle may detect an occurrence of a predefined condition related to diversion based on environment data. Further, minimum lateral shift required from a predefined distance for handling the detected predefined condition is determined and co-efficient value sets corresponding to the minimum lateral shift are determined based on pre-stored values that are generated based on a trial run. For each co-efficient value set, velocity and position data is determined at a plurality of time instances based on which one or more trajectories corresponding to each co-efficient value set are generated. Finally, an optimal trajectory is determined among the generated trajectories cumulative variation in orientation and total time required for navigating along the corresponding trajectory.

Abstract: A technique is provided for adaptively performing service function chaining based on network context. The technique includes receiving a request comprising a flexible SFC template (FST) for provisioning an SFC of a plurality of service functions (SFs). A service function path (SFP) is determined responsive to receipt of data packets corresponding to a pre-defined flow to be transmitted over the network. Monitoring of a pre-defined plurality of parameters associated with the SFP and a network context is performed after implementing the SFP. Based on the monitoring of the pre-defined plurality of parameters and SFP modification criteria, a requirement for modifying the SFP is identified. Responsive to the identification, the SFP is modified and the performance of the modified SFP is monitored based on one or more of the plurality of parameters, a network context after modifying the SFP, and a pre-defined plurality of service performance thresholds.

Abstract: The present invention discloses method and application deployment system for dynamic deployment and vertical scaling of applications in a cloud environment. The application deployment system receives information of one or more system parameters of a target system and performs one or more processing operations on information to identify storage access details for a predefined time period of the target system. Further, an ideal configuration for the target system is estimated based on current requirement of one or more system parameters and the identified storage access details by using at least one of a rule-based model, a neural network model, and statistical model. Thereafter, the application deployment system performs dynamic deployment of the one or more applications based on the ideal configuration.

Abstract: A method and system of language independent iterative learning mechanism for Natural Language Processing (NLP) tasks is disclosed. The method includes identifying at least one NLP feature associated with a set of words within a sentence for an NLP task. The method includes creating a pattern associated with the sentence for the NLP task, based on the at least one NLP feature associated with the set of words and the linkage relationship between each subset of two adjacent words. The method further includes computing a confidence score corresponding to the pattern, based on a comparison within a trained dataset. The method further includes assigning a pattern category to the pattern, based on the confidence score and a predefined threshold score. The method further includes executing the NLP task based on the assigned pattern category.

Abstract: This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

Abstract: A method and a system for controlling devices through voice interaction are disclosed. In an embodiment, the method may include identifying at least one feature of a target device and an action to be performed on the at least one feature, based on an intent and an object determined from a voice input received from a user. The method may further include determining a correspondence between the at least one feature and the action to be performed using a trained neural network. The method may further include comparing a current operational state of the at least one feature with an operational threshold of the at least one feature. The method may further include performing the action on the at least one feature based on the determined correspondence, when the current operational state is within limits of the operational threshold.

Abstract: A method and a system for implementing neural network models on edge devices in an Internet of Things (IoT) network are disclosed. In an embodiment, the method may include receiving a neural network model trained and configured to detect objects from images, and iteratively assigning a new value to each of a plurality of parameters associated with the neural network model to generate a re-configured neural network model in each iteration. The method may further include deploying for a current iteration the re-configured neural network on the edge device. The method may further include computing for the current iteration, a trade-off value based on a detection accuracy associated with the at least one object detected in the image and resource utilization data associated with the edge device, and selecting the re-configured neural network model, based on the trade-off value calculated for the current iteration.

Abstract: Methods, non-transitory computer readable media, and Industrial Internet-of-Things (IIoT) management apparatuses that generate event data based on an overall event comprising a plurality of dynamic multi-cloud events. Each of the events is associated with at least one of a plurality of types of IIoT resource devices or a plurality of IIoT participant devices. A current overall event hierarchy of the events is established, derived, or introduced based on one of a plurality of predefined hierarchies. A relationship between one or more socio environment and economic factors and each of the events is identified. One or more controls for each of the events are derived based on the identified relationship. Action plan data is generated for an execution in a multi-cloud environment based on the events, the derived controls, and a profiled participant role associated with one or more of the participant devices. The action plan data is distributed in the environment.