Abstract: A method and road assistance system for safe handling an autonomous vehicle during emergency failure situation is disclosed. The road assistance system establishes a network connection with autonomous vehicle, where request for the network connection is transmitted by the autonomous vehicle on detection of operation failures. Upon establishing the network connection with the autonomous vehicle, vehicle failure information is received from the autonomous vehicle. The road assistance system determines type of failure in the autonomous vehicle based on the vehicle failure information. Further, current surrounding environment information is obtained for the autonomous vehicle from one of autonomous vehicle and one or more sensing devices located in proximity to the autonomous vehicle. Thereafter, the road assistance system generates at least one control instruction based on the type of failure and the current surrounding environment information.

Abstract: The disclosure relates to system and method for managing security risk of information technology (IT) systems in an enterprise. The method includes determining valid trustware components that need to be evaluated for security risk of an IT system within the enterprise; correlating information associated with each of the valid trustware components in a set of data repositories; generating a mapping list comprising the valid trustware components, test cases corresponding to each of the valid trustware components, and test environments corresponding to each of the valid trustware components based on the correlation; triggering trustware security units for testing the valid trustware components based on the mapping list; and identifying security issues associated with the valid trustware components based on the testing. The trustware security units are arranged in a sequential manner or a parallel manner to align with execution of the test cases corresponding to each of the valid trustware components.

Abstract: This disclosure relates to system and method for cleaning surfaces using drones. The method includes identifying, by a first drone, a cleaning strip in contact with a surface. The cleaning strip connects a second drone and a third drone. The second drone is communicatively coupled with the third drone. The first drone is communicatively coupled with each of the second drone and the third drone. The method further includes releasing a cleaning agent at a preconfigured pressure upon a current target region of the surface near the cleaning strip. The method further includes relocating the cleaning strip upon the current target region. The method further includes performing a set of oscillations through the cleaning strip across the current target region. Each of the set of oscillations includes coordinating a displacement of the cleaning strip by a predefined distance, alternating towards each of ends of the cleaning strip.

Abstract: A method and system for diagnosing autonomous vehicles is disclosed. The method includes identifying anomaly in a set of navigation parameters from a plurality of navigation parameters associated with the autonomous vehicle, such that each of the set of navigation parameters is above an associated risk threshold. The method further includes validating anomaly identified for each of the set of navigation parameters. The method includes generating a set of quality parameters for the set of navigation parameters in response to validating anomaly in the set of navigation parameters. The method further includes generating values of at least one motion parameter associated with the autonomous vehicle based on values of each of the set of quality parameters fed into a trained Artificial Intelligence (AI) model. The method includes controlling the at least one motion parameter based on the generated values.

Abstract: A method and system for data transformation based on machine learning is disclosed. The method includes generating a matrix for a plurality of input vectors based on a machine learning model. The method further includes comparing for each of the plurality of input vectors, the intent value in the matrix with a predefined intent threshold, wherein, for an intent value below the predefined intent threshold, an associated function is unavailable. The method further includes determining a first set of vectors from the plurality of input vectors based on the comparing, wherein for each input vector in the first set, the associated intent value is below the predefined intent threshold. The method further includes mapping, by the data transformation device, each input vector in the first set with an intent value above the predefined intent threshold and an associated function.

Abstract: A method and system for performing real-time sentiment modulation in conversation systems is disclosed. The method includes generating an impact table comprising a plurality of sentiment vectors and a plurality of emotion vectors associated with the plurality of sentences. The method further includes generating for each of the plurality of sentences, a dependency vector based on the associated sentiment vector and the associated emotion vector. The method further includes stacking the dependency vector generated to generate a waveform representing variance in sentiment and emotions across words within the plurality of sentences. The method further includes altering at least one portion of the waveform based on a desired emotional output to generate a reshaped waveform. The method further includes generating a set of rephrased sentences associated with the at least one portion, based on the reshaped waveform, the set of sentences, a user defined sentiment output.

Abstract: This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.

Abstract: A system and method for testing a Light Detection and Ranging (LiDAR) sensor is disclosed. The system includes the LiDAR sensor radiating a plurality of LiDAR rays, a plurality of bars positioned at a predetermined distance from the LiDAR sensor, and a testing device. The method includes triggering a LiDAR sensor to radiate a plurality of LiDAR rays. The method further includes determining at least one intersection point at each of a plurality of bars upon intersection of at least one LiDAR ray from the plurality of LiDAR rays with each of the plurality of bars. The method further includes computing at least one operational parameter for the LiDAR sensor based on the intersection points, and determining one or more test results based on the one or more operational parameters.

Abstract: A method and a system for Augmented Reality (AR) content creation is disclosed. The method includes creating a feature vector corresponding to each of a sequence of frames extracted from a video, based on a plurality of features captured. The method further includes determining a vector distance between each of two consecutive frames from the sequence of frames, based on the feature vector associated with each of the two consecutive frames. The method further includes dividing the video into a plurality of frames based on the determined vector distance. The method further includes creating a storyline based on an object and an action associated with the object in each of the plurality of frames, and generating a set of instructions for a user based on the storyline created for each of the plurality of frames and real-time video stream capturing a current state of a user environment.

Abstract: This disclosure relates to method and system for detecting and tracking at least one object in a rotational environment. The method includes receiving a set of first features based on first data and a set of second features based on second data, detecting at least one object based on the set of first features using a Convolutional Neural Network (CNN) based predictive model, determining a set of first parameters for the at least one object, detecting the at least one object based on the set of second features using the CNN based predictive model, determining a set of second parameters for the at least one object, and tracking the at least one object based on the set of first parameters and the set of second parameters. It should be noted that the first data and the second data sequentially belong to an input dataset that includes images or video frames.

Abstract: This disclosure relates to method and system for optimizing memory requirement for training an artificial neural network (ANN) model employed for natural language processing (NLP). In one embodiment, the method may include receiving a plurality of training parameters and a plurality of model parameters, selecting a set of model parameters from among the plurality of model parameters for training the ANN model based on a characteristic and an architecture of the ANN model, masking the set of model parameters in one or more layers of the ANN model based on a set of pre-defined rules to generate a set of masked model parameters, determining an amount of memory required for training the ANN model based on the set of masked model parameters, and providing the set of masked model parameters for training the ANN model when the amount of memory required is less than a determined threshold.

Abstract: The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

Abstract: Disclosed herein is a device, system and method for determining error in robotic manipulator-to-camera calibration. The method includes detecting a test object by a camera coupled to a robotic manipulator. One or more test points are identified on the test object based on a CAD model and pre-defined contact points corresponding to the test object. Arm poses are determined for the robotic manipulator to reach the test points on the 3D test object by using current robotic manipulator-to-camera calibration. While driving an end effector of the robotic manipulator based on the arm poses, any contact of the end effector on the 3D test object is recorded upon receiving a feedback from the 3D test object. An error is determined in the current robotic manipulator-to-camera calibration based on current position of the end effector relative to the one or more test points on the 3D test object.

Abstract: This disclosure relates to initial learning of a classifier for automating extraction of structured data from unstructured or semi-structured data.

Abstract: The disclosure relates to method and system for performing real-time monitoring of a container containing a non-gaseous fluid substance. The method includes receiving, from a sensing device installed in the container and by a monitoring device, a dataset corresponding to the container and to a non-gaseous fluid substance in the container. The dataset may be acquired and transmitted by the sensing device at a preconfigured time-interval and may include level data corresponding to a level of the non-gaseous fluid substance in the container, temperature data corresponding to a temperature inside the container, and movement data corresponding to movement of the container or a lid of the container. The method further includes performing, by the monitoring device, a time-series analysis on the dataset for determining an anomaly; and generating, by the monitoring device, a graded alert based on the anomaly.

Abstract: A method and system for generating user driven adaptive object visualizations using Generative Adversarial Network (GAN) models is disclosed. The method includes the steps of generating a first set of object vectors for an object based on at least one input received from a user. The first set of vectors corresponds to a first set of visualizations for the object The method further includes capturing at least one tacit reaction type of the user in response to user interaction with each of the first set of visualizations, computing a score for each portion of each of the first set of visualizations, identifying a plurality of portions from at least one of the first set of object visualizations, generating a second set of object vectors, and processing the second set of object vectors sequentially through a plurality of GAN models to generate a final object visualization of the object.

Abstract: This disclosure relates to method and system for providing personalized driving or navigation assistance. The method may include receiving sensory data with respect to a vehicle from a plurality of sensors and multi-channel input data with respect to one or more passengers inside the vehicle from a plurality of onboard monitoring devices, performing fusion of the sensory data and the multi-channel input data to generate multimodal fusion data, determining one or more contextual events based on the multi-modal fusion data using a machine learning model, wherein the machine learning model is trained using an incremental learning process and comprises a supervised machine learning model and an unsupervised machine learning model, analysing the one or more contextual events to generate a personalized driving recommendation, and providing the personalized driving recommendation to a driver passenger or a navigation device.

Abstract: A method and system for adaptive determination of network slice configuration and resource requirement is disclosed. The method includes creating a service requirement set based on a service request. The method further includes determining enhancements and adjustments to be done in the service requirement set based on a plurality of user entitlements corresponding to the user associated with the service request. The method further includes generating an updated service requirement set based on the enhancements and adjustments applied on the service requirement set. The method further includes determining a network slice template hierarchy based on the updated service requirement set. The method further includes determining a resource requirement corresponding to each of the plurality of network sub-nets based on the updated service requirement set, the plurality of user entitlements, and dynamic network context.

Abstract: The present invention relates to a method of purchasing one or more goods from a physical store through a virtual environment. The method includes obtaining one or more physical store details and one or more user details from a user device for authenticating a physical user. Further, a unique avatar indicative of a virtual user corresponding to the physical user is generated. Furthermore, the virtual environment comprising one or more virtual stores is generated from a real-time video. The virtual environment is provided to the user device. Subsequently, an optimal path for traversing the virtual user in the virtual environment is determined in real-time based on one or more shopping details and a time period associated with the one or more shopping details. Finally, the virtual user is navigated in the one or more virtual stores through the optimal path for purchasing the one or more goods.

Abstract: Disclosed herein is method and fault detection system for detecting faults in one or more products. In an embodiment, method comprises generating plurality of wavelet coefficients corresponding to transformed images of each of the one or more products and determining a set of invariant features from the plurality of wavelet coefficients. Further, a dynamic set of invariant features is generated by grouping invariant features into a set of groups based on type of the one or more products. Subsequently, the dynamic set of invariant features is quantized based on a predetermined quantization threshold and a representative coefficient signature is associated for each group in the dynamic set of invariant features. Finally, faults in the one or more products are detected by comparing coefficient signatures associated with the one or more products with the representative coefficient signature of each group in the dynamic set of invariant features.