Abstract: Methods and apparatus for managing configuration sessions on remotely configurable network components, and a non-transitory computer readable medium for managing configuration sessions on such components. The method includes configuring a remote configuration interface to transmit a heartbeat signal or keep alive message within a defined interval while the remote configuration interface remains operational and closing a remote configuration session on a remotely configurable network component if a heartbeat signal or keep alive message is not received within a period corresponding to the defined interval.

Abstract: Methods and apparatus for managing configuration sessions on remotely configurable network components, and a non-transitory computer readable medium for managing configuration sessions on such components. The method includes configuring a remote configuration interface to transmit a heartbeat signal or keep alive message within a defined interval while the remote configuration interface remains operational and closing a remote configuration session on a remotely configurable network component if a heartbeat signal or keep alive message is not received within a period corresponding to the defined interval.

Abstract: The present disclosure provides a method and a system for triggering of Internet of things (IoT) devices. The system comprising a processor causes the processor to receive a change in output of first IoT device from a plurality of IoT devices, wherein the plurality of IoT devices are connected to each other, determine one or more connectivity paths between the plurality of IoT devices including the first IoT device based on stored connectivity paths between the plurality of IoT devices in a database, select one of the determined one or more connectivity paths between the plurality of IoT devices, wherein the selecting is based on similarity of metadata of each of the plurality of IoT devices in each of the one or more connectivity paths and trigger one or more IoT devices of the plurality of IoT devices in the selected connectivity path.

Abstract: This technology relates to virtual assistance including a method and system for providing gesture-based interaction with a virtual product from a remote location. A virtual assistance system identifies in real-time, direct or personified user actions performed by a user on the virtual product from images and videos. Further, user actions are mapped with predefined user actions related to virtual product to determine values associated with predefined qualifiers and predefined consequences associated with the mapped predefined user actions. Furthermore, pre-stored images and pre-stored videos corresponding to the mapped predefined user actions, values associated with the predefined qualifiers and the predefined consequences, and detected active objects are extracted.

Abstract: The present disclosure provides a method and a system for triggering of Internet of things (IoT) devices. The system comprising a processor causes the processor to receive a change in output of first IoT device from a plurality of IoT devices, wherein the plurality of IoT devices are connected to each other, determine one or more connectivity paths between the plurality of IoT devices including the first IoT device based on stored connectivity paths between the plurality of IoT devices in a database, select one of the determined one or more connectivity paths between the plurality of IoT devices, wherein the selecting is based on similarity of metadata of each of the plurality of IoT devices in each of the one or more connectivity paths and trigger one or more IoT devices of the plurality of IoT devices in the selected connectivity path.

Abstract: Systems and methods of providing real-time assistance to a presenter for rendering of a content are disclosed. In one embodiment, the method may include receiving a multi-modal input from the presenter with respect to the rendering of the content by the presenter, and performing a real-time analysis of at least one of the multi-modal input or a historical rendering of the content by the presenter. The method may further include dynamically determining a need for providing assistance to the presenter based on the real-time analysis. The method may further include dynamically generating, in response to the need, a supporting visual content based on the real-time analysis and a plurality of contents in a content database, and dynamically rendering the supporting visual content on a rendering device in possession of the presenter based on the real-time analysis.

Abstract: A method and system for compressing videos using deep learning is disclosed. The method includes segmenting each of a plurality of frames associated with a video into a plurality of super blocks. The method further includes determining a block size for partition of each of the plurality of super blocks into a plurality of sub blocks, based on a feature of each of the plurality of super blocks using a Convolutional Neural Network (CNN). The method further includes generating a prediction data for each of the plurality of sub blocks based on a motion vector predicted and learned by the CNN. The method further includes determining a residual data for each of the plurality of sub blocks by subtracting the prediction data from an associated original data. The method includes generating a transformed quantized residual data using each of a transformation algorithm and a quantization algorithm.

Abstract: This disclosure relates generally to drones, and more particularly to method and system for performing inspection and maintenance tasks of three-dimensional structures (3D) using drones. In one embodiment, a method for performing a task with respect to a 3D structure is disclosed. The method includes receiving a simulated 3D view of the 3D structure. The simulated 3D view comprises a hierarchy of augmented views to different degrees. The method further includes configuring one or more paths for performing a task on the 3D structure based on the hierarchy of augmented views, historical data on substantially similar tasks, and a capability of the at least one drone. The method further includes learning maneuverability and operations with respect to the one or more paths and the task based on the historical data on substantially similar tasks, and effecting performance of the task based on the learning through the at least one drone.

Abstract: A camera for generating distortion free images and a method thereof is disclosed. The camera includes a plurality of lenses, wherein each of the plurality of lenses has a dedicated sensor. The camera further includes a processor communicatively coupled to the plurality of lenses. The camera further includes a memory communicatively coupled to the processor and having instructions stored thereon, causing the processor, on execution to capture a plurality of images through the plurality of lenses and to generate a single distortion free image from the plurality of images based on a deep learning technique trained using a mapping of each of a plurality of sets of low resolution images generated in one or more environments to an associated distortion free image, wherein one or more low resolution images in each of the plurality of sets are distorted.

Abstract: A method and system are described for controlling an Internet of Things (IoT) device using multi-modal gesture commands. The method includes receiving one or more multi-modal gesture commands comprising at least one of one or more personalized gesture commands and one or more personalized voice commands of a user. The method includes detecting one or more multi-modal gesture commands using at least one of a gesture grammar database and a voice grammar database. The method includes determining one or more control parameters and IoT device status information associated with a plurality of IoT devices in response to the detection. The method includes identifying IoT device that user intends to control from plurality of IoT devices based on user requirement, IoT device status information, and line of sight information associated with user. The method includes controlling identified IoT device based on one or more control parameters and IoT device status information.

Abstract: Disclosed subject matter relates to digital media including a method and system for generating a contextual audio related to an image. An audio generating system may determine scene-theme and viewer theme of scene in the image. Further, audio files matching scene-objects and the contextual data may be retrieved in real-time and relevant audio files from audio files may be identified based on relationship between scene-theme, scene-objects, viewer theme, contextual data and metadata of audio files. A contribution weightage may be assigned to the relevant and substitute audio files based on contextual data and may be correlated based on contribution weightage, thereby generating the contextual audio related to the image. The present disclosure provides a feature wherein the contextual audio generated for an image may provide a holistic audio effect in accordance with context of the image, thus recreating the audio that might have been present when the image was captured.

Abstract: The present disclosure relates to method and system for determining intent of subject using behavioural pattern by intent determination system. The intent determination system receiving video data associated with subject from one or more information sources, extracting pre-defined number of femes before target frame from video data of subject, obtaining plurality of behavioural parameters of subject for each of extracted frames, determining score for plurality of behavioural parameters of extracted femes using trained intent model, calculating weighted average score for plurality of behavioural parameters of extracted frames based on score of each of behavioural parameters, analysing emotion of subject for pre-defined number of frames based on weighted average score and determining intent of subject based on emotion of subject analysed. The present disclosure removes the need of human intervention in determining intent of subject.

Abstract: This disclosure relates generally to drones, and more particularly to method and system for performing inspection and maintenance tasks of three-dimensional structures (3D) using drones. In one embodiment, a method for performing a task with respect to a 3D structure is disclosed. The method includes receiving a simulated 3D view of the 3D structure. The simulated 3D view comprises a hierarchy of augmented views to different degrees. The method further includes configuring one or more paths for performing a task on the 3D structure based on the hierarchy of augmented views, historical data on substantially similar tasks, and a capability of the at least one drone. The method further includes learning maneuverability and operations with respect to the one or more paths and the task based on the historical data on substantially similar tasks, and effecting performance of the task based on the learning through the at least one drone.

Abstract: Disclosed herein is a method and system for performing a task based on user input. One or more requirements related to the task are extracted from the user input. Based on the requirements, plurality of resources required for performing the task are retrieved and integrated to generate action sequences. Further, a simulated model is generated based on the action sequences and provided to the user for receiving user feedback. Finally, the action sequences are implemented based on the user feedback for performing the task. In an embodiment, the method of present disclosure is capable of automatically selecting and integrating resources required for implementing a task, thereby helps in reducing overall time required for implementing a task intended by the user.

Abstract: Disclosed herein is a method and system for performing context-based transformation of a video. In an embodiment, a scene descriptor and a textual descriptor are generated for each scene corresponding to the video. Further, an audio context descriptor is generated based on semantic analysis of the textual descriptor. Subsequently, the audio context descriptor and the scene descriptor are correlated to generate a scene context descriptor for each scene. Finally, the video is translated using the scene context descriptor, thereby transforming the video based on context. In some embodiments, the method of present disclosure is capable of automatically changing one or more attributes, such as color of one or more scenes in the video, in response to change in the context of audio/speech signals corresponding to the video. Thus, the present method helps in effective rendering of a video to users.

Abstract: The present disclosure discloses method and video generation system for generating video content based on user data. The video generation system receives user data sequentially from user, where each sequence of user data is converted into text data. One or more objects, relations, emotions, and actions from user data is identified by evaluating text data, a scene descriptor is generated for each sequence of user data, by associating one or more objects with one or more relations, emotions, and actions. The method comprises performing consistency check for scene descriptor of each sequence of user data, based on one or more previously stored scene descriptors, performing, one or more modifications to inconsistent scene descriptors, identified based on consistency check, generating, segments for each of scene descriptor and generating video content for by combining video segments associated with each of scene descriptor.

Abstract: Systems and methods for removing rain streak distortion from a distorted video are described. The system receives sample non-distorted images and sample distorted images of a video. The sample non-distorted images are indicative of non-raining condition and the sample distorted images are indicative of raining condition in the video. The system further determines first temporal information from the sample distorted images and second temporal information from the sample non-distorted images. The first temporal information indicative of a change in the rain streak distortion pattern and the second temporal information indicative of a change in a non-rain streak distortion pattern. Further, the system correlates the first temporal information with the second temporal information to generate a training model comprising one or more trained weights.

Abstract: A testing device for performing testing across a plurality of smart devices is disclosed. The testing device may be configured to register the plurality of smart devices to be accessed for performing testing. At least one time-window at which each smart device is idle may be determined, by the testing device, for the plurality of smart devices. Upon gathering the testing criteria and time duration for performing a testing operation, the testing device may be configured to dynamically create a test group that includes one or more smart devices from the plurality of smart devices such that the one or more smart devices in the test group satisfy the testing criteria and the at least one time-window of smart devices in the test group is within the time duration.

Abstract: In one embodiment, a method for rendering multimedia content on a user device is disclosed. The method includes receiving, by a content rendering device, multimedia content from a multimedia server. The multimedia content is stored in the multimedia server in form of chunks. The method includes transforming, by the content rendering device, at least one chunk of the multimedia content based on user preferences. The transforming comprises adjusting time duration of the multimedia content based on a user-defined time duration. Further, the method includes rendering, by the content rendering device, the transformed multimedia content on the user device.

Abstract: Systems and methods for removing rain streak distortion from a distorted video are described. The system receives sample non-distorted images and sample distorted images of a video. The sample non-distorted images are indicative of non-raining condition and the sample distorted images are indicative of raining condition in the video. The system further determines first temporal information from the sample distorted images and second temporal information from the sample non-distorted images. The first temporal information indicative of a change in the rain streak distortion pattern and the second temporal information indicative of a change in a non-rain streak distortion pattern. Further, the system correlates the first temporal information with the second temporal information to generate a training model comprising one or more trained weights.